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Contributions to this software is made +under the terms of *both* these licenses. diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/licenses/LICENSE.APACHE b/personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/licenses/LICENSE.APACHE new file mode 100644 index 0000000000000000000000000000000000000000..f433b1a53f5b830a205fd2df78e2b34974656c7b --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/licenses/LICENSE.APACHE @@ -0,0 +1,177 @@ + + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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Redistributions of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + 2. Redistributions in binary form must reproduce the above copyright + notice, this list of conditions and the following disclaimer in the + documentation and/or other materials provided with the distribution. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/INSTALLER b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..5c69047b2eb8235994febeeae1da4a82365a240a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/INSTALLER @@ -0,0 +1 @@ +uv \ No newline at end of file diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/METADATA b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..12345f88ebe873247f9298feca3e469e7e8af87f --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/METADATA @@ -0,0 +1,152 @@ +Metadata-Version: 2.4 +Name: pluggy +Version: 1.6.0 +Summary: plugin and hook calling mechanisms for python +Author-email: Holger Krekel +License: MIT +Classifier: Development Status :: 6 - Mature +Classifier: Intended Audience :: Developers +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: POSIX +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: MacOS :: MacOS X +Classifier: Topic :: Software Development :: Testing +Classifier: Topic :: Software Development :: Libraries +Classifier: Topic :: Utilities +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Requires-Python: >=3.9 +Description-Content-Type: text/x-rst +License-File: LICENSE +Provides-Extra: dev +Requires-Dist: pre-commit; extra == "dev" +Requires-Dist: tox; extra == "dev" +Provides-Extra: testing +Requires-Dist: pytest; extra == "testing" +Requires-Dist: pytest-benchmark; extra == "testing" +Requires-Dist: coverage; extra == "testing" +Dynamic: license-file + +==================================================== +pluggy - A minimalist production ready plugin system +==================================================== + +|pypi| |conda-forge| |versions| |github-actions| |gitter| |black| |codecov| + +This is the core framework used by the `pytest`_, `tox`_, and `devpi`_ projects. + +Please `read the docs`_ to learn more! + +A definitive example +==================== +.. code-block:: python + + import pluggy + + hookspec = pluggy.HookspecMarker("myproject") + hookimpl = pluggy.HookimplMarker("myproject") + + + class MySpec: + """A hook specification namespace.""" + + @hookspec + def myhook(self, arg1, arg2): + """My special little hook that you can customize.""" + + + class Plugin_1: + """A hook implementation namespace.""" + + @hookimpl + def myhook(self, arg1, arg2): + print("inside Plugin_1.myhook()") + return arg1 + arg2 + + + class Plugin_2: + """A 2nd hook implementation namespace.""" + + @hookimpl + def myhook(self, arg1, arg2): + print("inside Plugin_2.myhook()") + return arg1 - arg2 + + + # create a manager and add the spec + pm = pluggy.PluginManager("myproject") + pm.add_hookspecs(MySpec) + + # register plugins + pm.register(Plugin_1()) + pm.register(Plugin_2()) + + # call our ``myhook`` hook + results = pm.hook.myhook(arg1=1, arg2=2) + print(results) + + +Running this directly gets us:: + + $ python docs/examples/toy-example.py + inside Plugin_2.myhook() + inside Plugin_1.myhook() + [-1, 3] + + +.. badges + +.. |pypi| image:: https://img.shields.io/pypi/v/pluggy.svg + :target: https://pypi.org/pypi/pluggy + +.. |versions| image:: https://img.shields.io/pypi/pyversions/pluggy.svg + :target: https://pypi.org/pypi/pluggy + +.. |github-actions| image:: https://github.com/pytest-dev/pluggy/workflows/main/badge.svg + :target: https://github.com/pytest-dev/pluggy/actions + +.. |conda-forge| image:: https://img.shields.io/conda/vn/conda-forge/pluggy.svg + :target: https://anaconda.org/conda-forge/pytest + +.. |gitter| image:: https://badges.gitter.im/pytest-dev/pluggy.svg + :alt: Join the chat at https://gitter.im/pytest-dev/pluggy + :target: https://gitter.im/pytest-dev/pluggy?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge + +.. |black| image:: https://img.shields.io/badge/code%20style-black-000000.svg + :target: https://github.com/ambv/black + +.. |codecov| image:: https://codecov.io/gh/pytest-dev/pluggy/branch/master/graph/badge.svg + :target: https://codecov.io/gh/pytest-dev/pluggy + :alt: Code coverage Status + +.. links +.. _pytest: + http://pytest.org +.. _tox: + https://tox.readthedocs.org +.. _devpi: + http://doc.devpi.net +.. _read the docs: + https://pluggy.readthedocs.io/en/latest/ + + +Support pluggy +-------------- + +`Open Collective`_ is an online funding platform for open and transparent communities. +It provides tools to raise money and share your finances in full transparency. + +It is the platform of choice for individuals and companies that want to make one-time or +monthly donations directly to the project. + +``pluggy`` is part of the ``pytest-dev`` project, see more details in the `pytest collective`_. + +.. _Open Collective: https://opencollective.com +.. _pytest collective: https://opencollective.com/pytest diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/RECORD b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..5f164ef32bdb4078627aab79c4668dc48a6a135a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/RECORD @@ -0,0 +1,16 @@ +pluggy-1.6.0.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2 +pluggy-1.6.0.dist-info/METADATA,sha256=dDjDXuJaCV63QW-EtGHC10Qlxec0rVTDkSRTxlJE4Bw,4811 +pluggy-1.6.0.dist-info/RECORD,, +pluggy-1.6.0.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +pluggy-1.6.0.dist-info/WHEEL,sha256=Nw36Djuh_5VDukK0H78QzOX-_FQEo6V37m3nkm96gtU,91 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b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/WHEEL b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..e9653ae033ea36c4cecbdb5a1e4f6b376733b414 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: setuptools (80.7.1) +Root-Is-Purelib: true +Tag: py3-none-any + diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/licenses/LICENSE b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..85f4dd63d2da8e31d7e84d5180f016fdfe315c2c --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/licenses/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2015 holger krekel (rather uses bitbucket/hpk42) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/top_level.txt b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..11bdb5c1f5fcdd91af5d587c352039cb8476af49 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/top_level.txt @@ -0,0 +1 @@ +pluggy diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8a651f499d9da137fd61a1aefcd8a031a86372c2 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/__init__.py @@ -0,0 +1,30 @@ +__all__ = [ + "__version__", + "PluginManager", + "PluginValidationError", + "HookCaller", + "HookCallError", + "HookspecOpts", + "HookimplOpts", + "HookImpl", + "HookRelay", + "HookspecMarker", + "HookimplMarker", + "Result", + "PluggyWarning", + "PluggyTeardownRaisedWarning", +] +from ._hooks import HookCaller +from ._hooks import HookImpl +from ._hooks import HookimplMarker +from ._hooks import HookimplOpts +from ._hooks import HookRelay +from ._hooks import HookspecMarker +from ._hooks import HookspecOpts +from ._manager import PluginManager +from ._manager import PluginValidationError +from ._result import HookCallError +from ._result import Result +from ._version import version as __version__ +from ._warnings import PluggyTeardownRaisedWarning +from ._warnings import PluggyWarning diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_callers.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_callers.py new file mode 100644 index 0000000000000000000000000000000000000000..472d5dd05be442ad0dbe8ea5b8ca98b86e569fdc --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_callers.py @@ -0,0 +1,169 @@ +""" +Call loop machinery +""" + +from __future__ import annotations + +from collections.abc import Generator +from collections.abc import Mapping +from collections.abc import Sequence +from typing import cast +from typing import NoReturn +import warnings + +from ._hooks import HookImpl +from ._result import HookCallError +from ._result import Result +from ._warnings import PluggyTeardownRaisedWarning + + +# Need to distinguish between old- and new-style hook wrappers. +# Wrapping with a tuple is the fastest type-safe way I found to do it. +Teardown = Generator[None, object, object] + + +def run_old_style_hookwrapper( + hook_impl: HookImpl, hook_name: str, args: Sequence[object] +) -> Teardown: + """ + backward compatibility wrapper to run a old style hookwrapper as a wrapper + """ + + teardown: Teardown = cast(Teardown, hook_impl.function(*args)) + try: + next(teardown) + except StopIteration: + _raise_wrapfail(teardown, "did not yield") + try: + res = yield + result = Result(res, None) + except BaseException as exc: + result = Result(None, exc) + try: + teardown.send(result) + except StopIteration: + pass + except BaseException as e: + _warn_teardown_exception(hook_name, hook_impl, e) + raise + else: + _raise_wrapfail(teardown, "has second yield") + finally: + teardown.close() + return result.get_result() + + +def _raise_wrapfail( + wrap_controller: Generator[None, object, object], + msg: str, +) -> NoReturn: + co = wrap_controller.gi_code # type: ignore[attr-defined] + raise RuntimeError( + f"wrap_controller at {co.co_name!r} {co.co_filename}:{co.co_firstlineno} {msg}" + ) + + +def _warn_teardown_exception( + hook_name: str, hook_impl: HookImpl, e: BaseException +) -> None: + msg = "A plugin raised an exception during an old-style hookwrapper teardown.\n" + msg += f"Plugin: {hook_impl.plugin_name}, Hook: {hook_name}\n" + msg += f"{type(e).__name__}: {e}\n" + msg += "For more information see https://pluggy.readthedocs.io/en/stable/api_reference.html#pluggy.PluggyTeardownRaisedWarning" # noqa: E501 + warnings.warn(PluggyTeardownRaisedWarning(msg), stacklevel=6) + + +def _multicall( + hook_name: str, + hook_impls: Sequence[HookImpl], + caller_kwargs: Mapping[str, object], + firstresult: bool, +) -> object | list[object]: + """Execute a call into multiple python functions/methods and return the + result(s). + + ``caller_kwargs`` comes from HookCaller.__call__(). + """ + __tracebackhide__ = True + results: list[object] = [] + exception = None + try: # run impl and wrapper setup functions in a loop + teardowns: list[Teardown] = [] + try: + for hook_impl in reversed(hook_impls): + try: + args = [caller_kwargs[argname] for argname in hook_impl.argnames] + except KeyError as e: + # coverage bug - this is tested + for argname in hook_impl.argnames: # pragma: no cover + if argname not in caller_kwargs: + raise HookCallError( + f"hook call must provide argument {argname!r}" + ) from e + + if hook_impl.hookwrapper: + function_gen = run_old_style_hookwrapper(hook_impl, hook_name, args) + + next(function_gen) # first yield + teardowns.append(function_gen) + + elif hook_impl.wrapper: + try: + # If this cast is not valid, a type error is raised below, + # which is the desired response. + res = hook_impl.function(*args) + function_gen = cast(Generator[None, object, object], res) + next(function_gen) # first yield + teardowns.append(function_gen) + except StopIteration: + _raise_wrapfail(function_gen, "did not yield") + else: + res = hook_impl.function(*args) + if res is not None: + results.append(res) + if firstresult: # halt further impl calls + break + except BaseException as exc: + exception = exc + finally: + if firstresult: # first result hooks return a single value + result = results[0] if results else None + else: + result = results + + # run all wrapper post-yield blocks + for teardown in reversed(teardowns): + try: + if exception is not None: + try: + teardown.throw(exception) + except RuntimeError as re: + # StopIteration from generator causes RuntimeError + # even for coroutine usage - see #544 + if ( + isinstance(exception, StopIteration) + and re.__cause__ is exception + ): + teardown.close() + continue + else: + raise + else: + teardown.send(result) + # Following is unreachable for a well behaved hook wrapper. + # Try to force finalizers otherwise postponed till GC action. + # Note: close() may raise if generator handles GeneratorExit. + teardown.close() + except StopIteration as si: + result = si.value + exception = None + continue + except BaseException as e: + exception = e + continue + _raise_wrapfail(teardown, "has second yield") + + if exception is not None: + raise exception + else: + return result diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_hooks.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_hooks.py new file mode 100644 index 0000000000000000000000000000000000000000..97fef0d75fc2c8c3fee866ce8d2cc19f9ab54c7c --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_hooks.py @@ -0,0 +1,714 @@ +""" +Internal hook annotation, representation and calling machinery. +""" + +from __future__ import annotations + +from collections.abc import Generator +from collections.abc import Mapping +from collections.abc import Sequence +from collections.abc import Set +import inspect +import sys +from types import ModuleType +from typing import Any +from typing import Callable +from typing import Final +from typing import final +from typing import Optional +from typing import overload +from typing import TYPE_CHECKING +from typing import TypedDict +from typing import TypeVar +from typing import Union +import warnings + +from ._result import Result + + +_T = TypeVar("_T") +_F = TypeVar("_F", bound=Callable[..., object]) +_Namespace = Union[ModuleType, type] +_Plugin = object +_HookExec = Callable[ + [str, Sequence["HookImpl"], Mapping[str, object], bool], + Union[object, list[object]], +] +_HookImplFunction = Callable[..., Union[_T, Generator[None, Result[_T], None]]] + + +class HookspecOpts(TypedDict): + """Options for a hook specification.""" + + #: Whether the hook is :ref:`first result only `. + firstresult: bool + #: Whether the hook is :ref:`historic `. + historic: bool + #: Whether the hook :ref:`warns when implemented `. + warn_on_impl: Warning | None + #: Whether the hook warns when :ref:`certain arguments are requested + #: `. + #: + #: .. versionadded:: 1.5 + warn_on_impl_args: Mapping[str, Warning] | None + + +class HookimplOpts(TypedDict): + """Options for a hook implementation.""" + + #: Whether the hook implementation is a :ref:`wrapper `. + wrapper: bool + #: Whether the hook implementation is an :ref:`old-style wrapper + #: `. + hookwrapper: bool + #: Whether validation against a hook specification is :ref:`optional + #: `. + optionalhook: bool + #: Whether to try to order this hook implementation :ref:`first + #: `. + tryfirst: bool + #: Whether to try to order this hook implementation :ref:`last + #: `. + trylast: bool + #: The name of the hook specification to match, see :ref:`specname`. + specname: str | None + + +@final +class HookspecMarker: + """Decorator for marking functions as hook specifications. + + Instantiate it with a project_name to get a decorator. + Calling :meth:`PluginManager.add_hookspecs` later will discover all marked + functions if the :class:`PluginManager` uses the same project name. + """ + + __slots__ = ("project_name",) + + def __init__(self, project_name: str) -> None: + self.project_name: Final = project_name + + @overload + def __call__( + self, + function: _F, + firstresult: bool = False, + historic: bool = False, + warn_on_impl: Warning | None = None, + warn_on_impl_args: Mapping[str, Warning] | None = None, + ) -> _F: ... + + @overload # noqa: F811 + def __call__( # noqa: F811 + self, + function: None = ..., + firstresult: bool = ..., + historic: bool = ..., + warn_on_impl: Warning | None = ..., + warn_on_impl_args: Mapping[str, Warning] | None = ..., + ) -> Callable[[_F], _F]: ... + + def __call__( # noqa: F811 + self, + function: _F | None = None, + firstresult: bool = False, + historic: bool = False, + warn_on_impl: Warning | None = None, + warn_on_impl_args: Mapping[str, Warning] | None = None, + ) -> _F | Callable[[_F], _F]: + """If passed a function, directly sets attributes on the function + which will make it discoverable to :meth:`PluginManager.add_hookspecs`. + + If passed no function, returns a decorator which can be applied to a + function later using the attributes supplied. + + :param firstresult: + If ``True``, the 1:N hook call (N being the number of registered + hook implementation functions) will stop at I<=N when the I'th + function returns a non-``None`` result. See :ref:`firstresult`. + + :param historic: + If ``True``, every call to the hook will be memorized and replayed + on plugins registered after the call was made. See :ref:`historic`. + + :param warn_on_impl: + If given, every implementation of this hook will trigger the given + warning. See :ref:`warn_on_impl`. + + :param warn_on_impl_args: + If given, every implementation of this hook which requests one of + the arguments in the dict will trigger the corresponding warning. + See :ref:`warn_on_impl`. + + .. versionadded:: 1.5 + """ + + def setattr_hookspec_opts(func: _F) -> _F: + if historic and firstresult: + raise ValueError("cannot have a historic firstresult hook") + opts: HookspecOpts = { + "firstresult": firstresult, + "historic": historic, + "warn_on_impl": warn_on_impl, + "warn_on_impl_args": warn_on_impl_args, + } + setattr(func, self.project_name + "_spec", opts) + return func + + if function is not None: + return setattr_hookspec_opts(function) + else: + return setattr_hookspec_opts + + +@final +class HookimplMarker: + """Decorator for marking functions as hook implementations. + + Instantiate it with a ``project_name`` to get a decorator. + Calling :meth:`PluginManager.register` later will discover all marked + functions if the :class:`PluginManager` uses the same project name. + """ + + __slots__ = ("project_name",) + + def __init__(self, project_name: str) -> None: + self.project_name: Final = project_name + + @overload + def __call__( + self, + function: _F, + hookwrapper: bool = ..., + optionalhook: bool = ..., + tryfirst: bool = ..., + trylast: bool = ..., + specname: str | None = ..., + wrapper: bool = ..., + ) -> _F: ... + + @overload # noqa: F811 + def __call__( # noqa: F811 + self, + function: None = ..., + hookwrapper: bool = ..., + optionalhook: bool = ..., + tryfirst: bool = ..., + trylast: bool = ..., + specname: str | None = ..., + wrapper: bool = ..., + ) -> Callable[[_F], _F]: ... + + def __call__( # noqa: F811 + self, + function: _F | None = None, + hookwrapper: bool = False, + optionalhook: bool = False, + tryfirst: bool = False, + trylast: bool = False, + specname: str | None = None, + wrapper: bool = False, + ) -> _F | Callable[[_F], _F]: + """If passed a function, directly sets attributes on the function + which will make it discoverable to :meth:`PluginManager.register`. + + If passed no function, returns a decorator which can be applied to a + function later using the attributes supplied. + + :param optionalhook: + If ``True``, a missing matching hook specification will not result + in an error (by default it is an error if no matching spec is + found). See :ref:`optionalhook`. + + :param tryfirst: + If ``True``, this hook implementation will run as early as possible + in the chain of N hook implementations for a specification. See + :ref:`callorder`. + + :param trylast: + If ``True``, this hook implementation will run as late as possible + in the chain of N hook implementations for a specification. See + :ref:`callorder`. + + :param wrapper: + If ``True`` ("new-style hook wrapper"), the hook implementation + needs to execute exactly one ``yield``. The code before the + ``yield`` is run early before any non-hook-wrapper function is run. + The code after the ``yield`` is run after all non-hook-wrapper + functions have run. The ``yield`` receives the result value of the + inner calls, or raises the exception of inner calls (including + earlier hook wrapper calls). The return value of the function + becomes the return value of the hook, and a raised exception becomes + the exception of the hook. See :ref:`hookwrapper`. + + :param hookwrapper: + If ``True`` ("old-style hook wrapper"), the hook implementation + needs to execute exactly one ``yield``. The code before the + ``yield`` is run early before any non-hook-wrapper function is run. + The code after the ``yield`` is run after all non-hook-wrapper + function have run The ``yield`` receives a :class:`Result` object + representing the exception or result outcome of the inner calls + (including earlier hook wrapper calls). This option is mutually + exclusive with ``wrapper``. See :ref:`old_style_hookwrapper`. + + :param specname: + If provided, the given name will be used instead of the function + name when matching this hook implementation to a hook specification + during registration. See :ref:`specname`. + + .. versionadded:: 1.2.0 + The ``wrapper`` parameter. + """ + + def setattr_hookimpl_opts(func: _F) -> _F: + opts: HookimplOpts = { + "wrapper": wrapper, + "hookwrapper": hookwrapper, + "optionalhook": optionalhook, + "tryfirst": tryfirst, + "trylast": trylast, + "specname": specname, + } + setattr(func, self.project_name + "_impl", opts) + return func + + if function is None: + return setattr_hookimpl_opts + else: + return setattr_hookimpl_opts(function) + + +def normalize_hookimpl_opts(opts: HookimplOpts) -> None: + opts.setdefault("tryfirst", False) + opts.setdefault("trylast", False) + opts.setdefault("wrapper", False) + opts.setdefault("hookwrapper", False) + opts.setdefault("optionalhook", False) + opts.setdefault("specname", None) + + +_PYPY = hasattr(sys, "pypy_version_info") + + +def varnames(func: object) -> tuple[tuple[str, ...], tuple[str, ...]]: + """Return tuple of positional and keywrord argument names for a function, + method, class or callable. + + In case of a class, its ``__init__`` method is considered. + For methods the ``self`` parameter is not included. + """ + if inspect.isclass(func): + try: + func = func.__init__ + except AttributeError: # pragma: no cover - pypy special case + return (), () + elif not inspect.isroutine(func): # callable object? + try: + func = getattr(func, "__call__", func) + except Exception: # pragma: no cover - pypy special case + return (), () + + try: + # func MUST be a function or method here or we won't parse any args. + sig = inspect.signature( + func.__func__ if inspect.ismethod(func) else func # type:ignore[arg-type] + ) + except TypeError: # pragma: no cover + return (), () + + _valid_param_kinds = ( + inspect.Parameter.POSITIONAL_ONLY, + inspect.Parameter.POSITIONAL_OR_KEYWORD, + ) + _valid_params = { + name: param + for name, param in sig.parameters.items() + if param.kind in _valid_param_kinds + } + args = tuple(_valid_params) + defaults = ( + tuple( + param.default + for param in _valid_params.values() + if param.default is not param.empty + ) + or None + ) + + if defaults: + index = -len(defaults) + args, kwargs = args[:index], tuple(args[index:]) + else: + kwargs = () + + # strip any implicit instance arg + # pypy3 uses "obj" instead of "self" for default dunder methods + if not _PYPY: + implicit_names: tuple[str, ...] = ("self",) + else: # pragma: no cover + implicit_names = ("self", "obj") + if args: + qualname: str = getattr(func, "__qualname__", "") + if inspect.ismethod(func) or ("." in qualname and args[0] in implicit_names): + args = args[1:] + + return args, kwargs + + +@final +class HookRelay: + """Hook holder object for performing 1:N hook calls where N is the number + of registered plugins.""" + + __slots__ = ("__dict__",) + + def __init__(self) -> None: + """:meta private:""" + + if TYPE_CHECKING: + + def __getattr__(self, name: str) -> HookCaller: ... + + +# Historical name (pluggy<=1.2), kept for backward compatibility. +_HookRelay = HookRelay + + +_CallHistory = list[tuple[Mapping[str, object], Optional[Callable[[Any], None]]]] + + +class HookCaller: + """A caller of all registered implementations of a hook specification.""" + + __slots__ = ( + "name", + "spec", + "_hookexec", + "_hookimpls", + "_call_history", + ) + + def __init__( + self, + name: str, + hook_execute: _HookExec, + specmodule_or_class: _Namespace | None = None, + spec_opts: HookspecOpts | None = None, + ) -> None: + """:meta private:""" + #: Name of the hook getting called. + self.name: Final = name + self._hookexec: Final = hook_execute + # The hookimpls list. The caller iterates it *in reverse*. Format: + # 1. trylast nonwrappers + # 2. nonwrappers + # 3. tryfirst nonwrappers + # 4. trylast wrappers + # 5. wrappers + # 6. tryfirst wrappers + self._hookimpls: Final[list[HookImpl]] = [] + self._call_history: _CallHistory | None = None + # TODO: Document, or make private. + self.spec: HookSpec | None = None + if specmodule_or_class is not None: + assert spec_opts is not None + self.set_specification(specmodule_or_class, spec_opts) + + # TODO: Document, or make private. + def has_spec(self) -> bool: + return self.spec is not None + + # TODO: Document, or make private. + def set_specification( + self, + specmodule_or_class: _Namespace, + spec_opts: HookspecOpts, + ) -> None: + if self.spec is not None: + raise ValueError( + f"Hook {self.spec.name!r} is already registered " + f"within namespace {self.spec.namespace}" + ) + self.spec = HookSpec(specmodule_or_class, self.name, spec_opts) + if spec_opts.get("historic"): + self._call_history = [] + + def is_historic(self) -> bool: + """Whether this caller is :ref:`historic `.""" + return self._call_history is not None + + def _remove_plugin(self, plugin: _Plugin) -> None: + for i, method in enumerate(self._hookimpls): + if method.plugin == plugin: + del self._hookimpls[i] + return + raise ValueError(f"plugin {plugin!r} not found") + + def get_hookimpls(self) -> list[HookImpl]: + """Get all registered hook implementations for this hook.""" + return self._hookimpls.copy() + + def _add_hookimpl(self, hookimpl: HookImpl) -> None: + """Add an implementation to the callback chain.""" + for i, method in enumerate(self._hookimpls): + if method.hookwrapper or method.wrapper: + splitpoint = i + break + else: + splitpoint = len(self._hookimpls) + if hookimpl.hookwrapper or hookimpl.wrapper: + start, end = splitpoint, len(self._hookimpls) + else: + start, end = 0, splitpoint + + if hookimpl.trylast: + self._hookimpls.insert(start, hookimpl) + elif hookimpl.tryfirst: + self._hookimpls.insert(end, hookimpl) + else: + # find last non-tryfirst method + i = end - 1 + while i >= start and self._hookimpls[i].tryfirst: + i -= 1 + self._hookimpls.insert(i + 1, hookimpl) + + def __repr__(self) -> str: + return f"" + + def _verify_all_args_are_provided(self, kwargs: Mapping[str, object]) -> None: + # This is written to avoid expensive operations when not needed. + if self.spec: + for argname in self.spec.argnames: + if argname not in kwargs: + notincall = ", ".join( + repr(argname) + for argname in self.spec.argnames + # Avoid self.spec.argnames - kwargs.keys() + # it doesn't preserve order. + if argname not in kwargs.keys() + ) + warnings.warn( + f"Argument(s) {notincall} which are declared in the hookspec " + "cannot be found in this hook call", + stacklevel=2, + ) + break + + def __call__(self, **kwargs: object) -> Any: + """Call the hook. + + Only accepts keyword arguments, which should match the hook + specification. + + Returns the result(s) of calling all registered plugins, see + :ref:`calling`. + """ + assert not self.is_historic(), ( + "Cannot directly call a historic hook - use call_historic instead." + ) + self._verify_all_args_are_provided(kwargs) + firstresult = self.spec.opts.get("firstresult", False) if self.spec else False + # Copy because plugins may register other plugins during iteration (#438). + return self._hookexec(self.name, self._hookimpls.copy(), kwargs, firstresult) + + def call_historic( + self, + result_callback: Callable[[Any], None] | None = None, + kwargs: Mapping[str, object] | None = None, + ) -> None: + """Call the hook with given ``kwargs`` for all registered plugins and + for all plugins which will be registered afterwards, see + :ref:`historic`. + + :param result_callback: + If provided, will be called for each non-``None`` result obtained + from a hook implementation. + """ + assert self._call_history is not None + kwargs = kwargs or {} + self._verify_all_args_are_provided(kwargs) + self._call_history.append((kwargs, result_callback)) + # Historizing hooks don't return results. + # Remember firstresult isn't compatible with historic. + # Copy because plugins may register other plugins during iteration (#438). + res = self._hookexec(self.name, self._hookimpls.copy(), kwargs, False) + if result_callback is None: + return + if isinstance(res, list): + for x in res: + result_callback(x) + + def call_extra( + self, methods: Sequence[Callable[..., object]], kwargs: Mapping[str, object] + ) -> Any: + """Call the hook with some additional temporarily participating + methods using the specified ``kwargs`` as call parameters, see + :ref:`call_extra`.""" + assert not self.is_historic(), ( + "Cannot directly call a historic hook - use call_historic instead." + ) + self._verify_all_args_are_provided(kwargs) + opts: HookimplOpts = { + "wrapper": False, + "hookwrapper": False, + "optionalhook": False, + "trylast": False, + "tryfirst": False, + "specname": None, + } + hookimpls = self._hookimpls.copy() + for method in methods: + hookimpl = HookImpl(None, "", method, opts) + # Find last non-tryfirst nonwrapper method. + i = len(hookimpls) - 1 + while i >= 0 and ( + # Skip wrappers. + (hookimpls[i].hookwrapper or hookimpls[i].wrapper) + # Skip tryfirst nonwrappers. + or hookimpls[i].tryfirst + ): + i -= 1 + hookimpls.insert(i + 1, hookimpl) + firstresult = self.spec.opts.get("firstresult", False) if self.spec else False + return self._hookexec(self.name, hookimpls, kwargs, firstresult) + + def _maybe_apply_history(self, method: HookImpl) -> None: + """Apply call history to a new hookimpl if it is marked as historic.""" + if self.is_historic(): + assert self._call_history is not None + for kwargs, result_callback in self._call_history: + res = self._hookexec(self.name, [method], kwargs, False) + if res and result_callback is not None: + # XXX: remember firstresult isn't compat with historic + assert isinstance(res, list) + result_callback(res[0]) + + +# Historical name (pluggy<=1.2), kept for backward compatibility. +_HookCaller = HookCaller + + +class _SubsetHookCaller(HookCaller): + """A proxy to another HookCaller which manages calls to all registered + plugins except the ones from remove_plugins.""" + + # This class is unusual: in inhertits from `HookCaller` so all of + # the *code* runs in the class, but it delegates all underlying *data* + # to the original HookCaller. + # `subset_hook_caller` used to be implemented by creating a full-fledged + # HookCaller, copying all hookimpls from the original. This had problems + # with memory leaks (#346) and historic calls (#347), which make a proxy + # approach better. + # An alternative implementation is to use a `_getattr__`/`__getattribute__` + # proxy, however that adds more overhead and is more tricky to implement. + + __slots__ = ( + "_orig", + "_remove_plugins", + ) + + def __init__(self, orig: HookCaller, remove_plugins: Set[_Plugin]) -> None: + self._orig = orig + self._remove_plugins = remove_plugins + self.name = orig.name # type: ignore[misc] + self._hookexec = orig._hookexec # type: ignore[misc] + + @property # type: ignore[misc] + def _hookimpls(self) -> list[HookImpl]: + return [ + impl + for impl in self._orig._hookimpls + if impl.plugin not in self._remove_plugins + ] + + @property + def spec(self) -> HookSpec | None: # type: ignore[override] + return self._orig.spec + + @property + def _call_history(self) -> _CallHistory | None: # type: ignore[override] + return self._orig._call_history + + def __repr__(self) -> str: + return f"<_SubsetHookCaller {self.name!r}>" + + +@final +class HookImpl: + """A hook implementation in a :class:`HookCaller`.""" + + __slots__ = ( + "function", + "argnames", + "kwargnames", + "plugin", + "opts", + "plugin_name", + "wrapper", + "hookwrapper", + "optionalhook", + "tryfirst", + "trylast", + ) + + def __init__( + self, + plugin: _Plugin, + plugin_name: str, + function: _HookImplFunction[object], + hook_impl_opts: HookimplOpts, + ) -> None: + """:meta private:""" + #: The hook implementation function. + self.function: Final = function + argnames, kwargnames = varnames(self.function) + #: The positional parameter names of ``function```. + self.argnames: Final = argnames + #: The keyword parameter names of ``function```. + self.kwargnames: Final = kwargnames + #: The plugin which defined this hook implementation. + self.plugin: Final = plugin + #: The :class:`HookimplOpts` used to configure this hook implementation. + self.opts: Final = hook_impl_opts + #: The name of the plugin which defined this hook implementation. + self.plugin_name: Final = plugin_name + #: Whether the hook implementation is a :ref:`wrapper `. + self.wrapper: Final = hook_impl_opts["wrapper"] + #: Whether the hook implementation is an :ref:`old-style wrapper + #: `. + self.hookwrapper: Final = hook_impl_opts["hookwrapper"] + #: Whether validation against a hook specification is :ref:`optional + #: `. + self.optionalhook: Final = hook_impl_opts["optionalhook"] + #: Whether to try to order this hook implementation :ref:`first + #: `. + self.tryfirst: Final = hook_impl_opts["tryfirst"] + #: Whether to try to order this hook implementation :ref:`last + #: `. + self.trylast: Final = hook_impl_opts["trylast"] + + def __repr__(self) -> str: + return f"" + + +@final +class HookSpec: + __slots__ = ( + "namespace", + "function", + "name", + "argnames", + "kwargnames", + "opts", + "warn_on_impl", + "warn_on_impl_args", + ) + + def __init__(self, namespace: _Namespace, name: str, opts: HookspecOpts) -> None: + self.namespace = namespace + self.function: Callable[..., object] = getattr(namespace, name) + self.name = name + self.argnames, self.kwargnames = varnames(self.function) + self.opts = opts + self.warn_on_impl = opts.get("warn_on_impl") + self.warn_on_impl_args = opts.get("warn_on_impl_args") diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_manager.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_manager.py new file mode 100644 index 0000000000000000000000000000000000000000..ff1e3ce6e300eaed456bf67cf61aa4d15fceeb1f --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_manager.py @@ -0,0 +1,523 @@ +from __future__ import annotations + +from collections.abc import Iterable +from collections.abc import Mapping +from collections.abc import Sequence +import inspect +import types +from typing import Any +from typing import Callable +from typing import cast +from typing import Final +from typing import TYPE_CHECKING +import warnings + +from . import _tracing +from ._callers import _multicall +from ._hooks import _HookImplFunction +from ._hooks import _Namespace +from ._hooks import _Plugin +from ._hooks import _SubsetHookCaller +from ._hooks import HookCaller +from ._hooks import HookImpl +from ._hooks import HookimplOpts +from ._hooks import HookRelay +from ._hooks import HookspecOpts +from ._hooks import normalize_hookimpl_opts +from ._result import Result + + +if TYPE_CHECKING: + # importtlib.metadata import is slow, defer it. + import importlib.metadata + + +_BeforeTrace = Callable[[str, Sequence[HookImpl], Mapping[str, Any]], None] +_AfterTrace = Callable[[Result[Any], str, Sequence[HookImpl], Mapping[str, Any]], None] + + +def _warn_for_function(warning: Warning, function: Callable[..., object]) -> None: + func = cast(types.FunctionType, function) + warnings.warn_explicit( + warning, + type(warning), + lineno=func.__code__.co_firstlineno, + filename=func.__code__.co_filename, + ) + + +class PluginValidationError(Exception): + """Plugin failed validation. + + :param plugin: The plugin which failed validation. + :param message: Error message. + """ + + def __init__(self, plugin: _Plugin, message: str) -> None: + super().__init__(message) + #: The plugin which failed validation. + self.plugin = plugin + + +class DistFacade: + """Emulate a pkg_resources Distribution""" + + def __init__(self, dist: importlib.metadata.Distribution) -> None: + self._dist = dist + + @property + def project_name(self) -> str: + name: str = self.metadata["name"] + return name + + def __getattr__(self, attr: str, default: Any | None = None) -> Any: + return getattr(self._dist, attr, default) + + def __dir__(self) -> list[str]: + return sorted(dir(self._dist) + ["_dist", "project_name"]) + + +class PluginManager: + """Core class which manages registration of plugin objects and 1:N hook + calling. + + You can register new hooks by calling :meth:`add_hookspecs(module_or_class) + `. + + You can register plugin objects (which contain hook implementations) by + calling :meth:`register(plugin) `. + + For debugging purposes you can call :meth:`PluginManager.enable_tracing` + which will subsequently send debug information to the trace helper. + + :param project_name: + The short project name. Prefer snake case. Make sure it's unique! + """ + + def __init__(self, project_name: str) -> None: + #: The project name. + self.project_name: Final = project_name + self._name2plugin: Final[dict[str, _Plugin]] = {} + self._plugin_distinfo: Final[list[tuple[_Plugin, DistFacade]]] = [] + #: The "hook relay", used to call a hook on all registered plugins. + #: See :ref:`calling`. + self.hook: Final = HookRelay() + #: The tracing entry point. See :ref:`tracing`. + self.trace: Final[_tracing.TagTracerSub] = _tracing.TagTracer().get( + "pluginmanage" + ) + self._inner_hookexec = _multicall + + def _hookexec( + self, + hook_name: str, + methods: Sequence[HookImpl], + kwargs: Mapping[str, object], + firstresult: bool, + ) -> object | list[object]: + # called from all hookcaller instances. + # enable_tracing will set its own wrapping function at self._inner_hookexec + return self._inner_hookexec(hook_name, methods, kwargs, firstresult) + + def register(self, plugin: _Plugin, name: str | None = None) -> str | None: + """Register a plugin and return its name. + + :param name: + The name under which to register the plugin. If not specified, a + name is generated using :func:`get_canonical_name`. + + :returns: + The plugin name. If the name is blocked from registering, returns + ``None``. + + If the plugin is already registered, raises a :exc:`ValueError`. + """ + plugin_name = name or self.get_canonical_name(plugin) + + if plugin_name in self._name2plugin: + if self._name2plugin.get(plugin_name, -1) is None: + return None # blocked plugin, return None to indicate no registration + raise ValueError( + "Plugin name already registered: " + f"{plugin_name}={plugin}\n{self._name2plugin}" + ) + + if plugin in self._name2plugin.values(): + raise ValueError( + "Plugin already registered under a different name: " + f"{plugin_name}={plugin}\n{self._name2plugin}" + ) + + # XXX if an error happens we should make sure no state has been + # changed at point of return + self._name2plugin[plugin_name] = plugin + + # register matching hook implementations of the plugin + for name in dir(plugin): + hookimpl_opts = self.parse_hookimpl_opts(plugin, name) + if hookimpl_opts is not None: + normalize_hookimpl_opts(hookimpl_opts) + method: _HookImplFunction[object] = getattr(plugin, name) + hookimpl = HookImpl(plugin, plugin_name, method, hookimpl_opts) + name = hookimpl_opts.get("specname") or name + hook: HookCaller | None = getattr(self.hook, name, None) + if hook is None: + hook = HookCaller(name, self._hookexec) + setattr(self.hook, name, hook) + elif hook.has_spec(): + self._verify_hook(hook, hookimpl) + hook._maybe_apply_history(hookimpl) + hook._add_hookimpl(hookimpl) + return plugin_name + + def parse_hookimpl_opts(self, plugin: _Plugin, name: str) -> HookimplOpts | None: + """Try to obtain a hook implementation from an item with the given name + in the given plugin which is being searched for hook impls. + + :returns: + The parsed hookimpl options, or None to skip the given item. + + This method can be overridden by ``PluginManager`` subclasses to + customize how hook implementation are picked up. By default, returns the + options for items decorated with :class:`HookimplMarker`. + """ + method: object = getattr(plugin, name) + if not inspect.isroutine(method): + return None + try: + res: HookimplOpts | None = getattr( + method, self.project_name + "_impl", None + ) + except Exception: # pragma: no cover + res = {} # type: ignore[assignment] #pragma: no cover + if res is not None and not isinstance(res, dict): + # false positive + res = None # type:ignore[unreachable] #pragma: no cover + return res + + def unregister( + self, plugin: _Plugin | None = None, name: str | None = None + ) -> Any | None: + """Unregister a plugin and all of its hook implementations. + + The plugin can be specified either by the plugin object or the plugin + name. If both are specified, they must agree. + + Returns the unregistered plugin, or ``None`` if not found. + """ + if name is None: + assert plugin is not None, "one of name or plugin needs to be specified" + name = self.get_name(plugin) + assert name is not None, "plugin is not registered" + + if plugin is None: + plugin = self.get_plugin(name) + if plugin is None: + return None + + hookcallers = self.get_hookcallers(plugin) + if hookcallers: + for hookcaller in hookcallers: + hookcaller._remove_plugin(plugin) + + # if self._name2plugin[name] == None registration was blocked: ignore + if self._name2plugin.get(name): + assert name is not None + del self._name2plugin[name] + + return plugin + + def set_blocked(self, name: str) -> None: + """Block registrations of the given name, unregister if already registered.""" + self.unregister(name=name) + self._name2plugin[name] = None + + def is_blocked(self, name: str) -> bool: + """Return whether the given plugin name is blocked.""" + return name in self._name2plugin and self._name2plugin[name] is None + + def unblock(self, name: str) -> bool: + """Unblocks a name. + + Returns whether the name was actually blocked. + """ + if self._name2plugin.get(name, -1) is None: + del self._name2plugin[name] + return True + return False + + def add_hookspecs(self, module_or_class: _Namespace) -> None: + """Add new hook specifications defined in the given ``module_or_class``. + + Functions are recognized as hook specifications if they have been + decorated with a matching :class:`HookspecMarker`. + """ + names = [] + for name in dir(module_or_class): + spec_opts = self.parse_hookspec_opts(module_or_class, name) + if spec_opts is not None: + hc: HookCaller | None = getattr(self.hook, name, None) + if hc is None: + hc = HookCaller(name, self._hookexec, module_or_class, spec_opts) + setattr(self.hook, name, hc) + else: + # Plugins registered this hook without knowing the spec. + hc.set_specification(module_or_class, spec_opts) + for hookfunction in hc.get_hookimpls(): + self._verify_hook(hc, hookfunction) + names.append(name) + + if not names: + raise ValueError( + f"did not find any {self.project_name!r} hooks in {module_or_class!r}" + ) + + def parse_hookspec_opts( + self, module_or_class: _Namespace, name: str + ) -> HookspecOpts | None: + """Try to obtain a hook specification from an item with the given name + in the given module or class which is being searched for hook specs. + + :returns: + The parsed hookspec options for defining a hook, or None to skip the + given item. + + This method can be overridden by ``PluginManager`` subclasses to + customize how hook specifications are picked up. By default, returns the + options for items decorated with :class:`HookspecMarker`. + """ + method = getattr(module_or_class, name) + opts: HookspecOpts | None = getattr(method, self.project_name + "_spec", None) + return opts + + def get_plugins(self) -> set[Any]: + """Return a set of all registered plugin objects.""" + return {x for x in self._name2plugin.values() if x is not None} + + def is_registered(self, plugin: _Plugin) -> bool: + """Return whether the plugin is already registered.""" + return any(plugin == val for val in self._name2plugin.values()) + + def get_canonical_name(self, plugin: _Plugin) -> str: + """Return a canonical name for a plugin object. + + Note that a plugin may be registered under a different name + specified by the caller of :meth:`register(plugin, name) `. + To obtain the name of a registered plugin use :meth:`get_name(plugin) + ` instead. + """ + name: str | None = getattr(plugin, "__name__", None) + return name or str(id(plugin)) + + def get_plugin(self, name: str) -> Any | None: + """Return the plugin registered under the given name, if any.""" + return self._name2plugin.get(name) + + def has_plugin(self, name: str) -> bool: + """Return whether a plugin with the given name is registered.""" + return self.get_plugin(name) is not None + + def get_name(self, plugin: _Plugin) -> str | None: + """Return the name the plugin is registered under, or ``None`` if + is isn't.""" + for name, val in self._name2plugin.items(): + if plugin == val: + return name + return None + + def _verify_hook(self, hook: HookCaller, hookimpl: HookImpl) -> None: + if hook.is_historic() and (hookimpl.hookwrapper or hookimpl.wrapper): + raise PluginValidationError( + hookimpl.plugin, + f"Plugin {hookimpl.plugin_name!r}\nhook {hook.name!r}\n" + "historic incompatible with yield/wrapper/hookwrapper", + ) + + assert hook.spec is not None + if hook.spec.warn_on_impl: + _warn_for_function(hook.spec.warn_on_impl, hookimpl.function) + + # positional arg checking + notinspec = set(hookimpl.argnames) - set(hook.spec.argnames) + if notinspec: + raise PluginValidationError( + hookimpl.plugin, + f"Plugin {hookimpl.plugin_name!r} for hook {hook.name!r}\n" + f"hookimpl definition: {_formatdef(hookimpl.function)}\n" + f"Argument(s) {notinspec} are declared in the hookimpl but " + "can not be found in the hookspec", + ) + + if hook.spec.warn_on_impl_args: + for hookimpl_argname in hookimpl.argnames: + argname_warning = hook.spec.warn_on_impl_args.get(hookimpl_argname) + if argname_warning is not None: + _warn_for_function(argname_warning, hookimpl.function) + + if ( + hookimpl.wrapper or hookimpl.hookwrapper + ) and not inspect.isgeneratorfunction(hookimpl.function): + raise PluginValidationError( + hookimpl.plugin, + f"Plugin {hookimpl.plugin_name!r} for hook {hook.name!r}\n" + f"hookimpl definition: {_formatdef(hookimpl.function)}\n" + "Declared as wrapper=True or hookwrapper=True " + "but function is not a generator function", + ) + + if hookimpl.wrapper and hookimpl.hookwrapper: + raise PluginValidationError( + hookimpl.plugin, + f"Plugin {hookimpl.plugin_name!r} for hook {hook.name!r}\n" + f"hookimpl definition: {_formatdef(hookimpl.function)}\n" + "The wrapper=True and hookwrapper=True options are mutually exclusive", + ) + + def check_pending(self) -> None: + """Verify that all hooks which have not been verified against a + hook specification are optional, otherwise raise + :exc:`PluginValidationError`.""" + for name in self.hook.__dict__: + if name[0] == "_": + continue + hook: HookCaller = getattr(self.hook, name) + if not hook.has_spec(): + for hookimpl in hook.get_hookimpls(): + if not hookimpl.optionalhook: + raise PluginValidationError( + hookimpl.plugin, + f"unknown hook {name!r} in plugin {hookimpl.plugin!r}", + ) + + def load_setuptools_entrypoints(self, group: str, name: str | None = None) -> int: + """Load modules from querying the specified setuptools ``group``. + + :param group: + Entry point group to load plugins. + :param name: + If given, loads only plugins with the given ``name``. + + :return: + The number of plugins loaded by this call. + """ + import importlib.metadata + + count = 0 + for dist in list(importlib.metadata.distributions()): + for ep in dist.entry_points: + if ( + ep.group != group + or (name is not None and ep.name != name) + # already registered + or self.get_plugin(ep.name) + or self.is_blocked(ep.name) + ): + continue + plugin = ep.load() + self.register(plugin, name=ep.name) + self._plugin_distinfo.append((plugin, DistFacade(dist))) + count += 1 + return count + + def list_plugin_distinfo(self) -> list[tuple[_Plugin, DistFacade]]: + """Return a list of (plugin, distinfo) pairs for all + setuptools-registered plugins.""" + return list(self._plugin_distinfo) + + def list_name_plugin(self) -> list[tuple[str, _Plugin]]: + """Return a list of (name, plugin) pairs for all registered plugins.""" + return list(self._name2plugin.items()) + + def get_hookcallers(self, plugin: _Plugin) -> list[HookCaller] | None: + """Get all hook callers for the specified plugin. + + :returns: + The hook callers, or ``None`` if ``plugin`` is not registered in + this plugin manager. + """ + if self.get_name(plugin) is None: + return None + hookcallers = [] + for hookcaller in self.hook.__dict__.values(): + for hookimpl in hookcaller.get_hookimpls(): + if hookimpl.plugin is plugin: + hookcallers.append(hookcaller) + return hookcallers + + def add_hookcall_monitoring( + self, before: _BeforeTrace, after: _AfterTrace + ) -> Callable[[], None]: + """Add before/after tracing functions for all hooks. + + Returns an undo function which, when called, removes the added tracers. + + ``before(hook_name, hook_impls, kwargs)`` will be called ahead + of all hook calls and receive a hookcaller instance, a list + of HookImpl instances and the keyword arguments for the hook call. + + ``after(outcome, hook_name, hook_impls, kwargs)`` receives the + same arguments as ``before`` but also a :class:`~pluggy.Result` object + which represents the result of the overall hook call. + """ + oldcall = self._inner_hookexec + + def traced_hookexec( + hook_name: str, + hook_impls: Sequence[HookImpl], + caller_kwargs: Mapping[str, object], + firstresult: bool, + ) -> object | list[object]: + before(hook_name, hook_impls, caller_kwargs) + outcome = Result.from_call( + lambda: oldcall(hook_name, hook_impls, caller_kwargs, firstresult) + ) + after(outcome, hook_name, hook_impls, caller_kwargs) + return outcome.get_result() + + self._inner_hookexec = traced_hookexec + + def undo() -> None: + self._inner_hookexec = oldcall + + return undo + + def enable_tracing(self) -> Callable[[], None]: + """Enable tracing of hook calls. + + Returns an undo function which, when called, removes the added tracing. + """ + hooktrace = self.trace.root.get("hook") + + def before( + hook_name: str, methods: Sequence[HookImpl], kwargs: Mapping[str, object] + ) -> None: + hooktrace.root.indent += 1 + hooktrace(hook_name, kwargs) + + def after( + outcome: Result[object], + hook_name: str, + methods: Sequence[HookImpl], + kwargs: Mapping[str, object], + ) -> None: + if outcome.exception is None: + hooktrace("finish", hook_name, "-->", outcome.get_result()) + hooktrace.root.indent -= 1 + + return self.add_hookcall_monitoring(before, after) + + def subset_hook_caller( + self, name: str, remove_plugins: Iterable[_Plugin] + ) -> HookCaller: + """Return a proxy :class:`~pluggy.HookCaller` instance for the named + method which manages calls to all registered plugins except the ones + from remove_plugins.""" + orig: HookCaller = getattr(self.hook, name) + plugins_to_remove = {plug for plug in remove_plugins if hasattr(plug, name)} + if plugins_to_remove: + return _SubsetHookCaller(orig, plugins_to_remove) + return orig + + +def _formatdef(func: Callable[..., object]) -> str: + return f"{func.__name__}{inspect.signature(func)}" diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_result.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_result.py new file mode 100644 index 0000000000000000000000000000000000000000..656a58416cae5d9c9967f0e0b28052252a513704 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_result.py @@ -0,0 +1,107 @@ +""" +Hook wrapper "result" utilities. +""" + +from __future__ import annotations + +from types import TracebackType +from typing import Callable +from typing import cast +from typing import final +from typing import Generic +from typing import Optional +from typing import TypeVar + + +_ExcInfo = tuple[type[BaseException], BaseException, Optional[TracebackType]] +ResultType = TypeVar("ResultType") + + +class HookCallError(Exception): + """Hook was called incorrectly.""" + + +@final +class Result(Generic[ResultType]): + """An object used to inspect and set the result in a :ref:`hook wrapper + `.""" + + __slots__ = ("_result", "_exception", "_traceback") + + def __init__( + self, + result: ResultType | None, + exception: BaseException | None, + ) -> None: + """:meta private:""" + self._result = result + self._exception = exception + # Exception __traceback__ is mutable, this keeps the original. + self._traceback = exception.__traceback__ if exception is not None else None + + @property + def excinfo(self) -> _ExcInfo | None: + """:meta private:""" + exc = self._exception + if exc is None: + return None + else: + return (type(exc), exc, self._traceback) + + @property + def exception(self) -> BaseException | None: + """:meta private:""" + return self._exception + + @classmethod + def from_call(cls, func: Callable[[], ResultType]) -> Result[ResultType]: + """:meta private:""" + __tracebackhide__ = True + result = exception = None + try: + result = func() + except BaseException as exc: + exception = exc + return cls(result, exception) + + def force_result(self, result: ResultType) -> None: + """Force the result(s) to ``result``. + + If the hook was marked as a ``firstresult`` a single value should + be set, otherwise set a (modified) list of results. Any exceptions + found during invocation will be deleted. + + This overrides any previous result or exception. + """ + self._result = result + self._exception = None + self._traceback = None + + def force_exception(self, exception: BaseException) -> None: + """Force the result to fail with ``exception``. + + This overrides any previous result or exception. + + .. versionadded:: 1.1.0 + """ + self._result = None + self._exception = exception + self._traceback = exception.__traceback__ if exception is not None else None + + def get_result(self) -> ResultType: + """Get the result(s) for this hook call. + + If the hook was marked as a ``firstresult`` only a single value + will be returned, otherwise a list of results. + """ + __tracebackhide__ = True + exc = self._exception + tb = self._traceback + if exc is None: + return cast(ResultType, self._result) + else: + raise exc.with_traceback(tb) + + +# Historical name (pluggy<=1.2), kept for backward compatibility. +_Result = Result diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_tracing.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_tracing.py new file mode 100644 index 0000000000000000000000000000000000000000..f0b36db1524ffe1dac4c93561d952ef0a7dbfd8a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_tracing.py @@ -0,0 +1,72 @@ +""" +Tracing utils +""" + +from __future__ import annotations + +from collections.abc import Sequence +from typing import Any +from typing import Callable + + +_Writer = Callable[[str], object] +_Processor = Callable[[tuple[str, ...], tuple[Any, ...]], object] + + +class TagTracer: + def __init__(self) -> None: + self._tags2proc: dict[tuple[str, ...], _Processor] = {} + self._writer: _Writer | None = None + self.indent = 0 + + def get(self, name: str) -> TagTracerSub: + return TagTracerSub(self, (name,)) + + def _format_message(self, tags: Sequence[str], args: Sequence[object]) -> str: + if isinstance(args[-1], dict): + extra = args[-1] + args = args[:-1] + else: + extra = {} + + content = " ".join(map(str, args)) + indent = " " * self.indent + + lines = ["{}{} [{}]\n".format(indent, content, ":".join(tags))] + + for name, value in extra.items(): + lines.append(f"{indent} {name}: {value}\n") + + return "".join(lines) + + def _processmessage(self, tags: tuple[str, ...], args: tuple[object, ...]) -> None: + if self._writer is not None and args: + self._writer(self._format_message(tags, args)) + try: + processor = self._tags2proc[tags] + except KeyError: + pass + else: + processor(tags, args) + + def setwriter(self, writer: _Writer | None) -> None: + self._writer = writer + + def setprocessor(self, tags: str | tuple[str, ...], processor: _Processor) -> None: + if isinstance(tags, str): + tags = tuple(tags.split(":")) + else: + assert isinstance(tags, tuple) + self._tags2proc[tags] = processor + + +class TagTracerSub: + def __init__(self, root: TagTracer, tags: tuple[str, ...]) -> None: + self.root = root + self.tags = tags + + def __call__(self, *args: object) -> None: + self.root._processmessage(self.tags, args) + + def get(self, name: str) -> TagTracerSub: + return self.__class__(self.root, self.tags + (name,)) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_version.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..6b8420c0cf04fcea74d1a3e80e6648132930972e --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_version.py @@ -0,0 +1,21 @@ +# file generated by setuptools-scm +# don't change, don't track in version control + +__all__ = ["__version__", "__version_tuple__", "version", "version_tuple"] + +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple + from typing import Union + + VERSION_TUPLE = Tuple[Union[int, str], ...] +else: + VERSION_TUPLE = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE + +__version__ = version = '1.6.0' +__version_tuple__ = version_tuple = (1, 6, 0) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_warnings.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_warnings.py new file mode 100644 index 0000000000000000000000000000000000000000..6356c770c7d1f26ef4f90a4781b1386209f8169c --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/_warnings.py @@ -0,0 +1,27 @@ +from typing import final + + +class PluggyWarning(UserWarning): + """Base class for all warnings emitted by pluggy.""" + + __module__ = "pluggy" + + +@final +class PluggyTeardownRaisedWarning(PluggyWarning): + """A plugin raised an exception during an :ref:`old-style hookwrapper + ` teardown. + + Such exceptions are not handled by pluggy, and may cause subsequent + teardowns to be executed at unexpected times, or be skipped entirely. + + This is an issue in the plugin implementation. + + If the exception is unintended, fix the underlying cause. + + If the exception is intended, switch to :ref:`new-style hook wrappers + `, or use :func:`result.force_exception() + ` to set the exception instead of raising. + """ + + __module__ = "pluggy" diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/py.typed b/personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/py.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/py.py new file mode 100644 index 0000000000000000000000000000000000000000..5c661e66c1fa1c66721b3a330a5bc583937d08c9 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/py.py @@ -0,0 +1,15 @@ +# shim for pylib going away +# if pylib is installed this file will get skipped +# (`py/__init__.py` has higher precedence) +from __future__ import annotations + +import sys + +import _pytest._py.error as error +import _pytest._py.path as path + + +sys.modules["py.error"] = error +sys.modules["py.path"] = path + +__all__ = ["error", "path"] diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/INSTALLER b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..5c69047b2eb8235994febeeae1da4a82365a240a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/INSTALLER @@ -0,0 +1 @@ +uv \ No newline at end of file diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/METADATA b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..845db99724933fd9a937ba80ebf74fcb9594d197 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/METADATA @@ -0,0 +1,1279 @@ +Metadata-Version: 2.4 +Name: pydantic +Version: 2.13.3 +Summary: Data validation using Python type hints +Project-URL: Homepage, https://github.com/pydantic/pydantic +Project-URL: Documentation, https://docs.pydantic.dev +Project-URL: Funding, https://github.com/sponsors/samuelcolvin +Project-URL: Source, https://github.com/pydantic/pydantic +Project-URL: Changelog, https://docs.pydantic.dev/latest/changelog/ +Author-email: Samuel Colvin , Eric Jolibois , Hasan Ramezani , Adrian Garcia Badaracco <1755071+adriangb@users.noreply.github.com>, Terrence Dorsey , David Montague , Serge Matveenko , Marcelo Trylesinski , Sydney Runkle , David Hewitt , Alex Hall , Victorien Plot +License-Expression: MIT +License-File: LICENSE +Classifier: Development Status :: 5 - Production/Stable +Classifier: Framework :: Hypothesis +Classifier: Framework :: Pydantic +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Topic :: Internet +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Requires-Python: >=3.9 +Requires-Dist: annotated-types>=0.6.0 +Requires-Dist: pydantic-core==2.46.3 +Requires-Dist: typing-extensions>=4.14.1 +Requires-Dist: typing-inspection>=0.4.2 +Provides-Extra: email +Requires-Dist: email-validator>=2.0.0; extra == 'email' +Provides-Extra: timezone +Requires-Dist: tzdata; (python_version >= '3.9' and platform_system == 'Windows') and extra == 'timezone' +Description-Content-Type: text/markdown + +# Pydantic Validation + +[![CI](https://img.shields.io/github/actions/workflow/status/pydantic/pydantic/ci.yml?branch=main&logo=github&label=CI)](https://github.com/pydantic/pydantic/actions?query=event%3Apush+branch%3Amain+workflow%3ACI) +[![Coverage](https://coverage-badge.samuelcolvin.workers.dev/pydantic/pydantic.svg)](https://coverage-badge.samuelcolvin.workers.dev/redirect/pydantic/pydantic) +[![pypi](https://img.shields.io/pypi/v/pydantic.svg)](https://pypi.python.org/pypi/pydantic) +[![CondaForge](https://img.shields.io/conda/v/conda-forge/pydantic.svg)](https://anaconda.org/conda-forge/pydantic) +[![downloads](https://static.pepy.tech/badge/pydantic/month)](https://pepy.tech/project/pydantic) +[![versions](https://img.shields.io/pypi/pyversions/pydantic.svg)](https://github.com/pydantic/pydantic) +[![license](https://img.shields.io/github/license/pydantic/pydantic.svg)](https://github.com/pydantic/pydantic/blob/main/LICENSE) +[![Pydantic v2](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/pydantic/pydantic/main/docs/badge/v2.json)](https://docs.pydantic.dev/latest/contributing/#badges) +[![llms.txt](https://img.shields.io/badge/llms.txt-green)](https://docs.pydantic.dev/latest/llms.txt) + +Data validation using Python type hints. + +Fast and extensible, Pydantic plays nicely with your linters/IDE/brain. +Define how data should be in pure, canonical Python 3.9+; validate it with Pydantic. + +## Pydantic Logfire :fire: + +We've launched Pydantic Logfire to help you monitor your applications. +[Learn more](https://pydantic.dev/logfire/?utm_source=pydantic_validation) + +## Pydantic V1.10 vs. V2 + +Pydantic V2 is a ground-up rewrite that offers many new features, performance improvements, and some breaking changes compared to Pydantic V1. + +If you're using Pydantic V1 you may want to look at the +[pydantic V1.10 Documentation](https://docs.pydantic.dev/) or, +[`1.10.X-fixes` git branch](https://github.com/pydantic/pydantic/tree/1.10.X-fixes). Pydantic V2 also ships with the latest version of Pydantic V1 built in so that you can incrementally upgrade your code base and projects: `from pydantic import v1 as pydantic_v1`. + +## Help + +See [documentation](https://docs.pydantic.dev/) for more details. + +## Installation + +Install using `pip install -U pydantic` or `conda install pydantic -c conda-forge`. +For more installation options to make Pydantic even faster, +see the [Install](https://docs.pydantic.dev/install/) section in the documentation. + +## A Simple Example + +```python +from datetime import datetime +from typing import Optional +from pydantic import BaseModel + +class User(BaseModel): + id: int + name: str = 'John Doe' + signup_ts: Optional[datetime] = None + friends: list[int] = [] + +external_data = {'id': '123', 'signup_ts': '2017-06-01 12:22', 'friends': [1, '2', b'3']} +user = User(**external_data) +print(user) +#> User id=123 name='John Doe' signup_ts=datetime.datetime(2017, 6, 1, 12, 22) friends=[1, 2, 3] +print(user.id) +#> 123 +``` + +## Contributing + +For guidance on setting up a development environment and how to make a +contribution to Pydantic, see +[Contributing to Pydantic](https://docs.pydantic.dev/contributing/). + +## Reporting a Security Vulnerability + +See our [security policy](https://github.com/pydantic/pydantic/security/policy). + +## Changelog + + + + + +## v2.13.3 (2026-04-20) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.3) + +### What's Changed + +#### Fixes + +* Handle `AttributeError` subclasses with `from_attributes` by [@Viicos](https://github.com/Viicos) in [#13096](https://github.com/pydantic/pydantic/pull/13096) + +## v2.13.2 (2026-04-17) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.2) + +### What's Changed + +#### Fixes + +* Fix `ValidationInfo.field_name` missing with `model_validate_json()` by [@Viicos](https://github.com/Viicos) in [#13084](https://github.com/pydantic/pydantic/pull/13084) + +## v2.13.1 (2026-04-15) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.1) + +### What's Changed + +#### Fixes + +* Fix `ValidationInfo.data` missing with `model_validate_json()` by [@davidhewitt](https://github.com/davidhewitt) in [#13079](https://github.com/pydantic/pydantic/pull/13079) + +## v2.13.0 (2026-04-13) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.0) + +The highlights of the v2.13 release are available in the [blog post](https://pydantic.dev/articles/pydantic-v2-13-release). +Several minor changes (considered non-breaking changes according to our [versioning policy](https://pydantic.dev/docs/validation/2.13/get-started/version-policy/#pydantic-v2)) +are also included in this release. Make sure to look into them before upgrading. + +This release contains the updated `pydantic.v1` namespace, matching version 1.10.26 which includes support for Python 3.14. + +### What's Changed + +See the beta releases for all changes sinces 2.12. + +#### New Features + +* Allow default factories of private attributes to take validated model data by [@Viicos](https://github.com/Viicos) in [#13013](https://github.com/pydantic/pydantic/pull/13013) + +#### Changes + +* Warn when serializing fixed length tuples with too few items by [@arvindsaripalli](https://github.com/arvindsaripalli) in [#13016](https://github.com/pydantic/pydantic/pull/13016) + +#### Fixes + +* Change type of `Any` when synthesizing `_build_sources` for `BaseSettings.__init__()` signature in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#13049](https://github.com/pydantic/pydantic/pull/13049) +* Fix model equality when using runtime `extra` configuration by [@Viicos](https://github.com/Viicos) in [#13062](https://github.com/pydantic/pydantic/pull/13062) + +#### Packaging + +* Add zizmor for GitHub Actions workflow linting by [@Viicos](https://github.com/Viicos) in [#13039](https://github.com/pydantic/pydantic/pull/13039) +* Update jiter to v0.14.0 to fix a segmentation fault on musl Linux by [@Viicos](https://github.com/Viicos) in [#13064](https://github.com/pydantic/pydantic/pull/13064) + +### New Contributors + +* [@arvindsaripalli](https://github.com/arvindsaripalli) made their first contribution in [#13016](https://github.com/pydantic/pydantic/pull/13016) + +## v2.13.0b3 (2026-03-31) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.0b3) + +### What's Changed + +#### New Features + +* Add `ascii_only` option to `StringConstraints` by [@ai-man-codes](https://github.com/ai-man-codes) in [#12907](https://github.com/pydantic/pydantic/pull/12907) +* Support `exclude_if` in computed fields by [@andresliszt](https://github.com/andresliszt) in [#12748](https://github.com/pydantic/pydantic/pull/12748) +* Push down constraints in unions involving `MISSING` sentinel by [@Viicos](https://github.com/Viicos) in [#12908](https://github.com/pydantic/pydantic/pull/12908) + +#### Changes + +* Track extra fields set after init in `model_fields_set` by [@navalprakhar](https://github.com/navalprakhar) in [#12817](https://github.com/pydantic/pydantic/pull/12817) +* Do not include annotations that are not part of named tuple fields by [@galuszkak](https://github.com/galuszkak) in [#12951](https://github.com/pydantic/pydantic/pull/12951) +* No longer fall back to trying all union members when the variant selected by discriminator fails to serialize by [@navalprakhar](https://github.com/navalprakhar) in [#12825](https://github.com/pydantic/pydantic/pull/12825) + +#### Fixes + +* Support discriminator metadata outside union type alias by [@Viicos](https://github.com/Viicos) in [#12785](https://github.com/pydantic/pydantic/pull/12785) +* Respect `extras_schema` when only `extra_fields_behavior` is set on the config in JSON Schema generation for typed dictionaries by [@Viicos](https://github.com/Viicos) in [#12810](https://github.com/pydantic/pydantic/pull/12810) +* Ensure `__pydantic_private__` is set in `model_construct()` with user-defined `model_post_init()` by [@nightcityblade](https://github.com/nightcityblade) in [#12816](https://github.com/pydantic/pydantic/pull/12816) +* Handle all schema generation errors in `InstanceOf` by [@Viicos](https://github.com/Viicos) in [#12705](https://github.com/pydantic/pydantic/pull/12705) +* Allow dynamic models created with `create_model()` to be used as annotations in the Mypy plugin by [@Br1an67](https://github.com/Br1an67) in [#12879](https://github.com/pydantic/pydantic/pull/12879) +* Check for `PlaceholderNode` in Mypy plugin by [@Viicos](https://github.com/Viicos) in [#12929](https://github.com/pydantic/pydantic/pull/12929) +* Try other branches in smart union in case of omit errors by [@mikeedjones](https://github.com/mikeedjones) in [#12758](https://github.com/pydantic/pydantic/pull/12758) +* Patch unset attributes with `MISSING` during model serialization with `exclude_unset` by [@davidhewitt](https://github.com/davidhewitt) in [#12905](https://github.com/pydantic/pydantic/pull/12905) +* Ensure custom `__init__()` is called when using `model_validate_strings()` by [@siewcapital](https://github.com/siewcapital) in [#12897](https://github.com/pydantic/pydantic/pull/12897) + +#### Packaging + +* Add riscv64 build target for manylinux by [@boosterl](https://github.com/boosterl) in [#12723](https://github.com/pydantic/pydantic/pull/12723) + +### New Contributors + +* [@kelsonbrito50](https://github.com/kelsonbrito50) made their first contribution in [#12860](https://github.com/pydantic/pydantic/pull/12860) +* [@boosterl](https://github.com/boosterl) made their first contribution in [#12723](https://github.com/pydantic/pydantic/pull/12723) +* [@adityagiri3600](https://github.com/adityagiri3600) made their first contribution in [#12868](https://github.com/pydantic/pydantic/pull/12868) +* [@navalprakhar](https://github.com/navalprakhar) made their first contribution in [#12817](https://github.com/pydantic/pydantic/pull/12817) +* [@Br1an67](https://github.com/Br1an67) made their first contribution in [#12879](https://github.com/pydantic/pydantic/pull/12879) +* [@rmorshea](https://github.com/rmorshea) made their first contribution in [#12910](https://github.com/pydantic/pydantic/pull/12910) +* [@N3XT3R1337](https://github.com/N3XT3R1337) made their first contribution in [#12922](https://github.com/pydantic/pydantic/pull/12922) +* [@ai-man-codes](https://github.com/ai-man-codes) made their first contribution in [#12907](https://github.com/pydantic/pydantic/pull/12907) +* [@Yume05-dev](https://github.com/Yume05-dev) made their first contribution in [#12953](https://github.com/pydantic/pydantic/pull/12953) +* [@galuszkak](https://github.com/galuszkak) made their first contribution in [#12951](https://github.com/pydantic/pydantic/pull/12951) +* [@siewcapital](https://github.com/siewcapital) made their first contribution in [#12897](https://github.com/pydantic/pydantic/pull/12897) + +## v2.13.0b2 (2026-02-24) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.0b2) + +### What's Changed + +#### Fixes + +* Fix backported V1 namespace by [@Viicos](https://github.com/Viicos) in [#12855](https://github.com/pydantic/pydantic/pull/12855) +* Allow any type form to be used in `validate_as()` by [@bledden](https://github.com/bledden) in [#12846](https://github.com/pydantic/pydantic/pull/12846) +* Fix walrus operator precedence in `UrlConstraints.__get_pydantic_core_schema__()` by [@bysiber](https://github.com/bysiber) in [#12826](https://github.com/pydantic/pydantic/pull/12826) + +### New Contributors + +* [@bledden](https://github.com/bledden) made their first contribution in [#12846](https://github.com/pydantic/pydantic/pull/12846) +* [@bysiber](https://github.com/bysiber) made their first contribution in [#12827](https://github.com/pydantic/pydantic/pull/12827) + +## v2.13.0b1 (2026-02-23) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.13.0b1) + +This is the first beta release of the 2.13 version, mainly providing bug fixes and performance improvements +for validation and serialization. + +Notable changes include: + +* Add a new `polymorphic_serialization` option, solving issues with `serialize_as_any` introduced in 2.12. +* Latest V1.10.26 release under the `pydantic.v1` namespace. This version includes support for Python 3.14. +* The [`pydantic-core`](https://github.com/pydantic/pydantic-core/) repository was merged inside the main `pydantic` one. + +### What's Changed + +#### New Features + +* Add `polymorphic_serialization` option by [@davidhewitt](https://github.com/davidhewitt) in [#12518](https://github.com/pydantic/pydantic/pull/12518) +* Support Root models with `Literal` root types as discriminator field types by [@YassinNouh21](https://github.com/YassinNouh21) in [#12680](https://github.com/pydantic/pydantic/pull/12680) + +#### Changes + +* Migrate `pydantic-core` CI by [@Viicos](https://github.com/Viicos) in [#12752](https://github.com/pydantic/pydantic/pull/12752) +* Import `pydantic-core` into pydantic by [@davidhewitt](https://github.com/davidhewitt) in [#12481](https://github.com/pydantic/pydantic/pull/12481) +* Backport V1 changes up to v1.10.26 by [@Viicos](https://github.com/Viicos) in [#12663](https://github.com/pydantic/pydantic/pull/12663) +* Use the `complex()` constructor unconditionally when validating `complex` Python data by [@tanmaymunjal](https://github.com/tanmaymunjal) in [#12498](https://github.com/pydantic/pydantic/pull/12498) +* Add support for three-tuple input for `Decimal` by [@tanmaymunjal](https://github.com/tanmaymunjal) in [#12500](https://github.com/pydantic/pydantic/pull/12500) +* Align `@field_serializer` logic with `@field_validator` by [@Viicos](https://github.com/Viicos) in [#12577](https://github.com/pydantic/pydantic/pull/12577) +* Make `PydanticUserError` a `RuntimeError` instead of a `TypeError` by [@poliakovva](https://github.com/poliakovva) in [#12579](https://github.com/pydantic/pydantic/pull/12579) +* Remove redundant serialization attempts in nested unions by [@davidhewitt](https://github.com/davidhewitt) in [#12604](https://github.com/pydantic/pydantic/pull/12604) +* Copy `root` value when making root model shallow copies by [@YassinNouh21](https://github.com/YassinNouh21) in [#12679](https://github.com/pydantic/pydantic/pull/12679) +* Ensure deterministic JSON schema defaults by sorting sets by [@drshvik](https://github.com/drshvik) in [#12760](https://github.com/pydantic/pydantic/pull/12760) + +#### Performance + +* Refactor `DecoratorInfos.build()` implementation by [@Viicos](https://github.com/Viicos) in [#12536](https://github.com/pydantic/pydantic/pull/12536) +* Cache compiled regex in `pydantic-core` by [@Viicos](https://github.com/Viicos) in [#12549](https://github.com/pydantic/pydantic/pull/12549) +* Optimize creation of `Literal` validators by [@davidhewitt](https://github.com/davidhewitt) in [#12569](https://github.com/pydantic/pydantic/pull/12569) +* Optimize implementation of `LookupKey` by [@davidhewitt](https://github.com/davidhewitt) in [#12571](https://github.com/pydantic/pydantic/pull/12571) +* Use python strings for field names by [@davidhewitt](https://github.com/davidhewitt) in [#12631](https://github.com/pydantic/pydantic/pull/12631) +* Optimize datetime formatting code by [@davidhewitt](https://github.com/davidhewitt) in [#12626](https://github.com/pydantic/pydantic/pull/12626) +* Validate JSON model data by iteration by [@davidhewitt](https://github.com/davidhewitt) in [#12550](https://github.com/pydantic/pydantic/pull/12550) +* Optimize annotations evaluation of Pydantic models by [@Viicos](https://github.com/Viicos) in [#12681](https://github.com/pydantic/pydantic/pull/12681) +* Optimize `FieldInfo._copy()` by [@Viicos](https://github.com/Viicos) in [#12727](https://github.com/pydantic/pydantic/pull/12727) + +#### Fixes + +* Fix `FieldInfo` rebuilding when parameterizing generic models with an `Annotated` type by [@Viicos](https://github.com/Viicos) in [#12463](https://github.com/pydantic/pydantic/pull/12463) +* Fix nested model schema deduplication in JSON schema generation by [@marwan-alloreview](https://github.com/marwan-alloreview) in [#12494](https://github.com/pydantic/pydantic/pull/12494) +* Fix `InitVar` being ignored when using with the `pydantic.Field()` function by [@Viicos](https://github.com/Viicos) in [#12495](https://github.com/pydantic/pydantic/pull/12495) +* Fix support for enums with `NamedTuple` as values by [@Viicos](https://github.com/Viicos) in [#12506](https://github.com/pydantic/pydantic/pull/12506) +* Do not delete mock validator/serializer in `rebuild_dataclass()` by [@Viicos](https://github.com/Viicos) in [#12513](https://github.com/pydantic/pydantic/pull/12513) +* Require test suite to pass with free threading, switch back to global generic types cache by [@davidhewitt](https://github.com/davidhewitt) in [#12537](https://github.com/pydantic/pydantic/pull/12537) +* Refactor `__pydantic_extra__` annotation handling by [@Viicos](https://github.com/Viicos) in [#12563](https://github.com/pydantic/pydantic/pull/12563) +* Do not add claim of UUID "safety" provision by [@davidhewitt](https://github.com/davidhewitt) in [#12567](https://github.com/pydantic/pydantic/pull/12567) +* Use Python hash to perform lookup in tagged union serializer by [@davidhewitt](https://github.com/davidhewitt) in [#12594](https://github.com/pydantic/pydantic/pull/12594) +* Do not emit serialization warning `MISSING` sentinel is present in a nested model by [@Viicos](https://github.com/Viicos) in [#12635](https://github.com/pydantic/pydantic/pull/12635) +* Do not eagerly evaluate annotations in signature logic by [@Viicos](https://github.com/Viicos) in [#12660](https://github.com/pydantic/pydantic/pull/12660) +* Fix serialization of typed dict unions when `exclude_none` is set by [@davidhewitt](https://github.com/davidhewitt) in [#12677](https://github.com/pydantic/pydantic/pull/12677) +* Do not reuse prebuilt serializers/validators on rebuilds by [@lmmx](https://github.com/lmmx) in [#12689](https://github.com/pydantic/pydantic/pull/12689) +* Fix type annotation of `field_definitions` in `create_model()` by [@lehmann-hqs](https://github.com/lehmann-hqs) in [#12734](https://github.com/pydantic/pydantic/pull/12734) +* Fix incorrect dataclass constructor signature when overriding class `kw_only` with `Field()` by [@jfadia](https://github.com/jfadia) in [#12741](https://github.com/pydantic/pydantic/pull/12741) +* Use `typing.Union` when replacing types under Python 3.14 by [@Viicos](https://github.com/Viicos) in [#12733](https://github.com/pydantic/pydantic/pull/12733) +* Improve ImportString error when internal imports fail by [@tsembp](https://github.com/tsembp) in [#12740](https://github.com/pydantic/pydantic/pull/12740) +* Fix serializing complex numbers with negative zero imaginary part by [@lhnwrk](https://github.com/lhnwrk) in [#12770](https://github.com/pydantic/pydantic/pull/12770) +* Preserve custom docstrings on stdlib dataclasses in JSON schema by [@nightcityblade](https://github.com/nightcityblade) in [#12815](https://github.com/pydantic/pydantic/pull/12815) + +#### Packaging + +* Bump Rust url dependency from 2.5.4 to 2.5.7 in `pydantic-core` by [@dependabot](https://github.com/dependabot)[bot] in [#12508](https://github.com/pydantic/pydantic/pull/12508) +* Bump Rust minimum version to 1.88, use edition 2024 by [@davidhewitt](https://github.com/davidhewitt) and [@Viicos](https://github.com/Viicos) in [#12551](https://github.com/pydantic/pydantic/pull/12551) and [#12752](https://github.com/pydantic/pydantic/pull/12752) +* Bump PyO3 to 0.28, jiter to 0.13 by [@davidhewitt](https://github.com/davidhewitt) in [#12767](https://github.com/pydantic/pydantic/pull/12767) + +### New Contributors + +* [@marwan-alloreview](https://github.com/marwan-alloreview) made their first contribution in [#12494](https://github.com/pydantic/pydantic/pull/12494) +* [@tanmaymunjal](https://github.com/tanmaymunjal) made their first contribution in [#12498](https://github.com/pydantic/pydantic/pull/12498) +* [@poliakovva](https://github.com/poliakovva) made their first contribution in [#12579](https://github.com/pydantic/pydantic/pull/12579) +* [@lehmann-hqs](https://github.com/lehmann-hqs) made their first contribution in [#12734](https://github.com/pydantic/pydantic/pull/12734) +* [@jfadia](https://github.com/jfadia) made their first contribution in [#12741](https://github.com/pydantic/pydantic/pull/12741) +* [@tsembp](https://github.com/tsembp) made their first contribution in [#12740](https://github.com/pydantic/pydantic/pull/12740) +* [@drshvik](https://github.com/drshvik) made their first contribution in [#12760](https://github.com/pydantic/pydantic/pull/12760) +* [@lhnwrk](https://github.com/lhnwrk) made their first contribution in [#12770](https://github.com/pydantic/pydantic/pull/12770) +* [@nightcityblade](https://github.com/nightcityblade) made their first contribution in [#12815](https://github.com/pydantic/pydantic/pull/12811) + +## v2.12.5 (2025-11-26) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.5) + +This is the fifth 2.12 patch release, addressing an issue with the `MISSING` sentinel and providing several documentation improvements. + +The next 2.13 minor release will be published in a couple weeks, and will include a new *polymorphic serialization* feature addressing +the remaining unexpected changes to the *serialize as any* behavior. + +* Fix pickle error when using `model_construct()` on a model with `MISSING` as a default value by [@ornariece](https://github.com/ornariece) in [#12522](https://github.com/pydantic/pydantic/pull/12522). +* Several updates to the documentation by [@Viicos](https://github.com/Viicos). + +## v2.12.4 (2025-11-05) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.4) + +This is the fourth 2.12 patch release, fixing more regressions, and reverting a change in the `build()` method +of the [`AnyUrl` and Dsn types](https://docs.pydantic.dev/latest/api/networks/). + +This patch release also fixes an issue with the serialization of IP address types, when `serialize_as_any` is used. The next patch release +will try to address the remaining issues with *serialize as any* behavior by introducing a new *polymorphic serialization* feature, that +should be used in most cases in place of *serialize as any*. + +* Fix issue with forward references in parent `TypedDict` classes by [@Viicos](https://github.com/Viicos) in [#12427](https://github.com/pydantic/pydantic/pull/12427). + + This issue is only relevant on Python 3.14 and greater. + +* Exclude fields with `exclude_if` from JSON Schema required fields by [@Viicos](https://github.com/Viicos) in [#12430](https://github.com/pydantic/pydantic/pull/12430) +* Revert URL percent-encoding of credentials in the `build()` method + of the [`AnyUrl` and Dsn types](https://docs.pydantic.dev/latest/api/networks/) by [@davidhewitt](https://github.com/davidhewitt) in + [pydantic-core#1833](https://github.com/pydantic/pydantic-core/pull/1833). + + This was initially considered as a bugfix, but caused regressions and as such was fully reverted. The next release will include + an opt-in option to percent-encode components of the URL. + +* Add type inference for IP address types by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1868](https://github.com/pydantic/pydantic-core/pull/1868). + + The 2.12 changes to the `serialize_as_any` behavior made it so that IP address types could not properly serialize to JSON. + +* Avoid getting default values from defaultdict by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1853](https://github.com/pydantic/pydantic-core/pull/1853). + + This fixes a subtle regression in the validation behavior of the [`collections.defaultdict`](https://docs.python.org/3/library/collections.html#collections.defaultdict) + type. + +* Fix issue with field serializers on nested typed dictionaries by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1879](https://github.com/pydantic/pydantic-core/pull/1879). +* Add more `pydantic-core` builds for the three-threaded version of Python 3.14 by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1864](https://github.com/pydantic/pydantic-core/pull/1864). + +## v2.12.3 (2025-10-17) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.3) + +### What's Changed + +This is the third 2.12 patch release, fixing issues related to the `FieldInfo` class, and reverting a change to the supported +[*after* model validator](https://docs.pydantic.dev/latest/concepts/validators/#model-validators) function signatures. + +* Raise a warning when an invalid after model validator function signature is raised by [@Viicos](https://github.com/Viicos) in [#12414](https://github.com/pydantic/pydantic/pull/12414). + Starting in 2.12.0, using class methods for *after* model validators raised an error, but the error wasn't raised concistently. We decided + to emit a deprecation warning instead. +* Add [`FieldInfo.asdict()`](https://docs.pydantic.dev/latest/api/fields/#pydantic.fields.FieldInfo.asdict) method, improve documentation around `FieldInfo` by [@Viicos](https://github.com/Viicos) in [#12411](https://github.com/pydantic/pydantic/pull/12411). + This also add back support for mutations on `FieldInfo` classes, that are reused as `Annotated` metadata. **However**, note that this is still + *not* a supported pattern. Instead, please refer to the [added example](https://docs.pydantic.dev/latest/examples/dynamic_models/) in the documentation. + +The [blog post](https://pydantic.dev/articles/pydantic-v2-12-release#changes) section on changes was also updated to document the changes related to `serialize_as_any`. + +## v2.12.2 (2025-10-14) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.2) + +### What's Changed + +#### Fixes + +* Release a new `pydantic-core` version, as a corrupted CPython 3.10 `manylinux2014_aarch64` wheel got uploaded ([pydantic-core#1843](https://github.com/pydantic/pydantic-core/pull/1843)). +* Fix issue with recursive generic models with a parent model class by [@Viicos](https://github.com/Viicos) in [#12398](https://github.com/pydantic/pydantic/pull/12398) + +## v2.12.1 (2025-10-13) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.1) + +### What's Changed + +This is the first 2.12 patch release, addressing most (but not all yet) regressions from the initial 2.12.0 release. + +#### Fixes + +* Do not evaluate annotations when inspecting validators and serializers by [@Viicos](https://github.com/Viicos) in [#12355](https://github.com/pydantic/pydantic/pull/12355) +* Make sure `None` is converted as `NoneType` in Python 3.14 by [@Viicos](https://github.com/Viicos) in [#12370](https://github.com/pydantic/pydantic/pull/12370) +* Backport V1 runtime warning when using Python 3.14 by [@Viicos](https://github.com/Viicos) in [#12367](https://github.com/pydantic/pydantic/pull/12367) +* Fix error message for invalid validator signatures by [@Viicos](https://github.com/Viicos) in [#12366](https://github.com/pydantic/pydantic/pull/12366) +* Populate field name in `ValidationInfo` for validation of default value by [@Viicos](https://github.com/Viicos) in [pydantic-core#1826](https://github.com/pydantic/pydantic-core/pull/1826) +* Encode credentials in `MultiHostUrl` builder by [@willswire](https://github.com/willswire) in [pydantic-core#1829](https://github.com/pydantic/pydantic-core/pull/1829) +* Respect field serializers when using `serialize_as_any` serialization flag by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1829](https://github.com/pydantic/pydantic-core/pull/1829) +* Fix various `RootModel` serialization issues by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1836](https://github.com/pydantic/pydantic-core/pull/1836) + +### New Contributors + +* [@willswire](https://github.com/willswire) made their first contribution in [pydantic-core#1829](https://github.com/pydantic/pydantic-core/pull/1829) + +## v2.12.0 (2025-10-07) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.0) + +This is the final 2.12 release. It features the work of 20 external contributors and provides useful new features, along with initial Python 3.14 support. +Several minor changes (considered non-breaking changes according to our [versioning policy](https://docs.pydantic.dev/2.12/version-policy/#pydantic-v2)) +are also included in this release. Make sure to look into them before upgrading. + +**Note that Pydantic V1 is not compatible with Python 3.14 and greater**. + +### What's Changed + +See the beta releases for all changes sinces 2.11. + +#### New Features + +* Add `extra` parameter to the validate functions by [@anvilpete](https://github.com/anvilpete) in [#12233](https://github.com/pydantic/pydantic/pull/12233) +* Add `exclude_computed_fields` serialization option by [@Viicos](https://github.com/Viicos) in [#12334](https://github.com/pydantic/pydantic/pull/12334) +* Add `preverse_empty_path` URL options by [@Viicos](https://github.com/Viicos) in [#12336](https://github.com/pydantic/pydantic/pull/12336) +* Add `union_format` parameter to JSON Schema generation by [@Viicos](https://github.com/Viicos) in [#12147](https://github.com/pydantic/pydantic/pull/12147) +* Add `__qualname__` parameter for `create_model` by [@Atry](https://github.com/Atry) in [#12001](https://github.com/pydantic/pydantic/pull/12001) + +#### Fixes + +* Do not try to infer name from lambda definitions in pipelines API by [@Viicos](https://github.com/Viicos) in [#12289](https://github.com/pydantic/pydantic/pull/12289) +* Use proper namespace for functions in `TypeAdapter` by [@Viicos](https://github.com/Viicos) in [#12324](https://github.com/pydantic/pydantic/pull/12324) +* Use `Any` for context type annotation in `TypeAdapter` by [@inducer](https://github.com/inducer) in [#12279](https://github.com/pydantic/pydantic/pull/12279) +* Expose `FieldInfo` in `pydantic.fields.__all__` by [@Viicos](https://github.com/Viicos) in [#12339](https://github.com/pydantic/pydantic/pull/12339) +* Respect `validation_alias` in `@validate_call` by [@Viicos](https://github.com/Viicos) in [#12340](https://github.com/pydantic/pydantic/pull/12340) +* Use `Any` as context annotation in plugin API by [@Viicos](https://github.com/Viicos) in [#12341](https://github.com/pydantic/pydantic/pull/12341) +* Use proper `stacklevel` in warnings when possible by [@Viicos](https://github.com/Viicos) in [#12342](https://github.com/pydantic/pydantic/pull/12342) + +#### Packaging + +* Update V1 copy to v1.10.24 by [@Viicos](https://github.com/Viicos) in [#12338](https://github.com/pydantic/pydantic/pull/12338) + +### New Contributors + +* [@anvilpete](https://github.com/anvilpete) made their first contribution in [#12233](https://github.com/pydantic/pydantic/pull/12233) +* [@JonathanWindell](https://github.com/JonathanWindell) made their first contribution in [#12327](https://github.com/pydantic/pydantic/pull/12327) +* [@inducer](https://github.com/inducer) made their first contribution in [#12279](https://github.com/pydantic/pydantic/pull/12279) +* [@Atry](https://github.com/Atry) made their first contribution in [#12001](https://github.com/pydantic/pydantic/pull/12001) + +## v2.12.0b1 (2025-10-03) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.0b1) + +This is the first beta release of the upcoming 2.12 release. + +### What's Changed + +#### New Features + +* Add support for `exclude_if` at the field level by [@andresliszt](https://github.com/andresliszt) in [#12141](https://github.com/pydantic/pydantic/pull/12141) +* Add `ValidateAs` annotation helper by [@Viicos](https://github.com/Viicos) in [#11942](https://github.com/pydantic/pydantic/pull/11942) +* Add configuration options for validation and JSON serialization of temporal types by [@ollz272](https://github.com/ollz272) in [#12068](https://github.com/pydantic/pydantic/pull/12068) +* Add support for PEP 728 by [@Viicos](https://github.com/Viicos) in [#12179](https://github.com/pydantic/pydantic/pull/12179) +* Add field name in serialization error by [@NicolasPllr1](https://github.com/NicolasPllr1) in [pydantic-core#1799](https://github.com/pydantic/pydantic-core/pull/1799) +* Add option to preserve empty URL paths by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1789](https://github.com/pydantic/pydantic-core/pull/1789) + +#### Changes + +* Raise error if an incompatible `pydantic-core` version is installed by [@Viicos](https://github.com/Viicos) in [#12196](https://github.com/pydantic/pydantic/pull/12196) +* Remove runtime warning for experimental features by [@Viicos](https://github.com/Viicos) in [#12265](https://github.com/pydantic/pydantic/pull/12265) +* Warn if registering virtual subclasses on Pydantic models by [@Viicos](https://github.com/Viicos) in [#11669](https://github.com/pydantic/pydantic/pull/11669) + +#### Fixes + +* Fix `__getattr__()` behavior on Pydantic models when a property raised an `AttributeError` and extra values are present by [@raspuchin](https://github.com/raspuchin) in [#12106](https://github.com/pydantic/pydantic/pull/12106) +* Add test to prevent regression with Pydantic models used as annotated metadata by [@Viicos](https://github.com/Viicos) in [#12133](https://github.com/pydantic/pydantic/pull/12133) +* Allow to use property setters on Pydantic dataclasses with `validate_assignment` set by [@Viicos](https://github.com/Viicos) in [#12173](https://github.com/pydantic/pydantic/pull/12173) +* Fix mypy v2 plugin for upcoming mypy release by [@cdce8p](https://github.com/cdce8p) in [#12209](https://github.com/pydantic/pydantic/pull/12209) +* Respect custom title in functions JSON Schema by [@Viicos](https://github.com/Viicos) in [#11892](https://github.com/pydantic/pydantic/pull/11892) +* Fix `ImportString` JSON serialization for objects with a `name` attribute by [@chr1sj0nes](https://github.com/chr1sj0nes) in [#12219](https://github.com/pydantic/pydantic/pull/12219) +* Do not error on fields overridden by methods in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#12290](https://github.com/pydantic/pydantic/pull/12290) + +#### Packaging + +* Bump `pydantic-core` to v2.40.1 by [@Viicos](https://github.com/Viicos) in [#12314](https://github.com/pydantic/pydantic/pull/12314) + +### New Contributors + +* [@raspuchin](https://github.com/raspuchin) made their first contribution in [#12106](https://github.com/pydantic/pydantic/pull/12106) +* [@chr1sj0nes](https://github.com/chr1sj0nes) made their first contribution in [#12219](https://github.com/pydantic/pydantic/pull/12219) + +## v2.12.0a1 (2025-07-26) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.12.0a1) + +This is the first alpha release of the upcoming 2.12 release, which adds initial support for Python 3.14. + +### What's Changed + +#### New Features + +* Add `__pydantic_on_complete__()` hook that is called once model is fully ready to be used by [@DouweM](https://github.com/DouweM) in [#11762](https://github.com/pydantic/pydantic/pull/11762) +* Add initial support for Python 3.14 by [@Viicos](https://github.com/Viicos) in [#11991](https://github.com/pydantic/pydantic/pull/11991) +* Add regex patterns to JSON schema for `Decimal` type by [@Dima-Bulavenko](https://github.com/Dima-Bulavenko) in [#11987](https://github.com/pydantic/pydantic/pull/11987) +* Add support for `doc` attribute on dataclass fields by [@Viicos](https://github.com/Viicos) in [#12077](https://github.com/pydantic/pydantic/pull/12077) +* Add experimental `MISSING` sentinel by [@Viicos](https://github.com/Viicos) in [#11883](https://github.com/pydantic/pydantic/pull/11883) + +#### Changes + +* Allow config and bases to be specified together in `create_model()` by [@Viicos](https://github.com/Viicos) in [#11714](https://github.com/pydantic/pydantic/pull/11714) +* Move some field logic out of the `GenerateSchema` class by [@Viicos](https://github.com/Viicos) in [#11733](https://github.com/pydantic/pydantic/pull/11733) +* Always make use of `inspect.getsourcelines()` for docstring extraction on Python 3.13 and greater by [@Viicos](https://github.com/Viicos) in [#11829](https://github.com/pydantic/pydantic/pull/11829) +* Only support the latest Mypy version by [@Viicos](https://github.com/Viicos) in [#11832](https://github.com/pydantic/pydantic/pull/11832) +* Do not implicitly convert after model validators to class methods by [@Viicos](https://github.com/Viicos) in [#11957](https://github.com/pydantic/pydantic/pull/11957) +* Refactor `FieldInfo` creation implementation by [@Viicos](https://github.com/Viicos) in [#11898](https://github.com/pydantic/pydantic/pull/11898) +* Make `Secret` covariant by [@bluenote10](https://github.com/bluenote10) in [#12008](https://github.com/pydantic/pydantic/pull/12008) +* Emit warning when field-specific metadata is used in invalid contexts by [@Viicos](https://github.com/Viicos) in [#12028](https://github.com/pydantic/pydantic/pull/12028) + +#### Fixes + +* Properly fetch plain serializer function when serializing default value in JSON Schema by [@Viicos](https://github.com/Viicos) in [#11721](https://github.com/pydantic/pydantic/pull/11721) +* Remove generics cache workaround by [@Viicos](https://github.com/Viicos) in [#11755](https://github.com/pydantic/pydantic/pull/11755) +* Remove coercion of decimal constraints by [@Viicos](https://github.com/Viicos) in [#11772](https://github.com/pydantic/pydantic/pull/11772) +* Fix crash when expanding root type in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#11735](https://github.com/pydantic/pydantic/pull/11735) +* Only mark model as complete once all fields are complete by [@DouweM](https://github.com/DouweM) in [#11759](https://github.com/pydantic/pydantic/pull/11759) +* Do not provide `field_name` in validator core schemas by [@DouweM](https://github.com/DouweM) in [#11761](https://github.com/pydantic/pydantic/pull/11761) +* Fix issue with recursive generic models by [@Viicos](https://github.com/Viicos) in [#11775](https://github.com/pydantic/pydantic/pull/11775) +* Fix qualified name comparison of private attributes during namespace inspection by [@karta9821](https://github.com/karta9821) in [#11803](https://github.com/pydantic/pydantic/pull/11803) +* Make sure Pydantic dataclasses with slots and `validate_assignment` can be unpickled by [@Viicos](https://github.com/Viicos) in [#11769](https://github.com/pydantic/pydantic/pull/11769) +* Traverse `function-before` schemas during schema gathering by [@Viicos](https://github.com/Viicos) in [#11801](https://github.com/pydantic/pydantic/pull/11801) +* Fix check for stdlib dataclasses by [@Viicos](https://github.com/Viicos) in [#11822](https://github.com/pydantic/pydantic/pull/11822) +* Check if `FieldInfo` is complete after applying type variable map by [@Viicos](https://github.com/Viicos) in [#11855](https://github.com/pydantic/pydantic/pull/11855) +* Do not delete mock validator/serializer in `model_rebuild()` by [@Viicos](https://github.com/Viicos) in [#11890](https://github.com/pydantic/pydantic/pull/11890) +* Rebuild dataclass fields before schema generation by [@Viicos](https://github.com/Viicos) in [#11949](https://github.com/pydantic/pydantic/pull/11949) +* Always store the original field assignment on `FieldInfo` by [@Viicos](https://github.com/Viicos) in [#11946](https://github.com/pydantic/pydantic/pull/11946) +* Do not use deprecated methods as default field values by [@Viicos](https://github.com/Viicos) in [#11914](https://github.com/pydantic/pydantic/pull/11914) +* Allow callable discriminator to be applied on PEP 695 type aliases by [@Viicos](https://github.com/Viicos) in [#11941](https://github.com/pydantic/pydantic/pull/11941) +* Suppress core schema generation warning when using `SkipValidation` by [@ygsh0816](https://github.com/ygsh0816) in [#12002](https://github.com/pydantic/pydantic/pull/12002) +* Do not emit typechecking error for invalid `Field()` default with `validate_default` set to `True` by [@Viicos](https://github.com/Viicos) in [#11988](https://github.com/pydantic/pydantic/pull/11988) +* Refactor logic to support Pydantic's `Field()` function in dataclasses by [@Viicos](https://github.com/Viicos) in [#12051](https://github.com/pydantic/pydantic/pull/12051) + +#### Packaging + +* Update project metadata to use PEP 639 by [@Viicos](https://github.com/Viicos) in [#11694](https://github.com/pydantic/pydantic/pull/11694) +* Bump `mkdocs-llmstxt` to v0.2.0 by [@Viicos](https://github.com/Viicos) in [#11725](https://github.com/pydantic/pydantic/pull/11725) +* Bump `pydantic-core` to v2.35.1 by [@Viicos](https://github.com/Viicos) in [#11963](https://github.com/pydantic/pydantic/pull/11963) +* Bump dawidd6/action-download-artifact from 10 to 11 by [@dependabot](https://github.com/dependabot)[bot] in [#12033](https://github.com/pydantic/pydantic/pull/12033) +* Bump astral-sh/setup-uv from 5 to 6 by [@dependabot](https://github.com/dependabot)[bot] in [#11826](https://github.com/pydantic/pydantic/pull/11826) +* Update mypy to 1.17.0 by [@Viicos](https://github.com/Viicos) in [#12076](https://github.com/pydantic/pydantic/pull/12076) + +### New Contributors + +* [@parth-paradkar](https://github.com/parth-paradkar) made their first contribution in [#11695](https://github.com/pydantic/pydantic/pull/11695) +* [@dqkqd](https://github.com/dqkqd) made their first contribution in [#11739](https://github.com/pydantic/pydantic/pull/11739) +* [@fhightower](https://github.com/fhightower) made their first contribution in [#11722](https://github.com/pydantic/pydantic/pull/11722) +* [@gbaian10](https://github.com/gbaian10) made their first contribution in [#11766](https://github.com/pydantic/pydantic/pull/11766) +* [@DouweM](https://github.com/DouweM) made their first contribution in [#11759](https://github.com/pydantic/pydantic/pull/11759) +* [@bowenliang123](https://github.com/bowenliang123) made their first contribution in [#11719](https://github.com/pydantic/pydantic/pull/11719) +* [@rawwar](https://github.com/rawwar) made their first contribution in [#11799](https://github.com/pydantic/pydantic/pull/11799) +* [@karta9821](https://github.com/karta9821) made their first contribution in [#11803](https://github.com/pydantic/pydantic/pull/11803) +* [@jinnovation](https://github.com/jinnovation) made their first contribution in [#11834](https://github.com/pydantic/pydantic/pull/11834) +* [@zmievsa](https://github.com/zmievsa) made their first contribution in [#11861](https://github.com/pydantic/pydantic/pull/11861) +* [@Otto-AA](https://github.com/Otto-AA) made their first contribution in [#11860](https://github.com/pydantic/pydantic/pull/11860) +* [@ygsh0816](https://github.com/ygsh0816) made their first contribution in [#12002](https://github.com/pydantic/pydantic/pull/12002) +* [@lukland](https://github.com/lukland) made their first contribution in [#12015](https://github.com/pydantic/pydantic/pull/12015) +* [@Dima-Bulavenko](https://github.com/Dima-Bulavenko) made their first contribution in [#11987](https://github.com/pydantic/pydantic/pull/11987) +* [@GSemikozov](https://github.com/GSemikozov) made their first contribution in [#12050](https://github.com/pydantic/pydantic/pull/12050) +* [@hannah-heywa](https://github.com/hannah-heywa) made their first contribution in [#12082](https://github.com/pydantic/pydantic/pull/12082) + +## v2.11.10 (2025-10-04) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.10) + +### What's Changed + +#### Fixes + +* Backport v1.10.24 changes by [@Viicos](https://github.com/Viicos) + +## v2.11.9 (2025-09-13) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.9) + +### What's Changed + +#### Fixes + +* Backport v1.10.23 changes by [@Viicos](https://github.com/Viicos) + +## v2.11.8 (2025-09-13) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.8) + +### What's Changed + +#### Fixes + +* Fix mypy plugin for mypy 1.18 by [@cdce8p](https://github.com/cdce8p) in [#12209](https://github.com/pydantic/pydantic/pull/12209) + +## v2.11.7 (2025-06-14) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.7) + +### What's Changed + +#### Fixes + +* Copy `FieldInfo` instance if necessary during `FieldInfo` build by [@Viicos](https://github.com/Viicos) in [#11898](https://github.com/pydantic/pydantic/pull/11898) + +## v2.11.6 (2025-06-13) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.6) + +### What's Changed + +#### Fixes + +* Rebuild dataclass fields before schema generation by [@Viicos](https://github.com/Viicos) in [#11949](https://github.com/pydantic/pydantic/pull/11949) +* Always store the original field assignment on `FieldInfo` by [@Viicos](https://github.com/Viicos) in [#11946](https://github.com/pydantic/pydantic/pull/11946) + +## v2.11.5 (2025-05-22) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.5) + +### What's Changed + +#### Fixes + +* Check if `FieldInfo` is complete after applying type variable map by [@Viicos](https://github.com/Viicos) in [#11855](https://github.com/pydantic/pydantic/pull/11855) +* Do not delete mock validator/serializer in `model_rebuild()` by [@Viicos](https://github.com/Viicos) in [#11890](https://github.com/pydantic/pydantic/pull/11890) +* Do not duplicate metadata on model rebuild by [@Viicos](https://github.com/Viicos) in [#11902](https://github.com/pydantic/pydantic/pull/11902) + +## v2.11.4 (2025-04-29) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.4) + +### What's Changed + +#### Changes + +* Allow config and bases to be specified together in `create_model()` by [@Viicos](https://github.com/Viicos) in [#11714](https://github.com/pydantic/pydantic/pull/11714). + This change was backported as it was previously possible (although not meant to be supported) + to provide `model_config` as a field, which would make it possible to provide both configuration + and bases. + +#### Fixes + +* Remove generics cache workaround by [@Viicos](https://github.com/Viicos) in [#11755](https://github.com/pydantic/pydantic/pull/11755) +* Remove coercion of decimal constraints by [@Viicos](https://github.com/Viicos) in [#11772](https://github.com/pydantic/pydantic/pull/11772) +* Fix crash when expanding root type in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#11735](https://github.com/pydantic/pydantic/pull/11735) +* Fix issue with recursive generic models by [@Viicos](https://github.com/Viicos) in [#11775](https://github.com/pydantic/pydantic/pull/11775) +* Traverse `function-before` schemas during schema gathering by [@Viicos](https://github.com/Viicos) in [#11801](https://github.com/pydantic/pydantic/pull/11801) + +#### Packaging + +* Bump `mkdocs-llmstxt` to v0.2.0 by [@Viicos](https://github.com/Viicos) in [#11725](https://github.com/pydantic/pydantic/pull/11725) + +## v2.11.3 (2025-04-08) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.3) + +### What's Changed + +#### Fixes + +* Preserve field description when rebuilding model fields by [@Viicos](https://github.com/Viicos) in [#11698](https://github.com/pydantic/pydantic/pull/11698) + +#### Packaging + +* Update V1 copy to v1.10.21 by [@Viicos](https://github.com/Viicos) in [#11706](https://github.com/pydantic/pydantic/pull/11706) + +## v2.11.2 (2025-04-03) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.2) + +### What's Changed + +#### Fixes + +* Bump `pydantic-core` to v2.33.1 by [@Viicos](https://github.com/Viicos) in [#11678](https://github.com/pydantic/pydantic/pull/11678) +* Make sure `__pydantic_private__` exists before setting private attributes by [@Viicos](https://github.com/Viicos) in [#11666](https://github.com/pydantic/pydantic/pull/11666) +* Do not override `FieldInfo._complete` when using field from parent class by [@Viicos](https://github.com/Viicos) in [#11668](https://github.com/pydantic/pydantic/pull/11668) +* Provide the available definitions when applying discriminated unions by [@Viicos](https://github.com/Viicos) in [#11670](https://github.com/pydantic/pydantic/pull/11670) +* Do not expand root type in the mypy plugin for variables by [@Viicos](https://github.com/Viicos) in [#11676](https://github.com/pydantic/pydantic/pull/11676) +* Mention the attribute name in model fields deprecation message by [@Viicos](https://github.com/Viicos) in [#11674](https://github.com/pydantic/pydantic/pull/11674) +* Properly validate parameterized mappings by [@Viicos](https://github.com/Viicos) in [#11658](https://github.com/pydantic/pydantic/pull/11658) + +## v2.11.1 (2025-03-28) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.1) + +### What's Changed + +#### Fixes + +* Do not override `'definitions-ref'` schemas containing serialization schemas or metadata by [@Viicos](https://github.com/Viicos) in [#11644](https://github.com/pydantic/pydantic/pull/11644) + +## v2.11.0 (2025-03-27) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.0) + +### What's Changed + +Pydantic v2.11 is a version strongly focused on build time performance of Pydantic models (and core schema generation in general). +See the [blog post](https://pydantic.dev/articles/pydantic-v2-11-release) for more details. + +#### New Features + +* Add `encoded_string()` method to the URL types by [@YassinNouh21](https://github.com/YassinNouh21) in [#11580](https://github.com/pydantic/pydantic/pull/11580) +* Add support for `defer_build` with `@validate_call` decorator by [@Viicos](https://github.com/Viicos) in [#11584](https://github.com/pydantic/pydantic/pull/11584) +* Allow `@with_config` decorator to be used with keyword arguments by [@Viicos](https://github.com/Viicos) in [#11608](https://github.com/pydantic/pydantic/pull/11608) +* Simplify customization of default value inclusion in JSON Schema generation by [@Viicos](https://github.com/Viicos) in [#11634](https://github.com/pydantic/pydantic/pull/11634) +* Add `generate_arguments_schema()` function by [@Viicos](https://github.com/Viicos) in [#11572](https://github.com/pydantic/pydantic/pull/11572) + +#### Fixes + +* Allow generic typed dictionaries to be used for unpacked variadic keyword parameters by [@Viicos](https://github.com/Viicos) in [#11571](https://github.com/pydantic/pydantic/pull/11571) +* Fix runtime error when computing model string representation involving cached properties and self-referenced models by [@Viicos](https://github.com/Viicos) in [#11579](https://github.com/pydantic/pydantic/pull/11579) +* Preserve other steps when using the ellipsis in the pipeline API by [@Viicos](https://github.com/Viicos) in [#11626](https://github.com/pydantic/pydantic/pull/11626) +* Fix deferred discriminator application logic by [@Viicos](https://github.com/Viicos) in [#11591](https://github.com/pydantic/pydantic/pull/11591) + +#### Packaging + +* Bump `pydantic-core` to v2.33.0 by [@Viicos](https://github.com/Viicos) in [#11631](https://github.com/pydantic/pydantic/pull/11631) + +### New Contributors + +* [@cmenon12](https://github.com/cmenon12) made their first contribution in [#11562](https://github.com/pydantic/pydantic/pull/11562) +* [@Jeukoh](https://github.com/Jeukoh) made their first contribution in [#11611](https://github.com/pydantic/pydantic/pull/11611) + +## v2.11.0b2 (2025-03-17) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.0b2) + +### What's Changed + +#### New Features + +* Add experimental support for free threading by [@Viicos](https://github.com/Viicos) in [#11516](https://github.com/pydantic/pydantic/pull/11516) + +#### Fixes + +* Fix `NotRequired` qualifier not taken into account in stringified annotation by [@Viicos](https://github.com/Viicos) in [#11559](https://github.com/pydantic/pydantic/pull/11559) + +#### Packaging + +* Bump `pydantic-core` to v2.32.0 by [@Viicos](https://github.com/Viicos) in [#11567](https://github.com/pydantic/pydantic/pull/11567) + +### New Contributors + +* [@joren485](https://github.com/joren485) made their first contribution in [#11547](https://github.com/pydantic/pydantic/pull/11547) + +## v2.11.0b1 (2025-03-06) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.0b1) + +### What's Changed + +#### New Features + +* Support unsubstituted type variables with both a default and a bound or constraints by [@FyZzyss](https://github.com/FyZzyss) in https://github.com/pydantic/pydantic/pull/10789 +* Add a `default_factory_takes_validated_data` property to `FieldInfo` by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11034 +* Raise a better error when a generic alias is used inside `type[]` by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11088 +* Properly support PEP 695 generics syntax by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11189 +* Properly support type variable defaults by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11332 +* Add support for validating v6, v7, v8 UUIDs by [@astei](https://github.com/astei) in https://github.com/pydantic/pydantic/pull/11436 +* Improve alias configuration APIs by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11468 + +#### Changes + +* Rework `create_model` field definitions format by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11032 +* Raise a deprecation warning when a field is annotated as final with a default value by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11168 +* Deprecate accessing `model_fields` and `model_computed_fields` on instances by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11169 +* **Breaking Change:** Move core schema generation logic for path types inside the `GenerateSchema` class by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/10846 +* Remove Python 3.8 Support by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11258 +* Optimize calls to `get_type_ref` by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/10863 +* Disable `pydantic-core` core schema validation by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11271 + +#### Performance + +* Only evaluate `FieldInfo` annotations if required during schema building by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/10769 +* Improve `__setattr__` performance of Pydantic models by caching setter functions by [@MarkusSintonen](https://github.com/MarkusSintonen) in https://github.com/pydantic/pydantic/pull/10868 +* Improve annotation application performance by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11186 +* Improve performance of `_typing_extra` module by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11255 +* Refactor and optimize schema cleaning logic by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11244 +* Create a single dictionary when creating a `CoreConfig` instance by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11384 +* Bump `pydantic-core` and thus use `SchemaValidator` and `SchemaSerializer` caching by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11402 +* Reuse cached core schemas for parametrized generic Pydantic models by [@MarkusSintonen](https://github.com/MarkusSintonen) in https://github.com/pydantic/pydantic/pull/11434 + +#### Fixes + +* Improve `TypeAdapter` instance repr by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/10872 +* Use the correct frame when instantiating a parametrized `TypeAdapter` by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/10893 +* Infer final fields with a default value as class variables in the mypy plugin by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11121 +* Recursively unpack `Literal` values if using PEP 695 type aliases by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11114 +* Override `__subclasscheck__` on `ModelMetaclass` to avoid memory leak and performance issues by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11116 +* Remove unused `_extract_get_pydantic_json_schema()` parameter by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11155 +* Improve discriminated union error message for invalid union variants by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11161 +* Unpack PEP 695 type aliases if using the `Annotated` form by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11109 +* Add missing stacklevel in `deprecated_instance_property` warning by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11200 +* Copy `WithJsonSchema` schema to avoid sharing mutated data by [@thejcannon](https://github.com/thejcannon) in https://github.com/pydantic/pydantic/pull/11014 +* Do not cache parametrized models when in the process of parametrizing another model by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/10704 +* Add discriminated union related metadata entries to the `CoreMetadata` definition by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11216 +* Consolidate schema definitions logic in the `_Definitions` class by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11208 +* Support initializing root model fields with values of the `root` type in the mypy plugin by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11212 +* Fix various issues with dataclasses and `use_attribute_docstrings` by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11246 +* Only compute normalized decimal places if necessary in `decimal_places_validator` by [@misrasaurabh1](https://github.com/misrasaurabh1) in https://github.com/pydantic/pydantic/pull/11281 +* Add support for `validation_alias` in the mypy plugin by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11295 +* Fix JSON Schema reference collection with `"examples"` keys by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11305 +* Do not transform model serializer functions as class methods in the mypy plugin by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11298 +* Simplify `GenerateJsonSchema.literal_schema()` implementation by [@misrasaurabh1](https://github.com/misrasaurabh1) in https://github.com/pydantic/pydantic/pull/11321 +* Add additional allowed schemes for `ClickHouseDsn` by [@Maze21127](https://github.com/Maze21127) in https://github.com/pydantic/pydantic/pull/11319 +* Coerce decimal constraints to `Decimal` instances by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11350 +* Use the correct JSON Schema mode when handling function schemas by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11367 +* Improve exception message when encountering recursion errors during type evaluation by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11356 +* Always include `additionalProperties: True` for arbitrary dictionary schemas by [@austinyu](https://github.com/austinyu) in https://github.com/pydantic/pydantic/pull/11392 +* Expose `fallback` parameter in serialization methods by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11398 +* Fix path serialization behavior by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11416 +* Do not reuse validators and serializers during model rebuild by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11429 +* Collect model fields when rebuilding a model by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11388 +* Allow cached properties to be altered on frozen models by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11432 +* Fix tuple serialization for `Sequence` types by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11435 +* Fix: do not check for `__get_validators__` on classes where `__get_pydantic_core_schema__` is also defined by [@tlambert03](https://github.com/tlambert03) in https://github.com/pydantic/pydantic/pull/11444 +* Allow callable instances to be used as serializers by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11451 +* Improve error thrown when overriding field with a property by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11459 +* Fix JSON Schema generation with referenceable core schemas holding JSON metadata by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11475 +* Support strict specification on union member types by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11481 +* Implicitly set `validate_by_name` to `True` when `validate_by_alias` is `False` by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11503 +* Change type of `Any` when synthesizing `BaseSettings.__init__` signature in the mypy plugin by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11497 +* Support type variable defaults referencing other type variables by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11520 +* Fix `ValueError` on year zero by [@davidhewitt](https://github.com/davidhewitt) in https://github.com/pydantic/pydantic-core/pull/1583 +* `dataclass` `InitVar` shouldn't be required on serialization by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic-core/pull/1602 + +#### Packaging + +* Add a `check_pydantic_core_version()` function by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11324 +* Remove `greenlet` development dependency by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11351 +* Use the `typing-inspection` library by [@Viicos](https://github.com/Viicos) in https://github.com/pydantic/pydantic/pull/11479 +* Bump `pydantic-core` to `v2.31.1` by [@sydney-runkle](https://github.com/sydney-runkle) in https://github.com/pydantic/pydantic/pull/11526 + +## New Contributors + +* [@FyZzyss](https://github.com/FyZzyss) made their first contribution in https://github.com/pydantic/pydantic/pull/10789 +* [@tamird](https://github.com/tamird) made their first contribution in https://github.com/pydantic/pydantic/pull/10948 +* [@felixxm](https://github.com/felixxm) made their first contribution in https://github.com/pydantic/pydantic/pull/11077 +* [@alexprabhat99](https://github.com/alexprabhat99) made their first contribution in https://github.com/pydantic/pydantic/pull/11082 +* [@Kharianne](https://github.com/Kharianne) made their first contribution in https://github.com/pydantic/pydantic/pull/11111 +* [@mdaffad](https://github.com/mdaffad) made their first contribution in https://github.com/pydantic/pydantic/pull/11177 +* [@thejcannon](https://github.com/thejcannon) made their first contribution in https://github.com/pydantic/pydantic/pull/11014 +* [@thomasfrimannkoren](https://github.com/thomasfrimannkoren) made their first contribution in https://github.com/pydantic/pydantic/pull/11251 +* [@usernameMAI](https://github.com/usernameMAI) made their first contribution in https://github.com/pydantic/pydantic/pull/11275 +* [@ananiavito](https://github.com/ananiavito) made their first contribution in https://github.com/pydantic/pydantic/pull/11302 +* [@pawamoy](https://github.com/pawamoy) made their first contribution in https://github.com/pydantic/pydantic/pull/11311 +* [@Maze21127](https://github.com/Maze21127) made their first contribution in https://github.com/pydantic/pydantic/pull/11319 +* [@kauabh](https://github.com/kauabh) made their first contribution in https://github.com/pydantic/pydantic/pull/11369 +* [@jaceklaskowski](https://github.com/jaceklaskowski) made their first contribution in https://github.com/pydantic/pydantic/pull/11353 +* [@tmpbeing](https://github.com/tmpbeing) made their first contribution in https://github.com/pydantic/pydantic/pull/11375 +* [@petyosi](https://github.com/petyosi) made their first contribution in https://github.com/pydantic/pydantic/pull/11405 +* [@austinyu](https://github.com/austinyu) made their first contribution in https://github.com/pydantic/pydantic/pull/11392 +* [@mikeedjones](https://github.com/mikeedjones) made their first contribution in https://github.com/pydantic/pydantic/pull/11402 +* [@astei](https://github.com/astei) made their first contribution in https://github.com/pydantic/pydantic/pull/11436 +* [@dsayling](https://github.com/dsayling) made their first contribution in https://github.com/pydantic/pydantic/pull/11522 +* [@sobolevn](https://github.com/sobolevn) made their first contribution in https://github.com/pydantic/pydantic-core/pull/1645 + +## v2.11.0a2 (2025-02-10) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.0a2) + +### What's Changed + +Pydantic v2.11 is a version strongly focused on build time performance of Pydantic models (and core schema generation in general). +This is another early alpha release, meant to collect early feedback from users having issues with core schema builds. + +#### Performance + +* Create a single dictionary when creating a `CoreConfig` instance by [@sydney-runkle](https://github.com/sydney-runkle) in [#11384](https://github.com/pydantic/pydantic/pull/11384) + +#### Fixes + +* Use the correct JSON Schema mode when handling function schemas by [@Viicos](https://github.com/Viicos) in [#11367](https://github.com/pydantic/pydantic/pull/11367) +* Fix JSON Schema reference logic with `examples` keys by [@Viicos](https://github.com/Viicos) in [#11366](https://github.com/pydantic/pydantic/pull/11366) +* Improve exception message when encountering recursion errors during type evaluation by [@Viicos](https://github.com/Viicos) in [#11356](https://github.com/pydantic/pydantic/pull/11356) +* Always include `additionalProperties: True` for arbitrary dictionary schemas by [@austinyu](https://github.com/austinyu) in [#11392](https://github.com/pydantic/pydantic/pull/11392) +* Expose `fallback` parameter in serialization methods by [@Viicos](https://github.com/Viicos) in [#11398](https://github.com/pydantic/pydantic/pull/11398) +* Fix path serialization behavior by [@sydney-runkle](https://github.com/sydney-runkle) in [#11416](https://github.com/pydantic/pydantic/pull/11416) + +#### Packaging + +* Bump `ruff` from 0.9.2 to 0.9.5 by [@Viicos](https://github.com/Viicos) in [#11407](https://github.com/pydantic/pydantic/pull/11407) +* Bump `pydantic-core` to v2.29.0 by [@mikeedjones](https://github.com/mikeedjones) in [#11402](https://github.com/pydantic/pydantic/pull/11402) +* Use locally-built rust with symbols & pgo by [@davidhewitt](https://github.com/davidhewitt) in [#11403](https://github.com/pydantic/pydantic/pull/11403) + +### New Contributors + +* [@kauabh](https://github.com/kauabh) made their first contribution in [#11369](https://github.com/pydantic/pydantic/pull/11369) +* [@jaceklaskowski](https://github.com/jaceklaskowski) made their first contribution in [#11353](https://github.com/pydantic/pydantic/pull/11353) +* [@tmpbeing](https://github.com/tmpbeing) made their first contribution in [#11375](https://github.com/pydantic/pydantic/pull/11375) +* [@petyosi](https://github.com/petyosi) made their first contribution in [#11405](https://github.com/pydantic/pydantic/pull/11405) +* [@austinyu](https://github.com/austinyu) made their first contribution in [#11392](https://github.com/pydantic/pydantic/pull/11392) +* [@mikeedjones](https://github.com/mikeedjones) made their first contribution in [#11402](https://github.com/pydantic/pydantic/pull/11402) + +## v2.11.0a1 (2025-01-30) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.11.0a1) + +### What's Changed + +Pydantic v2.11 is a version strongly focused on build time performance of Pydantic models (and core schema generation in general). +This is an early alpha release, meant to collect early feedback from users having issues with core schema builds. + +#### New Features + +* Support unsubstituted type variables with both a default and a bound or constraints by [@FyZzyss](https://github.com/FyZzyss) in [#10789](https://github.com/pydantic/pydantic/pull/10789) +* Add a `default_factory_takes_validated_data` property to `FieldInfo` by [@Viicos](https://github.com/Viicos) in [#11034](https://github.com/pydantic/pydantic/pull/11034) +* Raise a better error when a generic alias is used inside `type[]` by [@Viicos](https://github.com/Viicos) in [#11088](https://github.com/pydantic/pydantic/pull/11088) +* Properly support PEP 695 generics syntax by [@Viicos](https://github.com/Viicos) in [#11189](https://github.com/pydantic/pydantic/pull/11189) +* Properly support type variable defaults by [@Viicos](https://github.com/Viicos) in [#11332](https://github.com/pydantic/pydantic/pull/11332) + +#### Changes + +* Rework `create_model` field definitions format by [@Viicos](https://github.com/Viicos) in [#11032](https://github.com/pydantic/pydantic/pull/11032) +* Raise a deprecation warning when a field is annotated as final with a default value by [@Viicos](https://github.com/Viicos) in [#11168](https://github.com/pydantic/pydantic/pull/11168) +* Deprecate accessing `model_fields` and `model_computed_fields` on instances by [@Viicos](https://github.com/Viicos) in [#11169](https://github.com/pydantic/pydantic/pull/11169) +* Move core schema generation logic for path types inside the `GenerateSchema` class by [@sydney-runkle](https://github.com/sydney-runkle) in [#10846](https://github.com/pydantic/pydantic/pull/10846) +* Move `deque` schema gen to `GenerateSchema` class by [@sydney-runkle](https://github.com/sydney-runkle) in [#11239](https://github.com/pydantic/pydantic/pull/11239) +* Move `Mapping` schema gen to `GenerateSchema` to complete removal of `prepare_annotations_for_known_type` workaround by [@sydney-runkle](https://github.com/sydney-runkle) in [#11247](https://github.com/pydantic/pydantic/pull/11247) +* Remove Python 3.8 Support by [@sydney-runkle](https://github.com/sydney-runkle) in [#11258](https://github.com/pydantic/pydantic/pull/11258) +* Disable `pydantic-core` core schema validation by [@sydney-runkle](https://github.com/sydney-runkle) in [#11271](https://github.com/pydantic/pydantic/pull/11271) + +#### Performance + +* Only evaluate `FieldInfo` annotations if required during schema building by [@Viicos](https://github.com/Viicos) in [#10769](https://github.com/pydantic/pydantic/pull/10769) +* Optimize calls to `get_type_ref` by [@Viicos](https://github.com/Viicos) in [#10863](https://github.com/pydantic/pydantic/pull/10863) +* Improve `__setattr__` performance of Pydantic models by caching setter functions by [@MarkusSintonen](https://github.com/MarkusSintonen) in [#10868](https://github.com/pydantic/pydantic/pull/10868) +* Improve annotation application performance by [@Viicos](https://github.com/Viicos) in [#11186](https://github.com/pydantic/pydantic/pull/11186) +* Improve performance of `_typing_extra` module by [@Viicos](https://github.com/Viicos) in [#11255](https://github.com/pydantic/pydantic/pull/11255) +* Refactor and optimize schema cleaning logic by [@Viicos](https://github.com/Viicos) and [@MarkusSintonen](https://github.com/MarkusSintonen) in [#11244](https://github.com/pydantic/pydantic/pull/11244) + +#### Fixes + +* Add validation tests for `_internal/_validators.py` by [@tkasuz](https://github.com/tkasuz) in [#10763](https://github.com/pydantic/pydantic/pull/10763) +* Improve `TypeAdapter` instance repr by [@sydney-runkle](https://github.com/sydney-runkle) in [#10872](https://github.com/pydantic/pydantic/pull/10872) +* Revert "ci: use locally built pydantic-core with debug symbols by [@sydney-runkle](https://github.com/sydney-runkle) in [#10942](https://github.com/pydantic/pydantic/pull/10942) +* Re-enable all FastAPI tests by [@tamird](https://github.com/tamird) in [#10948](https://github.com/pydantic/pydantic/pull/10948) +* Fix typo in HISTORY.md. by [@felixxm](https://github.com/felixxm) in [#11077](https://github.com/pydantic/pydantic/pull/11077) +* Infer final fields with a default value as class variables in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#11121](https://github.com/pydantic/pydantic/pull/11121) +* Recursively unpack `Literal` values if using PEP 695 type aliases by [@Viicos](https://github.com/Viicos) in [#11114](https://github.com/pydantic/pydantic/pull/11114) +* Override `__subclasscheck__` on `ModelMetaclass` to avoid memory leak and performance issues by [@Viicos](https://github.com/Viicos) in [#11116](https://github.com/pydantic/pydantic/pull/11116) +* Remove unused `_extract_get_pydantic_json_schema()` parameter by [@Viicos](https://github.com/Viicos) in [#11155](https://github.com/pydantic/pydantic/pull/11155) +* Add FastAPI and SQLModel to third-party tests by [@sydney-runkle](https://github.com/sydney-runkle) in [#11044](https://github.com/pydantic/pydantic/pull/11044) +* Fix conditional expressions syntax for third-party tests by [@Viicos](https://github.com/Viicos) in [#11162](https://github.com/pydantic/pydantic/pull/11162) +* Move FastAPI tests to third-party workflow by [@Viicos](https://github.com/Viicos) in [#11164](https://github.com/pydantic/pydantic/pull/11164) +* Improve discriminated union error message for invalid union variants by [@Viicos](https://github.com/Viicos) in [#11161](https://github.com/pydantic/pydantic/pull/11161) +* Unpack PEP 695 type aliases if using the `Annotated` form by [@Viicos](https://github.com/Viicos) in [#11109](https://github.com/pydantic/pydantic/pull/11109) +* Include `openapi-python-client` check in issue creation for third-party failures, use `main` branch by [@sydney-runkle](https://github.com/sydney-runkle) in [#11182](https://github.com/pydantic/pydantic/pull/11182) +* Add pandera third-party tests by [@Viicos](https://github.com/Viicos) in [#11193](https://github.com/pydantic/pydantic/pull/11193) +* Add ODMantic third-party tests by [@sydney-runkle](https://github.com/sydney-runkle) in [#11197](https://github.com/pydantic/pydantic/pull/11197) +* Add missing stacklevel in `deprecated_instance_property` warning by [@Viicos](https://github.com/Viicos) in [#11200](https://github.com/pydantic/pydantic/pull/11200) +* Copy `WithJsonSchema` schema to avoid sharing mutated data by [@thejcannon](https://github.com/thejcannon) in [#11014](https://github.com/pydantic/pydantic/pull/11014) +* Do not cache parametrized models when in the process of parametrizing another model by [@Viicos](https://github.com/Viicos) in [#10704](https://github.com/pydantic/pydantic/pull/10704) +* Re-enable Beanie third-party tests by [@Viicos](https://github.com/Viicos) in [#11214](https://github.com/pydantic/pydantic/pull/11214) +* Add discriminated union related metadata entries to the `CoreMetadata` definition by [@Viicos](https://github.com/Viicos) in [#11216](https://github.com/pydantic/pydantic/pull/11216) +* Consolidate schema definitions logic in the `_Definitions` class by [@Viicos](https://github.com/Viicos) in [#11208](https://github.com/pydantic/pydantic/pull/11208) +* Support initializing root model fields with values of the `root` type in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#11212](https://github.com/pydantic/pydantic/pull/11212) +* Fix various issues with dataclasses and `use_attribute_docstrings` by [@Viicos](https://github.com/Viicos) in [#11246](https://github.com/pydantic/pydantic/pull/11246) +* Only compute normalized decimal places if necessary in `decimal_places_validator` by [@misrasaurabh1](https://github.com/misrasaurabh1) in [#11281](https://github.com/pydantic/pydantic/pull/11281) +* Fix two misplaced sentences in validation errors documentation by [@ananiavito](https://github.com/ananiavito) in [#11302](https://github.com/pydantic/pydantic/pull/11302) +* Fix mkdocstrings inventory example in documentation by [@pawamoy](https://github.com/pawamoy) in [#11311](https://github.com/pydantic/pydantic/pull/11311) +* Add support for `validation_alias` in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#11295](https://github.com/pydantic/pydantic/pull/11295) +* Do not transform model serializer functions as class methods in the mypy plugin by [@Viicos](https://github.com/Viicos) in [#11298](https://github.com/pydantic/pydantic/pull/11298) +* Simplify `GenerateJsonSchema.literal_schema()` implementation by [@misrasaurabh1](https://github.com/misrasaurabh1) in [#11321](https://github.com/pydantic/pydantic/pull/11321) +* Add additional allowed schemes for `ClickHouseDsn` by [@Maze21127](https://github.com/Maze21127) in [#11319](https://github.com/pydantic/pydantic/pull/11319) +* Coerce decimal constraints to `Decimal` instances by [@Viicos](https://github.com/Viicos) in [#11350](https://github.com/pydantic/pydantic/pull/11350) +* Fix `ValueError` on year zero by [@davidhewitt](https://github.com/davidhewitt) in [pydantic-core#1583](https://github.com/pydantic/pydantic-core/pull/1583) + +#### Packaging + +* Bump dawidd6/action-download-artifact from 6 to 7 by [@dependabot](https://github.com/dependabot) in [#11018](https://github.com/pydantic/pydantic/pull/11018) +* Re-enable memray related tests on Python 3.12+ by [@Viicos](https://github.com/Viicos) in [#11191](https://github.com/pydantic/pydantic/pull/11191) +* Bump astral-sh/setup-uv to 5 by [@dependabot](https://github.com/dependabot) in [#11205](https://github.com/pydantic/pydantic/pull/11205) +* Bump `ruff` to v0.9.0 by [@sydney-runkle](https://github.com/sydney-runkle) in [#11254](https://github.com/pydantic/pydantic/pull/11254) +* Regular `uv.lock` deps update by [@sydney-runkle](https://github.com/sydney-runkle) in [#11333](https://github.com/pydantic/pydantic/pull/11333) +* Add a `check_pydantic_core_version()` function by [@Viicos](https://github.com/Viicos) in [#11324](https://github.com/pydantic/pydantic/pull/11324) +* Remove `greenlet` development dependency by [@Viicos](https://github.com/Viicos) in [#11351](https://github.com/pydantic/pydantic/pull/11351) +* Bump `pydantic-core` to v2.28.0 by [@Viicos](https://github.com/Viicos) in [#11364](https://github.com/pydantic/pydantic/pull/11364) + +### New Contributors + +* [@FyZzyss](https://github.com/FyZzyss) made their first contribution in [#10789](https://github.com/pydantic/pydantic/pull/10789) +* [@tamird](https://github.com/tamird) made their first contribution in [#10948](https://github.com/pydantic/pydantic/pull/10948) +* [@felixxm](https://github.com/felixxm) made their first contribution in [#11077](https://github.com/pydantic/pydantic/pull/11077) +* [@alexprabhat99](https://github.com/alexprabhat99) made their first contribution in [#11082](https://github.com/pydantic/pydantic/pull/11082) +* [@Kharianne](https://github.com/Kharianne) made their first contribution in [#11111](https://github.com/pydantic/pydantic/pull/11111) +* [@mdaffad](https://github.com/mdaffad) made their first contribution in [#11177](https://github.com/pydantic/pydantic/pull/11177) +* [@thejcannon](https://github.com/thejcannon) made their first contribution in [#11014](https://github.com/pydantic/pydantic/pull/11014) +* [@thomasfrimannkoren](https://github.com/thomasfrimannkoren) made their first contribution in [#11251](https://github.com/pydantic/pydantic/pull/11251) +* [@usernameMAI](https://github.com/usernameMAI) made their first contribution in [#11275](https://github.com/pydantic/pydantic/pull/11275) +* [@ananiavito](https://github.com/ananiavito) made their first contribution in [#11302](https://github.com/pydantic/pydantic/pull/11302) +* [@pawamoy](https://github.com/pawamoy) made their first contribution in [#11311](https://github.com/pydantic/pydantic/pull/11311) +* [@Maze21127](https://github.com/Maze21127) made their first contribution in [#11319](https://github.com/pydantic/pydantic/pull/11319) + +## v2.10.6 (2025-01-23) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.6) + +### What's Changed + +#### Fixes + +* Fix JSON Schema reference collection with `'examples'` keys by [@Viicos](https://github.com/Viicos) in [#11325](https://github.com/pydantic/pydantic/pull/11325) +* Fix url python serialization by [@sydney-runkle](https://github.com/sydney-runkle) in [#11331](https://github.com/pydantic/pydantic/pull/11331) + +## v2.10.5 (2025-01-08) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.5) + +### What's Changed + +#### Fixes + +* Remove custom MRO implementation of Pydantic models by [@Viicos](https://github.com/Viicos) in [#11184](https://github.com/pydantic/pydantic/pull/11184) +* Fix URL serialization for unions by [@sydney-runkle](https://github.com/sydney-runkle) in [#11233](https://github.com/pydantic/pydantic/pull/11233) + +## v2.10.4 (2024-12-18) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.4) + +### What's Changed + +#### Fixes + +* Fix for comparison of `AnyUrl` objects by [@alexprabhat99](https://github.com/alexprabhat99) in [#11082](https://github.com/pydantic/pydantic/pull/11082) +* Properly fetch PEP 695 type params for functions, do not fetch annotations from signature by [@Viicos](https://github.com/Viicos) in [#11093](https://github.com/pydantic/pydantic/pull/11093) +* Include JSON Schema input core schema in function schemas by [@Viicos](https://github.com/Viicos) in [#11085](https://github.com/pydantic/pydantic/pull/11085) +* Add `len` to `_BaseUrl` to avoid TypeError by [@Kharianne](https://github.com/Kharianne) in [#11111](https://github.com/pydantic/pydantic/pull/11111) +* Make sure the type reference is removed from the seen references by [@Viicos](https://github.com/Viicos) in [#11143](https://github.com/pydantic/pydantic/pull/11143) + +### New Contributors + +* [@FyZzyss](https://github.com/FyZzyss) made their first contribution in [#10789](https://github.com/pydantic/pydantic/pull/10789) +* [@tamird](https://github.com/tamird) made their first contribution in [#10948](https://github.com/pydantic/pydantic/pull/10948) +* [@felixxm](https://github.com/felixxm) made their first contribution in [#11077](https://github.com/pydantic/pydantic/pull/11077) +* [@alexprabhat99](https://github.com/alexprabhat99) made their first contribution in [#11082](https://github.com/pydantic/pydantic/pull/11082) +* [@Kharianne](https://github.com/Kharianne) made their first contribution in [#11111](https://github.com/pydantic/pydantic/pull/11111) + +#### Packaging + +* Bump `pydantic-core` to v2.27.2 by [@davidhewitt](https://github.com/davidhewitt) in [#11138](https://github.com/pydantic/pydantic/pull/11138) + +## v2.10.3 (2024-12-03) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.3) + +### What's Changed + +#### Fixes + +* Set fields when `defer_build` is set on Pydantic dataclasses by [@Viicos](https://github.com/Viicos) in [#10984](https://github.com/pydantic/pydantic/pull/10984) +* Do not resolve the JSON Schema reference for `dict` core schema keys by [@Viicos](https://github.com/Viicos) in [#10989](https://github.com/pydantic/pydantic/pull/10989) +* Use the globals of the function when evaluating the return type for `PlainSerializer` and `WrapSerializer` functions by [@Viicos](https://github.com/Viicos) in [#11008](https://github.com/pydantic/pydantic/pull/11008) +* Fix host required enforcement for urls to be compatible with v2.9 behavior by [@sydney-runkle](https://github.com/sydney-runkle) in [#11027](https://github.com/pydantic/pydantic/pull/11027) +* Add a `default_factory_takes_validated_data` property to `FieldInfo` by [@Viicos](https://github.com/Viicos) in [#11034](https://github.com/pydantic/pydantic/pull/11034) +* Fix url json schema in `serialization` mode by [@sydney-runkle](https://github.com/sydney-runkle) in [#11035](https://github.com/pydantic/pydantic/pull/11035) + +## v2.10.2 (2024-11-25) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.2) + +### What's Changed + +#### Fixes + +* Only evaluate FieldInfo annotations if required during schema building by [@Viicos](https://github.com/Viicos) in [#10769](https://github.com/pydantic/pydantic/pull/10769) +* Do not evaluate annotations for private fields by [@Viicos](https://github.com/Viicos) in [#10962](https://github.com/pydantic/pydantic/pull/10962) +* Support serialization as any for `Secret` types and `Url` types by [@sydney-runkle](https://github.com/sydney-runkle) in [#10947](https://github.com/pydantic/pydantic/pull/10947) +* Fix type hint of `Field.default` to be compatible with Python 3.8 and 3.9 by [@Viicos](https://github.com/Viicos) in [#10972](https://github.com/pydantic/pydantic/pull/10972) +* Add hashing support for URL types by [@sydney-runkle](https://github.com/sydney-runkle) in [#10975](https://github.com/pydantic/pydantic/pull/10975) +* Hide `BaseModel.__replace__` definition from type checkers by [@Viicos](https://github.com/Viicos) in [#10979](https://github.com/pydantic/pydantic/pull/10979) + +## v2.10.1 (2024-11-21) + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.1) + +### What's Changed + +#### Fixes + +* Use the correct frame when instantiating a parametrized `TypeAdapter` by [@Viicos](https://github.com/Viicos) in [#10893](https://github.com/pydantic/pydantic/pull/10893) +* Relax check for validated data in `default_factory` utils by [@sydney-runkle](https://github.com/sydney-runkle) in [#10909](https://github.com/pydantic/pydantic/pull/10909) +* Fix type checking issue with `model_fields` and `model_computed_fields` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10911](https://github.com/pydantic/pydantic/pull/10911) +* Use the parent configuration during schema generation for stdlib `dataclass`es by [@sydney-runkle](https://github.com/sydney-runkle) in [#10928](https://github.com/pydantic/pydantic/pull/10928) +* Use the `globals` of the function when evaluating the return type of serializers and `computed_field`s by [@Viicos](https://github.com/Viicos) in [#10929](https://github.com/pydantic/pydantic/pull/10929) +* Fix URL constraint application by [@sydney-runkle](https://github.com/sydney-runkle) in [#10922](https://github.com/pydantic/pydantic/pull/10922) +* Fix URL equality with different validation methods by [@sydney-runkle](https://github.com/sydney-runkle) in [#10934](https://github.com/pydantic/pydantic/pull/10934) +* Fix JSON schema title when specified as `''` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10936](https://github.com/pydantic/pydantic/pull/10936) +* Fix `python` mode serialization for `complex` inference by [@sydney-runkle](https://github.com/sydney-runkle) in [pydantic-core#1549](https://github.com/pydantic/pydantic-core/pull/1549) + +#### Packaging + +* Bump `pydantic-core` version to `v2.27.1` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10938](https://github.com/pydantic/pydantic/pull/10938) + +### New Contributors + +## v2.10.0 (2024-11-20) + +The code released in v2.10.0 is practically identical to that of v2.10.0b2. + +[GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.0) + +See the [v2.10 release blog post](https://pydantic.dev/articles/pydantic-v2-10-release) for the highlights! + +### What's Changed + +#### New Features + +* Support `fractions.Fraction` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10318](https://github.com/pydantic/pydantic/pull/10318) +* Support `Hashable` for json validation by [@sydney-runkle](https://github.com/sydney-runkle) in [#10324](https://github.com/pydantic/pydantic/pull/10324) +* Add a `SocketPath` type for `linux` systems by [@theunkn0wn1](https://github.com/theunkn0wn1) in [#10378](https://github.com/pydantic/pydantic/pull/10378) +* Allow arbitrary refs in JSON schema `examples` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10417](https://github.com/pydantic/pydantic/pull/10417) +* Support `defer_build` for Pydantic dataclasses by [@Viicos](https://github.com/Viicos) in [#10313](https://github.com/pydantic/pydantic/pull/10313) +* Adding v1 / v2 incompatibility warning for nested v1 model by [@sydney-runkle](https://github.com/sydney-runkle) in [#10431](https://github.com/pydantic/pydantic/pull/10431) +* Add support for unpacked `TypedDict` to type hint variadic keyword arguments with `@validate_call` by [@Viicos](https://github.com/Viicos) in [#10416](https://github.com/pydantic/pydantic/pull/10416) +* Support compiled patterns in `protected_namespaces` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10522](https://github.com/pydantic/pydantic/pull/10522) +* Add support for `propertyNames` in JSON schema by [@FlorianSW](https://github.com/FlorianSW) in [#10478](https://github.com/pydantic/pydantic/pull/10478) +* Adding `__replace__` protocol for Python 3.13+ support by [@sydney-runkle](https://github.com/sydney-runkle) in [#10596](https://github.com/pydantic/pydantic/pull/10596) +* Expose public `sort` method for JSON schema generation by [@sydney-runkle](https://github.com/sydney-runkle) in [#10595](https://github.com/pydantic/pydantic/pull/10595) +* Add runtime validation of `@validate_call` callable argument by [@kc0506](https://github.com/kc0506) in [#10627](https://github.com/pydantic/pydantic/pull/10627) +* Add `experimental_allow_partial` support by [@samuelcolvin](https://github.com/samuelcolvin) in [#10748](https://github.com/pydantic/pydantic/pull/10748) +* Support default factories taking validated data as an argument by [@Viicos](https://github.com/Viicos) in [#10678](https://github.com/pydantic/pydantic/pull/10678) +* Allow subclassing `ValidationError` and `PydanticCustomError` by [@Youssefares](https://github.com/Youssefares) in [pydantic/pydantic-core#1413](https://github.com/pydantic/pydantic-core/pull/1413) +* Add `trailing-strings` support to `experimental_allow_partial` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10825](https://github.com/pydantic/pydantic/pull/10825) +* Add `rebuild()` method for `TypeAdapter` and simplify `defer_build` patterns by [@sydney-runkle](https://github.com/sydney-runkle) in [#10537](https://github.com/pydantic/pydantic/pull/10537) +* Improve `TypeAdapter` instance repr by [@sydney-runkle](https://github.com/sydney-runkle) in [#10872](https://github.com/pydantic/pydantic/pull/10872) + +#### Changes + +* Don't allow customization of `SchemaGenerator` until interface is more stable by [@sydney-runkle](https://github.com/sydney-runkle) in [#10303](https://github.com/pydantic/pydantic/pull/10303) +* Cleanly `defer_build` on `TypeAdapters`, removing experimental flag by [@sydney-runkle](https://github.com/sydney-runkle) in [#10329](https://github.com/pydantic/pydantic/pull/10329) +* Fix `mro` of generic subclass by [@kc0506](https://github.com/kc0506) in [#10100](https://github.com/pydantic/pydantic/pull/10100) +* Strip whitespaces on JSON Schema title generation by [@sydney-runkle](https://github.com/sydney-runkle) in [#10404](https://github.com/pydantic/pydantic/pull/10404) +* Use `b64decode` and `b64encode` for `Base64Bytes` type by [@sydney-runkle](https://github.com/sydney-runkle) in [#10486](https://github.com/pydantic/pydantic/pull/10486) +* Relax protected namespace config default by [@sydney-runkle](https://github.com/sydney-runkle) in [#10441](https://github.com/pydantic/pydantic/pull/10441) +* Revalidate parametrized generics if instance's origin is subclass of OG class by [@sydney-runkle](https://github.com/sydney-runkle) in [#10666](https://github.com/pydantic/pydantic/pull/10666) +* Warn if configuration is specified on the `@dataclass` decorator and with the `__pydantic_config__` attribute by [@sydney-runkle](https://github.com/sydney-runkle) in [#10406](https://github.com/pydantic/pydantic/pull/10406) +* Recommend against using `Ellipsis` (...) with `Field` by [@Viicos](https://github.com/Viicos) in [#10661](https://github.com/pydantic/pydantic/pull/10661) +* Migrate to subclassing instead of annotated approach for pydantic url types by [@sydney-runkle](https://github.com/sydney-runkle) in [#10662](https://github.com/pydantic/pydantic/pull/10662) +* Change JSON schema generation of `Literal`s and `Enums` by [@Viicos](https://github.com/Viicos) in [#10692](https://github.com/pydantic/pydantic/pull/10692) +* Simplify unions involving `Any` or `Never` when replacing type variables by [@Viicos](https://github.com/Viicos) in [#10338](https://github.com/pydantic/pydantic/pull/10338) +* Do not require padding when decoding `base64` bytes by [@bschoenmaeckers](https://github.com/bschoenmaeckers) in [pydantic/pydantic-core#1448](https://github.com/pydantic/pydantic-core/pull/1448) +* Support dates all the way to 1BC by [@changhc](https://github.com/changhc) in [pydantic/speedate#77](https://github.com/pydantic/speedate/pull/77) + +#### Performance + +* Schema cleaning: skip unnecessary copies during schema walking by [@Viicos](https://github.com/Viicos) in [#10286](https://github.com/pydantic/pydantic/pull/10286) +* Refactor namespace logic for annotations evaluation by [@Viicos](https://github.com/Viicos) in [#10530](https://github.com/pydantic/pydantic/pull/10530) +* Improve email regexp on edge cases by [@AlekseyLobanov](https://github.com/AlekseyLobanov) in [#10601](https://github.com/pydantic/pydantic/pull/10601) +* `CoreMetadata` refactor with an emphasis on documentation, schema build time performance, and reducing complexity by [@sydney-runkle](https://github.com/sydney-runkle) in [#10675](https://github.com/pydantic/pydantic/pull/10675) + +#### Packaging + +* Bump `pydantic-core` to `v2.27.0` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10825](https://github.com/pydantic/pydantic/pull/10825) +* Replaced pdm with uv by [@frfahim](https://github.com/frfahim) in [#10727](https://github.com/pydantic/pydantic/pull/10727) + +#### Fixes + +* Remove guarding check on `computed_field` with `field_serializer` by [@nix010](https://github.com/nix010) in [#10390](https://github.com/pydantic/pydantic/pull/10390) +* Fix `Predicate` issue in `v2.9.0` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10321](https://github.com/pydantic/pydantic/pull/10321) +* Fixing `annotated-types` bound by [@sydney-runkle](https://github.com/sydney-runkle) in [#10327](https://github.com/pydantic/pydantic/pull/10327) +* Turn `tzdata` install requirement into optional `timezone` dependency by [@jakob-keller](https://github.com/jakob-keller) in [#10331](https://github.com/pydantic/pydantic/pull/10331) +* Use correct types namespace when building `namedtuple` core schemas by [@Viicos](https://github.com/Viicos) in [#10337](https://github.com/pydantic/pydantic/pull/10337) +* Fix evaluation of stringified annotations during namespace inspection by [@Viicos](https://github.com/Viicos) in [#10347](https://github.com/pydantic/pydantic/pull/10347) +* Fix `IncEx` type alias definition by [@Viicos](https://github.com/Viicos) in [#10339](https://github.com/pydantic/pydantic/pull/10339) +* Do not error when trying to evaluate annotations of private attributes by [@Viicos](https://github.com/Viicos) in [#10358](https://github.com/pydantic/pydantic/pull/10358) +* Fix nested type statement by [@kc0506](https://github.com/kc0506) in [#10369](https://github.com/pydantic/pydantic/pull/10369) +* Improve typing of `ModelMetaclass.mro` by [@Viicos](https://github.com/Viicos) in [#10372](https://github.com/pydantic/pydantic/pull/10372) +* Fix class access of deprecated `computed_field`s by [@Viicos](https://github.com/Viicos) in [#10391](https://github.com/pydantic/pydantic/pull/10391) +* Make sure `inspect.iscoroutinefunction` works on coroutines decorated with `@validate_call` by [@MovisLi](https://github.com/MovisLi) in [#10374](https://github.com/pydantic/pydantic/pull/10374) +* Fix `NameError` when using `validate_call` with PEP 695 on a class by [@kc0506](https://github.com/kc0506) in [#10380](https://github.com/pydantic/pydantic/pull/10380) +* Fix `ZoneInfo` with various invalid types by [@sydney-runkle](https://github.com/sydney-runkle) in [#10408](https://github.com/pydantic/pydantic/pull/10408) +* Fix `PydanticUserError` on empty `model_config` with annotations by [@cdwilson](https://github.com/cdwilson) in [#10412](https://github.com/pydantic/pydantic/pull/10412) +* Fix variance issue in `_IncEx` type alias, only allow `True` by [@Viicos](https://github.com/Viicos) in [#10414](https://github.com/pydantic/pydantic/pull/10414) +* Fix serialization schema generation when using `PlainValidator` by [@Viicos](https://github.com/Viicos) in [#10427](https://github.com/pydantic/pydantic/pull/10427) +* Fix schema generation error when serialization schema holds references by [@Viicos](https://github.com/Viicos) in [#10444](https://github.com/pydantic/pydantic/pull/10444) +* Inline references if possible when generating schema for `json_schema_input_type` by [@Viicos](https://github.com/Viicos) in [#10439](https://github.com/pydantic/pydantic/pull/10439) +* Fix recursive arguments in `Representation` by [@Viicos](https://github.com/Viicos) in [#10480](https://github.com/pydantic/pydantic/pull/10480) +* Fix representation for builtin function types by [@kschwab](https://github.com/kschwab) in [#10479](https://github.com/pydantic/pydantic/pull/10479) +* Add python validators for decimal constraints (`max_digits` and `decimal_places`) by [@sydney-runkle](https://github.com/sydney-runkle) in [#10506](https://github.com/pydantic/pydantic/pull/10506) +* Only fetch `__pydantic_core_schema__` from the current class during schema generation by [@Viicos](https://github.com/Viicos) in [#10518](https://github.com/pydantic/pydantic/pull/10518) +* Fix `stacklevel` on deprecation warnings for `BaseModel` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10520](https://github.com/pydantic/pydantic/pull/10520) +* Fix warning `stacklevel` in `BaseModel.__init__` by [@Viicos](https://github.com/Viicos) in [#10526](https://github.com/pydantic/pydantic/pull/10526) +* Improve error handling for in-evaluable refs for discriminator application by [@sydney-runkle](https://github.com/sydney-runkle) in [#10440](https://github.com/pydantic/pydantic/pull/10440) +* Change the signature of `ConfigWrapper.core_config` to take the title directly by [@Viicos](https://github.com/Viicos) in [#10562](https://github.com/pydantic/pydantic/pull/10562) +* Do not use the previous config from the stack for dataclasses without config by [@Viicos](https://github.com/Viicos) in [#10576](https://github.com/pydantic/pydantic/pull/10576) +* Fix serialization for IP types with `mode='python'` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10594](https://github.com/pydantic/pydantic/pull/10594) +* Support constraint application for `Base64Etc` types by [@sydney-runkle](https://github.com/sydney-runkle) in [#10584](https://github.com/pydantic/pydantic/pull/10584) +* Fix `validate_call` ignoring `Field` in `Annotated` by [@kc0506](https://github.com/kc0506) in [#10610](https://github.com/pydantic/pydantic/pull/10610) +* Raise an error when `Self` is invalid by [@kc0506](https://github.com/kc0506) in [#10609](https://github.com/pydantic/pydantic/pull/10609) +* Using `core_schema.InvalidSchema` instead of metadata injection + checks by [@sydney-runkle](https://github.com/sydney-runkle) in [#10523](https://github.com/pydantic/pydantic/pull/10523) +* Tweak type alias logic by [@kc0506](https://github.com/kc0506) in [#10643](https://github.com/pydantic/pydantic/pull/10643) +* Support usage of `type` with `typing.Self` and type aliases by [@kc0506](https://github.com/kc0506) in [#10621](https://github.com/pydantic/pydantic/pull/10621) +* Use overloads for `Field` and `PrivateAttr` functions by [@Viicos](https://github.com/Viicos) in [#10651](https://github.com/pydantic/pydantic/pull/10651) +* Clean up the `mypy` plugin implementation by [@Viicos](https://github.com/Viicos) in [#10669](https://github.com/pydantic/pydantic/pull/10669) +* Properly check for `typing_extensions` variant of `TypeAliasType` by [@Daraan](https://github.com/Daraan) in [#10713](https://github.com/pydantic/pydantic/pull/10713) +* Allow any mapping in `BaseModel.model_copy()` by [@Viicos](https://github.com/Viicos) in [#10751](https://github.com/pydantic/pydantic/pull/10751) +* Fix `isinstance` behavior for urls by [@sydney-runkle](https://github.com/sydney-runkle) in [#10766](https://github.com/pydantic/pydantic/pull/10766) +* Ensure `cached_property` can be set on Pydantic models by [@Viicos](https://github.com/Viicos) in [#10774](https://github.com/pydantic/pydantic/pull/10774) +* Fix equality checks for primitives in literals by [@sydney-runkle](https://github.com/sydney-runkle) in [pydantic/pydantic-core#1459](https://github.com/pydantic/pydantic-core/pull/1459) +* Properly enforce `host_required` for URLs by [@Viicos](https://github.com/Viicos) in [pydantic/pydantic-core#1488](https://github.com/pydantic/pydantic-core/pull/1488) +* Fix when `coerce_numbers_to_str` enabled and string has invalid Unicode character by [@andrey-berenda](https://github.com/andrey-berenda) in [pydantic/pydantic-core#1515](https://github.com/pydantic/pydantic-core/pull/1515) +* Fix serializing `complex` values in `Enum`s by [@changhc](https://github.com/changhc) in [pydantic/pydantic-core#1524](https://github.com/pydantic/pydantic-core/pull/1524) +* Refactor `_typing_extra` module by [@Viicos](https://github.com/Viicos) in [#10725](https://github.com/pydantic/pydantic/pull/10725) +* Support intuitive equality for urls by [@sydney-runkle](https://github.com/sydney-runkle) in [#10798](https://github.com/pydantic/pydantic/pull/10798) +* Add `bytearray` to `TypeAdapter.validate_json` signature by [@samuelcolvin](https://github.com/samuelcolvin) in [#10802](https://github.com/pydantic/pydantic/pull/10802) +* Ensure class access of method descriptors is performed when used as a default with `Field` by [@Viicos](https://github.com/Viicos) in [#10816](https://github.com/pydantic/pydantic/pull/10816) +* Fix circular import with `validate_call` by [@sydney-runkle](https://github.com/sydney-runkle) in [#10807](https://github.com/pydantic/pydantic/pull/10807) +* Fix error when using type aliases referencing other type aliases by [@Viicos](https://github.com/Viicos) in [#10809](https://github.com/pydantic/pydantic/pull/10809) +* Fix `IncEx` type alias to be compatible with mypy by [@Viicos](https://github.com/Viicos) in [#10813](https://github.com/pydantic/pydantic/pull/10813) +* Make `__signature__` a lazy property, do not deepcopy defaults by [@Viicos](https://github.com/Viicos) in [#10818](https://github.com/pydantic/pydantic/pull/10818) +* Make `__signature__` lazy for dataclasses, too by [@sydney-runkle](https://github.com/sydney-runkle) in [#10832](https://github.com/pydantic/pydantic/pull/10832) +* Subclass all single host url classes from `AnyUrl` to preserve behavior from v2.9 by [@sydney-runkle](https://github.com/sydney-runkle) in [#10856](https://github.com/pydantic/pydantic/pull/10856) + +### New Contributors + +* [@jakob-keller](https://github.com/jakob-keller) made their first contribution in [#10331](https://github.com/pydantic/pydantic/pull/10331) +* [@MovisLi](https://github.com/MovisLi) made their first contribution in [#10374](https://github.com/pydantic/pydantic/pull/10374) +* [@joaopalmeiro](https://github.com/joaopalmeiro) made their first contribution in [#10405](https://github.com/pydantic/pydantic/pull/10405) +* [@theunkn0wn1](https://github.com/theunkn0wn1) made their first contribution in [#10378](https://github.com/pydantic/pydantic/pull/10378) +* [@cdwilson](https://github.com/cdwilson) made their first contribution in [#10412](https://github.com/pydantic/pydantic/pull/10412) +* [@dlax](https://github.com/dlax) made their first contribution in [#10421](https://github.com/pydantic/pydantic/pull/10421) +* [@kschwab](https://github.com/kschwab) made their first contribution in [#10479](https://github.com/pydantic/pydantic/pull/10479) +* [@santibreo](https://github.com/santibreo) made their first contribution in [#10453](https://github.com/pydantic/pydantic/pull/10453) +* [@FlorianSW](https://github.com/FlorianSW) made their first contribution in [#10478](https://github.com/pydantic/pydantic/pull/10478) +* [@tkasuz](https://github.com/tkasuz) made their first contribution in [#10555](https://github.com/pydantic/pydantic/pull/10555) +* [@AlekseyLobanov](https://github.com/AlekseyLobanov) made their first contribution in [#10601](https://github.com/pydantic/pydantic/pull/10601) +* [@NiclasvanEyk](https://github.com/NiclasvanEyk) made their first contribution in [#10667](https://github.com/pydantic/pydantic/pull/10667) +* [@mschoettle](https://github.com/mschoettle) made their first contribution in [#10677](https://github.com/pydantic/pydantic/pull/10677) +* [@Daraan](https://github.com/Daraan) made their first contribution in [#10713](https://github.com/pydantic/pydantic/pull/10713) +* [@k4nar](https://github.com/k4nar) made their first contribution in [#10736](https://github.com/pydantic/pydantic/pull/10736) +* [@UriyaHarpeness](https://github.com/UriyaHarpeness) made their first contribution in [#10740](https://github.com/pydantic/pydantic/pull/10740) +* [@frfahim](https://github.com/frfahim) made their first contribution in [#10727](https://github.com/pydantic/pydantic/pull/10727) + +## v2.10.0b2 (2024-11-13) + +Pre-release, see [the GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.0b2) for details. + +## v2.10.0b1 (2024-11-06) + +Pre-release, see [the GitHub release](https://github.com/pydantic/pydantic/releases/tag/v2.10.0b1) for details. + + +... see [here](https://docs.pydantic.dev/changelog/#v0322-2019-08-17) for earlier changes. diff --git 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100644 index 0000000000000000000000000000000000000000..b1b94fd58e7e9ed0ef3449473bc48de68afcc3fe --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: hatchling 1.29.0 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/licenses/LICENSE b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..488c6260c10f2e88fa1fae58a63fccec8d600cd1 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/licenses/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2017 to present Pydantic Services Inc. and individual contributors. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..01212849e733d8f07df79b27f328a294c54d7fe3 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/__init__.py @@ -0,0 +1,456 @@ +from importlib import import_module +from typing import TYPE_CHECKING +from warnings import warn + +from ._migration import getattr_migration +from .version import VERSION, _ensure_pydantic_core_version + +_ensure_pydantic_core_version() +del _ensure_pydantic_core_version + +if TYPE_CHECKING: + # import of virtually everything is supported via `__getattr__` below, + # but we need them here for type checking and IDE support + import pydantic_core + from pydantic_core.core_schema import ( + FieldSerializationInfo, + SerializationInfo, + SerializerFunctionWrapHandler, + ValidationInfo, + ValidatorFunctionWrapHandler, + ) + + from . import dataclasses + from .aliases import AliasChoices, AliasGenerator, AliasPath + from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler + from .config import ConfigDict, with_config + from .errors import * + from .fields import Field, PrivateAttr, computed_field + from .functional_serializers import ( + PlainSerializer, + SerializeAsAny, + WrapSerializer, + field_serializer, + model_serializer, + ) + from .functional_validators import ( + AfterValidator, + BeforeValidator, + InstanceOf, + ModelWrapValidatorHandler, + PlainValidator, + SkipValidation, + ValidateAs, + WrapValidator, + field_validator, + model_validator, + ) + from .json_schema import WithJsonSchema + from .main import * + from .networks import * + from .type_adapter import TypeAdapter + from .types import * + from .validate_call_decorator import validate_call + from .warnings import ( + PydanticDeprecatedSince20, + PydanticDeprecatedSince26, + PydanticDeprecatedSince29, + PydanticDeprecatedSince210, + PydanticDeprecatedSince211, + PydanticDeprecatedSince212, + PydanticDeprecationWarning, + PydanticExperimentalWarning, + ) + + # this encourages pycharm to import `ValidationError` from here, not pydantic_core + ValidationError = pydantic_core.ValidationError + from .deprecated.class_validators import root_validator, validator + from .deprecated.config import BaseConfig, Extra + from .deprecated.tools import * + from .root_model import RootModel + +__version__ = VERSION +__all__ = ( + # dataclasses + 'dataclasses', + # functional validators + 'field_validator', + 'model_validator', + 'AfterValidator', + 'BeforeValidator', + 'PlainValidator', + 'WrapValidator', + 'SkipValidation', + 'ValidateAs', + 'InstanceOf', + 'ModelWrapValidatorHandler', + # JSON Schema + 'WithJsonSchema', + # deprecated V1 functional validators, these are imported via `__getattr__` below + 'root_validator', + 'validator', + # functional serializers + 'field_serializer', + 'model_serializer', + 'PlainSerializer', + 'SerializeAsAny', + 'WrapSerializer', + # config + 'ConfigDict', + 'with_config', + # deprecated V1 config, these are imported via `__getattr__` below + 'BaseConfig', + 'Extra', + # validate_call + 'validate_call', + # errors + 'PydanticErrorCodes', + 'PydanticUserError', + 'PydanticSchemaGenerationError', + 'PydanticImportError', + 'PydanticUndefinedAnnotation', + 'PydanticInvalidForJsonSchema', + 'PydanticForbiddenQualifier', + # fields + 'Field', + 'computed_field', + 'PrivateAttr', + # alias + 'AliasChoices', + 'AliasGenerator', + 'AliasPath', + # main + 'BaseModel', + 'create_model', + # network + 'AnyUrl', + 'AnyHttpUrl', + 'FileUrl', + 'HttpUrl', + 'FtpUrl', + 'WebsocketUrl', + 'AnyWebsocketUrl', + 'UrlConstraints', + 'EmailStr', + 'NameEmail', + 'IPvAnyAddress', + 'IPvAnyInterface', + 'IPvAnyNetwork', + 'PostgresDsn', + 'CockroachDsn', + 'AmqpDsn', + 'RedisDsn', + 'MongoDsn', + 'KafkaDsn', + 'NatsDsn', + 'MySQLDsn', + 'MariaDBDsn', + 'ClickHouseDsn', + 'SnowflakeDsn', + 'validate_email', + # root_model + 'RootModel', + # deprecated tools, these are imported via `__getattr__` below + 'parse_obj_as', + 'schema_of', + 'schema_json_of', + # types + 'Strict', + 'StrictStr', + 'conbytes', + 'conlist', + 'conset', + 'confrozenset', + 'constr', + 'StringConstraints', + 'ImportString', + 'conint', + 'PositiveInt', + 'NegativeInt', + 'NonNegativeInt', + 'NonPositiveInt', + 'confloat', + 'PositiveFloat', + 'NegativeFloat', + 'NonNegativeFloat', + 'NonPositiveFloat', + 'FiniteFloat', + 'condecimal', + 'condate', + 'UUID1', + 'UUID3', + 'UUID4', + 'UUID5', + 'UUID6', + 'UUID7', + 'UUID8', + 'FilePath', + 'DirectoryPath', + 'NewPath', + 'Json', + 'Secret', + 'SecretStr', + 'SecretBytes', + 'SocketPath', + 'StrictBool', + 'StrictBytes', + 'StrictInt', + 'StrictFloat', + 'PaymentCardNumber', + 'ByteSize', + 'PastDate', + 'FutureDate', + 'PastDatetime', + 'FutureDatetime', + 'AwareDatetime', + 'NaiveDatetime', + 'AllowInfNan', + 'EncoderProtocol', + 'EncodedBytes', + 'EncodedStr', + 'Base64Encoder', + 'Base64Bytes', + 'Base64Str', + 'Base64UrlBytes', + 'Base64UrlStr', + 'GetPydanticSchema', + 'Tag', + 'Discriminator', + 'JsonValue', + 'FailFast', + # type_adapter + 'TypeAdapter', + # version + '__version__', + 'VERSION', + # warnings + 'PydanticDeprecatedSince20', + 'PydanticDeprecatedSince26', + 'PydanticDeprecatedSince29', + 'PydanticDeprecatedSince210', + 'PydanticDeprecatedSince211', + 'PydanticDeprecatedSince212', + 'PydanticDeprecationWarning', + 'PydanticExperimentalWarning', + # annotated handlers + 'GetCoreSchemaHandler', + 'GetJsonSchemaHandler', + # pydantic_core + 'ValidationError', + 'ValidationInfo', + 'SerializationInfo', + 'ValidatorFunctionWrapHandler', + 'FieldSerializationInfo', + 'SerializerFunctionWrapHandler', + 'OnErrorOmit', +) + +# A mapping of {: (package, )} defining dynamic imports +_dynamic_imports: 'dict[str, tuple[str, str]]' = { + 'dataclasses': (__spec__.parent, '__module__'), + # functional validators + 'field_validator': (__spec__.parent, '.functional_validators'), + 'model_validator': (__spec__.parent, '.functional_validators'), + 'AfterValidator': (__spec__.parent, '.functional_validators'), + 'BeforeValidator': (__spec__.parent, '.functional_validators'), + 'PlainValidator': (__spec__.parent, '.functional_validators'), + 'WrapValidator': (__spec__.parent, '.functional_validators'), + 'SkipValidation': (__spec__.parent, '.functional_validators'), + 'InstanceOf': (__spec__.parent, '.functional_validators'), + 'ValidateAs': (__spec__.parent, '.functional_validators'), + 'ModelWrapValidatorHandler': (__spec__.parent, '.functional_validators'), + # JSON Schema + 'WithJsonSchema': (__spec__.parent, '.json_schema'), + # functional serializers + 'field_serializer': (__spec__.parent, '.functional_serializers'), + 'model_serializer': (__spec__.parent, '.functional_serializers'), + 'PlainSerializer': (__spec__.parent, '.functional_serializers'), + 'SerializeAsAny': (__spec__.parent, '.functional_serializers'), + 'WrapSerializer': (__spec__.parent, '.functional_serializers'), + # config + 'ConfigDict': (__spec__.parent, '.config'), + 'with_config': (__spec__.parent, '.config'), + # validate call + 'validate_call': (__spec__.parent, '.validate_call_decorator'), + # errors + 'PydanticErrorCodes': (__spec__.parent, '.errors'), + 'PydanticUserError': (__spec__.parent, '.errors'), + 'PydanticSchemaGenerationError': (__spec__.parent, '.errors'), + 'PydanticImportError': (__spec__.parent, '.errors'), + 'PydanticUndefinedAnnotation': (__spec__.parent, '.errors'), + 'PydanticInvalidForJsonSchema': (__spec__.parent, '.errors'), + 'PydanticForbiddenQualifier': (__spec__.parent, '.errors'), + # fields + 'Field': (__spec__.parent, '.fields'), + 'computed_field': (__spec__.parent, '.fields'), + 'PrivateAttr': (__spec__.parent, '.fields'), + # alias + 'AliasChoices': (__spec__.parent, '.aliases'), + 'AliasGenerator': (__spec__.parent, '.aliases'), + 'AliasPath': (__spec__.parent, '.aliases'), + # main + 'BaseModel': (__spec__.parent, '.main'), + 'create_model': (__spec__.parent, '.main'), + # network + 'AnyUrl': (__spec__.parent, '.networks'), + 'AnyHttpUrl': (__spec__.parent, '.networks'), + 'FileUrl': (__spec__.parent, '.networks'), + 'HttpUrl': (__spec__.parent, '.networks'), + 'FtpUrl': (__spec__.parent, '.networks'), + 'WebsocketUrl': (__spec__.parent, '.networks'), + 'AnyWebsocketUrl': (__spec__.parent, '.networks'), + 'UrlConstraints': (__spec__.parent, '.networks'), + 'EmailStr': (__spec__.parent, '.networks'), + 'NameEmail': (__spec__.parent, '.networks'), + 'IPvAnyAddress': (__spec__.parent, '.networks'), + 'IPvAnyInterface': (__spec__.parent, '.networks'), + 'IPvAnyNetwork': (__spec__.parent, '.networks'), + 'PostgresDsn': (__spec__.parent, '.networks'), + 'CockroachDsn': (__spec__.parent, '.networks'), + 'AmqpDsn': (__spec__.parent, '.networks'), + 'RedisDsn': (__spec__.parent, '.networks'), + 'MongoDsn': (__spec__.parent, '.networks'), + 'KafkaDsn': (__spec__.parent, '.networks'), + 'NatsDsn': (__spec__.parent, '.networks'), + 'MySQLDsn': (__spec__.parent, '.networks'), + 'MariaDBDsn': (__spec__.parent, '.networks'), + 'ClickHouseDsn': (__spec__.parent, '.networks'), + 'SnowflakeDsn': (__spec__.parent, '.networks'), + 'validate_email': (__spec__.parent, '.networks'), + # root_model + 'RootModel': (__spec__.parent, '.root_model'), + # types + 'Strict': (__spec__.parent, '.types'), + 'StrictStr': (__spec__.parent, '.types'), + 'conbytes': (__spec__.parent, '.types'), + 'conlist': (__spec__.parent, '.types'), + 'conset': (__spec__.parent, '.types'), + 'confrozenset': (__spec__.parent, '.types'), + 'constr': (__spec__.parent, '.types'), + 'StringConstraints': (__spec__.parent, '.types'), + 'ImportString': (__spec__.parent, '.types'), + 'conint': (__spec__.parent, '.types'), + 'PositiveInt': (__spec__.parent, '.types'), + 'NegativeInt': (__spec__.parent, '.types'), + 'NonNegativeInt': (__spec__.parent, '.types'), + 'NonPositiveInt': (__spec__.parent, '.types'), + 'confloat': (__spec__.parent, '.types'), + 'PositiveFloat': (__spec__.parent, '.types'), + 'NegativeFloat': (__spec__.parent, '.types'), + 'NonNegativeFloat': (__spec__.parent, '.types'), + 'NonPositiveFloat': (__spec__.parent, '.types'), + 'FiniteFloat': (__spec__.parent, '.types'), + 'condecimal': (__spec__.parent, '.types'), + 'condate': (__spec__.parent, '.types'), + 'UUID1': (__spec__.parent, '.types'), + 'UUID3': (__spec__.parent, '.types'), + 'UUID4': (__spec__.parent, '.types'), + 'UUID5': (__spec__.parent, '.types'), + 'UUID6': (__spec__.parent, '.types'), + 'UUID7': (__spec__.parent, '.types'), + 'UUID8': (__spec__.parent, '.types'), + 'FilePath': (__spec__.parent, '.types'), + 'DirectoryPath': (__spec__.parent, '.types'), + 'NewPath': (__spec__.parent, '.types'), + 'Json': (__spec__.parent, '.types'), + 'Secret': (__spec__.parent, '.types'), + 'SecretStr': (__spec__.parent, '.types'), + 'SecretBytes': (__spec__.parent, '.types'), + 'StrictBool': (__spec__.parent, '.types'), + 'StrictBytes': (__spec__.parent, '.types'), + 'StrictInt': (__spec__.parent, '.types'), + 'StrictFloat': (__spec__.parent, '.types'), + 'PaymentCardNumber': (__spec__.parent, '.types'), + 'ByteSize': (__spec__.parent, '.types'), + 'PastDate': (__spec__.parent, '.types'), + 'SocketPath': (__spec__.parent, '.types'), + 'FutureDate': (__spec__.parent, '.types'), + 'PastDatetime': (__spec__.parent, '.types'), + 'FutureDatetime': (__spec__.parent, '.types'), + 'AwareDatetime': (__spec__.parent, '.types'), + 'NaiveDatetime': (__spec__.parent, '.types'), + 'AllowInfNan': (__spec__.parent, '.types'), + 'EncoderProtocol': (__spec__.parent, '.types'), + 'EncodedBytes': (__spec__.parent, '.types'), + 'EncodedStr': (__spec__.parent, '.types'), + 'Base64Encoder': (__spec__.parent, '.types'), + 'Base64Bytes': (__spec__.parent, '.types'), + 'Base64Str': (__spec__.parent, '.types'), + 'Base64UrlBytes': (__spec__.parent, '.types'), + 'Base64UrlStr': (__spec__.parent, '.types'), + 'GetPydanticSchema': (__spec__.parent, '.types'), + 'Tag': (__spec__.parent, '.types'), + 'Discriminator': (__spec__.parent, '.types'), + 'JsonValue': (__spec__.parent, '.types'), + 'OnErrorOmit': (__spec__.parent, '.types'), + 'FailFast': (__spec__.parent, '.types'), + # type_adapter + 'TypeAdapter': (__spec__.parent, '.type_adapter'), + # warnings + 'PydanticDeprecatedSince20': (__spec__.parent, '.warnings'), + 'PydanticDeprecatedSince26': (__spec__.parent, '.warnings'), + 'PydanticDeprecatedSince29': (__spec__.parent, '.warnings'), + 'PydanticDeprecatedSince210': (__spec__.parent, '.warnings'), + 'PydanticDeprecatedSince211': (__spec__.parent, '.warnings'), + 'PydanticDeprecatedSince212': (__spec__.parent, '.warnings'), + 'PydanticDeprecationWarning': (__spec__.parent, '.warnings'), + 'PydanticExperimentalWarning': (__spec__.parent, '.warnings'), + # annotated handlers + 'GetCoreSchemaHandler': (__spec__.parent, '.annotated_handlers'), + 'GetJsonSchemaHandler': (__spec__.parent, '.annotated_handlers'), + # pydantic_core stuff + 'ValidationError': ('pydantic_core', '.'), + 'ValidationInfo': ('pydantic_core', '.core_schema'), + 'SerializationInfo': ('pydantic_core', '.core_schema'), + 'ValidatorFunctionWrapHandler': ('pydantic_core', '.core_schema'), + 'FieldSerializationInfo': ('pydantic_core', '.core_schema'), + 'SerializerFunctionWrapHandler': ('pydantic_core', '.core_schema'), + # deprecated, mostly not included in __all__ + 'root_validator': (__spec__.parent, '.deprecated.class_validators'), + 'validator': (__spec__.parent, '.deprecated.class_validators'), + 'BaseConfig': (__spec__.parent, '.deprecated.config'), + 'Extra': (__spec__.parent, '.deprecated.config'), + 'parse_obj_as': (__spec__.parent, '.deprecated.tools'), + 'schema_of': (__spec__.parent, '.deprecated.tools'), + 'schema_json_of': (__spec__.parent, '.deprecated.tools'), + # deprecated dynamic imports + 'FieldValidationInfo': ('pydantic_core', '.core_schema'), + 'GenerateSchema': (__spec__.parent, '._internal._generate_schema'), +} +_deprecated_dynamic_imports = {'FieldValidationInfo', 'GenerateSchema'} + +_getattr_migration = getattr_migration(__name__) + + +def __getattr__(attr_name: str) -> object: + if attr_name in _deprecated_dynamic_imports: + from pydantic.warnings import PydanticDeprecatedSince20 + + warn( + f'Importing {attr_name} from `pydantic` is deprecated. This feature is either no longer supported, or is not public.', + PydanticDeprecatedSince20, + stacklevel=2, + ) + + dynamic_attr = _dynamic_imports.get(attr_name) + if dynamic_attr is None: + return _getattr_migration(attr_name) + + package, module_name = dynamic_attr + + if module_name == '__module__': + result = import_module(f'.{attr_name}', package=package) + globals()[attr_name] = result + return result + else: + module = import_module(module_name, package=package) + result = getattr(module, attr_name) + g = globals() + for k, (_, v_module_name) in _dynamic_imports.items(): + if v_module_name == module_name and k not in _deprecated_dynamic_imports: + g[k] = getattr(module, k) + return result + + +def __dir__() -> list[str]: + return list(__all__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_config.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_config.py new file mode 100644 index 0000000000000000000000000000000000000000..73b5870891629a6f1351ef825cc1e2a3d1728268 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_config.py @@ -0,0 +1,386 @@ +from __future__ import annotations as _annotations + +import warnings +from contextlib import contextmanager +from re import Pattern +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Literal, + cast, +) + +from pydantic_core import core_schema +from typing_extensions import Self + +from ..aliases import AliasGenerator +from ..config import ConfigDict, ExtraValues, JsonDict, JsonEncoder, JsonSchemaExtraCallable +from ..errors import PydanticUserError +from ..warnings import PydanticDeprecatedSince20, PydanticDeprecatedSince210 + +if TYPE_CHECKING: + from .._internal._schema_generation_shared import GenerateSchema + from ..fields import ComputedFieldInfo, FieldInfo + +DEPRECATION_MESSAGE = 'Support for class-based `config` is deprecated, use ConfigDict instead.' + + +class ConfigWrapper: + """Internal wrapper for Config which exposes ConfigDict items as attributes.""" + + __slots__ = ('config_dict',) + + config_dict: ConfigDict + + # all annotations are copied directly from ConfigDict, and should be kept up to date, a test will fail if they + # stop matching + title: str | None + str_to_lower: bool + str_to_upper: bool + str_strip_whitespace: bool + str_min_length: int + str_max_length: int | None + extra: ExtraValues | None + frozen: bool + populate_by_name: bool + use_enum_values: bool + validate_assignment: bool + arbitrary_types_allowed: bool + from_attributes: bool + # whether to use the actual key provided in the data (e.g. alias or first alias for "field required" errors) instead of field_names + # to construct error `loc`s, default `True` + loc_by_alias: bool + alias_generator: Callable[[str], str] | AliasGenerator | None + model_title_generator: Callable[[type], str] | None + field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None + ignored_types: tuple[type, ...] + allow_inf_nan: bool + json_schema_extra: JsonDict | JsonSchemaExtraCallable | None + json_encoders: dict[type[object], JsonEncoder] | None + + # new in V2 + strict: bool + # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never' + revalidate_instances: Literal['always', 'never', 'subclass-instances'] + ser_json_timedelta: Literal['iso8601', 'float'] + ser_json_temporal: Literal['iso8601', 'seconds', 'milliseconds'] + val_temporal_unit: Literal['seconds', 'milliseconds', 'infer'] + ser_json_bytes: Literal['utf8', 'base64', 'hex'] + val_json_bytes: Literal['utf8', 'base64', 'hex'] + ser_json_inf_nan: Literal['null', 'constants', 'strings'] + # whether to validate default values during validation, default False + validate_default: bool + validate_return: bool + protected_namespaces: tuple[str | Pattern[str], ...] + hide_input_in_errors: bool + defer_build: bool + plugin_settings: dict[str, object] | None + schema_generator: type[GenerateSchema] | None + json_schema_serialization_defaults_required: bool + json_schema_mode_override: Literal['validation', 'serialization', None] + coerce_numbers_to_str: bool + regex_engine: Literal['rust-regex', 'python-re'] + validation_error_cause: bool + use_attribute_docstrings: bool + cache_strings: bool | Literal['all', 'keys', 'none'] + validate_by_alias: bool + validate_by_name: bool + serialize_by_alias: bool + url_preserve_empty_path: bool + polymorphic_serialization: bool + + def __init__(self, config: ConfigDict | dict[str, Any] | type[Any] | None, *, check: bool = True): + if check: + self.config_dict = prepare_config(config) + else: + self.config_dict = cast(ConfigDict, config) + + @classmethod + def for_model( + cls, + bases: tuple[type[Any], ...], + namespace: dict[str, Any], + raw_annotations: dict[str, Any], + kwargs: dict[str, Any], + ) -> Self: + """Build a new `ConfigWrapper` instance for a `BaseModel`. + + The config wrapper built based on (in descending order of priority): + - options from `kwargs` + - options from the `namespace` + - options from the base classes (`bases`) + + Args: + bases: A tuple of base classes. + namespace: The namespace of the class being created. + raw_annotations: The (non-evaluated) annotations of the model. + kwargs: The kwargs passed to the class being created. + + Returns: + A `ConfigWrapper` instance for `BaseModel`. + """ + config_new = ConfigDict() + for base in bases: + config = getattr(base, 'model_config', None) + if config: + config_new.update(config.copy()) + + config_class_from_namespace = namespace.get('Config') + config_dict_from_namespace = namespace.get('model_config') + + if raw_annotations.get('model_config') and config_dict_from_namespace is None: + raise PydanticUserError( + '`model_config` cannot be used as a model field name. Use `model_config` for model configuration.', + code='model-config-invalid-field-name', + ) + + if config_class_from_namespace and config_dict_from_namespace: + raise PydanticUserError('"Config" and "model_config" cannot be used together', code='config-both') + + config_from_namespace = config_dict_from_namespace or prepare_config(config_class_from_namespace) + + config_new.update(config_from_namespace) + + for k in list(kwargs.keys()): + if k in config_keys: + config_new[k] = kwargs.pop(k) + + return cls(config_new) + + # we don't show `__getattr__` to type checkers so missing attributes cause errors + if not TYPE_CHECKING: # pragma: no branch + + def __getattr__(self, name: str) -> Any: + try: + return self.config_dict[name] + except KeyError: + try: + return config_defaults[name] + except KeyError: + raise AttributeError(f'Config has no attribute {name!r}') from None + + def core_config(self, title: str | None) -> core_schema.CoreConfig: + """Create a pydantic-core config. + + We don't use getattr here since we don't want to populate with defaults. + + Args: + title: The title to use if not set in config. + + Returns: + A `CoreConfig` object created from config. + """ + config = self.config_dict + + if config.get('schema_generator') is not None: + warnings.warn( + 'The `schema_generator` setting has been deprecated since v2.10. This setting no longer has any effect.', + PydanticDeprecatedSince210, + stacklevel=2, + ) + + if (populate_by_name := config.get('populate_by_name')) is not None: + # We include this patch for backwards compatibility purposes, but this config setting will be deprecated in v3.0, and likely removed in v4.0. + # Thus, the above warning and this patch can be removed then as well. + if config.get('validate_by_name') is None: + config['validate_by_alias'] = True + config['validate_by_name'] = populate_by_name + + # We dynamically patch validate_by_name to be True if validate_by_alias is set to False + # and validate_by_name is not explicitly set. + if config.get('validate_by_alias') is False and config.get('validate_by_name') is None: + config['validate_by_name'] = True + + if (not config.get('validate_by_alias', True)) and (not config.get('validate_by_name', False)): + raise PydanticUserError( + 'At least one of `validate_by_alias` or `validate_by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return core_schema.CoreConfig( + **{ # pyright: ignore[reportArgumentType] + k: v + for k, v in ( + ('title', config.get('title') or title or None), + ('extra_fields_behavior', config.get('extra')), + ('allow_inf_nan', config.get('allow_inf_nan')), + ('str_strip_whitespace', config.get('str_strip_whitespace')), + ('str_to_lower', config.get('str_to_lower')), + ('str_to_upper', config.get('str_to_upper')), + ('strict', config.get('strict')), + ('ser_json_timedelta', config.get('ser_json_timedelta')), + ('ser_json_temporal', config.get('ser_json_temporal')), + ('val_temporal_unit', config.get('val_temporal_unit')), + ('ser_json_bytes', config.get('ser_json_bytes')), + ('val_json_bytes', config.get('val_json_bytes')), + ('ser_json_inf_nan', config.get('ser_json_inf_nan')), + ('from_attributes', config.get('from_attributes')), + ('loc_by_alias', config.get('loc_by_alias')), + ('revalidate_instances', config.get('revalidate_instances')), + ('validate_default', config.get('validate_default')), + ('str_max_length', config.get('str_max_length')), + ('str_min_length', config.get('str_min_length')), + ('hide_input_in_errors', config.get('hide_input_in_errors')), + ('coerce_numbers_to_str', config.get('coerce_numbers_to_str')), + ('regex_engine', config.get('regex_engine')), + ('validation_error_cause', config.get('validation_error_cause')), + ('cache_strings', config.get('cache_strings')), + ('validate_by_alias', config.get('validate_by_alias')), + ('validate_by_name', config.get('validate_by_name')), + ('serialize_by_alias', config.get('serialize_by_alias')), + ('url_preserve_empty_path', config.get('url_preserve_empty_path')), + ('polymorphic_serialization', config.get('polymorphic_serialization')), + ) + if v is not None + } + ) + + def __repr__(self): + c = ', '.join(f'{k}={v!r}' for k, v in self.config_dict.items()) + return f'ConfigWrapper({c})' + + +class ConfigWrapperStack: + """A stack of `ConfigWrapper` instances.""" + + def __init__(self, config_wrapper: ConfigWrapper): + self._config_wrapper_stack: list[ConfigWrapper] = [config_wrapper] + + @property + def tail(self) -> ConfigWrapper: + return self._config_wrapper_stack[-1] + + @contextmanager + def push(self, config_wrapper: ConfigWrapper | ConfigDict | None): + if config_wrapper is None: + yield + return + + if not isinstance(config_wrapper, ConfigWrapper): + config_wrapper = ConfigWrapper(config_wrapper, check=False) + + self._config_wrapper_stack.append(config_wrapper) + try: + yield + finally: + self._config_wrapper_stack.pop() + + +config_defaults = ConfigDict( + title=None, + str_to_lower=False, + str_to_upper=False, + str_strip_whitespace=False, + str_min_length=0, + str_max_length=None, + # let the model / dataclass decide how to handle it + extra=None, + frozen=False, + populate_by_name=False, + use_enum_values=False, + validate_assignment=False, + arbitrary_types_allowed=False, + from_attributes=False, + loc_by_alias=True, + alias_generator=None, + model_title_generator=None, + field_title_generator=None, + ignored_types=(), + allow_inf_nan=True, + json_schema_extra=None, + strict=False, + revalidate_instances='never', + ser_json_timedelta='iso8601', + ser_json_temporal='iso8601', + val_temporal_unit='infer', + ser_json_bytes='utf8', + val_json_bytes='utf8', + ser_json_inf_nan='null', + validate_default=False, + validate_return=False, + protected_namespaces=('model_validate', 'model_dump'), + hide_input_in_errors=False, + json_encoders=None, + defer_build=False, + schema_generator=None, + plugin_settings=None, + json_schema_serialization_defaults_required=False, + json_schema_mode_override=None, + coerce_numbers_to_str=False, + regex_engine='rust-regex', + validation_error_cause=False, + use_attribute_docstrings=False, + cache_strings=True, + validate_by_alias=True, + validate_by_name=False, + serialize_by_alias=False, + url_preserve_empty_path=False, + polymorphic_serialization=False, +) + + +def prepare_config(config: ConfigDict | dict[str, Any] | type[Any] | None) -> ConfigDict: + """Create a `ConfigDict` instance from an existing dict, a class (e.g. old class-based config) or None. + + Args: + config: The input config. + + Returns: + A ConfigDict object created from config. + """ + if config is None: + return ConfigDict() + + if not isinstance(config, dict): + warnings.warn(DEPRECATION_MESSAGE, PydanticDeprecatedSince20, stacklevel=4) + config = {k: getattr(config, k) for k in dir(config) if not k.startswith('__')} + + config_dict = cast(ConfigDict, config) + check_deprecated(config_dict) + return config_dict + + +config_keys = set(ConfigDict.__annotations__.keys()) + + +V2_REMOVED_KEYS = { + 'allow_mutation', + 'error_msg_templates', + 'fields', + 'getter_dict', + 'smart_union', + 'underscore_attrs_are_private', + 'json_loads', + 'json_dumps', + 'copy_on_model_validation', + 'post_init_call', +} +V2_RENAMED_KEYS = { + 'allow_population_by_field_name': 'validate_by_name', + 'anystr_lower': 'str_to_lower', + 'anystr_strip_whitespace': 'str_strip_whitespace', + 'anystr_upper': 'str_to_upper', + 'keep_untouched': 'ignored_types', + 'max_anystr_length': 'str_max_length', + 'min_anystr_length': 'str_min_length', + 'orm_mode': 'from_attributes', + 'schema_extra': 'json_schema_extra', + 'validate_all': 'validate_default', +} + + +def check_deprecated(config_dict: ConfigDict) -> None: + """Check for deprecated config keys and warn the user. + + Args: + config_dict: The input config. + """ + deprecated_removed_keys = V2_REMOVED_KEYS & config_dict.keys() + deprecated_renamed_keys = V2_RENAMED_KEYS.keys() & config_dict.keys() + if deprecated_removed_keys or deprecated_renamed_keys: + renamings = {k: V2_RENAMED_KEYS[k] for k in sorted(deprecated_renamed_keys)} + renamed_bullets = [f'* {k!r} has been renamed to {v!r}' for k, v in renamings.items()] + removed_bullets = [f'* {k!r} has been removed' for k in sorted(deprecated_removed_keys)] + message = '\n'.join(['Valid config keys have changed in V2:'] + renamed_bullets + removed_bullets) + warnings.warn(message, UserWarning) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_core_metadata.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_core_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..9f2510c03429b6de24063bec5937e4334077722d --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_core_metadata.py @@ -0,0 +1,97 @@ +from __future__ import annotations as _annotations + +from typing import TYPE_CHECKING, Any, TypedDict, cast +from warnings import warn + +if TYPE_CHECKING: + from ..config import JsonDict, JsonSchemaExtraCallable + from ._schema_generation_shared import ( + GetJsonSchemaFunction, + ) + + +class CoreMetadata(TypedDict, total=False): + """A `TypedDict` for holding the metadata dict of the schema. + + Attributes: + pydantic_js_functions: List of JSON schema functions that resolve refs during application. + pydantic_js_annotation_functions: List of JSON schema functions that don't resolve refs during application. + pydantic_js_prefer_positional_arguments: Whether JSON schema generator will + prefer positional over keyword arguments for an 'arguments' schema. + custom validation function. Only applies to before, plain, and wrap validators. + pydantic_js_updates: key / value pair updates to apply to the JSON schema for a type. + pydantic_js_extra: WIP, either key/value pair updates to apply to the JSON schema, or a custom callable. + pydantic_internal_union_tag_key: Used internally by the `Tag` metadata to specify the tag used for a discriminated union. + pydantic_internal_union_discriminator: Used internally to specify the discriminator value for a discriminated union + when the discriminator was applied to a `'definition-ref'` schema, and that reference was missing at the time + of the annotation application. + + TODO: Perhaps we should move this structure to pydantic-core. At the moment, though, + it's easier to iterate on if we leave it in pydantic until we feel there is a semi-stable API. + + TODO: It's unfortunate how functionally oriented JSON schema generation is, especially that which occurs during + the core schema generation process. It's inevitable that we need to store some json schema related information + on core schemas, given that we generate JSON schemas directly from core schemas. That being said, debugging related + issues is quite difficult when JSON schema information is disguised via dynamically defined functions. + """ + + pydantic_js_functions: list[GetJsonSchemaFunction] + pydantic_js_annotation_functions: list[GetJsonSchemaFunction] + pydantic_js_prefer_positional_arguments: bool + pydantic_js_updates: JsonDict + pydantic_js_extra: JsonDict | JsonSchemaExtraCallable + pydantic_internal_union_tag_key: str + pydantic_internal_union_discriminator: str + + +def update_core_metadata( + core_metadata: Any, + /, + *, + pydantic_js_functions: list[GetJsonSchemaFunction] | None = None, + pydantic_js_annotation_functions: list[GetJsonSchemaFunction] | None = None, + pydantic_js_updates: JsonDict | None = None, + pydantic_js_extra: JsonDict | JsonSchemaExtraCallable | None = None, +) -> None: + from ..json_schema import PydanticJsonSchemaWarning + + """Update CoreMetadata instance in place. When we make modifications in this function, they + take effect on the `core_metadata` reference passed in as the first (and only) positional argument. + + First, cast to `CoreMetadata`, then finish with a cast to `dict[str, Any]` for core schema compatibility. + We do this here, instead of before / after each call to this function so that this typing hack + can be easily removed if/when we move `CoreMetadata` to `pydantic-core`. + + For parameter descriptions, see `CoreMetadata` above. + """ + core_metadata = cast(CoreMetadata, core_metadata) + + if pydantic_js_functions: + core_metadata.setdefault('pydantic_js_functions', []).extend(pydantic_js_functions) + + if pydantic_js_annotation_functions: + core_metadata.setdefault('pydantic_js_annotation_functions', []).extend(pydantic_js_annotation_functions) + + if pydantic_js_updates: + if (existing_updates := core_metadata.get('pydantic_js_updates')) is not None: + core_metadata['pydantic_js_updates'] = {**existing_updates, **pydantic_js_updates} + else: + core_metadata['pydantic_js_updates'] = pydantic_js_updates + + if pydantic_js_extra is not None: + existing_pydantic_js_extra = core_metadata.get('pydantic_js_extra') + if existing_pydantic_js_extra is None: + core_metadata['pydantic_js_extra'] = pydantic_js_extra + if isinstance(existing_pydantic_js_extra, dict): + if isinstance(pydantic_js_extra, dict): + core_metadata['pydantic_js_extra'] = {**existing_pydantic_js_extra, **pydantic_js_extra} + if callable(pydantic_js_extra): + warn( + 'Composing `dict` and `callable` type `json_schema_extra` is not supported.' + 'The `callable` type is being ignored.' + "If you'd like support for this behavior, please open an issue on pydantic.", + PydanticJsonSchemaWarning, + ) + if callable(existing_pydantic_js_extra): + # if ever there's a case of a callable, we'll just keep the last json schema extra spec + core_metadata['pydantic_js_extra'] = pydantic_js_extra diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_core_utils.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_core_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..caa51e8c4630458020486407c032f0a8b78bba75 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_core_utils.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +import inspect +from collections.abc import Mapping, Sequence +from typing import TYPE_CHECKING, Any, Union + +from pydantic_core import CoreSchema, core_schema +from typing_extensions import TypeGuard, get_args, get_origin +from typing_inspection import typing_objects + +from . import _repr +from ._typing_extra import is_generic_alias + +if TYPE_CHECKING: + from rich.console import Console + +AnyFunctionSchema = Union[ + core_schema.AfterValidatorFunctionSchema, + core_schema.BeforeValidatorFunctionSchema, + core_schema.WrapValidatorFunctionSchema, + core_schema.PlainValidatorFunctionSchema, +] + + +FunctionSchemaWithInnerSchema = Union[ + core_schema.AfterValidatorFunctionSchema, + core_schema.BeforeValidatorFunctionSchema, + core_schema.WrapValidatorFunctionSchema, +] + +CoreSchemaField = Union[ + core_schema.ModelField, core_schema.DataclassField, core_schema.TypedDictField, core_schema.ComputedField +] +CoreSchemaOrField = Union[core_schema.CoreSchema, CoreSchemaField] + +_CORE_SCHEMA_FIELD_TYPES = {'typed-dict-field', 'dataclass-field', 'model-field', 'computed-field'} +_FUNCTION_WITH_INNER_SCHEMA_TYPES = {'function-before', 'function-after', 'function-wrap'} +_LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES = {'list', 'set', 'frozenset'} + + +def is_core_schema( + schema: CoreSchemaOrField, +) -> TypeGuard[CoreSchema]: + return schema['type'] not in _CORE_SCHEMA_FIELD_TYPES + + +def is_core_schema_field( + schema: CoreSchemaOrField, +) -> TypeGuard[CoreSchemaField]: + return schema['type'] in _CORE_SCHEMA_FIELD_TYPES + + +def is_function_with_inner_schema( + schema: CoreSchemaOrField, +) -> TypeGuard[FunctionSchemaWithInnerSchema]: + return schema['type'] in _FUNCTION_WITH_INNER_SCHEMA_TYPES + + +def is_list_like_schema_with_items_schema( + schema: CoreSchema, +) -> TypeGuard[core_schema.ListSchema | core_schema.SetSchema | core_schema.FrozenSetSchema]: + return schema['type'] in _LIST_LIKE_SCHEMA_WITH_ITEMS_TYPES + + +def get_type_ref(type_: Any, args_override: tuple[type[Any], ...] | None = None) -> str: + """Produces the ref to be used for this type by pydantic_core's core schemas. + + This `args_override` argument was added for the purpose of creating valid recursive references + when creating generic models without needing to create a concrete class. + """ + origin = get_origin(type_) or type_ + + args = get_args(type_) if is_generic_alias(type_) else (args_override or ()) + generic_metadata = getattr(type_, '__pydantic_generic_metadata__', None) + if generic_metadata: + origin = generic_metadata['origin'] or origin + args = generic_metadata['args'] or args + + module_name = getattr(origin, '__module__', '') + if typing_objects.is_typealiastype(origin): + type_ref = f'{module_name}.{origin.__name__}:{id(origin)}' + else: + try: + qualname = getattr(origin, '__qualname__', f'') + except Exception: + qualname = getattr(origin, '__qualname__', '') + type_ref = f'{module_name}.{qualname}:{id(origin)}' + + arg_refs: list[str] = [] + for arg in args: + if isinstance(arg, str): + # Handle string literals as a special case; we may be able to remove this special handling if we + # wrap them in a ForwardRef at some point. + arg_ref = f'{arg}:str-{id(arg)}' + else: + arg_ref = f'{_repr.display_as_type(arg)}:{id(arg)}' + arg_refs.append(arg_ref) + if arg_refs: + type_ref = f'{type_ref}[{",".join(arg_refs)}]' + return type_ref + + +def get_ref(s: core_schema.CoreSchema) -> None | str: + """Get the ref from the schema if it has one. + This exists just for type checking to work correctly. + """ + return s.get('ref', None) + + +def _clean_schema_for_pretty_print(obj: Any, strip_metadata: bool = True) -> Any: # pragma: no cover + """A utility function to remove irrelevant information from a core schema.""" + if isinstance(obj, Mapping): + new_dct = {} + for k, v in obj.items(): + if k == 'metadata' and strip_metadata: + new_metadata = {} + + for meta_k, meta_v in v.items(): + if meta_k in ('pydantic_js_functions', 'pydantic_js_annotation_functions'): + new_metadata['js_metadata'] = '' + else: + new_metadata[meta_k] = _clean_schema_for_pretty_print(meta_v, strip_metadata=strip_metadata) + + if list(new_metadata.keys()) == ['js_metadata']: + new_metadata = {''} + + new_dct[k] = new_metadata + # Remove some defaults: + elif k in ('custom_init', 'root_model') and not v: + continue + else: + new_dct[k] = _clean_schema_for_pretty_print(v, strip_metadata=strip_metadata) + + return new_dct + elif isinstance(obj, Sequence) and not isinstance(obj, str): + return [_clean_schema_for_pretty_print(v, strip_metadata=strip_metadata) for v in obj] + else: + return obj + + +def pretty_print_core_schema( + val: Any, + *, + console: Console | None = None, + max_depth: int | None = None, + strip_metadata: bool = True, +) -> None: # pragma: no cover + """Pretty-print a core schema using the `rich` library. + + Args: + val: The core schema to print, or a Pydantic model/dataclass/type adapter + (in which case the cached core schema is fetched and printed). + console: A rich console to use when printing. Defaults to the global rich console instance. + max_depth: The number of nesting levels which may be printed. + strip_metadata: Whether to strip metadata in the output. If `True` any known core metadata + attributes will be stripped (but custom attributes are kept). Defaults to `True`. + """ + # lazy import: + from rich.pretty import pprint + + # circ. imports: + from pydantic import BaseModel, TypeAdapter + from pydantic.dataclasses import is_pydantic_dataclass + + if (inspect.isclass(val) and issubclass(val, BaseModel)) or is_pydantic_dataclass(val): + val = val.__pydantic_core_schema__ + if isinstance(val, TypeAdapter): + val = val.core_schema + cleaned_schema = _clean_schema_for_pretty_print(val, strip_metadata=strip_metadata) + + pprint(cleaned_schema, console=console, max_depth=max_depth) + + +pps = pretty_print_core_schema diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_dataclasses.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_dataclasses.py new file mode 100644 index 0000000000000000000000000000000000000000..f00c8b77723ba2c269d858c0c7c6bb9f04fba7f2 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_dataclasses.py @@ -0,0 +1,315 @@ +"""Private logic for creating pydantic dataclasses.""" + +from __future__ import annotations as _annotations + +import copy +import dataclasses +import sys +import warnings +from collections.abc import Generator +from contextlib import contextmanager +from functools import partial +from typing import TYPE_CHECKING, Any, ClassVar, Protocol, cast + +from pydantic_core import ( + ArgsKwargs, + SchemaSerializer, + SchemaValidator, + core_schema, +) +from typing_extensions import TypeAlias, TypeIs + +from ..errors import PydanticUndefinedAnnotation +from ..fields import FieldInfo +from ..plugin._schema_validator import PluggableSchemaValidator, create_schema_validator +from ..warnings import PydanticDeprecatedSince20 +from . import _config, _decorators +from ._fields import collect_dataclass_fields +from ._generate_schema import GenerateSchema, InvalidSchemaError +from ._generics import get_standard_typevars_map +from ._mock_val_ser import set_dataclass_mocks +from ._namespace_utils import NsResolver +from ._signature import generate_pydantic_signature +from ._utils import LazyClassAttribute + +if TYPE_CHECKING: + from _typeshed import DataclassInstance as StandardDataclass + + from ..config import ConfigDict + + class PydanticDataclass(StandardDataclass, Protocol): + """A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. + + Attributes: + __pydantic_config__: Pydantic-specific configuration settings for the dataclass. + __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. + __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. + __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. + __pydantic_fields__: Metadata about the fields defined on the dataclass. + __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. + __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. + """ + + __pydantic_config__: ClassVar[ConfigDict] + __pydantic_complete__: ClassVar[bool] + __pydantic_core_schema__: ClassVar[core_schema.CoreSchema] + __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] + __pydantic_fields__: ClassVar[dict[str, FieldInfo]] + __pydantic_serializer__: ClassVar[SchemaSerializer] + __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] + + @classmethod + def __pydantic_fields_complete__(cls) -> bool: ... + + +def set_dataclass_fields( + cls: type[StandardDataclass], + config_wrapper: _config.ConfigWrapper, + ns_resolver: NsResolver | None = None, +) -> None: + """Collect and set `cls.__pydantic_fields__`. + + Args: + cls: The class. + config_wrapper: The config wrapper instance. + ns_resolver: Namespace resolver to use when getting dataclass annotations. + """ + typevars_map = get_standard_typevars_map(cls) + fields = collect_dataclass_fields( + cls, ns_resolver=ns_resolver, typevars_map=typevars_map, config_wrapper=config_wrapper + ) + + cls.__pydantic_fields__ = fields # type: ignore + + +def complete_dataclass( + cls: type[Any], + config_wrapper: _config.ConfigWrapper, + *, + raise_errors: bool = True, + ns_resolver: NsResolver | None = None, + _force_build: bool = False, +) -> bool: + """Finish building a pydantic dataclass. + + This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`. + + This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`. + + Args: + cls: The class. + config_wrapper: The config wrapper instance. + raise_errors: Whether to raise errors, defaults to `True`. + ns_resolver: The namespace resolver instance to use when collecting dataclass fields + and during schema building. + _force_build: Whether to force building the dataclass, no matter if + [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. + + Returns: + `True` if building a pydantic dataclass is successfully completed, `False` otherwise. + + Raises: + PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. + """ + original_init = cls.__init__ + + # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied, + # and so that the mock validator is used if building was deferred: + def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None: + __tracebackhide__ = True + s = __dataclass_self__ + s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s) + + __init__.__qualname__ = f'{cls.__qualname__}.__init__' + + cls.__init__ = __init__ # type: ignore + cls.__pydantic_config__ = config_wrapper.config_dict # type: ignore + + set_dataclass_fields(cls, config_wrapper=config_wrapper, ns_resolver=ns_resolver) + + if not _force_build and config_wrapper.defer_build: + set_dataclass_mocks(cls) + return False + + if hasattr(cls, '__post_init_post_parse__'): + warnings.warn( + 'Support for `__post_init_post_parse__` has been dropped, the method will not be called', + PydanticDeprecatedSince20, + ) + + typevars_map = get_standard_typevars_map(cls) + gen_schema = GenerateSchema( + config_wrapper, + ns_resolver=ns_resolver, + typevars_map=typevars_map, + ) + + # set __signature__ attr only for the class, but not for its instances + # (because instances can define `__call__`, and `inspect.signature` shouldn't + # use the `__signature__` attribute and instead generate from `__call__`). + cls.__signature__ = LazyClassAttribute( + '__signature__', + partial( + generate_pydantic_signature, + # It's important that we reference the `original_init` here, + # as it is the one synthesized by the stdlib `dataclass` module: + init=original_init, + fields=cls.__pydantic_fields__, # type: ignore + validate_by_name=config_wrapper.validate_by_name, + extra=config_wrapper.extra, + is_dataclass=True, + ), + ) + + try: + schema = gen_schema.generate_schema(cls) + except PydanticUndefinedAnnotation as e: + if raise_errors: + raise + set_dataclass_mocks(cls, f'`{e.name}`') + return False + + core_config = config_wrapper.core_config(title=cls.__name__) + + try: + schema = gen_schema.clean_schema(schema) + except InvalidSchemaError: + set_dataclass_mocks(cls) + return False + + # We are about to set all the remaining required properties expected for this cast; + # __pydantic_decorators__ and __pydantic_fields__ should already be set + cls = cast('type[PydanticDataclass]', cls) + + cls.__pydantic_core_schema__ = schema + cls.__pydantic_validator__ = create_schema_validator( + schema, cls, cls.__module__, cls.__qualname__, 'dataclass', core_config, config_wrapper.plugin_settings + ) + cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config) + cls.__pydantic_complete__ = True + return True + + +def is_stdlib_dataclass(cls: type[Any], /) -> TypeIs[type[StandardDataclass]]: + """Returns `True` if the class is a stdlib dataclass and *not* a Pydantic dataclass. + + Unlike the stdlib `dataclasses.is_dataclass()` function, this does *not* include subclasses + of a dataclass that are themselves not dataclasses. + + Args: + cls: The class. + + Returns: + `True` if the class is a stdlib dataclass, `False` otherwise. + """ + return '__dataclass_fields__' in cls.__dict__ and not hasattr(cls, '__pydantic_validator__') + + +def as_dataclass_field(pydantic_field: FieldInfo) -> dataclasses.Field[Any]: + field_args: dict[str, Any] = {'default': pydantic_field} + + # Needed because if `doc` is set, the dataclass slots will be a dict (field name -> doc) instead of a tuple: + if sys.version_info >= (3, 14) and pydantic_field.description is not None: + field_args['doc'] = pydantic_field.description + + # Needed as the stdlib dataclass module processes kw_only in a specific way during class construction: + if sys.version_info >= (3, 10) and pydantic_field.kw_only is not None: + field_args['kw_only'] = pydantic_field.kw_only + + # Needed as the stdlib dataclass modules generates `__repr__()` during class construction: + if pydantic_field.repr is not True: + field_args['repr'] = pydantic_field.repr + + return dataclasses.field(**field_args) + + +DcFields: TypeAlias = dict[str, dataclasses.Field[Any]] + + +@contextmanager +def patch_base_fields(cls: type[Any]) -> Generator[None]: + """Temporarily patch the stdlib dataclasses bases of `cls` if the Pydantic `Field()` function is used. + + When creating a Pydantic dataclass, it is possible to inherit from stdlib dataclasses, where + the Pydantic `Field()` function is used. To create this Pydantic dataclass, we first apply + the stdlib `@dataclass` decorator on it. During the construction of the stdlib dataclass, + the `kw_only` and `repr` field arguments need to be understood by the stdlib *during* the + dataclass construction. To do so, we temporarily patch the fields dictionary of the affected + bases. + + For instance, with the following example: + + ```python {test="skip" lint="skip"} + import dataclasses as stdlib_dc + + import pydantic + import pydantic.dataclasses as pydantic_dc + + @stdlib_dc.dataclass + class A: + a: int = pydantic.Field(repr=False) + + # Notice that the `repr` attribute of the dataclass field is `True`: + A.__dataclass_fields__['a'] + #> dataclass.Field(default=FieldInfo(repr=False), repr=True, ...) + + @pydantic_dc.dataclass + class B(A): + b: int = pydantic.Field(repr=False) + ``` + + When passing `B` to the stdlib `@dataclass` decorator, it will look for fields in the parent classes + and reuse them directly. When this context manager is active, `A` will be temporarily patched to be + equivalent to: + + ```python {test="skip" lint="skip"} + @stdlib_dc.dataclass + class A: + a: int = stdlib_dc.field(default=Field(repr=False), repr=False) + ``` + + !!! note + This is only applied to the bases of `cls`, and not `cls` itself. The reason is that the Pydantic + dataclass decorator "owns" `cls` (in the previous example, `B`). As such, we instead modify the fields + directly (in the previous example, we simply do `setattr(B, 'b', as_dataclass_field(pydantic_field))`). + + !!! note + This approach is far from ideal, and can probably be the source of unwanted side effects/race conditions. + The previous implemented approach was mutating the `__annotations__` dict of `cls`, which is no longer a + safe operation in Python 3.14+, and resulted in unexpected behavior with field ordering anyway. + """ + # A list of two-tuples, the first element being a reference to the + # dataclass fields dictionary, the second element being a mapping between + # the field names that were modified, and their original `Field`: + original_fields_list: list[tuple[DcFields, DcFields]] = [] + + for base in cls.__mro__[1:]: + dc_fields: dict[str, dataclasses.Field[Any]] = base.__dict__.get('__dataclass_fields__', {}) + dc_fields_with_pydantic_field_defaults = { + field_name: field + for field_name, field in dc_fields.items() + if isinstance(field.default, FieldInfo) + # Only do the patching if one of the affected attributes is set: + and (field.default.description is not None or field.default.kw_only or field.default.repr is not True) + } + if dc_fields_with_pydantic_field_defaults: + original_fields_list.append((dc_fields, dc_fields_with_pydantic_field_defaults)) + for field_name, field in dc_fields_with_pydantic_field_defaults.items(): + default = cast(FieldInfo, field.default) + # `dataclasses.Field` isn't documented as working with `copy.copy()`. + # It is a class with `__slots__`, so should work (and we hope for the best): + new_dc_field = copy.copy(field) + # For base fields, no need to set `doc` from `FieldInfo.description`, this is only relevant + # for the class under construction and handled in `as_dataclass_field()`. + if sys.version_info >= (3, 10) and default.kw_only: + new_dc_field.kw_only = True + if default.repr is not True: + new_dc_field.repr = default.repr + dc_fields[field_name] = new_dc_field + + try: + yield + finally: + for fields, original_fields in original_fields_list: + for field_name, original_field in original_fields.items(): + fields[field_name] = original_field diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_decorators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_decorators.py new file mode 100644 index 0000000000000000000000000000000000000000..e24ad2cf924b0940b1d1d41f2ccbe4b20f380947 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_decorators.py @@ -0,0 +1,873 @@ +"""Logic related to validators applied to models etc. via the `@field_validator` and `@model_validator` decorators.""" + +from __future__ import annotations as _annotations + +import types +from collections import deque +from collections.abc import Iterable +from copy import copy +from dataclasses import dataclass, field +from functools import cached_property, partial, partialmethod +from inspect import Parameter, Signature, isdatadescriptor, ismethoddescriptor +from itertools import islice +from typing import TYPE_CHECKING, Any, Callable, ClassVar, Generic, Literal, TypeVar, Union + +from pydantic_core import PydanticUndefined, PydanticUndefinedType, core_schema +from typing_extensions import Self, TypeAlias, is_typeddict + +from ..errors import PydanticUserError +from ._core_utils import get_type_ref +from ._internal_dataclass import slots_true +from ._namespace_utils import GlobalsNamespace, MappingNamespace +from ._typing_extra import get_function_type_hints, signature_no_eval +from ._utils import can_be_positional + +if TYPE_CHECKING: + from ..fields import ComputedFieldInfo + from ..functional_validators import FieldValidatorModes + from ._config import ConfigWrapper + + +@dataclass(**slots_true) +class ValidatorDecoratorInfo: + """A container for data from `@validator` so that we can access it + while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@validator'. + fields: A tuple of field names the validator should be called on. + mode: The proposed validator mode. + each_item: For complex objects (sets, lists etc.) whether to validate individual + elements rather than the whole object. + always: Whether this method and other validators should be called even if the value is missing. + check_fields: Whether to check that the fields actually exist on the model. + """ + + decorator_repr: ClassVar[str] = '@validator' + + fields: tuple[str, ...] + mode: Literal['before', 'after'] + each_item: bool + always: bool + check_fields: bool | None + + +@dataclass(**slots_true) +class FieldValidatorDecoratorInfo: + """A container for data from `@field_validator` so that we can access it + while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@field_validator'. + fields: A tuple of field names the validator should be called on. + mode: The proposed validator mode. + check_fields: Whether to check that the fields actually exist on the model. + json_schema_input_type: The input type of the function. This is only used to generate + the appropriate JSON Schema (in validation mode) and can only specified + when `mode` is either `'before'`, `'plain'` or `'wrap'`. + """ + + decorator_repr: ClassVar[str] = '@field_validator' + + fields: tuple[str, ...] + mode: FieldValidatorModes + check_fields: bool | None + json_schema_input_type: Any + + +@dataclass(**slots_true) +class RootValidatorDecoratorInfo: + """A container for data from `@root_validator` so that we can access it + while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@root_validator'. + mode: The proposed validator mode. + """ + + decorator_repr: ClassVar[str] = '@root_validator' + mode: Literal['before', 'after'] + + +@dataclass(**slots_true) +class FieldSerializerDecoratorInfo: + """A container for data from `@field_serializer` so that we can access it + while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@field_serializer'. + fields: A tuple of field names the serializer should be called on. + mode: The proposed serializer mode. + return_type: The type of the serializer's return value. + when_used: The serialization condition. Accepts a string with values `'always'`, `'unless-none'`, `'json'`, + and `'json-unless-none'`. + check_fields: Whether to check that the fields actually exist on the model. + """ + + decorator_repr: ClassVar[str] = '@field_serializer' + fields: tuple[str, ...] + mode: Literal['plain', 'wrap'] + return_type: Any + when_used: core_schema.WhenUsed + check_fields: bool | None + + +@dataclass(**slots_true) +class ModelSerializerDecoratorInfo: + """A container for data from `@model_serializer` so that we can access it + while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@model_serializer'. + mode: The proposed serializer mode. + return_type: The type of the serializer's return value. + when_used: The serialization condition. Accepts a string with values `'always'`, `'unless-none'`, `'json'`, + and `'json-unless-none'`. + """ + + decorator_repr: ClassVar[str] = '@model_serializer' + mode: Literal['plain', 'wrap'] + return_type: Any + when_used: core_schema.WhenUsed + + +@dataclass(**slots_true) +class ModelValidatorDecoratorInfo: + """A container for data from `@model_validator` so that we can access it + while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@model_validator'. + mode: The proposed serializer mode. + """ + + decorator_repr: ClassVar[str] = '@model_validator' + mode: Literal['wrap', 'before', 'after'] + + +DecoratorInfo: TypeAlias = """Union[ + ValidatorDecoratorInfo, + FieldValidatorDecoratorInfo, + RootValidatorDecoratorInfo, + FieldSerializerDecoratorInfo, + ModelSerializerDecoratorInfo, + ModelValidatorDecoratorInfo, + ComputedFieldInfo, +]""" + +ReturnType = TypeVar('ReturnType') +DecoratedType: TypeAlias = ( + 'Union[classmethod[Any, Any, ReturnType], staticmethod[Any, ReturnType], Callable[..., ReturnType], property]' +) + + +@dataclass # can't use slots here since we set attributes on `__post_init__` +class PydanticDescriptorProxy(Generic[ReturnType]): + """Wrap a classmethod, staticmethod, property or unbound function + and act as a descriptor that allows us to detect decorated items + from the class' attributes. + + This class' __get__ returns the wrapped item's __get__ result, + which makes it transparent for classmethods and staticmethods. + + Attributes: + wrapped: The decorator that has to be wrapped. + decorator_info: The decorator info. + shim: A wrapper function to wrap V1 style function. + """ + + wrapped: DecoratedType[ReturnType] + decorator_info: DecoratorInfo + shim: Callable[[Callable[..., Any]], Callable[..., Any]] | None = None + + def __post_init__(self): + for attr in 'setter', 'deleter': + if hasattr(self.wrapped, attr): + f = partial(self._call_wrapped_attr, name=attr) + setattr(self, attr, f) + + def _call_wrapped_attr(self, func: Callable[[Any], None], *, name: str) -> PydanticDescriptorProxy[ReturnType]: + self.wrapped = getattr(self.wrapped, name)(func) + if isinstance(self.wrapped, property): + # update ComputedFieldInfo.wrapped_property + from ..fields import ComputedFieldInfo + + if isinstance(self.decorator_info, ComputedFieldInfo): + self.decorator_info.wrapped_property = self.wrapped + return self + + def __get__(self, obj: object | None, obj_type: type[object] | None = None) -> PydanticDescriptorProxy[ReturnType]: + try: + return self.wrapped.__get__(obj, obj_type) # pyright: ignore[reportReturnType] + except AttributeError: + # not a descriptor, e.g. a partial object + return self.wrapped # type: ignore[return-value] + + def __set_name__(self, instance: Any, name: str) -> None: + if hasattr(self.wrapped, '__set_name__'): + self.wrapped.__set_name__(instance, name) # pyright: ignore[reportFunctionMemberAccess] + + def __getattr__(self, name: str, /) -> Any: + """Forward checks for __isabstractmethod__ and such.""" + return getattr(self.wrapped, name) + + +DecoratorInfoType = TypeVar('DecoratorInfoType', bound=DecoratorInfo) + + +@dataclass(**slots_true) +class Decorator(Generic[DecoratorInfoType]): + """A generic container class to join together the decorator metadata + (metadata from decorator itself, which we have when the + decorator is called but not when we are building the core-schema) + and the bound function (which we have after the class itself is created). + + Attributes: + cls_ref: The class ref. + cls_var_name: The decorated function name. + func: The decorated function. + shim: A wrapper function to wrap V1 style function. + info: The decorator info. + """ + + cls_ref: str + cls_var_name: str + func: Callable[..., Any] + shim: Callable[[Any], Any] | None + info: DecoratorInfoType + + @staticmethod + def build( + cls_: Any, + *, + cls_var_name: str, + shim: Callable[[Any], Any] | None, + info: DecoratorInfoType, + ) -> Decorator[DecoratorInfoType]: + """Build a new decorator. + + Args: + cls_: The class. + cls_var_name: The decorated function name. + shim: A wrapper function to wrap V1 style function. + info: The decorator info. + + Returns: + The new decorator instance. + """ + func = get_attribute_from_bases(cls_, cls_var_name) + if shim is not None: + func = shim(func) + func = unwrap_wrapped_function(func, unwrap_partial=False) + if not callable(func): + # TODO most likely this branch can be removed when we drop support for Python 3.12: + # This branch will get hit for classmethod properties + attribute = get_attribute_from_base_dicts(cls_, cls_var_name) # prevents the binding call to `__get__` + if isinstance(attribute, PydanticDescriptorProxy): + func = unwrap_wrapped_function(attribute.wrapped) + return Decorator( + cls_ref=get_type_ref(cls_), + cls_var_name=cls_var_name, + func=func, + shim=shim, + info=info, + ) + + def bind_to_cls(self, cls: Any) -> Decorator[DecoratorInfoType]: + """Bind the decorator to a class. + + Args: + cls: the class. + + Returns: + The new decorator instance. + """ + return self.build( + cls, + cls_var_name=self.cls_var_name, + shim=self.shim, + info=copy(self.info), + ) + + +def get_bases(tp: type[Any]) -> tuple[type[Any], ...]: + """Get the base classes of a class or typeddict. + + Args: + tp: The type or class to get the bases. + + Returns: + The base classes. + """ + if is_typeddict(tp): + return tp.__orig_bases__ # type: ignore + try: + return tp.__bases__ + except AttributeError: + return () + + +def mro(tp: type[Any]) -> tuple[type[Any], ...]: + """Calculate the Method Resolution Order of bases using the C3 algorithm. + + See https://www.python.org/download/releases/2.3/mro/ + """ + # try to use the existing mro, for performance mainly + # but also because it helps verify the implementation below + if not is_typeddict(tp): + try: + return tp.__mro__ + except AttributeError: + # GenericAlias and some other cases + pass + + bases = get_bases(tp) + return (tp,) + mro_for_bases(bases) + + +def mro_for_bases(bases: tuple[type[Any], ...]) -> tuple[type[Any], ...]: + def merge_seqs(seqs: list[deque[type[Any]]]) -> Iterable[type[Any]]: + while True: + non_empty = [seq for seq in seqs if seq] + if not non_empty: + # Nothing left to process, we're done. + return + candidate: type[Any] | None = None + for seq in non_empty: # Find merge candidates among seq heads. + candidate = seq[0] + not_head = [s for s in non_empty if candidate in islice(s, 1, None)] + if not_head: + # Reject the candidate. + candidate = None + else: + break + if not candidate: + raise TypeError('Inconsistent hierarchy, no C3 MRO is possible') + yield candidate + for seq in non_empty: + # Remove candidate. + if seq[0] == candidate: + seq.popleft() + + seqs = [deque(mro(base)) for base in bases] + [deque(bases)] + return tuple(merge_seqs(seqs)) + + +_sentinel = object() + + +def get_attribute_from_bases(tp: type[Any] | tuple[type[Any], ...], name: str) -> Any: + """Get the attribute from the next class in the MRO that has it, + aiming to simulate calling the method on the actual class. + + The reason for iterating over the mro instead of just getting + the attribute (which would do that for us) is to support TypedDict, + which lacks a real __mro__, but can have a virtual one constructed + from its bases (as done here). + + Args: + tp: The type or class to search for the attribute. If a tuple, this is treated as a set of base classes. + name: The name of the attribute to retrieve. + + Returns: + Any: The attribute value, if found. + + Raises: + AttributeError: If the attribute is not found in any class in the MRO. + """ + if isinstance(tp, tuple): + for base in mro_for_bases(tp): + attribute = base.__dict__.get(name, _sentinel) + if attribute is not _sentinel: + attribute_get = getattr(attribute, '__get__', None) + if attribute_get is not None: + return attribute_get(None, tp) + return attribute + raise AttributeError(f'{name} not found in {tp}') + else: + try: + return getattr(tp, name) + except AttributeError: + return get_attribute_from_bases(mro(tp), name) + + +def get_attribute_from_base_dicts(tp: type[Any], name: str) -> Any: + """Get an attribute out of the `__dict__` following the MRO. + This prevents the call to `__get__` on the descriptor, and allows + us to get the original function for classmethod properties. + + Args: + tp: The type or class to search for the attribute. + name: The name of the attribute to retrieve. + + Returns: + Any: The attribute value, if found. + + Raises: + KeyError: If the attribute is not found in any class's `__dict__` in the MRO. + """ + for base in reversed(mro(tp)): + if name in base.__dict__: + return base.__dict__[name] + return tp.__dict__[name] # raise the error + + +@dataclass(**slots_true) +class DecoratorInfos: + """Mapping of name in the class namespace to decorator info. + + note that the name in the class namespace is the function or attribute name + not the field name! + """ + + validators: dict[str, Decorator[ValidatorDecoratorInfo]] = field(default_factory=dict) + field_validators: dict[str, Decorator[FieldValidatorDecoratorInfo]] = field(default_factory=dict) + root_validators: dict[str, Decorator[RootValidatorDecoratorInfo]] = field(default_factory=dict) + field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]] = field(default_factory=dict) + model_serializers: dict[str, Decorator[ModelSerializerDecoratorInfo]] = field(default_factory=dict) + model_validators: dict[str, Decorator[ModelValidatorDecoratorInfo]] = field(default_factory=dict) + computed_fields: dict[str, Decorator[ComputedFieldInfo]] = field(default_factory=dict) + + @classmethod + def build( + cls, + typ: type[Any], + # Default to `True` for backwards compatibility: + replace_wrapped_methods: bool = True, + ) -> Self: + """Build a `DecoratorInfos` instance for the given model, dataclass or `TypedDict` type. + + Decorators from parent classes are included, including "bare" classes (e.g. if `typ` + is a Pydantic model, non Pydantic parent model classes are also taken into account). + The collection of the decorators happens by respecting the MRO. + + If one of the bases has an `__pydantic_decorators__` attribute set, it is assumed to be + a `DecoratorInfos` instance and is used as-is. The `__pydantic_decorators__` attribute + is *not* being set on the provided `typ`. + + Args: + typ: The model, dataclass or `TypedDict` type to use when building the `DecoratorInfos` instance. + replace_wrapped_methods: Whether to replace the decorator's wrapped methods on `typ`. + This is useful e.g. for field validators which are initially class methods. This should + only be set to `True` if `typ` is a Pydantic model or dataclass (otherwise this results + in mutations of classes Pydantic doesn't "own"). + """ + # reminder: dicts are ordered and replacement does not alter the order + res = cls() + # Iterate over the bases, without the actual `typ`. + # `1:-1` because we don't need to include `object`/`TypedDict`: + for base in reversed(mro(typ)[1:-1]): + existing: DecoratorInfos | None = base.__dict__.get('__pydantic_decorators__') + if existing is None: + existing, _ = _decorator_infos_for_class(base, collect_to_replace=False) + res.validators.update({k: v.bind_to_cls(typ) for k, v in existing.validators.items()}) + res.field_validators.update({k: v.bind_to_cls(typ) for k, v in existing.field_validators.items()}) + res.root_validators.update({k: v.bind_to_cls(typ) for k, v in existing.root_validators.items()}) + res.field_serializers.update({k: v.bind_to_cls(typ) for k, v in existing.field_serializers.items()}) + res.model_serializers.update({k: v.bind_to_cls(typ) for k, v in existing.model_serializers.items()}) + res.model_validators.update({k: v.bind_to_cls(typ) for k, v in existing.model_validators.items()}) + res.computed_fields.update({k: v.bind_to_cls(typ) for k, v in existing.computed_fields.items()}) + + decorator_infos, to_replace = _decorator_infos_for_class(typ, collect_to_replace=True) + + res.validators.update(decorator_infos.validators) + res.field_validators.update(decorator_infos.field_validators) + res.root_validators.update(decorator_infos.root_validators) + res.field_serializers.update(decorator_infos.field_serializers) + res.model_serializers.update(decorator_infos.model_serializers) + res.model_validators.update(decorator_infos.model_validators) + res.computed_fields.update(decorator_infos.computed_fields) + + if replace_wrapped_methods and to_replace: + for name, value in to_replace: + setattr(typ, name, value) + + res._validate() + return res + + def _validate(self) -> None: + seen: set[str] = set() + for field_ser in self.field_serializers.values(): + for f_name in field_ser.info.fields: + if f_name in seen: + raise PydanticUserError( + f'Multiple field serializer functions were defined for field {f_name!r}, this is not allowed.', + code='multiple-field-serializers', + ) + seen.add(f_name) + + def update_from_config(self, config_wrapper: ConfigWrapper) -> None: + """Update the decorator infos from the configuration of the class they are attached to.""" + for name, computed_field_dec in self.computed_fields.items(): + computed_field_dec.info._update_from_config(config_wrapper, name) + + +def _decorator_infos_for_class( + typ: type[Any], + *, + collect_to_replace: bool, +) -> tuple[DecoratorInfos, list[tuple[str, Any]]]: + """Collect a `DecoratorInfos` for class, without looking into bases.""" + res = DecoratorInfos() + to_replace: list[tuple[str, Any]] = [] + + for var_name, var_value in vars(typ).items(): + if isinstance(var_value, PydanticDescriptorProxy): + info = var_value.decorator_info + if isinstance(info, ValidatorDecoratorInfo): + res.validators[var_name] = Decorator.build(typ, cls_var_name=var_name, shim=var_value.shim, info=info) + elif isinstance(info, FieldValidatorDecoratorInfo): + res.field_validators[var_name] = Decorator.build( + typ, cls_var_name=var_name, shim=var_value.shim, info=info + ) + elif isinstance(info, RootValidatorDecoratorInfo): + res.root_validators[var_name] = Decorator.build( + typ, cls_var_name=var_name, shim=var_value.shim, info=info + ) + elif isinstance(info, FieldSerializerDecoratorInfo): + res.field_serializers[var_name] = Decorator.build( + typ, cls_var_name=var_name, shim=var_value.shim, info=info + ) + elif isinstance(info, ModelValidatorDecoratorInfo): + res.model_validators[var_name] = Decorator.build( + typ, cls_var_name=var_name, shim=var_value.shim, info=info + ) + elif isinstance(info, ModelSerializerDecoratorInfo): + res.model_serializers[var_name] = Decorator.build( + typ, cls_var_name=var_name, shim=var_value.shim, info=info + ) + else: + from ..fields import ComputedFieldInfo + + isinstance(var_value, ComputedFieldInfo) + res.computed_fields[var_name] = Decorator.build(typ, cls_var_name=var_name, shim=None, info=info) + if collect_to_replace: + to_replace.append((var_name, var_value.wrapped)) + + return res, to_replace + + +def inspect_validator( + validator: Callable[..., Any], *, mode: FieldValidatorModes, type: Literal['field', 'model'] +) -> bool: + """Look at a field or model validator function and determine whether it takes an info argument. + + An error is raised if the function has an invalid signature. + + Args: + validator: The validator function to inspect. + mode: The proposed validator mode. + type: The type of validator, either 'field' or 'model'. + + Returns: + Whether the validator takes an info argument. + """ + try: + sig = signature_no_eval(validator) + except (ValueError, TypeError): + # `inspect.signature` might not be able to infer a signature, e.g. with C objects. + # In this case, we assume no info argument is present: + return False + n_positional = count_positional_required_params(sig) + if mode == 'wrap': + if n_positional == 3: + return True + elif n_positional == 2: + return False + else: + assert mode in {'before', 'after', 'plain'}, f"invalid mode: {mode!r}, expected 'before', 'after' or 'plain" + if n_positional == 2: + return True + elif n_positional == 1: + return False + + raise PydanticUserError( + f'Unrecognized {type} validator function signature for {validator} with `mode={mode}`: {sig}', + code='validator-signature', + ) + + +def inspect_field_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> tuple[bool, bool]: + """Look at a field serializer function and determine if it is a field serializer, + and whether it takes an info argument. + + An error is raised if the function has an invalid signature. + + Args: + serializer: The serializer function to inspect. + mode: The serializer mode, either 'plain' or 'wrap'. + + Returns: + Tuple of (is_field_serializer, info_arg). + """ + try: + sig = signature_no_eval(serializer) + except (ValueError, TypeError): + # `inspect.signature` might not be able to infer a signature, e.g. with C objects. + # In this case, we assume no info argument is present and this is not a method: + return (False, False) + + first = next(iter(sig.parameters.values()), None) + is_field_serializer = first is not None and first.name == 'self' + + n_positional = count_positional_required_params(sig) + if is_field_serializer: + # -1 to correct for self parameter + info_arg = _serializer_info_arg(mode, n_positional - 1) + else: + info_arg = _serializer_info_arg(mode, n_positional) + + if info_arg is None: + raise PydanticUserError( + f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}', + code='field-serializer-signature', + ) + + return is_field_serializer, info_arg + + +def inspect_annotated_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool: + """Look at a serializer function used via `Annotated` and determine whether it takes an info argument. + + An error is raised if the function has an invalid signature. + + Args: + serializer: The serializer function to check. + mode: The serializer mode, either 'plain' or 'wrap'. + + Returns: + info_arg + """ + try: + sig = signature_no_eval(serializer) + except (ValueError, TypeError): + # `inspect.signature` might not be able to infer a signature, e.g. with C objects. + # In this case, we assume no info argument is present: + return False + info_arg = _serializer_info_arg(mode, count_positional_required_params(sig)) + if info_arg is None: + raise PydanticUserError( + f'Unrecognized field_serializer function signature for {serializer} with `mode={mode}`:{sig}', + code='field-serializer-signature', + ) + else: + return info_arg + + +def inspect_model_serializer(serializer: Callable[..., Any], mode: Literal['plain', 'wrap']) -> bool: + """Look at a model serializer function and determine whether it takes an info argument. + + An error is raised if the function has an invalid signature. + + Args: + serializer: The serializer function to check. + mode: The serializer mode, either 'plain' or 'wrap'. + + Returns: + `info_arg` - whether the function expects an info argument. + """ + if isinstance(serializer, (staticmethod, classmethod)) or not is_instance_method_from_sig(serializer): + raise PydanticUserError( + '`@model_serializer` must be applied to instance methods', code='model-serializer-instance-method' + ) + + sig = signature_no_eval(serializer) + info_arg = _serializer_info_arg(mode, count_positional_required_params(sig)) + if info_arg is None: + raise PydanticUserError( + f'Unrecognized model_serializer function signature for {serializer} with `mode={mode}`:{sig}', + code='model-serializer-signature', + ) + else: + return info_arg + + +def _serializer_info_arg(mode: Literal['plain', 'wrap'], n_positional: int) -> bool | None: + if mode == 'plain': + if n_positional == 1: + # (input_value: Any, /) -> Any + return False + elif n_positional == 2: + # (model: Any, input_value: Any, /) -> Any + return True + else: + assert mode == 'wrap', f"invalid mode: {mode!r}, expected 'plain' or 'wrap'" + if n_positional == 2: + # (input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any + return False + elif n_positional == 3: + # (input_value: Any, serializer: SerializerFunctionWrapHandler, info: SerializationInfo, /) -> Any + return True + + return None + + +AnyDecoratorCallable: TypeAlias = ( + 'Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any], Callable[..., Any]]' +) + + +def is_instance_method_from_sig(function: AnyDecoratorCallable) -> bool: + """Whether the function is an instance method. + + It will consider a function as instance method if the first parameter of + function is `self`. + + Args: + function: The function to check. + + Returns: + `True` if the function is an instance method, `False` otherwise. + """ + sig = signature_no_eval(unwrap_wrapped_function(function)) + first = next(iter(sig.parameters.values()), None) + if first and first.name == 'self': + return True + return False + + +def ensure_classmethod_based_on_signature(function: AnyDecoratorCallable) -> Any: + """Apply the `@classmethod` decorator on the function. + + Args: + function: The function to apply the decorator on. + + Return: + The `@classmethod` decorator applied function. + """ + if not isinstance( + unwrap_wrapped_function(function, unwrap_class_static_method=False), classmethod + ) and _is_classmethod_from_sig(function): + return classmethod(function) # type: ignore[arg-type] + return function + + +def _is_classmethod_from_sig(function: AnyDecoratorCallable) -> bool: + sig = signature_no_eval(unwrap_wrapped_function(function)) + first = next(iter(sig.parameters.values()), None) + if first and first.name == 'cls': + return True + return False + + +def unwrap_wrapped_function( + func: Any, + *, + unwrap_partial: bool = True, + unwrap_class_static_method: bool = True, +) -> Any: + """Recursively unwraps a wrapped function until the underlying function is reached. + This handles property, functools.partial, functools.partialmethod, staticmethod, and classmethod. + + Args: + func: The function to unwrap. + unwrap_partial: If True (default), unwrap partial and partialmethod decorators. + unwrap_class_static_method: If True (default), also unwrap classmethod and staticmethod + decorators. If False, only unwrap partial and partialmethod decorators. + + Returns: + The underlying function of the wrapped function. + """ + # Define the types we want to check against as a single tuple. + unwrap_types = ( + (property, cached_property) + + ((partial, partialmethod) if unwrap_partial else ()) + + ((staticmethod, classmethod) if unwrap_class_static_method else ()) + ) + + while isinstance(func, unwrap_types): + if unwrap_class_static_method and isinstance(func, (classmethod, staticmethod)): + func = func.__func__ + elif isinstance(func, (partial, partialmethod)): + func = func.func + elif isinstance(func, property): + func = func.fget # arbitrary choice, convenient for computed fields + else: + # Make coverage happy as it can only get here in the last possible case + assert isinstance(func, cached_property) + func = func.func # type: ignore + + return func + + +_function_like = ( + partial, + partialmethod, + types.FunctionType, + types.BuiltinFunctionType, + types.MethodType, + types.WrapperDescriptorType, + types.MethodWrapperType, + types.MemberDescriptorType, +) + + +def get_callable_return_type( + callable_obj: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, +) -> Any | PydanticUndefinedType: + """Get the callable return type. + + Args: + callable_obj: The callable to analyze. + globalns: The globals namespace to use during type annotation evaluation. + localns: The locals namespace to use during type annotation evaluation. + + Returns: + The function return type. + """ + if isinstance(callable_obj, type): + # types are callables, and we assume the return type + # is the type itself (e.g. `int()` results in an instance of `int`). + return callable_obj + + if not isinstance(callable_obj, _function_like): + call_func = getattr(type(callable_obj), '__call__', None) # noqa: B004 + if call_func is not None: + callable_obj = call_func + + hints = get_function_type_hints( + unwrap_wrapped_function(callable_obj), + include_keys={'return'}, + globalns=globalns, + localns=localns, + ) + return hints.get('return', PydanticUndefined) + + +def count_positional_required_params(sig: Signature) -> int: + """Get the number of positional (required) arguments of a signature. + + This function should only be used to inspect signatures of validation and serialization functions. + The first argument (the value being serialized or validated) is counted as a required argument + even if a default value exists. + + Returns: + The number of positional arguments of a signature. + """ + parameters = list(sig.parameters.values()) + return sum( + 1 + for param in parameters + if can_be_positional(param) + # First argument is the value being validated/serialized, and can have a default value + # (e.g. `float`, which has signature `(x=0, /)`). We assume other parameters (the info arg + # for instance) should be required, and thus without any default value. + and (param.default is Parameter.empty or param is parameters[0]) + ) + + +def ensure_property(f: Any) -> Any: + """Ensure that a function is a `property` or `cached_property`, or is a valid descriptor. + + Args: + f: The function to check. + + Returns: + The function, or a `property` or `cached_property` instance wrapping the function. + """ + if ismethoddescriptor(f) or isdatadescriptor(f): + return f + else: + return property(f) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_decorators_v1.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_decorators_v1.py new file mode 100644 index 0000000000000000000000000000000000000000..34273779d77666f5cd775f6a2684641092db67ee --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_decorators_v1.py @@ -0,0 +1,174 @@ +"""Logic for V1 validators, e.g. `@validator` and `@root_validator`.""" + +from __future__ import annotations as _annotations + +from inspect import Parameter, signature +from typing import Any, Union, cast + +from pydantic_core import core_schema +from typing_extensions import Protocol + +from ..errors import PydanticUserError +from ._utils import can_be_positional + + +class V1OnlyValueValidator(Protocol): + """A simple validator, supported for V1 validators and V2 validators.""" + + def __call__(self, __value: Any) -> Any: ... + + +class V1ValidatorWithValues(Protocol): + """A validator with `values` argument, supported for V1 validators and V2 validators.""" + + def __call__(self, __value: Any, values: dict[str, Any]) -> Any: ... + + +class V1ValidatorWithValuesKwOnly(Protocol): + """A validator with keyword only `values` argument, supported for V1 validators and V2 validators.""" + + def __call__(self, __value: Any, *, values: dict[str, Any]) -> Any: ... + + +class V1ValidatorWithKwargs(Protocol): + """A validator with `kwargs` argument, supported for V1 validators and V2 validators.""" + + def __call__(self, __value: Any, **kwargs: Any) -> Any: ... + + +class V1ValidatorWithValuesAndKwargs(Protocol): + """A validator with `values` and `kwargs` arguments, supported for V1 validators and V2 validators.""" + + def __call__(self, __value: Any, values: dict[str, Any], **kwargs: Any) -> Any: ... + + +V1Validator = Union[ + V1ValidatorWithValues, V1ValidatorWithValuesKwOnly, V1ValidatorWithKwargs, V1ValidatorWithValuesAndKwargs +] + + +def can_be_keyword(param: Parameter) -> bool: + return param.kind in (Parameter.POSITIONAL_OR_KEYWORD, Parameter.KEYWORD_ONLY) + + +def make_generic_v1_field_validator(validator: V1Validator) -> core_schema.WithInfoValidatorFunction: + """Wrap a V1 style field validator for V2 compatibility. + + Args: + validator: The V1 style field validator. + + Returns: + A wrapped V2 style field validator. + + Raises: + PydanticUserError: If the signature is not supported or the parameters are + not available in Pydantic V2. + """ + sig = signature(validator) + + needs_values_kw = False + + for param_num, (param_name, parameter) in enumerate(sig.parameters.items()): + if can_be_keyword(parameter) and param_name in ('field', 'config'): + raise PydanticUserError( + 'The `field` and `config` parameters are not available in Pydantic V2, ' + 'please use the `info` parameter instead.', + code='validator-field-config-info', + ) + if parameter.kind is Parameter.VAR_KEYWORD: + needs_values_kw = True + elif can_be_keyword(parameter) and param_name == 'values': + needs_values_kw = True + elif can_be_positional(parameter) and param_num == 0: + # value + continue + elif parameter.default is Parameter.empty: # ignore params with defaults e.g. bound by functools.partial + raise PydanticUserError( + f'Unsupported signature for V1 style validator {validator}: {sig} is not supported.', + code='validator-v1-signature', + ) + + if needs_values_kw: + # (v, **kwargs), (v, values, **kwargs), (v, *, values, **kwargs) or (v, *, values) + val1 = cast(V1ValidatorWithValues, validator) + + def wrapper1(value: Any, info: core_schema.ValidationInfo) -> Any: + return val1(value, values=info.data) + + return wrapper1 + else: + val2 = cast(V1OnlyValueValidator, validator) + + def wrapper2(value: Any, _: core_schema.ValidationInfo) -> Any: + return val2(value) + + return wrapper2 + + +RootValidatorValues = dict[str, Any] +# technically tuple[model_dict, model_extra, fields_set] | tuple[dataclass_dict, init_vars] +RootValidatorFieldsTuple = tuple[Any, ...] + + +class V1RootValidatorFunction(Protocol): + """A simple root validator, supported for V1 validators and V2 validators.""" + + def __call__(self, __values: RootValidatorValues) -> RootValidatorValues: ... + + +class V2CoreBeforeRootValidator(Protocol): + """V2 validator with mode='before'.""" + + def __call__(self, __values: RootValidatorValues, __info: core_schema.ValidationInfo) -> RootValidatorValues: ... + + +class V2CoreAfterRootValidator(Protocol): + """V2 validator with mode='after'.""" + + def __call__( + self, __fields_tuple: RootValidatorFieldsTuple, __info: core_schema.ValidationInfo + ) -> RootValidatorFieldsTuple: ... + + +def make_v1_generic_root_validator( + validator: V1RootValidatorFunction, pre: bool +) -> V2CoreBeforeRootValidator | V2CoreAfterRootValidator: + """Wrap a V1 style root validator for V2 compatibility. + + Args: + validator: The V1 style field validator. + pre: Whether the validator is a pre validator. + + Returns: + A wrapped V2 style validator. + """ + if pre is True: + # mode='before' for pydantic-core + def _wrapper1(values: RootValidatorValues, _: core_schema.ValidationInfo) -> RootValidatorValues: + return validator(values) + + return _wrapper1 + + # mode='after' for pydantic-core + def _wrapper2(fields_tuple: RootValidatorFieldsTuple, _: core_schema.ValidationInfo) -> RootValidatorFieldsTuple: + if len(fields_tuple) == 2: + # dataclass, this is easy + values, init_vars = fields_tuple + values = validator(values) + return values, init_vars + else: + # ugly hack: to match v1 behaviour, we merge values and model_extra, then split them up based on fields + # afterwards + model_dict, model_extra, fields_set = fields_tuple + if model_extra: + fields = set(model_dict.keys()) + model_dict.update(model_extra) + model_dict_new = validator(model_dict) + for k in list(model_dict_new.keys()): + if k not in fields: + model_extra[k] = model_dict_new.pop(k) + else: + model_dict_new = validator(model_dict) + return model_dict_new, model_extra, fields_set + + return _wrapper2 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_discriminated_union.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_discriminated_union.py new file mode 100644 index 0000000000000000000000000000000000000000..fc52f30889816b7161f41ae030df05e46ec5f557 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_discriminated_union.py @@ -0,0 +1,494 @@ +from __future__ import annotations as _annotations + +from collections.abc import Hashable, Sequence +from typing import TYPE_CHECKING, Any, cast + +from pydantic_core import CoreSchema, core_schema + +from ..errors import PydanticUserError +from . import _core_utils +from ._core_utils import ( + CoreSchemaField, +) + +if TYPE_CHECKING: + from ..types import Discriminator + from ._core_metadata import CoreMetadata + + +class MissingDefinitionForUnionRef(Exception): + """Raised when applying a discriminated union discriminator to a schema + requires a definition that is not yet defined + """ + + def __init__(self, ref: str) -> None: + self.ref = ref + super().__init__(f'Missing definition for ref {self.ref!r}') + + +def set_discriminator_in_metadata(schema: CoreSchema, discriminator: Any) -> None: + metadata = cast('CoreMetadata', schema.setdefault('metadata', {})) + metadata['pydantic_internal_union_discriminator'] = discriminator + + +def apply_discriminator( + schema: core_schema.CoreSchema, + discriminator: str | Discriminator, + definitions: dict[str, core_schema.CoreSchema] | None = None, +) -> core_schema.CoreSchema: + """Applies the discriminator and returns a new core schema. + + Args: + schema: The input schema. + discriminator: The name of the field which will serve as the discriminator. + definitions: A mapping of schema ref to schema. + + Returns: + The new core schema. + + Raises: + TypeError: + - If `discriminator` is used with invalid union variant. + - If `discriminator` is used with `Union` type with one variant. + - If `discriminator` value mapped to multiple choices. + MissingDefinitionForUnionRef: + If the definition for ref is missing. + PydanticUserError: + - If a model in union doesn't have a discriminator field. + - If discriminator field has a non-string alias. + - If discriminator fields have different aliases. + - If discriminator field not of type `Literal`. + """ + from ..types import Discriminator + + if isinstance(discriminator, Discriminator): + if isinstance(discriminator.discriminator, str): + discriminator = discriminator.discriminator + else: + return discriminator._convert_schema(schema) + + return _ApplyInferredDiscriminator(discriminator, definitions or {}).apply(schema) + + +class _ApplyInferredDiscriminator: + """This class is used to convert an input schema containing a union schema into one where that union is + replaced with a tagged-union, with all the associated debugging and performance benefits. + + This is done by: + * Validating that the input schema is compatible with the provided discriminator + * Introspecting the schema to determine which discriminator values should map to which union choices + * Handling various edge cases such as 'definitions', 'default', 'nullable' schemas, and more + + I have chosen to implement the conversion algorithm in this class, rather than a function, + to make it easier to maintain state while recursively walking the provided CoreSchema. + """ + + def __init__(self, discriminator: str, definitions: dict[str, core_schema.CoreSchema]): + # `discriminator` should be the name of the field which will serve as the discriminator. + # It must be the python name of the field, and *not* the field's alias. Note that as of now, + # all members of a discriminated union _must_ use a field with the same name as the discriminator. + # This may change if/when we expose a way to manually specify the TaggedUnionSchema's choices. + self.discriminator = discriminator + + # `definitions` should contain a mapping of schema ref to schema for all schemas which might + # be referenced by some choice + self.definitions = definitions + + # `_discriminator_alias` will hold the value, if present, of the alias for the discriminator + # + # Note: following the v1 implementation, we currently disallow the use of different aliases + # for different choices. This is not a limitation of pydantic_core, but if we try to handle + # this, the inference logic gets complicated very quickly, and could result in confusing + # debugging challenges for users making subtle mistakes. + # + # Rather than trying to do the most powerful inference possible, I think we should eventually + # expose a way to more-manually control the way the TaggedUnionSchema is constructed through + # the use of a new type which would be placed as an Annotation on the Union type. This would + # provide the full flexibility/power of pydantic_core's TaggedUnionSchema where necessary for + # more complex cases, without over-complicating the inference logic for the common cases. + self._discriminator_alias: str | None = None + + # `_should_be_nullable` indicates whether the converted union has `None` as an allowed value. + # If `None` is an acceptable value of the (possibly-wrapped) union, we ignore it while + # constructing the TaggedUnionSchema, but set the `_should_be_nullable` attribute to True. + # Once we have constructed the TaggedUnionSchema, if `_should_be_nullable` is True, we ensure + # that the final schema gets wrapped as a NullableSchema. This has the same semantics on the + # python side, but resolves the issue that `None` cannot correspond to any discriminator values. + self._should_be_nullable = False + + # `_is_nullable` is used to track if the final produced schema will definitely be nullable; + # we set it to True if the input schema is wrapped in a nullable schema that we know will be preserved + # as an indication that, even if None is discovered as one of the union choices, we will not need to wrap + # the final value in another nullable schema. + # + # This is more complicated than just checking for the final outermost schema having type 'nullable' thanks + # to the possible presence of other wrapper schemas such as DefinitionsSchema, WithDefaultSchema, etc. + self._is_nullable = False + + # `_choices_to_handle` serves as a stack of choices to add to the tagged union. Initially, choices + # from the union in the wrapped schema will be appended to this list, and the recursive choice-handling + # algorithm may add more choices to this stack as (nested) unions are encountered. + self._choices_to_handle: list[core_schema.CoreSchema] = [] + + # `_tagged_union_choices` is built during the call to `apply`, and will hold the choices to be included + # in the output TaggedUnionSchema that will replace the union from the input schema + self._tagged_union_choices: dict[Hashable, core_schema.CoreSchema] = {} + + # `_used` is changed to True after applying the discriminator to prevent accidental reuse + self._used = False + + def apply(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema: + """Return a new CoreSchema based on `schema` that uses a tagged-union with the discriminator provided + to this class. + + Args: + schema: The input schema. + + Returns: + The new core schema. + + Raises: + TypeError: + - If `discriminator` is used with invalid union variant. + - If `discriminator` is used with `Union` type with one variant. + - If `discriminator` value mapped to multiple choices. + ValueError: + If the definition for ref is missing. + PydanticUserError: + - If a model in union doesn't have a discriminator field. + - If discriminator field has a non-string alias. + - If discriminator fields have different aliases. + - If discriminator field not of type `Literal`. + """ + assert not self._used + schema = self._apply_to_root(schema) + if self._should_be_nullable and not self._is_nullable: + schema = core_schema.nullable_schema(schema) + self._used = True + return schema + + def _apply_to_root(self, schema: core_schema.CoreSchema) -> core_schema.CoreSchema: + """This method handles the outer-most stage of recursion over the input schema: + unwrapping nullable or definitions schemas, and calling the `_handle_choice` + method iteratively on the choices extracted (recursively) from the possibly-wrapped union. + """ + if schema['type'] == 'nullable': + self._is_nullable = True + wrapped = self._apply_to_root(schema['schema']) + nullable_wrapper = schema.copy() + nullable_wrapper['schema'] = wrapped + return nullable_wrapper + + if schema['type'] == 'definitions': + wrapped = self._apply_to_root(schema['schema']) + definitions_wrapper = schema.copy() + definitions_wrapper['schema'] = wrapped + return definitions_wrapper + + if schema['type'] == 'definition-ref': + schema_ref = schema['schema_ref'] + if schema_ref not in self.definitions: # pragma: no cover + raise MissingDefinitionForUnionRef(schema_ref) + + def_schema = self.definitions[schema_ref] + # If using a referenceable union as discriminated (e.g. `type Pet = Cat | Dog; field: Pet = Field(discriminator=...)`): + if def_schema['type'] == 'union': + schema = def_schema.copy() + schema.pop('ref') + + if schema['type'] != 'union': + # If the schema is not a union, it probably means it just had a single member and + # was flattened by pydantic_core. + # However, it still may make sense to apply the discriminator to this schema, + # as a way to get discriminated-union-style error messages, so we allow this here. + schema = core_schema.union_schema([schema]) + + # Reverse the choices list before extending the stack so that they get handled in the order they occur + choices_schemas = [v[0] if isinstance(v, tuple) else v for v in schema['choices'][::-1]] + self._choices_to_handle.extend(choices_schemas) + while self._choices_to_handle: + choice = self._choices_to_handle.pop() + self._handle_choice(choice) + + if self._discriminator_alias is not None and self._discriminator_alias != self.discriminator: + # * We need to annotate `discriminator` as a union here to handle both branches of this conditional + # * We need to annotate `discriminator` as list[list[str | int]] and not list[list[str]] due to the + # invariance of list, and because list[list[str | int]] is the type of the discriminator argument + # to tagged_union_schema below + # * See the docstring of pydantic_core.core_schema.tagged_union_schema for more details about how to + # interpret the value of the discriminator argument to tagged_union_schema. (The list[list[str]] here + # is the appropriate way to provide a list of fallback attributes to check for a discriminator value.) + discriminator: str | list[list[str | int]] = [[self.discriminator], [self._discriminator_alias]] + else: + discriminator = self.discriminator + return core_schema.tagged_union_schema( + choices=self._tagged_union_choices, + discriminator=discriminator, + custom_error_type=schema.get('custom_error_type'), + custom_error_message=schema.get('custom_error_message'), + custom_error_context=schema.get('custom_error_context'), + strict=False, + from_attributes=True, + ref=schema.get('ref'), + metadata=schema.get('metadata'), + serialization=schema.get('serialization'), + ) + + def _handle_choice(self, choice: core_schema.CoreSchema) -> None: + """This method handles the "middle" stage of recursion over the input schema. + Specifically, it is responsible for handling each choice of the outermost union + (and any "coalesced" choices obtained from inner unions). + + Here, "handling" entails: + * Coalescing nested unions and compatible tagged-unions + * Tracking the presence of 'none' and 'nullable' schemas occurring as choices + * Validating that each allowed discriminator value maps to a unique choice + * Updating the _tagged_union_choices mapping that will ultimately be used to build the TaggedUnionSchema. + """ + if choice['type'] == 'definition-ref': + if choice['schema_ref'] not in self.definitions: + raise MissingDefinitionForUnionRef(choice['schema_ref']) + + if choice['type'] == 'none': + self._should_be_nullable = True + elif choice['type'] == 'definitions': + self._handle_choice(choice['schema']) + elif choice['type'] == 'nullable': + self._should_be_nullable = True + self._handle_choice(choice['schema']) # unwrap the nullable schema + elif choice['type'] == 'union': + # Reverse the choices list before extending the stack so that they get handled in the order they occur + choices_schemas = [v[0] if isinstance(v, tuple) else v for v in choice['choices'][::-1]] + self._choices_to_handle.extend(choices_schemas) + elif choice['type'] not in { + 'model', + 'typed-dict', + 'tagged-union', + 'lax-or-strict', + 'dataclass', + 'dataclass-args', + 'definition-ref', + } and not _core_utils.is_function_with_inner_schema(choice): + # We should eventually handle 'definition-ref' as well + err_str = f'The core schema type {choice["type"]!r} is not a valid discriminated union variant.' + if choice['type'] == 'list': + err_str += ( + ' If you are making use of a list of union types, make sure the discriminator is applied to the ' + 'union type and not the list (e.g. `list[Annotated[ | , Field(discriminator=...)]]`).' + ) + raise TypeError(err_str) + else: + if choice['type'] == 'tagged-union' and self._is_discriminator_shared(choice): + # In this case, this inner tagged-union is compatible with the outer tagged-union, + # and its choices can be coalesced into the outer TaggedUnionSchema. + subchoices = [x for x in choice['choices'].values() if not isinstance(x, (str, int))] + # Reverse the choices list before extending the stack so that they get handled in the order they occur + self._choices_to_handle.extend(subchoices[::-1]) + return + + inferred_discriminator_values = self._infer_discriminator_values_for_choice(choice, source_name=None) + self._set_unique_choice_for_values(choice, inferred_discriminator_values) + + def _is_discriminator_shared(self, choice: core_schema.TaggedUnionSchema) -> bool: + """This method returns a boolean indicating whether the discriminator for the `choice` + is the same as that being used for the outermost tagged union. This is used to + determine whether this TaggedUnionSchema choice should be "coalesced" into the top level, + or whether it should be treated as a separate (nested) choice. + """ + inner_discriminator = choice['discriminator'] + return inner_discriminator == self.discriminator or ( + isinstance(inner_discriminator, list) + and (self.discriminator in inner_discriminator or [self.discriminator] in inner_discriminator) + ) + + def _infer_discriminator_values_for_choice( # noqa C901 + self, choice: core_schema.CoreSchema, source_name: str | None + ) -> list[str | int]: + """This function recurses over `choice`, extracting all discriminator values that should map to this choice. + + `model_name` is accepted for the purpose of producing useful error messages. + """ + if choice['type'] == 'definitions': + return self._infer_discriminator_values_for_choice(choice['schema'], source_name=source_name) + + elif _core_utils.is_function_with_inner_schema(choice): + return self._infer_discriminator_values_for_choice(choice['schema'], source_name=source_name) + + elif choice['type'] == 'lax-or-strict': + return sorted( + set( + self._infer_discriminator_values_for_choice(choice['lax_schema'], source_name=None) + + self._infer_discriminator_values_for_choice(choice['strict_schema'], source_name=None) + ) + ) + + elif choice['type'] == 'tagged-union': + values: list[str | int] = [] + # Ignore str/int "choices" since these are just references to other choices + subchoices = [x for x in choice['choices'].values() if not isinstance(x, (str, int))] + for subchoice in subchoices: + subchoice_values = self._infer_discriminator_values_for_choice(subchoice, source_name=None) + values.extend(subchoice_values) + return values + + elif choice['type'] == 'union': + values = [] + for subchoice in choice['choices']: + subchoice_schema = subchoice[0] if isinstance(subchoice, tuple) else subchoice + subchoice_values = self._infer_discriminator_values_for_choice(subchoice_schema, source_name=None) + values.extend(subchoice_values) + return values + + elif choice['type'] == 'nullable': + self._should_be_nullable = True + return self._infer_discriminator_values_for_choice(choice['schema'], source_name=None) + + elif choice['type'] == 'model': + return self._infer_discriminator_values_for_choice(choice['schema'], source_name=choice['cls'].__name__) + + elif choice['type'] == 'dataclass': + return self._infer_discriminator_values_for_choice(choice['schema'], source_name=choice['cls'].__name__) + + elif choice['type'] == 'model-fields': + return self._infer_discriminator_values_for_model_choice(choice, source_name=source_name) + + elif choice['type'] == 'dataclass-args': + return self._infer_discriminator_values_for_dataclass_choice(choice, source_name=source_name) + + elif choice['type'] == 'typed-dict': + return self._infer_discriminator_values_for_typed_dict_choice(choice, source_name=source_name) + + elif choice['type'] == 'definition-ref': + schema_ref = choice['schema_ref'] + if schema_ref not in self.definitions: + raise MissingDefinitionForUnionRef(schema_ref) + return self._infer_discriminator_values_for_choice(self.definitions[schema_ref], source_name=source_name) + else: + err_str = f'The core schema type {choice["type"]!r} is not a valid discriminated union variant.' + if choice['type'] == 'list': + err_str += ( + ' If you are making use of a list of union types, make sure the discriminator is applied to the ' + 'union type and not the list (e.g. `list[Annotated[ | , Field(discriminator=...)]]`).' + ) + raise TypeError(err_str) + + def _infer_discriminator_values_for_typed_dict_choice( + self, choice: core_schema.TypedDictSchema, source_name: str | None = None + ) -> list[str | int]: + """This method just extracts the _infer_discriminator_values_for_choice logic specific to TypedDictSchema + for the sake of readability. + """ + source = 'TypedDict' if source_name is None else f'TypedDict {source_name!r}' + field = choice['fields'].get(self.discriminator) + if field is None: + raise PydanticUserError( + f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field' + ) + return self._infer_discriminator_values_for_field(field, source) + + def _infer_discriminator_values_for_model_choice( + self, choice: core_schema.ModelFieldsSchema, source_name: str | None = None + ) -> list[str | int]: + source = 'ModelFields' if source_name is None else f'Model {source_name!r}' + field = choice['fields'].get(self.discriminator) + if field is None: + raise PydanticUserError( + f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field' + ) + return self._infer_discriminator_values_for_field(field, source) + + def _infer_discriminator_values_for_dataclass_choice( + self, choice: core_schema.DataclassArgsSchema, source_name: str | None = None + ) -> list[str | int]: + source = 'DataclassArgs' if source_name is None else f'Dataclass {source_name!r}' + for field in choice['fields']: + if field['name'] == self.discriminator: + break + else: + raise PydanticUserError( + f'{source} needs a discriminator field for key {self.discriminator!r}', code='discriminator-no-field' + ) + return self._infer_discriminator_values_for_field(field, source) + + def _infer_discriminator_values_for_field(self, field: CoreSchemaField, source: str) -> list[str | int]: + if field['type'] == 'computed-field': + # This should never occur as a discriminator, as it is only relevant to serialization + return [] + alias = field.get('validation_alias', self.discriminator) + if not isinstance(alias, str): + raise PydanticUserError( + f'Alias {alias!r} is not supported in a discriminated union', code='discriminator-alias-type' + ) + if self._discriminator_alias is None: + self._discriminator_alias = alias + elif self._discriminator_alias != alias: + raise PydanticUserError( + f'Aliases for discriminator {self.discriminator!r} must be the same ' + f'(got {alias}, {self._discriminator_alias})', + code='discriminator-alias', + ) + return self._infer_discriminator_values_for_inner_schema(field['schema'], source) + + def _infer_discriminator_values_for_inner_schema( + self, schema: core_schema.CoreSchema, source: str + ) -> list[str | int]: + """When inferring discriminator values for a field, we typically extract the expected values from a literal + schema. This function does that, but also handles nested unions and defaults. + """ + if schema['type'] == 'literal': + return schema['expected'] + + elif schema['type'] == 'union': + # Generally when multiple values are allowed they should be placed in a single `Literal`, but + # we add this case to handle the situation where a field is annotated as a `Union` of `Literal`s. + # For example, this lets us handle `Union[Literal['key'], Union[Literal['Key'], Literal['KEY']]]` + values: list[Any] = [] + for choice in schema['choices']: + choice_schema = choice[0] if isinstance(choice, tuple) else choice + choice_values = self._infer_discriminator_values_for_inner_schema(choice_schema, source) + values.extend(choice_values) + return values + + elif schema['type'] == 'default': + # This will happen if the field has a default value; we ignore it while extracting the discriminator values + return self._infer_discriminator_values_for_inner_schema(schema['schema'], source) + + elif schema['type'] == 'function-after': + # After validators don't affect the discriminator values + return self._infer_discriminator_values_for_inner_schema(schema['schema'], source) + + elif schema['type'] == 'model' and schema.get('root_model'): + # Support RootModel[Literal[...]] as discriminator field type + return self._infer_discriminator_values_for_inner_schema(schema['schema'], source) + + elif schema['type'] in {'function-before', 'function-wrap', 'function-plain'}: + validator_type = repr(schema['type'].split('-')[1]) + raise PydanticUserError( + f'Cannot use a mode={validator_type} validator in the' + f' discriminator field {self.discriminator!r} of {source}', + code='discriminator-validator', + ) + + else: + raise PydanticUserError( + f'{source} needs field {self.discriminator!r} to be of type `Literal`', + code='discriminator-needs-literal', + ) + + def _set_unique_choice_for_values(self, choice: core_schema.CoreSchema, values: Sequence[str | int]) -> None: + """This method updates `self.tagged_union_choices` so that all provided (discriminator) `values` map to the + provided `choice`, validating that none of these values already map to another (different) choice. + """ + for discriminator_value in values: + if discriminator_value in self._tagged_union_choices: + # It is okay if `value` is already in tagged_union_choices as long as it maps to the same value. + # Because tagged_union_choices may map values to other values, we need to walk the choices dict + # until we get to a "real" choice, and confirm that is equal to the one assigned. + existing_choice = self._tagged_union_choices[discriminator_value] + if existing_choice != choice: + raise TypeError( + f'Value {discriminator_value!r} for discriminator ' + f'{self.discriminator!r} mapped to multiple choices' + ) + else: + self._tagged_union_choices[discriminator_value] = choice diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_docs_extraction.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_docs_extraction.py new file mode 100644 index 0000000000000000000000000000000000000000..6df77bf6c7786a480a9b5aa4c9a816944dc72ed2 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_docs_extraction.py @@ -0,0 +1,113 @@ +"""Utilities related to attribute docstring extraction.""" + +from __future__ import annotations + +import ast +import inspect +import sys +import textwrap +from typing import Any + + +class DocstringVisitor(ast.NodeVisitor): + def __init__(self) -> None: + super().__init__() + + self.target: str | None = None + self.attrs: dict[str, str] = {} + self.previous_node_type: type[ast.AST] | None = None + + def visit(self, node: ast.AST) -> Any: + node_result = super().visit(node) + self.previous_node_type = type(node) + return node_result + + def visit_AnnAssign(self, node: ast.AnnAssign) -> Any: + if isinstance(node.target, ast.Name): + self.target = node.target.id + + def visit_Expr(self, node: ast.Expr) -> Any: + if ( + isinstance(node.value, ast.Constant) + and isinstance(node.value.value, str) + and self.previous_node_type is ast.AnnAssign + ): + docstring = inspect.cleandoc(node.value.value) + if self.target: + self.attrs[self.target] = docstring + self.target = None + + +def _dedent_source_lines(source: list[str]) -> str: + # Required for nested class definitions, e.g. in a function block + dedent_source = textwrap.dedent(''.join(source)) + if dedent_source.startswith((' ', '\t')): + # We are in the case where there's a dedented (usually multiline) string + # at a lower indentation level than the class itself. We wrap our class + # in a function as a workaround. + dedent_source = f'def dedent_workaround():\n{dedent_source}' + return dedent_source + + +def _extract_source_from_frame(cls: type[Any]) -> list[str] | None: + frame = inspect.currentframe() + + while frame: + if inspect.getmodule(frame) is inspect.getmodule(cls): + lnum = frame.f_lineno + try: + lines, _ = inspect.findsource(frame) + except OSError: # pragma: no cover + # Source can't be retrieved (maybe because running in an interactive terminal), + # we don't want to error here. + pass + else: + block_lines = inspect.getblock(lines[lnum - 1 :]) + dedent_source = _dedent_source_lines(block_lines) + try: + block_tree = ast.parse(dedent_source) + except SyntaxError: + pass + else: + stmt = block_tree.body[0] + if isinstance(stmt, ast.FunctionDef) and stmt.name == 'dedent_workaround': + # `_dedent_source_lines` wrapped the class around the workaround function + stmt = stmt.body[0] + if isinstance(stmt, ast.ClassDef) and stmt.name == cls.__name__: + return block_lines + + frame = frame.f_back + + +def extract_docstrings_from_cls(cls: type[Any], use_inspect: bool = False) -> dict[str, str]: + """Map model attributes and their corresponding docstring. + + Args: + cls: The class of the Pydantic model to inspect. + use_inspect: Whether to skip usage of frames to find the object and use + the `inspect` module instead. + + Returns: + A mapping containing attribute names and their corresponding docstring. + """ + if use_inspect or sys.version_info >= (3, 13): + # On Python < 3.13, `inspect.getsourcelines()` might not work as expected + # if two classes have the same name in the same source file. + # On Python 3.13+, it will use the new `__firstlineno__` class attribute, + # making it way more robust. + try: + source, _ = inspect.getsourcelines(cls) + except OSError: # pragma: no cover + return {} + else: + # TODO remove this implementation when we drop support for Python 3.12: + source = _extract_source_from_frame(cls) + + if not source: + return {} + + dedent_source = _dedent_source_lines(source) + + visitor = DocstringVisitor() + visitor.visit(ast.parse(dedent_source)) + return visitor.attrs diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_fields.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_fields.py new file mode 100644 index 0000000000000000000000000000000000000000..9a2a96b9269c18a40550cb8bd4965e31e8e8c605 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_fields.py @@ -0,0 +1,729 @@ +"""Private logic related to fields (the `Field()` function and `FieldInfo` class), and arguments to `Annotated`.""" + +from __future__ import annotations as _annotations + +import dataclasses +import warnings +from collections.abc import Mapping +from functools import cache +from inspect import Parameter, ismethoddescriptor +from re import Pattern +from typing import TYPE_CHECKING, Any, Callable, TypeVar, cast + +from pydantic_core import PydanticUndefined +from typing_extensions import TypeIs +from typing_inspection.introspection import AnnotationSource + +from pydantic import PydanticDeprecatedSince211 +from pydantic.errors import PydanticUserError + +from ..aliases import AliasGenerator +from . import _generics, _typing_extra +from ._config import ConfigWrapper +from ._docs_extraction import extract_docstrings_from_cls +from ._import_utils import import_cached_base_model, import_cached_field_info +from ._internal_dataclass import slots_true +from ._namespace_utils import NsResolver +from ._repr import Representation +from ._utils import can_be_positional, get_first_not_none + +if TYPE_CHECKING: + from annotated_types import BaseMetadata + + from ..fields import FieldInfo + from ..main import BaseModel + from ._dataclasses import PydanticDataclass, StandardDataclass + from ._decorators import DecoratorInfos + + +class PydanticMetadata(Representation): + """Base class for annotation markers like `Strict`.""" + + __slots__ = () + + +@dataclasses.dataclass(**slots_true) # TODO: make kw_only when we drop support for 3.9. +class PydanticExtraInfo: + # TODO: make use of PEP 747: + annotation: Any + complete: bool + + +def pydantic_general_metadata(**metadata: Any) -> BaseMetadata: + """Create a new `_PydanticGeneralMetadata` class with the given metadata. + + Args: + **metadata: The metadata to add. + + Returns: + The new `_PydanticGeneralMetadata` class. + """ + return _general_metadata_cls()(metadata) # type: ignore + + +@cache +def _general_metadata_cls() -> type[BaseMetadata]: + """Do it this way to avoid importing `annotated_types` at import time.""" + from annotated_types import BaseMetadata + + class _PydanticGeneralMetadata(PydanticMetadata, BaseMetadata): + """Pydantic general metadata like `max_digits`.""" + + def __init__(self, metadata: Any): + self.__dict__ = metadata + + return _PydanticGeneralMetadata # type: ignore + + +def _check_protected_namespaces( + protected_namespaces: tuple[str | Pattern[str], ...], + ann_name: str, + bases: tuple[type[Any], ...], + cls_name: str, +) -> None: + BaseModel = import_cached_base_model() + + for protected_namespace in protected_namespaces: + ns_violation = False + if isinstance(protected_namespace, Pattern): + ns_violation = protected_namespace.match(ann_name) is not None + elif isinstance(protected_namespace, str): + ns_violation = ann_name.startswith(protected_namespace) + + if ns_violation: + for b in bases: + if hasattr(b, ann_name): + if not (issubclass(b, BaseModel) and ann_name in getattr(b, '__pydantic_fields__', {})): + raise ValueError( + f'Field {ann_name!r} conflicts with member {getattr(b, ann_name)}' + f' of protected namespace {protected_namespace!r}.' + ) + else: + valid_namespaces: list[str] = [] + for pn in protected_namespaces: + if isinstance(pn, Pattern): + if not pn.match(ann_name): + valid_namespaces.append(f're.compile({pn.pattern!r})') + else: + if not ann_name.startswith(pn): + valid_namespaces.append(f"'{pn}'") + + valid_namespaces_str = f'({", ".join(valid_namespaces)}{",)" if len(valid_namespaces) == 1 else ")"}' + + warnings.warn( + f'Field {ann_name!r} in {cls_name!r} conflicts with protected namespace {protected_namespace!r}.\n\n' + f"You may be able to solve this by setting the 'protected_namespaces' configuration to {valid_namespaces_str}.", + UserWarning, + stacklevel=5, + ) + + +def _update_fields_from_docstrings(cls: type[Any], fields: dict[str, FieldInfo], use_inspect: bool = False) -> None: + fields_docs = extract_docstrings_from_cls(cls, use_inspect=use_inspect) + for ann_name, field_info in fields.items(): + if field_info.description is None and ann_name in fields_docs: + field_info.description = fields_docs[ann_name] + + +def _apply_field_title_generator_to_field_info( + title_generator: Callable[[str, FieldInfo], str], + field_name: str, + field_info: FieldInfo, +): + if field_info.title is None: + title = title_generator(field_name, field_info) + if not isinstance(title, str): + raise TypeError(f'field_title_generator {title_generator} must return str, not {title.__class__}') + + field_info.title = title + + +def _apply_alias_generator_to_field_info( + alias_generator: Callable[[str], str] | AliasGenerator, field_name: str, field_info: FieldInfo +): + """Apply an alias generator to aliases on a `FieldInfo` instance if appropriate. + + Args: + alias_generator: A callable that takes a string and returns a string, or an `AliasGenerator` instance. + field_name: The name of the field from which to generate the alias. + field_info: The `FieldInfo` instance to which the alias generator is (maybe) applied. + """ + # Apply an alias_generator if + # 1. An alias is not specified + # 2. An alias is specified, but the priority is <= 1 + if ( + field_info.alias_priority is None + or field_info.alias_priority <= 1 + or field_info.alias is None + or field_info.validation_alias is None + or field_info.serialization_alias is None + ): + alias, validation_alias, serialization_alias = None, None, None + + if isinstance(alias_generator, AliasGenerator): + alias, validation_alias, serialization_alias = alias_generator.generate_aliases(field_name) + elif callable(alias_generator): + alias = alias_generator(field_name) + if not isinstance(alias, str): + raise TypeError(f'alias_generator {alias_generator} must return str, not {alias.__class__}') + + # if priority is not set, we set to 1 + # which supports the case where the alias_generator from a child class is used + # to generate an alias for a field in a parent class + if field_info.alias_priority is None or field_info.alias_priority <= 1: + field_info.alias_priority = 1 + + # if the priority is 1, then we set the aliases to the generated alias + if field_info.alias_priority == 1: + field_info.serialization_alias = get_first_not_none(serialization_alias, alias) + field_info.validation_alias = get_first_not_none(validation_alias, alias) + field_info.alias = alias + + # if any of the aliases are not set, then we set them to the corresponding generated alias + if field_info.alias is None: + field_info.alias = alias + if field_info.serialization_alias is None: + field_info.serialization_alias = get_first_not_none(serialization_alias, alias) + if field_info.validation_alias is None: + field_info.validation_alias = get_first_not_none(validation_alias, alias) + + +def update_field_from_config(config_wrapper: ConfigWrapper, field_name: str, field_info: FieldInfo) -> None: + """Update the `FieldInfo` instance from the configuration set on the model it belongs to. + + This will apply the title and alias generators from the configuration. + + Args: + config_wrapper: The configuration from the model. + field_name: The field name the `FieldInfo` instance is attached to. + field_info: The `FieldInfo` instance to update. + """ + field_title_generator = field_info.field_title_generator or config_wrapper.field_title_generator + if field_title_generator is not None: + _apply_field_title_generator_to_field_info(field_title_generator, field_name, field_info) + if config_wrapper.alias_generator is not None: + _apply_alias_generator_to_field_info(config_wrapper.alias_generator, field_name, field_info) + + +_deprecated_method_names = {'dict', 'json', 'copy', '_iter', '_copy_and_set_values', '_calculate_keys'} + +_deprecated_classmethod_names = { + 'parse_obj', + 'parse_raw', + 'parse_file', + 'from_orm', + 'construct', + 'schema', + 'schema_json', + 'validate', + 'update_forward_refs', + '_get_value', +} + + +def collect_model_fields( # noqa: C901 + cls: type[BaseModel], + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver, + *, + typevars_map: Mapping[TypeVar, Any] | None = None, +) -> tuple[dict[str, FieldInfo], PydanticExtraInfo | None, set[str]]: + """Collect the fields and class variables names of a nascent Pydantic model. + + The fields collection process is *lenient*, meaning it won't error if string annotations + fail to evaluate. If this happens, the original annotation (and assigned value, if any) + is stored on the created `FieldInfo` instance. + + The `rebuild_model_fields()` should be called at a later point (e.g. when rebuilding the model), + and will make use of these stored attributes. + + Args: + cls: BaseModel or dataclass. + config_wrapper: The config wrapper instance. + ns_resolver: Namespace resolver to use when getting model annotations. + typevars_map: A dictionary mapping type variables to their concrete types. + + Returns: + A three-tuple containing the model fields, the `PydanticExtraInfo` instance if the `__pydantic_extra__` annotation is set, + and class variables names. + + Raises: + NameError: + - If there is a conflict between a field name and protected namespaces. + - If there is a field other than `root` in `RootModel`. + - If a field shadows an attribute in the parent model. + """ + FieldInfo_ = import_cached_field_info() + BaseModel_ = import_cached_base_model() + + bases = cls.__bases__ + parent_fields_lookup: dict[str, FieldInfo] = {} + for base in reversed(bases): + if model_fields := getattr(base, '__pydantic_fields__', None): + parent_fields_lookup.update(model_fields) + + type_hints = _typing_extra.get_model_type_hints(cls, ns_resolver=ns_resolver) + + # `cls_annotations` is only used to determine if an annotation comes from a parent class + cls_annotations = _typing_extra.safe_get_annotations(cls) + + fields: dict[str, FieldInfo] = {} + + class_vars: set[str] = set() + for ann_name, (ann_type, evaluated) in type_hints.items(): + if ann_name == 'model_config': + # We never want to treat `model_config` as a field + # Note: we may need to change this logic if/when we introduce a `BareModel` class with no + # protected namespaces (where `model_config` might be allowed as a field name) + continue + + _check_protected_namespaces( + protected_namespaces=config_wrapper.protected_namespaces, + ann_name=ann_name, + bases=bases, + cls_name=cls.__name__, + ) + + if _typing_extra.is_classvar_annotation(ann_type): + class_vars.add(ann_name) + continue + + assigned_value = getattr(cls, ann_name, PydanticUndefined) + if assigned_value is not PydanticUndefined and ( + # One of the deprecated instance methods was used as a field name (e.g. `dict()`): + any(getattr(BaseModel_, depr_name, None) is assigned_value for depr_name in _deprecated_method_names) + # One of the deprecated class methods was used as a field name (e.g. `schema()`): + or ( + hasattr(assigned_value, '__func__') + and any( + getattr(getattr(BaseModel_, depr_name, None), '__func__', None) is assigned_value.__func__ # pyright: ignore[reportAttributeAccessIssue] + for depr_name in _deprecated_classmethod_names + ) + ) + ): + # Then `assigned_value` would be the method, even though no default was specified: + assigned_value = PydanticUndefined + + if not is_valid_field_name(ann_name): + continue + if cls.__pydantic_root_model__ and ann_name != 'root': + raise NameError( + f"Unexpected field with name {ann_name!r}; only 'root' is allowed as a field of a `RootModel`" + ) + + for base in bases: + if hasattr(base, ann_name): + if ann_name not in cls_annotations: + # Don't warn when a field exists in a parent class but has not been defined in the current class + continue + + # when building a generic model with `MyModel[int]`, the generic_origin check makes sure we don't get + # "... shadows an attribute" warnings + generic_origin = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin') + if base is generic_origin: + # Don't warn when "shadowing" of attributes in parametrized generics + continue + + dataclass_fields = { + field.name for field in (dataclasses.fields(base) if dataclasses.is_dataclass(base) else ()) + } + if ann_name in dataclass_fields: + # Don't warn when inheriting stdlib dataclasses whose fields are "shadowed" by defaults being set + # on the class instance. + continue + + warnings.warn( + f'Field name "{ann_name}" in "{cls.__qualname__}" shadows an attribute in parent ' + f'"{base.__qualname__}"', + UserWarning, + stacklevel=4, + ) + + if assigned_value is PydanticUndefined: # no assignment, just a plain annotation + if ann_name in cls_annotations or ann_name not in parent_fields_lookup: + # field is either: + # - present in the current model's annotations (and *not* from parent classes) + # - not found on any base classes; this seems to be caused by fields not getting + # generated due to models not being fully defined while initializing recursive models. + # Nothing stops us from just creating a `FieldInfo` for this type hint, so we do this. + field_info = FieldInfo_.from_annotation(ann_type, _source=AnnotationSource.CLASS) + field_info._original_annotation = ann_type + if not evaluated: + field_info._complete = False + # Store the original annotation that should be used to rebuild + # the field info later: + else: + # The field was present on one of the (possibly multiple) base classes, we make a copy directly from it. + parent_field_info = parent_fields_lookup[ann_name]._copy() + + # The only case where substituting the type variables is relevant (i.e. when `typevars_map` is not empty) + # is when a generic class is parameterized (e.g. `MyGenericModel[int, str]`), which creates a new class object + # (unlike the stdlib genercis that create a generic alias). In this case, we are guaranteed to only have to copy + # from the origin/parent model (e.g. `MyGenericModel`). + if typevars_map: + field_info = _recreate_field_info( + parent_field_info, ns_resolver=ns_resolver, typevars_map=typevars_map, lenient=True + ) + else: + field_info = parent_field_info + + else: # An assigned value is present (either the default value, or a `Field()` function) + if isinstance(assigned_value, FieldInfo_) and ismethoddescriptor(assigned_value.default): + # `assigned_value` was fetched using `getattr`, which triggers a call to `__get__` + # for descriptors, so we do the same if the `= field(default=...)` form is used. + # Note that we only do this for method descriptors for now, we might want to + # extend this to any descriptor in the future (by simply checking for + # `hasattr(assigned_value.default, '__get__')`). + default = assigned_value.default.__get__(None, cls) + assigned_value.default = default + assigned_value._attributes_set['default'] = default + + field_info = FieldInfo_.from_annotated_attribute(ann_type, assigned_value, _source=AnnotationSource.CLASS) + + # Store the original annotation and assignment value that could be used to rebuild the field info later. + field_info._original_assignment = assigned_value + field_info._original_annotation = ann_type + if not evaluated: + field_info._complete = False + elif 'final' in field_info._qualifiers and not field_info.is_required(): + warnings.warn( + f'Annotation {ann_name!r} is marked as final and has a default value. Pydantic treats {ann_name!r} as a ' + 'class variable, but it will be considered as a normal field in V3 to be aligned with dataclasses. If you ' + f'still want {ann_name!r} to be considered as a class variable, annotate it as: `ClassVar[] = .`', + category=PydanticDeprecatedSince211, + # Incorrect when `create_model` is used, but the chance that final with a default is used is low in that case: + stacklevel=4, + ) + class_vars.add(ann_name) + continue + + # attributes which are fields are removed from the class namespace: + # 1. To match the behaviour of annotation-only fields + # 2. To avoid false positives in the NameError check above + try: + delattr(cls, ann_name) + except AttributeError: + pass # indicates the attribute was on a parent class + + # Use cls.__dict__['__pydantic_decorators__'] instead of cls.__pydantic_decorators__ + # to make sure the decorators have already been built for this exact class + decorators: DecoratorInfos = cls.__dict__['__pydantic_decorators__'] + if ann_name in decorators.computed_fields: + raise TypeError( + f'Field {ann_name!r} of class {cls.__name__!r} overrides symbol of same name in a parent class. ' + 'This override with a computed_field is incompatible.' + ) + fields[ann_name] = field_info + + if field_info._complete: + # If not complete, this will be called in `rebuild_model_fields()`: + update_field_from_config(config_wrapper, ann_name, field_info) + + if config_wrapper.use_attribute_docstrings: + _update_fields_from_docstrings(cls, fields) + + pydantic_extra_info: PydanticExtraInfo | None = None + if '__pydantic_extra__' in type_hints: + ann, complete = type_hints['__pydantic_extra__'] + pydantic_extra_info = PydanticExtraInfo( + annotation=ann, + complete=complete, + ) + + return fields, pydantic_extra_info, class_vars + + +def rebuild_model_fields( + cls: type[BaseModel], + *, + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver, + typevars_map: Mapping[TypeVar, Any], +) -> tuple[dict[str, FieldInfo], PydanticExtraInfo | None]: + """Rebuild the (already present) model fields by trying to reevaluate annotations. + + This function should be called whenever a model with incomplete fields is encountered. + + Returns: + A two-tuple, the first element being the rebuilt fields, the second element being + the rebuild `PydanticExtraInfo` instance, if available. + + Raises: + NameError: If one of the annotations failed to evaluate. + + Note: + This function *doesn't* mutate the model fields in place, as it can be called during + schema generation, where you don't want to mutate other model's fields. + """ + rebuilt_fields: dict[str, FieldInfo] = {} + with ns_resolver.push(cls): + for f_name, field_info in cls.__pydantic_fields__.items(): + if field_info._complete: + rebuilt_fields[f_name] = field_info + else: + new_field = _recreate_field_info( + field_info, ns_resolver=ns_resolver, typevars_map=typevars_map, lenient=False + ) + update_field_from_config(config_wrapper, f_name, new_field) + rebuilt_fields[f_name] = new_field + + if cls.__pydantic_extra_info__ is not None and not cls.__pydantic_extra_info__.complete: + rebuilt_extra_info = PydanticExtraInfo( + annotation=_typing_extra.eval_type( + cls.__pydantic_extra_info__.annotation, *ns_resolver.types_namespace + ), + complete=True, + ) + else: + rebuilt_extra_info = cls.__pydantic_extra_info__ + + return rebuilt_fields, rebuilt_extra_info + + +def _recreate_field_info( + field_info: FieldInfo, + ns_resolver: NsResolver, + typevars_map: Mapping[TypeVar, Any], + *, + lenient: bool, +) -> FieldInfo: + FieldInfo_ = import_cached_field_info() + + existing_desc = field_info.description + if lenient: + ann = _generics.replace_types(field_info._original_annotation, typevars_map) + ann, evaluated = _typing_extra.try_eval_type( + ann, + *ns_resolver.types_namespace, + ) + else: + # Not the best pattern, maybe we could ship our own `eval_type()`, + # that would replace the type variables on the fly during evaluation. + ann = _typing_extra.eval_type( + field_info._original_annotation, + *ns_resolver.types_namespace, + ) + ann = _generics.replace_types(ann, typevars_map) + ann = _typing_extra.eval_type( + ann, + *ns_resolver.types_namespace, + ) + evaluated = True + + if (assign := field_info._original_assignment) is PydanticUndefined: + new_field = FieldInfo_.from_annotation(ann, _source=AnnotationSource.CLASS) + else: + new_field = FieldInfo_.from_annotated_attribute(ann, assign, _source=AnnotationSource.CLASS) + new_field._original_assignment = assign + new_field._original_annotation = ann + # The description might come from the docstring if `use_attribute_docstrings` was `True`: + new_field.description = new_field.description if new_field.description is not None else existing_desc + if not evaluated: + new_field._complete = False + + return new_field + + +def collect_dataclass_fields( + cls: type[StandardDataclass], + *, + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver | None = None, + typevars_map: dict[Any, Any] | None = None, +) -> dict[str, FieldInfo]: + """Collect the fields of a dataclass. + + Args: + cls: dataclass. + config_wrapper: The config wrapper instance. + ns_resolver: Namespace resolver to use when getting dataclass annotations. + Defaults to an empty instance. + typevars_map: A dictionary mapping type variables to their concrete types. + + Returns: + The dataclass fields. + """ + FieldInfo_ = import_cached_field_info() + + fields: dict[str, FieldInfo] = {} + ns_resolver = ns_resolver or NsResolver() + dataclass_fields = cls.__dataclass_fields__ + + # The logic here is similar to `_typing_extra.get_cls_type_hints`, + # although we do it manually as stdlib dataclasses already have annotations + # collected in each class: + for base in reversed(cls.__mro__): + if not dataclasses.is_dataclass(base): + continue + + with ns_resolver.push(base): + for ann_name, dataclass_field in dataclass_fields.items(): + base_anns = _typing_extra.safe_get_annotations(base) + + if ann_name not in base_anns: + # `__dataclass_fields__`contains every field, even the ones from base classes. + # Only collect the ones defined on `base`. + continue + + globalns, localns = ns_resolver.types_namespace + ann_type, evaluated = _typing_extra.try_eval_type(dataclass_field.type, globalns, localns) + + if _typing_extra.is_classvar_annotation(ann_type): + continue + + if ( + not dataclass_field.init + and dataclass_field.default is dataclasses.MISSING + and dataclass_field.default_factory is dataclasses.MISSING + ): + # TODO: We should probably do something with this so that validate_assignment behaves properly + # Issue: https://github.com/pydantic/pydantic/issues/5470 + continue + + if isinstance(dataclass_field.default, FieldInfo_): + if dataclass_field.default.init_var: + if dataclass_field.default.init is False: + raise PydanticUserError( + f'Dataclass field {ann_name} has init=False and init_var=True, but these are mutually exclusive.', + code='clashing-init-and-init-var', + ) + + # TODO: same note as above re validate_assignment + continue + field_info = FieldInfo_.from_annotated_attribute( + ann_type, dataclass_field.default, _source=AnnotationSource.DATACLASS + ) + field_info._original_assignment = dataclass_field.default + else: + field_info = FieldInfo_.from_annotated_attribute( + ann_type, dataclass_field, _source=AnnotationSource.DATACLASS + ) + field_info._original_assignment = dataclass_field + + if not evaluated: + field_info._complete = False + field_info._original_annotation = ann_type + + fields[ann_name] = field_info + update_field_from_config(config_wrapper, ann_name, field_info) + + if field_info.default is not PydanticUndefined and isinstance( + getattr(cls, ann_name, field_info), FieldInfo_ + ): + # We need this to fix the default when the "default" from __dataclass_fields__ is a pydantic.FieldInfo + setattr(cls, ann_name, field_info.default) + + if typevars_map: + for field in fields.values(): + # We don't pass any ns, as `field.annotation` + # was already evaluated. TODO: is this method relevant? + # Can't we juste use `_generics.replace_types`? + field.apply_typevars_map(typevars_map) + + if config_wrapper.use_attribute_docstrings: + _update_fields_from_docstrings( + cls, + fields, + # We can't rely on the (more reliable) frame inspection method + # for stdlib dataclasses: + use_inspect=not hasattr(cls, '__is_pydantic_dataclass__'), + ) + + return fields + + +def rebuild_dataclass_fields( + cls: type[PydanticDataclass], + *, + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver, + typevars_map: Mapping[TypeVar, Any], +) -> dict[str, FieldInfo]: + """Rebuild the (already present) dataclass fields by trying to reevaluate annotations. + + This function should be called whenever a dataclass with incomplete fields is encountered. + + Raises: + NameError: If one of the annotations failed to evaluate. + + Note: + This function *doesn't* mutate the dataclass fields in place, as it can be called during + schema generation, where you don't want to mutate other dataclass's fields. + """ + FieldInfo_ = import_cached_field_info() + + rebuilt_fields: dict[str, FieldInfo] = {} + with ns_resolver.push(cls): + for f_name, field_info in cls.__pydantic_fields__.items(): + if field_info._complete: + rebuilt_fields[f_name] = field_info + else: + existing_desc = field_info.description + ann = _typing_extra.eval_type( + field_info._original_annotation, + *ns_resolver.types_namespace, + ) + ann = _generics.replace_types(ann, typevars_map) + new_field = FieldInfo_.from_annotated_attribute( + ann, + field_info._original_assignment, + _source=AnnotationSource.DATACLASS, + ) + + # The description might come from the docstring if `use_attribute_docstrings` was `True`: + new_field.description = new_field.description if new_field.description is not None else existing_desc + update_field_from_config(config_wrapper, f_name, new_field) + rebuilt_fields[f_name] = new_field + + return rebuilt_fields + + +def is_valid_field_name(name: str) -> bool: + return not name.startswith('_') + + +def is_valid_privateattr_name(name: str) -> bool: + return name.startswith('_') and not name.startswith('__') + + +def takes_validated_data_argument( + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any], +) -> TypeIs[Callable[[dict[str, Any]], Any]]: + """Whether the provided default factory callable has a validated data parameter.""" + try: + sig = _typing_extra.signature_no_eval(default_factory) + except (ValueError, TypeError): + # `inspect.signature` might not be able to infer a signature, e.g. with C objects. + # In this case, we assume no data argument is present: + return False + + parameters = list(sig.parameters.values()) + + return len(parameters) == 1 and can_be_positional(parameters[0]) and parameters[0].default is Parameter.empty + + +def resolve_default_value( + default: Any, + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None, + *, + validated_data: dict[str, Any] | None = None, + call_default_factory: bool = False, +) -> Any: + """Resolve the default value using either a static default or a default_factory.""" + from ._utils import smart_deepcopy + + if default_factory is None: + return smart_deepcopy(default) + if call_default_factory: + if takes_validated_data_argument(default_factory=default_factory): + fac = cast('Callable[[dict[str, Any]], Any]', default_factory) + if validated_data is None: + raise ValueError( + "The default factory requires the 'validated_data' argument, which was not provided when calling 'get_default()'." + ) + return fac(validated_data) + else: + fac = cast('Callable[[], Any]', default_factory) + return fac() + + return PydanticUndefined diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_forward_ref.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_forward_ref.py new file mode 100644 index 0000000000000000000000000000000000000000..231f81d11b87ef05d4765cd61d88d7348eb9e401 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_forward_ref.py @@ -0,0 +1,23 @@ +from __future__ import annotations as _annotations + +from dataclasses import dataclass +from typing import Union + + +@dataclass +class PydanticRecursiveRef: + type_ref: str + + __name__ = 'PydanticRecursiveRef' + __hash__ = object.__hash__ + + def __call__(self) -> None: + """Defining __call__ is necessary for the `typing` module to let you use an instance of + this class as the result of resolving a standard ForwardRef. + """ + + def __or__(self, other): + return Union[self, other] # type: ignore + + def __ror__(self, other): + return Union[other, self] # type: ignore diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_generate_schema.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_generate_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..f97f76ed8ef3b76eb6daf9ad8e1e40dbb05b3518 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_generate_schema.py @@ -0,0 +1,2924 @@ +"""Convert python types to pydantic-core schema.""" + +from __future__ import annotations as _annotations + +import collections.abc +import dataclasses +import datetime +import inspect +import os +import pathlib +import re +import sys +import typing +import warnings +from collections.abc import Generator, Iterable, Iterator, Mapping +from contextlib import contextmanager +from copy import copy +from decimal import Decimal +from enum import Enum +from fractions import Fraction +from functools import partial +from inspect import Parameter, _ParameterKind +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from itertools import chain +from operator import attrgetter +from types import FunctionType, GenericAlias, LambdaType, MethodType +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Final, + ForwardRef, + Literal, + TypeVar, + Union, + cast, + overload, +) +from uuid import UUID +from zoneinfo import ZoneInfo + +import typing_extensions +from pydantic_core import ( + MISSING, + CoreSchema, + MultiHostUrl, + PydanticCustomError, + PydanticSerializationUnexpectedValue, + PydanticUndefined, + Url, + core_schema, + to_jsonable_python, +) +from typing_extensions import TypeAlias, TypeAliasType, get_args, get_origin, is_typeddict +from typing_inspection import typing_objects +from typing_inspection.introspection import AnnotationSource, get_literal_values, is_union_origin + +from ..aliases import AliasChoices, AliasPath +from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler +from ..config import ConfigDict, JsonDict, JsonEncoder, JsonSchemaExtraCallable +from ..errors import ( + PydanticForbiddenQualifier, + PydanticInvalidForJsonSchema, + PydanticSchemaGenerationError, + PydanticUndefinedAnnotation, + PydanticUserError, +) +from ..functional_validators import AfterValidator, BeforeValidator, FieldValidatorModes, PlainValidator, WrapValidator +from ..json_schema import JsonSchemaValue +from ..version import version_short +from ..warnings import ( + ArbitraryTypeWarning, + PydanticDeprecatedSince20, + TypedDictExtraConfigWarning, + UnsupportedFieldAttributeWarning, +) +from . import _decorators, _discriminated_union, _known_annotated_metadata, _repr, _typing_extra +from ._config import ConfigWrapper, ConfigWrapperStack +from ._core_metadata import CoreMetadata, update_core_metadata +from ._core_utils import ( + get_ref, + get_type_ref, + is_list_like_schema_with_items_schema, +) +from ._decorators import ( + Decorator, + DecoratorInfos, + FieldSerializerDecoratorInfo, + FieldValidatorDecoratorInfo, + ModelSerializerDecoratorInfo, + ModelValidatorDecoratorInfo, + RootValidatorDecoratorInfo, + ValidatorDecoratorInfo, + get_attribute_from_bases, + inspect_field_serializer, + inspect_model_serializer, + inspect_validator, +) +from ._docs_extraction import extract_docstrings_from_cls +from ._fields import ( + collect_dataclass_fields, + rebuild_dataclass_fields, + rebuild_model_fields, + takes_validated_data_argument, + update_field_from_config, +) +from ._forward_ref import PydanticRecursiveRef +from ._generics import get_standard_typevars_map, replace_types +from ._import_utils import import_cached_base_model, import_cached_field_info +from ._mock_val_ser import MockCoreSchema +from ._namespace_utils import NamespacesTuple, NsResolver +from ._schema_gather import MissingDefinitionError, gather_schemas_for_cleaning +from ._schema_generation_shared import CallbackGetCoreSchemaHandler +from ._utils import lenient_issubclass, smart_deepcopy + +if TYPE_CHECKING: + from ..fields import ComputedFieldInfo, FieldInfo + from ..main import BaseModel + from ..types import Discriminator + from ._dataclasses import StandardDataclass + from ._schema_generation_shared import GetJsonSchemaFunction + +_SUPPORTS_TYPEDDICT = sys.version_info >= (3, 12) + +FieldDecoratorInfo = Union[ValidatorDecoratorInfo, FieldValidatorDecoratorInfo, FieldSerializerDecoratorInfo] +FieldDecoratorInfoType = TypeVar('FieldDecoratorInfoType', bound=FieldDecoratorInfo) +AnyFieldDecorator = Union[ + Decorator[ValidatorDecoratorInfo], + Decorator[FieldValidatorDecoratorInfo], + Decorator[FieldSerializerDecoratorInfo], +] + +ModifyCoreSchemaWrapHandler: TypeAlias = GetCoreSchemaHandler +GetCoreSchemaFunction: TypeAlias = Callable[[Any, ModifyCoreSchemaWrapHandler], core_schema.CoreSchema] +ParametersCallback: TypeAlias = "Callable[[int, str, Any], Literal['skip'] | None]" + +TUPLE_TYPES: list[type] = [typing.Tuple, tuple] # noqa: UP006 +LIST_TYPES: list[type] = [typing.List, list, collections.abc.MutableSequence] # noqa: UP006 +SET_TYPES: list[type] = [typing.Set, set, collections.abc.MutableSet] # noqa: UP006 +FROZEN_SET_TYPES: list[type] = [typing.FrozenSet, frozenset, collections.abc.Set] # noqa: UP006 +DICT_TYPES: list[type] = [typing.Dict, dict] # noqa: UP006 +IP_TYPES: list[type] = [IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network] +SEQUENCE_TYPES: list[type] = [typing.Sequence, collections.abc.Sequence] +ITERABLE_TYPES: list[type] = [typing.Iterable, collections.abc.Iterable, typing.Generator, collections.abc.Generator] +TYPE_TYPES: list[type] = [typing.Type, type] # noqa: UP006 +PATTERN_TYPES: list[type] = [typing.Pattern, re.Pattern] +PATH_TYPES: list[type] = [ + os.PathLike, + pathlib.Path, + pathlib.PurePath, + pathlib.PosixPath, + pathlib.PurePosixPath, + pathlib.PureWindowsPath, +] +MAPPING_TYPES = [ + typing.Mapping, + typing.MutableMapping, + collections.abc.Mapping, + collections.abc.MutableMapping, + collections.OrderedDict, + typing_extensions.OrderedDict, + typing.DefaultDict, # noqa: UP006 + collections.defaultdict, +] +COUNTER_TYPES = [collections.Counter, typing.Counter] +DEQUE_TYPES: list[type] = [collections.deque, typing.Deque] # noqa: UP006 + +# Note: This does not play very well with type checkers. For example, +# `a: LambdaType = lambda x: x` will raise a type error by Pyright. +ValidateCallSupportedTypes = Union[ + LambdaType, + FunctionType, + MethodType, + partial, +] + +VALIDATE_CALL_SUPPORTED_TYPES = get_args(ValidateCallSupportedTypes) +UNSUPPORTED_STANDALONE_FIELDINFO_ATTRIBUTES: list[tuple[str, Any]] = [ + ('alias', None), + ('validation_alias', None), + ('serialization_alias', None), + # will be set if any alias is set, so disable it to avoid double warnings: + # 'alias_priority', + ('default', PydanticUndefined), + ('default_factory', None), + ('exclude', None), + ('deprecated', None), + ('repr', True), + ('validate_default', None), + ('frozen', None), + ('init', None), + ('init_var', None), + ('kw_only', None), +] +"""`FieldInfo` attributes (and their default value) that can't be used outside of a model (e.g. in a type adapter or a PEP 695 type alias).""" + +_mode_to_validator: dict[ + FieldValidatorModes, type[BeforeValidator | AfterValidator | PlainValidator | WrapValidator] +] = {'before': BeforeValidator, 'after': AfterValidator, 'plain': PlainValidator, 'wrap': WrapValidator} + + +def check_validator_fields_against_field_name( + info: FieldDecoratorInfo, + field: str, +) -> bool: + """Check if field name is in validator fields. + + Args: + info: The field info. + field: The field name to check. + + Returns: + `True` if field name is in validator fields, `False` otherwise. + """ + fields = info.fields + return '*' in fields or field in fields + + +def check_decorator_fields_exist(decorators: Iterable[AnyFieldDecorator], fields: Iterable[str]) -> None: + """Check if the defined fields in decorators exist in `fields` param. + + It ignores the check for a decorator if the decorator has `*` as field or `check_fields=False`. + + Args: + decorators: An iterable of decorators. + fields: An iterable of fields name. + + Raises: + PydanticUserError: If one of the field names does not exist in `fields` param. + """ + fields = set(fields) + for dec in decorators: + if '*' in dec.info.fields: + continue + if dec.info.check_fields is False: + continue + for field in dec.info.fields: + if field not in fields: + raise PydanticUserError( + f'Decorators defined with incorrect fields: {dec.cls_ref}.{dec.cls_var_name}' + " (use check_fields=False if you're inheriting from the model and intended this)", + code='decorator-missing-field', + ) + + +def filter_field_decorator_info_by_field( + validator_functions: Iterable[Decorator[FieldDecoratorInfoType]], field: str +) -> list[Decorator[FieldDecoratorInfoType]]: + return [dec for dec in validator_functions if check_validator_fields_against_field_name(dec.info, field)] + + +def apply_each_item_validators( + schema: core_schema.CoreSchema, + each_item_validators: list[Decorator[ValidatorDecoratorInfo]], +) -> core_schema.CoreSchema: + # This V1 compatibility shim should eventually be removed + + # fail early if each_item_validators is empty + if not each_item_validators: + return schema + + # push down any `each_item=True` validators + # note that this won't work for any Annotated types that get wrapped by a function validator + # but that's okay because that didn't exist in V1 + if schema['type'] == 'nullable': + schema['schema'] = apply_each_item_validators(schema['schema'], each_item_validators) + return schema + elif schema['type'] == 'tuple': + if (variadic_item_index := schema.get('variadic_item_index')) is not None: + schema['items_schema'][variadic_item_index] = apply_validators( + schema['items_schema'][variadic_item_index], + each_item_validators, + ) + elif is_list_like_schema_with_items_schema(schema): + inner_schema = schema.get('items_schema', core_schema.any_schema()) + schema['items_schema'] = apply_validators(inner_schema, each_item_validators) + elif schema['type'] == 'dict': + inner_schema = schema.get('values_schema', core_schema.any_schema()) + schema['values_schema'] = apply_validators(inner_schema, each_item_validators) + else: + raise TypeError( + f'`@validator(..., each_item=True)` cannot be applied to fields with a schema of {schema["type"]}' + ) + return schema + + +def _extract_json_schema_info_from_field_info( + info: FieldInfo | ComputedFieldInfo, +) -> tuple[JsonDict | None, JsonDict | JsonSchemaExtraCallable | None]: + json_schema_updates = { + 'title': info.title, + 'description': info.description, + 'deprecated': bool(info.deprecated) or info.deprecated == '' or None, + 'examples': to_jsonable_python(info.examples), + } + json_schema_updates = {k: v for k, v in json_schema_updates.items() if v is not None} + return (json_schema_updates or None, info.json_schema_extra) + + +JsonEncoders = dict[type[Any], JsonEncoder] + + +def _add_custom_serialization_from_json_encoders( + json_encoders: JsonEncoders | None, tp: Any, schema: CoreSchema +) -> CoreSchema: + """Iterate over the json_encoders and add the first matching encoder to the schema. + + Args: + json_encoders: A dictionary of types and their encoder functions. + tp: The type to check for a matching encoder. + schema: The schema to add the encoder to. + """ + if not json_encoders: + return schema + if 'serialization' in schema: + return schema + # Check the class type and its superclasses for a matching encoder + # Decimal.__class__.__mro__ (and probably other cases) doesn't include Decimal itself + # if the type is a GenericAlias (e.g. from list[int]) we need to use __class__ instead of .__mro__ + for base in (tp, *getattr(tp, '__mro__', tp.__class__.__mro__)[:-1]): + encoder = json_encoders.get(base) + if encoder is None: + continue + + warnings.warn( + f'`json_encoders` is deprecated. See https://docs.pydantic.dev/{version_short()}/concepts/serialization/#custom-serializers for alternatives', + PydanticDeprecatedSince20, + ) + + # TODO: in theory we should check that the schema accepts a serialization key + schema['serialization'] = core_schema.plain_serializer_function_ser_schema(encoder, when_used='json') + return schema + + return schema + + +GENERATE_SCHEMA_ERRORS = ( + PydanticForbiddenQualifier, + PydanticInvalidForJsonSchema, + PydanticSchemaGenerationError, + PydanticUndefinedAnnotation, +) +"""Errors raised during core schema generation. This does *not* include `InvalidSchemaError`, which is raised during schema cleaning.""" + + +class InvalidSchemaError(Exception): + """The core schema is invalid.""" + + +class GenerateSchema: + """Generate core schema for a Pydantic model, dataclass and types like `str`, `datetime`, ... .""" + + __slots__ = ( + '_config_wrapper_stack', + '_ns_resolver', + '_typevars_map', + 'field_name_stack', + 'model_type_stack', + 'defs', + ) + + def __init__( + self, + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver | None = None, + typevars_map: Mapping[TypeVar, Any] | None = None, + ) -> None: + # we need a stack for recursing into nested models + self._config_wrapper_stack = ConfigWrapperStack(config_wrapper) + self._ns_resolver = ns_resolver or NsResolver() + self._typevars_map = typevars_map + self.field_name_stack = _FieldNameStack() + self.model_type_stack = _ModelTypeStack() + self.defs = _Definitions() + + def __init_subclass__(cls) -> None: + super().__init_subclass__() + warnings.warn( + 'Subclassing `GenerateSchema` is not supported. The API is highly subject to change in minor versions.', + UserWarning, + stacklevel=2, + ) + + @property + def _config_wrapper(self) -> ConfigWrapper: + return self._config_wrapper_stack.tail + + @property + def _types_namespace(self) -> NamespacesTuple: + return self._ns_resolver.types_namespace + + @property + def _arbitrary_types(self) -> bool: + return self._config_wrapper.arbitrary_types_allowed + + # the following methods can be overridden but should be considered + # unstable / private APIs + def _list_schema(self, items_type: Any) -> CoreSchema: + return core_schema.list_schema(self.generate_schema(items_type)) + + def _dict_schema(self, keys_type: Any, values_type: Any) -> CoreSchema: + return core_schema.dict_schema(self.generate_schema(keys_type), self.generate_schema(values_type)) + + def _set_schema(self, items_type: Any) -> CoreSchema: + return core_schema.set_schema(self.generate_schema(items_type)) + + def _frozenset_schema(self, items_type: Any) -> CoreSchema: + return core_schema.frozenset_schema(self.generate_schema(items_type)) + + def _enum_schema(self, enum_type: type[Enum]) -> CoreSchema: + cases: list[Any] = list(enum_type.__members__.values()) + + enum_ref = get_type_ref(enum_type) + description = None if not enum_type.__doc__ else inspect.cleandoc(enum_type.__doc__) + if ( + description == 'An enumeration.' + ): # This is the default value provided by enum.EnumMeta.__new__; don't use it + description = None + js_updates = {'title': enum_type.__name__, 'description': description} + js_updates = {k: v for k, v in js_updates.items() if v is not None} + + sub_type: Literal['str', 'int', 'float'] | None = None + if issubclass(enum_type, int): + sub_type = 'int' + value_ser_type: core_schema.SerSchema = core_schema.simple_ser_schema('int') + elif issubclass(enum_type, str): + # this handles `StrEnum` (3.11 only), and also `Foobar(str, Enum)` + sub_type = 'str' + value_ser_type = core_schema.simple_ser_schema('str') + elif issubclass(enum_type, float): + sub_type = 'float' + value_ser_type = core_schema.simple_ser_schema('float') + else: + # TODO this is an ugly hack, how do we trigger an Any schema for serialization? + value_ser_type = core_schema.plain_serializer_function_ser_schema(lambda x: x) + + if cases: + + def get_json_schema(schema: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue: + json_schema = handler(schema) + original_schema = handler.resolve_ref_schema(json_schema) + original_schema.update(js_updates) + return json_schema + + # we don't want to add the missing to the schema if it's the default one + default_missing = getattr(enum_type._missing_, '__func__', None) is Enum._missing_.__func__ # pyright: ignore[reportFunctionMemberAccess] + enum_schema = core_schema.enum_schema( + enum_type, + cases, + sub_type=sub_type, + missing=None if default_missing else enum_type._missing_, + ref=enum_ref, + metadata={'pydantic_js_functions': [get_json_schema]}, + ) + + if self._config_wrapper.use_enum_values: + enum_schema = core_schema.no_info_after_validator_function( + attrgetter('value'), enum_schema, serialization=value_ser_type + ) + + return enum_schema + + else: + + def get_json_schema_no_cases(_, handler: GetJsonSchemaHandler) -> JsonSchemaValue: + json_schema = handler(core_schema.enum_schema(enum_type, cases, sub_type=sub_type, ref=enum_ref)) + original_schema = handler.resolve_ref_schema(json_schema) + original_schema.update(js_updates) + return json_schema + + # Use an isinstance check for enums with no cases. + # The most important use case for this is creating TypeVar bounds for generics that should + # be restricted to enums. This is more consistent than it might seem at first, since you can only + # subclass enum.Enum (or subclasses of enum.Enum) if all parent classes have no cases. + # We use the get_json_schema function when an Enum subclass has been declared with no cases + # so that we can still generate a valid json schema. + return core_schema.is_instance_schema( + enum_type, + metadata={'pydantic_js_functions': [get_json_schema_no_cases]}, + ) + + def _ip_schema(self, tp: Any) -> CoreSchema: + from ._validators import IP_VALIDATOR_LOOKUP, IpType + + ip_type_json_schema_format: dict[type[IpType], str] = { + IPv4Address: 'ipv4', + IPv4Network: 'ipv4network', + IPv4Interface: 'ipv4interface', + IPv6Address: 'ipv6', + IPv6Network: 'ipv6network', + IPv6Interface: 'ipv6interface', + } + + def ser_ip(ip: Any, info: core_schema.SerializationInfo) -> str | IpType: + if not isinstance(ip, (tp, str)): + raise PydanticSerializationUnexpectedValue( + f"Expected `{tp}` but got `{type(ip)}` with value `'{ip}'` - serialized value may not be as expected." + ) + if info.mode == 'python': + return ip + return str(ip) + + return core_schema.lax_or_strict_schema( + lax_schema=core_schema.no_info_plain_validator_function(IP_VALIDATOR_LOOKUP[tp]), + strict_schema=core_schema.json_or_python_schema( + json_schema=core_schema.no_info_after_validator_function(tp, core_schema.str_schema()), + python_schema=core_schema.is_instance_schema(tp), + ), + serialization=core_schema.plain_serializer_function_ser_schema(ser_ip, info_arg=True, when_used='always'), + metadata={ + 'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': ip_type_json_schema_format[tp]}] + }, + ) + + def _path_schema(self, tp: Any, path_type: Any) -> CoreSchema: + if tp is os.PathLike and (path_type not in {str, bytes} and not typing_objects.is_any(path_type)): + raise PydanticUserError( + '`os.PathLike` can only be used with `str`, `bytes` or `Any`', code='schema-for-unknown-type' + ) + + path_constructor = pathlib.PurePath if tp is os.PathLike else tp + strict_inner_schema = ( + core_schema.bytes_schema(strict=True) if (path_type is bytes) else core_schema.str_schema(strict=True) + ) + lax_inner_schema = core_schema.bytes_schema() if (path_type is bytes) else core_schema.str_schema() + + def path_validator(input_value: str | bytes) -> os.PathLike[Any]: # type: ignore + try: + if path_type is bytes: + if isinstance(input_value, bytes): + try: + input_value = input_value.decode() + except UnicodeDecodeError as e: + raise PydanticCustomError('bytes_type', 'Input must be valid bytes') from e + else: + raise PydanticCustomError('bytes_type', 'Input must be bytes') + elif not isinstance(input_value, str): + raise PydanticCustomError('path_type', 'Input is not a valid path') + + return path_constructor(input_value) # type: ignore + except TypeError as e: + raise PydanticCustomError('path_type', 'Input is not a valid path') from e + + def ser_path(path: Any, info: core_schema.SerializationInfo) -> str | os.PathLike[Any]: + if not isinstance(path, (tp, str)): + raise PydanticSerializationUnexpectedValue( + f"Expected `{tp}` but got `{type(path)}` with value `'{path}'` - serialized value may not be as expected." + ) + if info.mode == 'python': + return path + return str(path) + + instance_schema = core_schema.json_or_python_schema( + json_schema=core_schema.no_info_after_validator_function(path_validator, lax_inner_schema), + python_schema=core_schema.is_instance_schema(tp), + ) + + schema = core_schema.lax_or_strict_schema( + lax_schema=core_schema.union_schema( + [ + instance_schema, + core_schema.no_info_after_validator_function(path_validator, strict_inner_schema), + ], + custom_error_type='path_type', + custom_error_message=f'Input is not a valid path for {tp}', + ), + strict_schema=instance_schema, + serialization=core_schema.plain_serializer_function_ser_schema(ser_path, info_arg=True, when_used='always'), + metadata={'pydantic_js_functions': [lambda source, handler: {**handler(source), 'format': 'path'}]}, + ) + return schema + + def _deque_schema(self, items_type: Any) -> CoreSchema: + from ._serializers import serialize_sequence_via_list + from ._validators import deque_validator + + item_type_schema = self.generate_schema(items_type) + + # we have to use a lax list schema here, because we need to validate the deque's + # items via a list schema, but it's ok if the deque itself is not a list + list_schema = core_schema.list_schema(item_type_schema, strict=False) + + check_instance = core_schema.json_or_python_schema( + json_schema=list_schema, + python_schema=core_schema.is_instance_schema(collections.deque, cls_repr='Deque'), + ) + + lax_schema = core_schema.no_info_wrap_validator_function(deque_validator, list_schema) + + return core_schema.lax_or_strict_schema( + lax_schema=lax_schema, + strict_schema=core_schema.chain_schema([check_instance, lax_schema]), + serialization=core_schema.wrap_serializer_function_ser_schema( + serialize_sequence_via_list, schema=item_type_schema, info_arg=True + ), + ) + + def _mapping_schema(self, tp: Any, keys_type: Any, values_type: Any) -> CoreSchema: + from ._validators import MAPPING_ORIGIN_MAP, defaultdict_validator, get_defaultdict_default_default_factory + + mapped_origin = MAPPING_ORIGIN_MAP[tp] + keys_schema = self.generate_schema(keys_type) + with warnings.catch_warnings(): + # We kind of abused `Field()` default factories to be able to specify + # the `defaultdict`'s `default_factory`. As a consequence, we get warnings + # as normally `FieldInfo.default_factory` is unsupported in the context where + # `Field()` is used and our only solution is to ignore them (note that this might + # wrongfully ignore valid warnings, e.g. if the `value_type` is a PEP 695 type alias + # with unsupported metadata). + warnings.simplefilter('ignore', category=UnsupportedFieldAttributeWarning) + values_schema = self.generate_schema(values_type) + dict_schema = core_schema.dict_schema(keys_schema, values_schema, strict=False) + + if mapped_origin is dict: + schema = dict_schema + else: + check_instance = core_schema.json_or_python_schema( + json_schema=dict_schema, + python_schema=core_schema.is_instance_schema(mapped_origin), + ) + + if tp is collections.defaultdict: + default_default_factory = get_defaultdict_default_default_factory(values_type) + coerce_instance_wrap = partial( + core_schema.no_info_wrap_validator_function, + partial(defaultdict_validator, default_default_factory=default_default_factory), + ) + else: + coerce_instance_wrap = partial(core_schema.no_info_after_validator_function, mapped_origin) + + lax_schema = coerce_instance_wrap(dict_schema) + strict_schema = core_schema.chain_schema([check_instance, lax_schema]) + + schema = core_schema.lax_or_strict_schema( + lax_schema=lax_schema, + strict_schema=strict_schema, + serialization=core_schema.wrap_serializer_function_ser_schema( + lambda v, h: h(v), schema=dict_schema, info_arg=False + ), + ) + + return schema + + def _fraction_schema(self) -> CoreSchema: + """Support for [`fractions.Fraction`][fractions.Fraction].""" + from ._validators import fraction_validator + + # TODO: note, this is a fairly common pattern, re lax / strict for attempted type coercion, + # can we use a helper function to reduce boilerplate? + return core_schema.lax_or_strict_schema( + lax_schema=core_schema.no_info_plain_validator_function(fraction_validator), + strict_schema=core_schema.json_or_python_schema( + json_schema=core_schema.no_info_plain_validator_function(fraction_validator), + python_schema=core_schema.is_instance_schema(Fraction), + ), + # use str serialization to guarantee round trip behavior + serialization=core_schema.to_string_ser_schema(when_used='always'), + metadata={'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'fraction'}]}, + ) + + def _arbitrary_type_schema(self, tp: Any) -> CoreSchema: + if not isinstance(tp, type): + warnings.warn( + f'{tp!r} is not a Python type (it may be an instance of an object),' + ' Pydantic will allow any object with no validation since we cannot even' + ' enforce that the input is an instance of the given type.' + ' To get rid of this error wrap the type with `pydantic.SkipValidation`.', + ArbitraryTypeWarning, + ) + return core_schema.any_schema() + return core_schema.is_instance_schema(tp) + + def _unknown_type_schema(self, obj: Any) -> CoreSchema: + raise PydanticSchemaGenerationError( + f'Unable to generate pydantic-core schema for {obj!r}. ' + 'Set `arbitrary_types_allowed=True` in the model_config to ignore this error' + ' or implement `__get_pydantic_core_schema__` on your type to fully support it.' + '\n\nIf you got this error by calling handler() within' + ' `__get_pydantic_core_schema__` then you likely need to call' + ' `handler.generate_schema()` since we do not call' + ' `__get_pydantic_core_schema__` on `` otherwise to avoid infinite recursion.' + ) + + def _apply_discriminator_to_union( + self, schema: CoreSchema, discriminator: str | Discriminator | None + ) -> CoreSchema: + if discriminator is None: + return schema + try: + return _discriminated_union.apply_discriminator( + schema, + discriminator, + self.defs._definitions, + ) + except _discriminated_union.MissingDefinitionForUnionRef: + # defer until defs are resolved + _discriminated_union.set_discriminator_in_metadata( + schema, + discriminator, + ) + return schema + + def clean_schema(self, schema: CoreSchema) -> CoreSchema: + return self.defs.finalize_schema(schema) + + def _add_js_function(self, metadata_schema: CoreSchema, js_function: Callable[..., Any]) -> None: + metadata = metadata_schema.get('metadata', {}) + pydantic_js_functions = metadata.setdefault('pydantic_js_functions', []) + # because of how we generate core schemas for nested generic models + # we can end up adding `BaseModel.__get_pydantic_json_schema__` multiple times + # this check may fail to catch duplicates if the function is a `functools.partial` + # or something like that, but if it does it'll fail by inserting the duplicate + if js_function not in pydantic_js_functions: + pydantic_js_functions.append(js_function) + metadata_schema['metadata'] = metadata + + def generate_schema( + self, + obj: Any, + ) -> core_schema.CoreSchema: + """Generate core schema. + + Args: + obj: The object to generate core schema for. + + Returns: + The generated core schema. + + Raises: + PydanticUndefinedAnnotation: + If it is not possible to evaluate forward reference. + PydanticSchemaGenerationError: + If it is not possible to generate pydantic-core schema. + TypeError: + - If `alias_generator` returns a disallowed type (must be str, AliasPath or AliasChoices). + - If V1 style validator with `each_item=True` applied on a wrong field. + PydanticUserError: + - If `typing.TypedDict` is used instead of `typing_extensions.TypedDict` on Python < 3.12. + - If `__modify_schema__` method is used instead of `__get_pydantic_json_schema__`. + """ + schema = self._generate_schema_from_get_schema_method(obj, obj) + + if schema is None: + schema = self._generate_schema_inner(obj) + + metadata_js_function = _extract_get_pydantic_json_schema(obj) + if metadata_js_function is not None: + metadata_schema = resolve_original_schema(schema, self.defs) + if metadata_schema: + self._add_js_function(metadata_schema, metadata_js_function) + + schema = _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, obj, schema) + + return schema + + def _model_schema(self, cls: type[BaseModel]) -> core_schema.CoreSchema: + """Generate schema for a Pydantic model.""" + BaseModel_ = import_cached_base_model() + + with self.defs.get_schema_or_ref(cls) as (model_ref, maybe_schema): + if maybe_schema is not None: + return maybe_schema + + schema = cls.__dict__.get('__pydantic_core_schema__') + if schema is not None and not isinstance(schema, MockCoreSchema): + if schema['type'] == 'definitions': + schema = self.defs.unpack_definitions(schema) + ref = get_ref(schema) + if ref: + return self.defs.create_definition_reference_schema(schema) + else: + return schema + + config_wrapper = ConfigWrapper(cls.model_config, check=False) + + with self._config_wrapper_stack.push(config_wrapper), self._ns_resolver.push(cls): + core_config = self._config_wrapper.core_config(title=cls.__name__) + + if cls.__pydantic_fields_complete__ or cls is BaseModel_: + fields = getattr(cls, '__pydantic_fields__', {}) + extra_info = getattr(cls, '__pydantic_extra_info__', None) + else: + if '__pydantic_fields__' not in cls.__dict__: + # This happens when we have a loop in the schema generation: + # class Base[T](BaseModel): + # t: T + # + # class Other(BaseModel): + # b: 'Base[Other]' + # When we build fields for `Other`, we evaluate the forward annotation. + # At this point, `Other` doesn't have the model fields set. We create + # `Base[Other]`; model fields are successfully built, and we try to generate + # a schema for `t: Other`. As `Other.__pydantic_fields__` aren't set, we abort. + raise PydanticUndefinedAnnotation( + name=cls.__name__, + message=f'Class {cls.__name__!r} is not defined', + ) + try: + fields, extra_info = rebuild_model_fields( + cls, + config_wrapper=self._config_wrapper, + ns_resolver=self._ns_resolver, + typevars_map=self._typevars_map or {}, + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + decorators = cls.__pydantic_decorators__ + computed_fields = decorators.computed_fields + check_decorator_fields_exist( + chain( + decorators.field_validators.values(), + decorators.field_serializers.values(), + decorators.validators.values(), + ), + {*fields.keys(), *computed_fields.keys()}, + ) + + model_validators = decorators.model_validators.values() + + extras_schema = None + extras_keys_schema = None + if core_config.get('extra_fields_behavior') == 'allow' and extra_info is not None: + tp = get_origin(extra_info.annotation) + if tp not in DICT_TYPES: + raise PydanticSchemaGenerationError( + 'The type annotation for `__pydantic_extra__` must be `dict[str, ...]`' + ) + # See the comments in `_get_args_resolving_forward_refs()` for why we need + # to re-evaluate the annotation: + extra_keys_type, extra_items_type = self._get_args_resolving_forward_refs( + extra_info.annotation, + required=True, + ) + if extra_keys_type is not str: + extras_keys_schema = self.generate_schema(extra_keys_type) + if not typing_objects.is_any(extra_items_type): + extras_schema = self.generate_schema(extra_items_type) + + generic_origin: type[BaseModel] | None = getattr(cls, '__pydantic_generic_metadata__', {}).get('origin') + + if cls.__pydantic_root_model__: + # FIXME: should the common field metadata be used here? + inner_schema, _ = self._common_field_schema('root', fields['root'], decorators) + inner_schema = apply_model_validators(inner_schema, model_validators, 'inner') + model_schema = core_schema.model_schema( + cls, + inner_schema, + generic_origin=generic_origin, + custom_init=getattr(cls, '__pydantic_custom_init__', None), + root_model=True, + post_init=getattr(cls, '__pydantic_post_init__', None), + config=core_config, + ref=model_ref, + ) + else: + fields_schema: core_schema.CoreSchema = core_schema.model_fields_schema( + {k: self._generate_md_field_schema(k, v, decorators) for k, v in fields.items()}, + computed_fields=[ + self._computed_field_schema(d, decorators.field_serializers) + for d in computed_fields.values() + ], + extras_schema=extras_schema, + extras_keys_schema=extras_keys_schema, + model_name=cls.__name__, + ) + inner_schema = apply_validators(fields_schema, decorators.root_validators.values()) + inner_schema = apply_model_validators(inner_schema, model_validators, 'inner') + + model_schema = core_schema.model_schema( + cls, + inner_schema, + generic_origin=generic_origin, + custom_init=getattr(cls, '__pydantic_custom_init__', None), + root_model=False, + post_init=getattr(cls, '__pydantic_post_init__', None), + config=core_config, + ref=model_ref, + ) + + schema = self._apply_model_serializers(model_schema, decorators.model_serializers.values()) + schema = apply_model_validators(schema, model_validators, 'outer') + return self.defs.create_definition_reference_schema(schema) + + def _resolve_self_type(self, obj: Any) -> Any: + obj = self.model_type_stack.get() + if obj is None: + raise PydanticUserError('`typing.Self` is invalid in this context', code='invalid-self-type') + return obj + + def _generate_schema_from_get_schema_method(self, obj: Any, source: Any) -> core_schema.CoreSchema | None: + BaseModel_ = import_cached_base_model() + + get_schema = getattr(obj, '__get_pydantic_core_schema__', None) + is_base_model_get_schema = ( + getattr(get_schema, '__func__', None) is BaseModel_.__get_pydantic_core_schema__.__func__ # pyright: ignore[reportFunctionMemberAccess] + ) + + if ( + get_schema is not None + # BaseModel.__get_pydantic_core_schema__ is defined for backwards compatibility, + # to allow existing code to call `super().__get_pydantic_core_schema__` in Pydantic + # model that overrides `__get_pydantic_core_schema__`. However, it raises a deprecation + # warning stating that the method will be removed, and during the core schema gen we actually + # don't call the method: + and not is_base_model_get_schema + ): + # Some referenceable types might have a `__get_pydantic_core_schema__` method + # defined on it by users (e.g. on a dataclass). This generally doesn't play well + # as these types are already recognized by the `GenerateSchema` class and isn't ideal + # as we might end up calling `get_schema_or_ref` (expensive) on types that are actually + # not referenceable: + with self.defs.get_schema_or_ref(obj) as (_, maybe_schema): + if maybe_schema is not None: + return maybe_schema + + if obj is source: + ref_mode = 'unpack' + else: + ref_mode = 'to-def' + schema = get_schema( + source, CallbackGetCoreSchemaHandler(self._generate_schema_inner, self, ref_mode=ref_mode) + ) + if schema['type'] == 'definitions': + schema = self.defs.unpack_definitions(schema) + + ref = get_ref(schema) + if ref: + return self.defs.create_definition_reference_schema(schema) + + # Note: if schema is of type `'definition-ref'`, we might want to copy it as a + # safety measure (because these are inlined in place -- i.e. mutated directly) + return schema + + if get_schema is None and (validators := getattr(obj, '__get_validators__', None)) is not None: + from pydantic.v1 import BaseModel as BaseModelV1 + + if issubclass(obj, BaseModelV1): + warnings.warn( + f'Mixing V1 models and V2 models (or constructs, like `TypeAdapter`) is not supported. Please upgrade `{obj.__name__}` to V2.', + UserWarning, + ) + else: + warnings.warn( + '`__get_validators__` is deprecated and will be removed, use `__get_pydantic_core_schema__` instead.', + PydanticDeprecatedSince20, + ) + return core_schema.chain_schema([core_schema.with_info_plain_validator_function(v) for v in validators()]) + + def _resolve_forward_ref(self, obj: Any) -> Any: + # we assume that types_namespace has the target of forward references in its scope, + # but this could fail, for example, if calling Validator on an imported type which contains + # forward references to other types only defined in the module from which it was imported + # `Validator(SomeImportedTypeAliasWithAForwardReference)` + # or the equivalent for BaseModel + # class Model(BaseModel): + # x: SomeImportedTypeAliasWithAForwardReference + try: + obj = _typing_extra.eval_type_backport(obj, *self._types_namespace) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + # if obj is still a ForwardRef, it means we can't evaluate it, raise PydanticUndefinedAnnotation + if isinstance(obj, ForwardRef): + raise PydanticUndefinedAnnotation(obj.__forward_arg__, f'Unable to evaluate forward reference {obj}') + + if self._typevars_map: + obj = replace_types(obj, self._typevars_map) + + return obj + + @overload + def _get_args_resolving_forward_refs(self, obj: Any, required: Literal[True]) -> tuple[Any, ...]: ... + + @overload + def _get_args_resolving_forward_refs(self, obj: Any) -> tuple[Any, ...] | None: ... + + def _get_args_resolving_forward_refs(self, obj: Any, required: bool = False) -> tuple[Any, ...] | None: + args = get_args(obj) + if args: + if isinstance(obj, GenericAlias): + # PEP 585 generic aliases don't convert args to ForwardRefs, unlike `typing.List/Dict` etc. + # This was fixed in https://github.com/python/cpython/pull/30900 (Python 3.11). + # TODO: this shouldn't be necessary (probably even this `_get_args_resolving_forward_refs()` function) + # once we drop support for Python 3.10 *or* if we implement our own `typing._eval_type()` implementation. + args = (_typing_extra._make_forward_ref(a) if isinstance(a, str) else a for a in args) + args = tuple(self._resolve_forward_ref(a) if isinstance(a, ForwardRef) else a for a in args) + elif required: # pragma: no cover + raise TypeError(f'Expected {obj} to have generic parameters but it had none') + return args + + def _get_first_arg_or_any(self, obj: Any) -> Any: + args = self._get_args_resolving_forward_refs(obj) + if not args: + return Any + return args[0] + + def _get_first_two_args_or_any(self, obj: Any) -> tuple[Any, Any]: + args = self._get_args_resolving_forward_refs(obj) + if not args: + return (Any, Any) + if len(args) < 2: + origin = get_origin(obj) + raise TypeError(f'Expected two type arguments for {origin}, got 1') + return args[0], args[1] + + def _generate_schema_inner(self, obj: Any) -> core_schema.CoreSchema: + if typing_objects.is_self(obj): + obj = self._resolve_self_type(obj) + + if typing_objects.is_annotated(get_origin(obj)): + return self._annotated_schema(obj) + + if isinstance(obj, dict): + # we assume this is already a valid schema + return obj # type: ignore[return-value] + + if isinstance(obj, str): + obj = ForwardRef(obj) + + if isinstance(obj, ForwardRef): + return self.generate_schema(self._resolve_forward_ref(obj)) + + BaseModel = import_cached_base_model() + + if lenient_issubclass(obj, BaseModel): + with self.model_type_stack.push(obj): + return self._model_schema(obj) + + if isinstance(obj, PydanticRecursiveRef): + return core_schema.definition_reference_schema(schema_ref=obj.type_ref) + + return self.match_type(obj) + + def match_type(self, obj: Any) -> core_schema.CoreSchema: # noqa: C901 + """Main mapping of types to schemas. + + The general structure is a series of if statements starting with the simple cases + (non-generic primitive types) and then handling generics and other more complex cases. + + Each case either generates a schema directly, calls into a public user-overridable method + (like `GenerateSchema.tuple_variable_schema`) or calls into a private method that handles some + boilerplate before calling into the user-facing method (e.g. `GenerateSchema._tuple_schema`). + + The idea is that we'll evolve this into adding more and more user facing methods over time + as they get requested and we figure out what the right API for them is. + """ + if obj is str: + return core_schema.str_schema() + elif obj is bytes: + return core_schema.bytes_schema() + elif obj is int: + return core_schema.int_schema() + elif obj is float: + return core_schema.float_schema() + elif obj is bool: + return core_schema.bool_schema() + elif obj is complex: + return core_schema.complex_schema() + elif typing_objects.is_any(obj) or obj is object: + return core_schema.any_schema() + elif obj is datetime.date: + return core_schema.date_schema() + elif obj is datetime.datetime: + return core_schema.datetime_schema() + elif obj is datetime.time: + return core_schema.time_schema() + elif obj is datetime.timedelta: + return core_schema.timedelta_schema() + elif obj is Decimal: + return core_schema.decimal_schema() + elif obj is UUID: + return core_schema.uuid_schema() + elif obj is Url: + return core_schema.url_schema() + elif obj is Fraction: + return self._fraction_schema() + elif obj is MultiHostUrl: + return core_schema.multi_host_url_schema() + elif obj is None or obj is _typing_extra.NoneType: + return core_schema.none_schema() + if obj is MISSING: + return core_schema.missing_sentinel_schema() + elif obj in IP_TYPES: + return self._ip_schema(obj) + elif obj in TUPLE_TYPES: + return self._tuple_schema(obj) + elif obj in LIST_TYPES: + return self._list_schema(Any) + elif obj in SET_TYPES: + return self._set_schema(Any) + elif obj in FROZEN_SET_TYPES: + return self._frozenset_schema(Any) + elif obj in SEQUENCE_TYPES: + return self._sequence_schema(Any) + elif obj in ITERABLE_TYPES: + return self._iterable_schema(obj) + elif obj in DICT_TYPES: + return self._dict_schema(Any, Any) + elif obj in PATH_TYPES: + return self._path_schema(obj, Any) + elif obj in DEQUE_TYPES: + return self._deque_schema(Any) + elif obj in MAPPING_TYPES: + return self._mapping_schema(obj, Any, Any) + elif obj in COUNTER_TYPES: + return self._mapping_schema(obj, Any, int) + elif typing_objects.is_typealiastype(obj): + return self._type_alias_type_schema(obj) + elif obj is type: + return self._type_schema() + elif _typing_extra.is_callable(obj): + return core_schema.callable_schema() + elif typing_objects.is_literal(get_origin(obj)): + return self._literal_schema(obj) + elif is_typeddict(obj): + return self._typed_dict_schema(obj, None) + elif inspect.isclass(obj) and issubclass(obj, Enum): + # NOTE: this must come before the `is_namedtuple()` check as enums values + # can be namedtuples: + return self._enum_schema(obj) + elif _typing_extra.is_namedtuple(obj): + return self._namedtuple_schema(obj, None) + elif typing_objects.is_newtype(obj): + # NewType, can't use isinstance because it fails <3.10 + return self.generate_schema(obj.__supertype__) + elif obj in PATTERN_TYPES: + return self._pattern_schema(obj) + elif _typing_extra.is_hashable(obj): + return self._hashable_schema() + elif isinstance(obj, typing.TypeVar): + return self._unsubstituted_typevar_schema(obj) + elif _typing_extra.is_finalvar(obj): + if obj is Final: + return core_schema.any_schema() + return self.generate_schema( + self._get_first_arg_or_any(obj), + ) + elif isinstance(obj, VALIDATE_CALL_SUPPORTED_TYPES): + return self._call_schema(obj) # pyright: ignore[reportArgumentType] + elif obj is ZoneInfo: + return self._zoneinfo_schema() + + # dataclasses.is_dataclass coerces dc instances to types, but we only handle + # the case of a dc type here + if dataclasses.is_dataclass(obj): + return self._dataclass_schema(obj, None) # pyright: ignore[reportArgumentType] + + origin = get_origin(obj) + if origin is not None: + return self._match_generic_type(obj, origin) + + if self._arbitrary_types: + return self._arbitrary_type_schema(obj) + return self._unknown_type_schema(obj) + + def _match_generic_type(self, obj: Any, origin: Any) -> CoreSchema: # noqa: C901 + # Need to handle generic dataclasses before looking for the schema properties because attribute accesses + # on _GenericAlias delegate to the origin type, so lose the information about the concrete parametrization + # As a result, currently, there is no way to cache the schema for generic dataclasses. This may be possible + # to resolve by modifying the value returned by `Generic.__class_getitem__`, but that is a dangerous game. + if dataclasses.is_dataclass(origin): + return self._dataclass_schema(obj, origin) # pyright: ignore[reportArgumentType] + if _typing_extra.is_namedtuple(origin): + return self._namedtuple_schema(obj, origin) + + schema = self._generate_schema_from_get_schema_method(origin, obj) + if schema is not None: + return schema + + if typing_objects.is_typealiastype(origin): + return self._type_alias_type_schema(obj) + elif is_union_origin(origin): + return self._union_schema(obj) + elif origin in TUPLE_TYPES: + return self._tuple_schema(obj) + elif origin in LIST_TYPES: + return self._list_schema(self._get_first_arg_or_any(obj)) + elif origin in SET_TYPES: + return self._set_schema(self._get_first_arg_or_any(obj)) + elif origin in FROZEN_SET_TYPES: + return self._frozenset_schema(self._get_first_arg_or_any(obj)) + elif origin in DICT_TYPES: + return self._dict_schema(*self._get_first_two_args_or_any(obj)) + elif origin in PATH_TYPES: + return self._path_schema(origin, self._get_first_arg_or_any(obj)) + elif origin in DEQUE_TYPES: + return self._deque_schema(self._get_first_arg_or_any(obj)) + elif origin in MAPPING_TYPES: + return self._mapping_schema(origin, *self._get_first_two_args_or_any(obj)) + elif origin in COUNTER_TYPES: + return self._mapping_schema(origin, self._get_first_arg_or_any(obj), int) + elif is_typeddict(origin): + return self._typed_dict_schema(obj, origin) + elif origin in TYPE_TYPES: + return self._subclass_schema(obj) + elif origin in SEQUENCE_TYPES: + return self._sequence_schema(self._get_first_arg_or_any(obj)) + elif origin in ITERABLE_TYPES: + return self._iterable_schema(obj) + elif origin in PATTERN_TYPES: + return self._pattern_schema(obj) + + if self._arbitrary_types: + return self._arbitrary_type_schema(origin) + return self._unknown_type_schema(obj) + + def _generate_td_field_schema( + self, + name: str, + field_info: FieldInfo, + decorators: DecoratorInfos, + *, + required: bool = True, + ) -> core_schema.TypedDictField: + """Prepare a TypedDictField to represent a model or typeddict field.""" + schema, metadata = self._common_field_schema(name, field_info, decorators) + return core_schema.typed_dict_field( + schema, + required=False if not field_info.is_required() else required, + serialization_exclude=field_info.exclude, + validation_alias=_convert_to_aliases(field_info.validation_alias), + serialization_alias=field_info.serialization_alias, + serialization_exclude_if=field_info.exclude_if, + metadata=metadata, + ) + + def _generate_md_field_schema( + self, + name: str, + field_info: FieldInfo, + decorators: DecoratorInfos, + ) -> core_schema.ModelField: + """Prepare a ModelField to represent a model field.""" + schema, metadata = self._common_field_schema(name, field_info, decorators) + return core_schema.model_field( + schema, + serialization_exclude=field_info.exclude, + validation_alias=_convert_to_aliases(field_info.validation_alias), + serialization_alias=field_info.serialization_alias, + serialization_exclude_if=field_info.exclude_if, + frozen=field_info.frozen, + metadata=metadata, + ) + + def _generate_dc_field_schema( + self, + name: str, + field_info: FieldInfo, + decorators: DecoratorInfos, + ) -> core_schema.DataclassField: + """Prepare a DataclassField to represent the parameter/field, of a dataclass.""" + schema, metadata = self._common_field_schema(name, field_info, decorators) + return core_schema.dataclass_field( + name, + schema, + init=field_info.init, + init_only=field_info.init_var or None, + kw_only=None if field_info.kw_only else False, + serialization_exclude=field_info.exclude, + validation_alias=_convert_to_aliases(field_info.validation_alias), + serialization_alias=field_info.serialization_alias, + serialization_exclude_if=field_info.exclude_if, + frozen=field_info.frozen, + metadata=metadata, + ) + + def _common_field_schema( # C901 + self, name: str, field_info: FieldInfo, decorators: DecoratorInfos + ) -> tuple[CoreSchema, dict[str, Any]]: + source_type, annotations = field_info.annotation, field_info.metadata + + def set_discriminator(schema: CoreSchema) -> CoreSchema: + schema = self._apply_discriminator_to_union(schema, field_info.discriminator) + return schema + + # Convert `@field_validator` decorators to `Before/After/Plain/WrapValidator` instances: + validators_from_decorators = [ + _mode_to_validator[decorator.info.mode]._from_decorator(decorator) + for decorator in filter_field_decorator_info_by_field(decorators.field_validators.values(), name) + ] + + with self.field_name_stack.push(name): + if field_info.discriminator is not None: + schema = self._apply_annotations( + source_type, annotations + validators_from_decorators, transform_inner_schema=set_discriminator + ) + else: + schema = self._apply_annotations( + source_type, + annotations + validators_from_decorators, + ) + + # This V1 compatibility shim should eventually be removed + # push down any `each_item=True` validators + # note that this won't work for any Annotated types that get wrapped by a function validator + # but that's okay because that didn't exist in V1 + this_field_validators = filter_field_decorator_info_by_field(decorators.validators.values(), name) + if _validators_require_validate_default(this_field_validators): + field_info.validate_default = True + each_item_validators = [v for v in this_field_validators if v.info.each_item is True] + this_field_validators = [v for v in this_field_validators if v not in each_item_validators] + schema = apply_each_item_validators(schema, each_item_validators) + + schema = apply_validators(schema, this_field_validators) + + # the default validator needs to go outside of any other validators + # so that it is the topmost validator for the field validator + # which uses it to check if the field has a default value or not + if not field_info.is_required(): + schema = wrap_default(field_info, schema) + + schema = self._apply_field_serializers( + schema, filter_field_decorator_info_by_field(decorators.field_serializers.values(), name) + ) + + pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(field_info) + core_metadata: dict[str, Any] = {} + update_core_metadata( + core_metadata, pydantic_js_updates=pydantic_js_updates, pydantic_js_extra=pydantic_js_extra + ) + + return schema, core_metadata + + def _union_schema(self, union_type: Any) -> core_schema.CoreSchema: + """Generate schema for a Union.""" + args = self._get_args_resolving_forward_refs(union_type, required=True) + choices: list[CoreSchema] = [] + nullable = False + for arg in args: + if arg is None or arg is _typing_extra.NoneType: + nullable = True + else: + choices.append(self.generate_schema(arg)) + + if len(choices) == 1: + s = choices[0] + else: + choices_with_tags: list[CoreSchema | tuple[CoreSchema, str]] = [] + for choice in choices: + tag = cast(CoreMetadata, choice.get('metadata', {})).get('pydantic_internal_union_tag_key') + if tag is not None: + choices_with_tags.append((choice, tag)) + else: + choices_with_tags.append(choice) + s = core_schema.union_schema(choices_with_tags) + + if nullable: + s = core_schema.nullable_schema(s) + return s + + def _type_alias_type_schema(self, obj: TypeAliasType) -> CoreSchema: + with self.defs.get_schema_or_ref(obj) as (ref, maybe_schema): + if maybe_schema is not None: + return maybe_schema + + origin: TypeAliasType = get_origin(obj) or obj + typevars_map = get_standard_typevars_map(obj) + + with self._ns_resolver.push(origin): + try: + annotation = _typing_extra.eval_type(origin.__value__, *self._types_namespace) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + annotation = replace_types(annotation, typevars_map) + schema = self.generate_schema(annotation) + assert schema['type'] != 'definitions' + schema['ref'] = ref # type: ignore + return self.defs.create_definition_reference_schema(schema) + + def _literal_schema(self, literal_type: Any) -> CoreSchema: + """Generate schema for a Literal.""" + expected = list(get_literal_values(literal_type, type_check=False, unpack_type_aliases='eager')) + assert expected, f'literal "expected" cannot be empty, obj={literal_type}' + schema = core_schema.literal_schema(expected) + + if self._config_wrapper.use_enum_values and any(isinstance(v, Enum) for v in expected): + schema = core_schema.no_info_after_validator_function( + lambda v: v.value if isinstance(v, Enum) else v, schema + ) + + return schema + + def _typed_dict_schema(self, typed_dict_cls: Any, origin: Any) -> core_schema.CoreSchema: + """Generate a core schema for a `TypedDict` class. + + To be able to build a `DecoratorInfos` instance for the `TypedDict` class (which will include + validators, serializers, etc.), we need to have access to the original bases of the class + (see https://docs.python.org/3/library/types.html#types.get_original_bases). + However, the `__orig_bases__` attribute was only added in 3.12 (https://github.com/python/cpython/pull/103698). + + For this reason, we require Python 3.12 (or using the `typing_extensions` backport). + """ + FieldInfo = import_cached_field_info() + + with ( + self.model_type_stack.push(typed_dict_cls), + self.defs.get_schema_or_ref(typed_dict_cls) as ( + typed_dict_ref, + maybe_schema, + ), + ): + if maybe_schema is not None: + return maybe_schema + + typevars_map = get_standard_typevars_map(typed_dict_cls) + if origin is not None: + typed_dict_cls = origin + + if not _SUPPORTS_TYPEDDICT and type(typed_dict_cls).__module__ == 'typing': + raise PydanticUserError( + 'Please use `typing_extensions.TypedDict` instead of `typing.TypedDict` on Python < 3.12.', + code='typed-dict-version', + ) + + try: + # if a typed dictionary class doesn't have config, we use the parent's config, hence a default of `None` + # see https://github.com/pydantic/pydantic/issues/10917 + config: ConfigDict | None = get_attribute_from_bases(typed_dict_cls, '__pydantic_config__') + except AttributeError: + config = None + + with self._config_wrapper_stack.push(config): + core_config = self._config_wrapper.core_config(title=typed_dict_cls.__name__) + + required_keys: frozenset[str] = typed_dict_cls.__required_keys__ + + fields: dict[str, core_schema.TypedDictField] = {} + + decorators = DecoratorInfos.build(typed_dict_cls, replace_wrapped_methods=False) + decorators.update_from_config(self._config_wrapper) + + if self._config_wrapper.use_attribute_docstrings: + field_docstrings = extract_docstrings_from_cls(typed_dict_cls, use_inspect=True) + else: + field_docstrings = None + + try: + annotations = _typing_extra.get_cls_type_hints(typed_dict_cls, ns_resolver=self._ns_resolver) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + readonly_fields: list[str] = [] + + for field_name, annotation in annotations.items(): + field_info = FieldInfo.from_annotation(annotation, _source=AnnotationSource.TYPED_DICT) + field_info.annotation = replace_types(field_info.annotation, typevars_map) + + required = ( + field_name in required_keys or 'required' in field_info._qualifiers + ) and 'not_required' not in field_info._qualifiers + if 'read_only' in field_info._qualifiers: + readonly_fields.append(field_name) + + if ( + field_docstrings is not None + and field_info.description is None + and field_name in field_docstrings + ): + field_info.description = field_docstrings[field_name] + update_field_from_config(self._config_wrapper, field_name, field_info) + + fields[field_name] = self._generate_td_field_schema( + field_name, field_info, decorators, required=required + ) + + if readonly_fields: + fields_repr = ', '.join(repr(f) for f in readonly_fields) + plural = len(readonly_fields) >= 2 + warnings.warn( + f'Item{"s" if plural else ""} {fields_repr} on TypedDict class {typed_dict_cls.__name__!r} ' + f'{"are" if plural else "is"} using the `ReadOnly` qualifier. Pydantic will not protect items ' + 'from any mutation on dictionary instances.', + UserWarning, + ) + + extra_behavior: core_schema.ExtraBehavior = 'ignore' + extras_schema: CoreSchema | None = None # For 'allow', equivalent to `Any` - no validation performed. + + # `__closed__` is `None` when not specified (equivalent to `False`): + is_closed = bool(getattr(typed_dict_cls, '__closed__', False)) + extra_items = getattr(typed_dict_cls, '__extra_items__', typing_extensions.NoExtraItems) + if is_closed: + extra_behavior = 'forbid' + extras_schema = None + elif not typing_objects.is_noextraitems(extra_items): + extra_behavior = 'allow' + extras_schema = self.generate_schema(replace_types(extra_items, typevars_map)) + + if (config_extra := self._config_wrapper.extra) in ('allow', 'forbid'): + if is_closed and config_extra == 'allow': + warnings.warn( + f"TypedDict class {typed_dict_cls.__qualname__!r} is closed, but 'extra' configuration " + "is set to `'allow'`. The 'extra' configuration value will be ignored.", + category=TypedDictExtraConfigWarning, + ) + elif not typing_objects.is_noextraitems(extra_items) and config_extra == 'forbid': + warnings.warn( + f"TypedDict class {typed_dict_cls.__qualname__!r} allows extra items, but 'extra' configuration " + "is set to `'forbid'`. The 'extra' configuration value will be ignored.", + category=TypedDictExtraConfigWarning, + ) + else: + extra_behavior = config_extra + + td_schema = core_schema.typed_dict_schema( + fields, + cls=typed_dict_cls, + computed_fields=[ + self._computed_field_schema(d, decorators.field_serializers) + for d in decorators.computed_fields.values() + ], + extra_behavior=extra_behavior, + extras_schema=extras_schema, + ref=typed_dict_ref, + config=core_config, + ) + + schema = self._apply_model_serializers(td_schema, decorators.model_serializers.values()) + schema = apply_model_validators(schema, decorators.model_validators.values(), 'all') + return self.defs.create_definition_reference_schema(schema) + + def _namedtuple_schema(self, namedtuple_cls: Any, origin: Any) -> core_schema.CoreSchema: + """Generate schema for a NamedTuple.""" + with ( + self.model_type_stack.push(namedtuple_cls), + self.defs.get_schema_or_ref(namedtuple_cls) as ( + namedtuple_ref, + maybe_schema, + ), + ): + if maybe_schema is not None: + return maybe_schema + typevars_map = get_standard_typevars_map(namedtuple_cls) + if origin is not None: + namedtuple_cls = origin + + try: + annotations = _typing_extra.get_cls_type_hints(namedtuple_cls, ns_resolver=self._ns_resolver) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + # Filter annotations to only include fields that are actually in the NamedTuple + # (as subclassing an existing NamedTuple is not supported yet - see https://github.com/python/typing/issues/427) + # and use `Any` if no annotation exist (i.e. when using `collections.namedtuple()`). + annotations = {field_name: annotations.get(field_name, Any) for field_name in namedtuple_cls._fields} + + if typevars_map: + annotations = { + field_name: replace_types(annotation, typevars_map) + for field_name, annotation in annotations.items() + } + + arguments_schema = core_schema.arguments_schema( + [ + self._generate_parameter_schema( + field_name, + annotation, + source=AnnotationSource.NAMED_TUPLE, + default=namedtuple_cls._field_defaults.get(field_name, Parameter.empty), + ) + for field_name, annotation in annotations.items() + ], + metadata={'pydantic_js_prefer_positional_arguments': True}, + ) + schema = core_schema.call_schema(arguments_schema, namedtuple_cls, ref=namedtuple_ref) + return self.defs.create_definition_reference_schema(schema) + + def _generate_parameter_schema( + self, + name: str, + annotation: type[Any], + source: AnnotationSource, + default: Any = Parameter.empty, + mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None, + ) -> core_schema.ArgumentsParameter: + """Generate the definition of a field in a namedtuple or a parameter in a function signature. + + This definition is meant to be used for the `'arguments'` core schema, which will be replaced + in V3 by the `'arguments-v3`'. + """ + FieldInfo = import_cached_field_info() + + if default is Parameter.empty: + field = FieldInfo.from_annotation(annotation, _source=source) + else: + field = FieldInfo.from_annotated_attribute(annotation, default, _source=source) + + assert field.annotation is not None, 'field.annotation should not be None when generating a schema' + update_field_from_config(self._config_wrapper, name, field) + + with self.field_name_stack.push(name): + schema = self._apply_annotations( + field.annotation, + [field], + # Because we pass `field` as metadata above (required for attributes relevant for + # JSON Scheme generation), we need to ignore the potential warnings about `FieldInfo` + # attributes that will not be used: + check_unsupported_field_info_attributes=False, + ) + + if not field.is_required(): + schema = wrap_default(field, schema) + + parameter_schema = core_schema.arguments_parameter( + name, + schema, + mode=mode, + alias=_convert_to_aliases(field.validation_alias), + ) + + return parameter_schema + + def _generate_parameter_v3_schema( + self, + name: str, + annotation: Any, + source: AnnotationSource, + mode: Literal[ + 'positional_only', + 'positional_or_keyword', + 'keyword_only', + 'var_args', + 'var_kwargs_uniform', + 'var_kwargs_unpacked_typed_dict', + ], + default: Any = Parameter.empty, + ) -> core_schema.ArgumentsV3Parameter: + """Generate the definition of a parameter in a function signature. + + This definition is meant to be used for the `'arguments-v3'` core schema, which will replace + the `'arguments`' schema in V3. + """ + FieldInfo = import_cached_field_info() + + if default is Parameter.empty: + field = FieldInfo.from_annotation(annotation, _source=source) + else: + field = FieldInfo.from_annotated_attribute(annotation, default, _source=source) + update_field_from_config(self._config_wrapper, name, field) + + with self.field_name_stack.push(name): + schema = self._apply_annotations( + field.annotation, + [field], + # Because we pass `field` as metadata above (required for attributes relevant for + # JSON Scheme generation), we need to ignore the potential warnings about `FieldInfo` + # attributes that will not be used: + check_unsupported_field_info_attributes=False, + ) + + if not field.is_required(): + schema = wrap_default(field, schema) + + parameter_schema = core_schema.arguments_v3_parameter( + name=name, + schema=schema, + mode=mode, + alias=_convert_to_aliases(field.validation_alias), + ) + + return parameter_schema + + def _tuple_schema(self, tuple_type: Any) -> core_schema.CoreSchema: + """Generate schema for a Tuple, e.g. `tuple[int, str]` or `tuple[int, ...]`.""" + # TODO: do we really need to resolve type vars here? + typevars_map = get_standard_typevars_map(tuple_type) + params = self._get_args_resolving_forward_refs(tuple_type) + + if typevars_map and params: + params = tuple(replace_types(param, typevars_map) for param in params) + + # NOTE: subtle difference: `tuple[()]` gives `params=()`, whereas `typing.Tuple[()]` gives `params=((),)` + # This is only true for <3.11, on Python 3.11+ `typing.Tuple[()]` gives `params=()` + if not params: + if tuple_type in TUPLE_TYPES: + return core_schema.tuple_schema([core_schema.any_schema()], variadic_item_index=0) + else: + # special case for `tuple[()]` which means `tuple[]` - an empty tuple + return core_schema.tuple_schema([]) + elif params[-1] is Ellipsis: + if len(params) == 2: + return core_schema.tuple_schema([self.generate_schema(params[0])], variadic_item_index=0) + else: + # TODO: something like https://github.com/pydantic/pydantic/issues/5952 + raise ValueError('Variable tuples can only have one type') + elif len(params) == 1 and params[0] == (): + # special case for `tuple[()]` which means `tuple[]` - an empty tuple + # NOTE: This conditional can be removed when we drop support for Python 3.10. + return core_schema.tuple_schema([]) + else: + return core_schema.tuple_schema([self.generate_schema(param) for param in params]) + + def _type_schema(self) -> core_schema.CoreSchema: + return core_schema.custom_error_schema( + core_schema.is_instance_schema(type), + custom_error_type='is_type', + custom_error_message='Input should be a type', + ) + + def _zoneinfo_schema(self) -> core_schema.CoreSchema: + """Generate schema for a zone_info.ZoneInfo object""" + from ._validators import validate_str_is_valid_iana_tz + + metadata = {'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'zoneinfo'}]} + return core_schema.no_info_plain_validator_function( + validate_str_is_valid_iana_tz, + serialization=core_schema.to_string_ser_schema(), + metadata=metadata, + ) + + def _union_is_subclass_schema(self, union_type: Any) -> core_schema.CoreSchema: + """Generate schema for `type[Union[X, ...]]`.""" + args = self._get_args_resolving_forward_refs(union_type, required=True) + return core_schema.union_schema([self.generate_schema(type[args]) for args in args]) + + def _subclass_schema(self, type_: Any) -> core_schema.CoreSchema: + """Generate schema for a type, e.g. `type[int]`.""" + type_param = self._get_first_arg_or_any(type_) + + # Assume `type[Annotated[, ...]]` is equivalent to `type[]`: + type_param = _typing_extra.annotated_type(type_param) or type_param + + if typing_objects.is_any(type_param): + return self._type_schema() + elif typing_objects.is_typealiastype(type_param): + return self.generate_schema(type[type_param.__value__]) + elif typing_objects.is_typevar(type_param): + if type_param.__bound__: + if is_union_origin(get_origin(type_param.__bound__)): + return self._union_is_subclass_schema(type_param.__bound__) + return core_schema.is_subclass_schema(type_param.__bound__) + elif type_param.__constraints__: + return core_schema.union_schema([self.generate_schema(type[c]) for c in type_param.__constraints__]) + else: + return self._type_schema() + elif is_union_origin(get_origin(type_param)): + return self._union_is_subclass_schema(type_param) + else: + if typing_objects.is_self(type_param): + type_param = self._resolve_self_type(type_param) + if _typing_extra.is_generic_alias(type_param): + raise PydanticUserError( + 'Subscripting `type[]` with an already parametrized type is not supported. ' + f'Instead of using type[{type_param!r}], use type[{_repr.display_as_type(get_origin(type_param))}].', + code=None, + ) + if not inspect.isclass(type_param): + # when using type[None], this doesn't type convert to type[NoneType], and None isn't a class + # so we handle it manually here + if type_param is None: + return core_schema.is_subclass_schema(_typing_extra.NoneType) + raise TypeError(f'Expected a class, got {type_param!r}') + return core_schema.is_subclass_schema(type_param) + + def _sequence_schema(self, items_type: Any) -> core_schema.CoreSchema: + """Generate schema for a Sequence, e.g. `Sequence[int]`.""" + from ._serializers import serialize_sequence_via_list + + item_type_schema = self.generate_schema(items_type) + list_schema = core_schema.list_schema(item_type_schema) + + json_schema = smart_deepcopy(list_schema) + python_schema = core_schema.is_instance_schema(typing.Sequence, cls_repr='Sequence') + if not typing_objects.is_any(items_type): + from ._validators import sequence_validator + + python_schema = core_schema.chain_schema( + [python_schema, core_schema.no_info_wrap_validator_function(sequence_validator, list_schema)], + ) + + serialization = core_schema.wrap_serializer_function_ser_schema( + serialize_sequence_via_list, schema=item_type_schema, info_arg=True + ) + return core_schema.json_or_python_schema( + json_schema=json_schema, python_schema=python_schema, serialization=serialization + ) + + def _iterable_schema(self, type_: Any) -> core_schema.GeneratorSchema: + """Generate a schema for an `Iterable`.""" + item_type = self._get_first_arg_or_any(type_) + + return core_schema.generator_schema(self.generate_schema(item_type)) + + def _pattern_schema(self, pattern_type: Any) -> core_schema.CoreSchema: + from . import _validators + + metadata = {'pydantic_js_functions': [lambda _1, _2: {'type': 'string', 'format': 'regex'}]} + ser = core_schema.plain_serializer_function_ser_schema( + attrgetter('pattern'), when_used='json', return_schema=core_schema.str_schema() + ) + if pattern_type is typing.Pattern or pattern_type is re.Pattern: + # bare type + return core_schema.no_info_plain_validator_function( + _validators.pattern_either_validator, serialization=ser, metadata=metadata + ) + + param = self._get_args_resolving_forward_refs( + pattern_type, + required=True, + )[0] + if param is str: + return core_schema.no_info_plain_validator_function( + _validators.pattern_str_validator, serialization=ser, metadata=metadata + ) + elif param is bytes: + return core_schema.no_info_plain_validator_function( + _validators.pattern_bytes_validator, serialization=ser, metadata=metadata + ) + else: + raise PydanticSchemaGenerationError(f'Unable to generate pydantic-core schema for {pattern_type!r}.') + + def _hashable_schema(self) -> core_schema.CoreSchema: + return core_schema.custom_error_schema( + schema=core_schema.json_or_python_schema( + json_schema=core_schema.chain_schema( + [core_schema.any_schema(), core_schema.is_instance_schema(collections.abc.Hashable)] + ), + python_schema=core_schema.is_instance_schema(collections.abc.Hashable), + ), + custom_error_type='is_hashable', + custom_error_message='Input should be hashable', + ) + + def _dataclass_schema( + self, dataclass: type[StandardDataclass], origin: type[StandardDataclass] | None + ) -> core_schema.CoreSchema: + """Generate schema for a dataclass.""" + with ( + self.model_type_stack.push(dataclass), + self.defs.get_schema_or_ref(dataclass) as ( + dataclass_ref, + maybe_schema, + ), + ): + if maybe_schema is not None: + return maybe_schema + + schema = dataclass.__dict__.get('__pydantic_core_schema__') + if schema is not None and not isinstance(schema, MockCoreSchema): + if schema['type'] == 'definitions': + schema = self.defs.unpack_definitions(schema) + ref = get_ref(schema) + if ref: + return self.defs.create_definition_reference_schema(schema) + else: + return schema + + typevars_map = get_standard_typevars_map(dataclass) + if origin is not None: + dataclass = origin + + # if (plain) dataclass doesn't have config, we use the parent's config, hence a default of `None` + # (Pydantic dataclasses have an empty dict config by default). + # see https://github.com/pydantic/pydantic/issues/10917 + config = getattr(dataclass, '__pydantic_config__', None) + + from ..dataclasses import is_pydantic_dataclass + + with self._ns_resolver.push(dataclass), self._config_wrapper_stack.push(config): + if is_pydantic_dataclass(dataclass): + if dataclass.__pydantic_fields_complete__(): + # Copy the field info instances to avoid mutating the `FieldInfo` instances + # of the generic dataclass generic origin (e.g. `apply_typevars_map` below). + # Note that we don't apply `deepcopy` on `__pydantic_fields__` because we + # don't want to copy the `FieldInfo` attributes: + fields = { + f_name: copy(field_info) for f_name, field_info in dataclass.__pydantic_fields__.items() + } + if typevars_map: + for field in fields.values(): + field.apply_typevars_map(typevars_map, *self._types_namespace) + else: + try: + fields = rebuild_dataclass_fields( + dataclass, + config_wrapper=self._config_wrapper, + ns_resolver=self._ns_resolver, + typevars_map=typevars_map or {}, + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + else: + fields = collect_dataclass_fields( + dataclass, + typevars_map=typevars_map, + config_wrapper=self._config_wrapper, + ) + + if self._config_wrapper.extra == 'allow': + # disallow combination of init=False on a dataclass field and extra='allow' on a dataclass + for field_name, field in fields.items(): + if field.init is False: + raise PydanticUserError( + f'Field {field_name} has `init=False` and dataclass has config setting `extra="allow"`. ' + f'This combination is not allowed.', + code='dataclass-init-false-extra-allow', + ) + + decorators = dataclass.__dict__.get('__pydantic_decorators__') + if decorators is None: + decorators = DecoratorInfos.build(dataclass, replace_wrapped_methods=False) + decorators.update_from_config(self._config_wrapper) + # Move kw_only=False args to the start of the list, as this is how vanilla dataclasses work. + # Note that when kw_only is missing or None, it is treated as equivalent to kw_only=True + args = sorted( + (self._generate_dc_field_schema(k, v, decorators) for k, v in fields.items()), + key=lambda a: a.get('kw_only') is not False, + ) + has_post_init = hasattr(dataclass, '__post_init__') + has_slots = hasattr(dataclass, '__slots__') + + args_schema = core_schema.dataclass_args_schema( + dataclass.__name__, + args, + computed_fields=[ + self._computed_field_schema(d, decorators.field_serializers) + for d in decorators.computed_fields.values() + ], + collect_init_only=has_post_init, + ) + + inner_schema = apply_validators(args_schema, decorators.root_validators.values()) + + model_validators = decorators.model_validators.values() + inner_schema = apply_model_validators(inner_schema, model_validators, 'inner') + + core_config = self._config_wrapper.core_config(title=dataclass.__name__) + + dc_schema = core_schema.dataclass_schema( + dataclass, + inner_schema, + generic_origin=origin, + post_init=has_post_init, + ref=dataclass_ref, + fields=[field.name for field in dataclasses.fields(dataclass)], + slots=has_slots, + config=core_config, + # we don't use a custom __setattr__ for dataclasses, so we must + # pass along the frozen config setting to the pydantic-core schema + frozen=self._config_wrapper_stack.tail.frozen, + ) + schema = self._apply_model_serializers(dc_schema, decorators.model_serializers.values()) + schema = apply_model_validators(schema, model_validators, 'outer') + return self.defs.create_definition_reference_schema(schema) + + def _call_schema(self, function: ValidateCallSupportedTypes) -> core_schema.CallSchema: + """Generate schema for a Callable. + + TODO support functional validators once we support them in Config + """ + arguments_schema = self._arguments_schema(function) + + return_schema: core_schema.CoreSchema | None = None + config_wrapper = self._config_wrapper + if config_wrapper.validate_return: + sig = _typing_extra.signature_no_eval(function) + return_hint = sig.return_annotation + if return_hint is not sig.empty: + globalns, localns = self._types_namespace + type_hints = _typing_extra.get_function_type_hints( + function, globalns=globalns, localns=localns, include_keys={'return'} + ) + return_schema = self.generate_schema(type_hints['return']) + + return core_schema.call_schema( + arguments_schema, + function, + return_schema=return_schema, + ) + + def _arguments_schema( + self, function: ValidateCallSupportedTypes, parameters_callback: ParametersCallback | None = None + ) -> core_schema.ArgumentsSchema: + """Generate schema for a Signature.""" + mode_lookup: dict[_ParameterKind, Literal['positional_only', 'positional_or_keyword', 'keyword_only']] = { + Parameter.POSITIONAL_ONLY: 'positional_only', + Parameter.POSITIONAL_OR_KEYWORD: 'positional_or_keyword', + Parameter.KEYWORD_ONLY: 'keyword_only', + } + + sig = _typing_extra.signature_no_eval(function) + globalns, localns = self._types_namespace + type_hints = _typing_extra.get_function_type_hints(function, globalns=globalns, localns=localns) + + arguments_list: list[core_schema.ArgumentsParameter] = [] + var_args_schema: core_schema.CoreSchema | None = None + var_kwargs_schema: core_schema.CoreSchema | None = None + var_kwargs_mode: core_schema.VarKwargsMode | None = None + + for i, (name, p) in enumerate(sig.parameters.items()): + if p.annotation is sig.empty: + annotation = typing.cast(Any, Any) + else: + annotation = type_hints[name] + + if parameters_callback is not None: + result = parameters_callback(i, name, annotation) + if result == 'skip': + continue + + parameter_mode = mode_lookup.get(p.kind) + if parameter_mode is not None: + arg_schema = self._generate_parameter_schema( + name, annotation, AnnotationSource.FUNCTION, p.default, parameter_mode + ) + arguments_list.append(arg_schema) + elif p.kind == Parameter.VAR_POSITIONAL: + var_args_schema = self.generate_schema(annotation) + else: + assert p.kind == Parameter.VAR_KEYWORD, p.kind + + unpack_type = _typing_extra.unpack_type(annotation) + if unpack_type is not None: + origin = get_origin(unpack_type) or unpack_type + if not is_typeddict(origin): + raise PydanticUserError( + f'Expected a `TypedDict` class inside `Unpack[...]`, got {unpack_type!r}', + code='unpack-typed-dict', + ) + non_pos_only_param_names = { + name for name, p in sig.parameters.items() if p.kind != Parameter.POSITIONAL_ONLY + } + overlapping_params = non_pos_only_param_names.intersection(origin.__annotations__) + if overlapping_params: + raise PydanticUserError( + f'Typed dictionary {origin.__name__!r} overlaps with parameter' + f'{"s" if len(overlapping_params) >= 2 else ""} ' + f'{", ".join(repr(p) for p in sorted(overlapping_params))}', + code='overlapping-unpack-typed-dict', + ) + + var_kwargs_mode = 'unpacked-typed-dict' + var_kwargs_schema = self._typed_dict_schema(unpack_type, get_origin(unpack_type)) + else: + var_kwargs_mode = 'uniform' + var_kwargs_schema = self.generate_schema(annotation) + + return core_schema.arguments_schema( + arguments_list, + var_args_schema=var_args_schema, + var_kwargs_mode=var_kwargs_mode, + var_kwargs_schema=var_kwargs_schema, + validate_by_name=self._config_wrapper.validate_by_name, + ) + + def _arguments_v3_schema( + self, function: ValidateCallSupportedTypes, parameters_callback: ParametersCallback | None = None + ) -> core_schema.ArgumentsV3Schema: + mode_lookup: dict[ + _ParameterKind, Literal['positional_only', 'positional_or_keyword', 'var_args', 'keyword_only'] + ] = { + Parameter.POSITIONAL_ONLY: 'positional_only', + Parameter.POSITIONAL_OR_KEYWORD: 'positional_or_keyword', + Parameter.VAR_POSITIONAL: 'var_args', + Parameter.KEYWORD_ONLY: 'keyword_only', + } + + sig = _typing_extra.signature_no_eval(function) + globalns, localns = self._types_namespace + type_hints = _typing_extra.get_function_type_hints(function, globalns=globalns, localns=localns) + + parameters_list: list[core_schema.ArgumentsV3Parameter] = [] + + for i, (name, p) in enumerate(sig.parameters.items()): + if parameters_callback is not None: + result = parameters_callback(i, name, p.annotation) + if result == 'skip': + continue + + if p.annotation is Parameter.empty: + annotation = typing.cast(Any, Any) + else: + annotation = type_hints[name] + + parameter_mode = mode_lookup.get(p.kind) + if parameter_mode is None: + assert p.kind == Parameter.VAR_KEYWORD, p.kind + + unpack_type = _typing_extra.unpack_type(annotation) + if unpack_type is not None: + origin = get_origin(unpack_type) or unpack_type + if not is_typeddict(origin): + raise PydanticUserError( + f'Expected a `TypedDict` class inside `Unpack[...]`, got {unpack_type!r}', + code='unpack-typed-dict', + ) + non_pos_only_param_names = { + name for name, p in sig.parameters.items() if p.kind != Parameter.POSITIONAL_ONLY + } + overlapping_params = non_pos_only_param_names.intersection(origin.__annotations__) + if overlapping_params: + raise PydanticUserError( + f'Typed dictionary {origin.__name__!r} overlaps with parameter' + f'{"s" if len(overlapping_params) >= 2 else ""} ' + f'{", ".join(repr(p) for p in sorted(overlapping_params))}', + code='overlapping-unpack-typed-dict', + ) + parameter_mode = 'var_kwargs_unpacked_typed_dict' + annotation = unpack_type + else: + parameter_mode = 'var_kwargs_uniform' + + parameters_list.append( + self._generate_parameter_v3_schema( + name, annotation, AnnotationSource.FUNCTION, parameter_mode, default=p.default + ) + ) + + return core_schema.arguments_v3_schema( + parameters_list, + validate_by_name=self._config_wrapper.validate_by_name, + ) + + def _unsubstituted_typevar_schema(self, typevar: typing.TypeVar) -> core_schema.CoreSchema: + try: + has_default = typevar.has_default() # pyright: ignore[reportAttributeAccessIssue] + except AttributeError: + # Happens if using `typing.TypeVar` (and not `typing_extensions`) on Python < 3.13 + pass + else: + if has_default: + return self.generate_schema(typevar.__default__) # pyright: ignore[reportAttributeAccessIssue] + + if constraints := typevar.__constraints__: + return self._union_schema(typing.Union[constraints]) + + if bound := typevar.__bound__: + schema = self.generate_schema(bound) + schema['serialization'] = core_schema.simple_ser_schema('any') + return schema + + return core_schema.any_schema() + + def _computed_field_schema( + self, + d: Decorator[ComputedFieldInfo], + field_serializers: dict[str, Decorator[FieldSerializerDecoratorInfo]], + ) -> core_schema.ComputedField: + if d.info.return_type is not PydanticUndefined: + return_type = d.info.return_type + else: + try: + # Do not pass in globals as the function could be defined in a different module. + # Instead, let `get_callable_return_type` infer the globals to use, but still pass + # in locals that may contain a parent/rebuild namespace: + return_type = _decorators.get_callable_return_type(d.func, localns=self._types_namespace.locals) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + if return_type is PydanticUndefined: + raise PydanticUserError( + 'Computed field is missing return type annotation or specifying `return_type`' + ' to the `@computed_field` decorator (e.g. `@computed_field(return_type=int | str)`)', + code='model-field-missing-annotation', + ) + + return_type = replace_types(return_type, self._typevars_map) + # Create a new ComputedFieldInfo so that different type parametrizations of the same + # generic model's computed field can have different return types. + d.info = dataclasses.replace(d.info, return_type=return_type) + return_type_schema = self.generate_schema(return_type) + # Apply serializers to computed field if there exist + return_type_schema = self._apply_field_serializers( + return_type_schema, + filter_field_decorator_info_by_field(field_serializers.values(), d.cls_var_name), + ) + + pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(d.info) + core_metadata: dict[str, Any] = {} + update_core_metadata( + core_metadata, + pydantic_js_updates={'readOnly': True, **(pydantic_js_updates if pydantic_js_updates else {})}, + pydantic_js_extra=pydantic_js_extra, + ) + exclude_if = d.info.exclude_if + # TODO: Should we support exclude_if from annotations? + return core_schema.computed_field( + d.cls_var_name, + return_schema=return_type_schema, + alias=d.info.alias, + serialization_exclude_if=exclude_if, + metadata=core_metadata, + ) + + def _annotated_schema(self, annotated_type: Any) -> core_schema.CoreSchema: + """Generate schema for an Annotated type, e.g. `Annotated[int, Field(...)]` or `Annotated[int, Gt(0)]`.""" + FieldInfo = import_cached_field_info() + source_type, *annotations = self._get_args_resolving_forward_refs( + annotated_type, + required=True, + ) + schema = self._apply_annotations(source_type, annotations) + # put the default validator last so that TypeAdapter.get_default_value() works + # even if there are function validators involved + for annotation in annotations: + if isinstance(annotation, FieldInfo): + schema = wrap_default(annotation, schema) + return schema + + def _apply_annotations( + self, + source_type: Any, + annotations: list[Any], + transform_inner_schema: Callable[[CoreSchema], CoreSchema] = lambda x: x, + check_unsupported_field_info_attributes: bool = True, + ) -> CoreSchema: + """Apply arguments from `Annotated` or from `FieldInfo` to a schema. + + This gets called by `GenerateSchema._annotated_schema` but differs from it in that it does + not expect `source_type` to be an `Annotated` object, it expects it to be the first argument of that + (in other words, `GenerateSchema._annotated_schema` just unpacks `Annotated`, this process it). + """ + annotations = list(_known_annotated_metadata.expand_grouped_metadata(annotations)) + + pydantic_js_annotation_functions: list[GetJsonSchemaFunction] = [] + + def inner_handler(obj: Any) -> CoreSchema: + schema = self._generate_schema_from_get_schema_method(obj, source_type) + + if schema is None: + schema = self._generate_schema_inner(obj) + + metadata_js_function = _extract_get_pydantic_json_schema(obj) + if metadata_js_function is not None: + metadata_schema = resolve_original_schema(schema, self.defs) + if metadata_schema is not None: + self._add_js_function(metadata_schema, metadata_js_function) + return transform_inner_schema(schema) + + get_inner_schema = CallbackGetCoreSchemaHandler(inner_handler, self) + + for annotation in annotations: + if annotation is None: + continue + get_inner_schema = self._get_wrapped_inner_schema( + get_inner_schema, + annotation, + pydantic_js_annotation_functions, + check_unsupported_field_info_attributes=check_unsupported_field_info_attributes, + ) + + schema = get_inner_schema(source_type) + if pydantic_js_annotation_functions: + core_metadata = schema.setdefault('metadata', {}) + update_core_metadata(core_metadata, pydantic_js_annotation_functions=pydantic_js_annotation_functions) + return _add_custom_serialization_from_json_encoders(self._config_wrapper.json_encoders, source_type, schema) + + def _apply_single_annotation( + self, + schema: core_schema.CoreSchema, + metadata: Any, + check_unsupported_field_info_attributes: bool = True, + ) -> core_schema.CoreSchema: + FieldInfo = import_cached_field_info() + + if isinstance(metadata, FieldInfo): + if ( + check_unsupported_field_info_attributes + # HACK: we don't want to emit the warning for `FieldInfo` subclasses, because FastAPI does weird manipulations + # with its subclasses and their annotations: + and type(metadata) is FieldInfo + ): + for attr, value in (unsupported_attributes := self._get_unsupported_field_info_attributes(metadata)): + warnings.warn( + f'The {attr!r} attribute with value {value!r} was provided to the `Field()` function, ' + f'which has no effect in the context it was used. {attr!r} is field-specific metadata, ' + 'and can only be attached to a model field using `Annotated` metadata or by assignment. ' + 'This may have happened because an `Annotated` type alias using the `type` statement was ' + 'used, or if the `Field()` function was attached to a single member of a union type.', + category=UnsupportedFieldAttributeWarning, + ) + + if ( + metadata.default_factory_takes_validated_data + and self.model_type_stack.get() is None + and 'defaut_factory' not in unsupported_attributes + ): + warnings.warn( + "A 'default_factory' taking validated data as an argument was provided to the `Field()` function, " + 'but no validated data is available in the context it was used.', + category=UnsupportedFieldAttributeWarning, + ) + + for field_metadata in metadata.metadata: + schema = self._apply_single_annotation(schema, field_metadata) + + if metadata.discriminator is not None: + schema = self._apply_discriminator_to_union(schema, metadata.discriminator) + return schema + + if schema['type'] == 'nullable': + # for nullable schemas, metadata is automatically applied to the inner schema + inner = schema.get('schema', core_schema.any_schema()) + inner = self._apply_single_annotation(inner, metadata) + if inner: + schema['schema'] = inner + return schema + + if schema['type'] == 'union' and any( + choice['type'] == 'missing-sentinel' for choice in core_schema.iter_union_choices(schema) + ): + # Same behavior as for nullable schemas. This is a bit gross, but we have to support the same pattern + filtered_choices = [ + choice + for choice in schema['choices'] + if (choice[0] if isinstance(choice, tuple) else choice)['type'] != 'missing-sentinel' + ] + if len(filtered_choices) >= 2: + # e.g. `Annotated[int | str | MISSING, Constraint(...)]`. We apply `Constraint(...)` to `int | str`, + # and create a new union semantically equivalent to `Annotated[int | str, Constraint(...)] | MISSING`: + filtered_union = core_schema.union_schema(filtered_choices) + filtered_union = self._apply_single_annotation(filtered_union, metadata) + new_union = schema.copy() + new_union['choices'] = [ + filtered_union, + next( + choice + for choice in schema['choices'] + if (choice[0] if isinstance(choice, tuple) else choice)['type'] == 'missing-sentinel' + ), + ] + return new_union + elif len(filtered_choices) == 1: + # e.g. `Annotated[int | MISSING, Constraint(...)]`. We apply `Constraint(...)` to `int`, and reconstruct + # a new union preserving the order. + inner = filtered_choices[0][0] if isinstance(filtered_choices[0], tuple) else filtered_choices[0] + inner = self._apply_single_annotation(inner, metadata) + + # Create a new union schema, preserving the order of the union: + new_union = schema.copy() + new_union['choices'] = [ + (inner, choice[1]) + if isinstance(choice, tuple) and choice[0]['type'] != 'missing-sentinel' + else inner + if not isinstance(choice, tuple) and choice['type'] != 'missing-sentinel' + else choice + for choice in schema['choices'] + ] + return new_union + + original_schema = schema + ref = schema.get('ref') + if ref is not None: + schema = schema.copy() + new_ref = ref + f'_{repr(metadata)}' + if (existing := self.defs.get_schema_from_ref(new_ref)) is not None: + return existing + schema['ref'] = new_ref # pyright: ignore[reportGeneralTypeIssues] + elif schema['type'] == 'definition-ref': + ref = schema['schema_ref'] + if (referenced_schema := self.defs.get_schema_from_ref(ref)) is not None: + schema = referenced_schema.copy() + new_ref = ref + f'_{repr(metadata)}' + if (existing := self.defs.get_schema_from_ref(new_ref)) is not None: + return existing + schema['ref'] = new_ref # pyright: ignore[reportGeneralTypeIssues] + + maybe_updated_schema = _known_annotated_metadata.apply_known_metadata(metadata, schema) + + if maybe_updated_schema is not None: + return maybe_updated_schema + return original_schema + + def _apply_single_annotation_json_schema( + self, schema: core_schema.CoreSchema, metadata: Any + ) -> core_schema.CoreSchema: + FieldInfo = import_cached_field_info() + + if isinstance(metadata, FieldInfo): + for field_metadata in metadata.metadata: + schema = self._apply_single_annotation_json_schema(schema, field_metadata) + + pydantic_js_updates, pydantic_js_extra = _extract_json_schema_info_from_field_info(metadata) + core_metadata = schema.setdefault('metadata', {}) + update_core_metadata( + core_metadata, pydantic_js_updates=pydantic_js_updates, pydantic_js_extra=pydantic_js_extra + ) + return schema + + def _get_unsupported_field_info_attributes(self, field_info: FieldInfo) -> list[tuple[str, Any]]: + """Get the list of unsupported `FieldInfo` attributes when not directly used in `Annotated` for field annotations.""" + unused_metadata: list[tuple[str, Any]] = [] + for unused_metadata_name, unset_value in UNSUPPORTED_STANDALONE_FIELDINFO_ATTRIBUTES: + if ( + (unused_metadata_value := getattr(field_info, unused_metadata_name)) is not unset_value + # `default` and `default_factory` can still be used with a type adapter, so only include them + # if used with a model-like class: + and ( + unused_metadata_name not in ('default', 'default_factory') + or self.model_type_stack.get() is not None + ) + # Setting `alias` will set `validation/serialization_alias` as well, so we want to avoid duplicate warnings: + and ( + unused_metadata_name not in ('validation_alias', 'serialization_alias') + or 'alias' not in field_info._attributes_set + ) + ): + unused_metadata.append((unused_metadata_name, unused_metadata_value)) + + return unused_metadata + + def _get_wrapped_inner_schema( + self, + get_inner_schema: GetCoreSchemaHandler, + annotation: Any, + pydantic_js_annotation_functions: list[GetJsonSchemaFunction], + check_unsupported_field_info_attributes: bool = False, + ) -> CallbackGetCoreSchemaHandler: + annotation_get_schema: GetCoreSchemaFunction | None = getattr(annotation, '__get_pydantic_core_schema__', None) + + def new_handler(source: Any) -> core_schema.CoreSchema: + if annotation_get_schema is not None: + schema = annotation_get_schema(source, get_inner_schema) + else: + schema = get_inner_schema(source) + schema = self._apply_single_annotation( + schema, + annotation, + check_unsupported_field_info_attributes=check_unsupported_field_info_attributes, + ) + schema = self._apply_single_annotation_json_schema(schema, annotation) + + metadata_js_function = _extract_get_pydantic_json_schema(annotation) + if metadata_js_function is not None: + pydantic_js_annotation_functions.append(metadata_js_function) + return schema + + return CallbackGetCoreSchemaHandler(new_handler, self) + + def _apply_field_serializers( + self, + schema: core_schema.CoreSchema, + serializers: list[Decorator[FieldSerializerDecoratorInfo]], + ) -> core_schema.CoreSchema: + """Apply field serializers to a schema.""" + if serializers: + schema = copy(schema) + if schema['type'] == 'definitions': + inner_schema = schema['schema'] + schema['schema'] = self._apply_field_serializers(inner_schema, serializers) + return schema + elif 'ref' in schema: + schema = self.defs.create_definition_reference_schema(schema) + + # use the last serializer to make it easy to override a serializer set on a parent model + serializer = serializers[-1] + is_field_serializer, info_arg = inspect_field_serializer(serializer.func, serializer.info.mode) + + if serializer.info.return_type is not PydanticUndefined: + return_type = serializer.info.return_type + else: + try: + # Do not pass in globals as the function could be defined in a different module. + # Instead, let `get_callable_return_type` infer the globals to use, but still pass + # in locals that may contain a parent/rebuild namespace: + return_type = _decorators.get_callable_return_type( + serializer.func, localns=self._types_namespace.locals + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + if return_type is PydanticUndefined: + return_schema = None + else: + return_schema = self.generate_schema(return_type) + + if serializer.info.mode == 'wrap': + schema['serialization'] = core_schema.wrap_serializer_function_ser_schema( + serializer.func, + is_field_serializer=is_field_serializer, + info_arg=info_arg, + return_schema=return_schema, + when_used=serializer.info.when_used, + ) + else: + assert serializer.info.mode == 'plain' + schema['serialization'] = core_schema.plain_serializer_function_ser_schema( + serializer.func, + is_field_serializer=is_field_serializer, + info_arg=info_arg, + return_schema=return_schema, + when_used=serializer.info.when_used, + ) + return schema + + def _apply_model_serializers( + self, schema: core_schema.CoreSchema, serializers: Iterable[Decorator[ModelSerializerDecoratorInfo]] + ) -> core_schema.CoreSchema: + """Apply model serializers to a schema.""" + ref: str | None = schema.pop('ref', None) # type: ignore + if serializers: + serializer = list(serializers)[-1] + info_arg = inspect_model_serializer(serializer.func, serializer.info.mode) + + if serializer.info.return_type is not PydanticUndefined: + return_type = serializer.info.return_type + else: + try: + # Do not pass in globals as the function could be defined in a different module. + # Instead, let `get_callable_return_type` infer the globals to use, but still pass + # in locals that may contain a parent/rebuild namespace: + return_type = _decorators.get_callable_return_type( + serializer.func, localns=self._types_namespace.locals + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + if return_type is PydanticUndefined: + return_schema = None + else: + return_schema = self.generate_schema(return_type) + + if serializer.info.mode == 'wrap': + ser_schema: core_schema.SerSchema = core_schema.wrap_serializer_function_ser_schema( + serializer.func, + info_arg=info_arg, + return_schema=return_schema, + when_used=serializer.info.when_used, + ) + else: + # plain + ser_schema = core_schema.plain_serializer_function_ser_schema( + serializer.func, + info_arg=info_arg, + return_schema=return_schema, + when_used=serializer.info.when_used, + ) + schema['serialization'] = ser_schema + if ref: + schema['ref'] = ref # type: ignore + return schema + + +_VALIDATOR_F_MATCH: Mapping[ + tuple[FieldValidatorModes, Literal['no-info', 'with-info']], + Callable[[Callable[..., Any], core_schema.CoreSchema], core_schema.CoreSchema], +] = { + ('before', 'no-info'): lambda f, schema: core_schema.no_info_before_validator_function(f, schema), + ('after', 'no-info'): lambda f, schema: core_schema.no_info_after_validator_function(f, schema), + ('plain', 'no-info'): lambda f, _: core_schema.no_info_plain_validator_function(f), + ('wrap', 'no-info'): lambda f, schema: core_schema.no_info_wrap_validator_function(f, schema), + ('before', 'with-info'): lambda f, schema: core_schema.with_info_before_validator_function(f, schema), + ('after', 'with-info'): lambda f, schema: core_schema.with_info_after_validator_function(f, schema), + ('plain', 'with-info'): lambda f, _: core_schema.with_info_plain_validator_function(f), + ('wrap', 'with-info'): lambda f, schema: core_schema.with_info_wrap_validator_function(f, schema), +} + + +# TODO V3: this function is only used for deprecated decorators. It should +# be removed once we drop support for those. +def apply_validators( + schema: core_schema.CoreSchema, + validators: Iterable[Decorator[RootValidatorDecoratorInfo]] + | Iterable[Decorator[ValidatorDecoratorInfo]] + | Iterable[Decorator[FieldValidatorDecoratorInfo]], +) -> core_schema.CoreSchema: + """Apply validators to a schema. + + Args: + schema: The schema to apply validators on. + validators: An iterable of validators. + field_name: The name of the field if validators are being applied to a model field. + + Returns: + The updated schema. + """ + for validator in validators: + # Actually, type could be 'field' or 'model', but this is only used for deprecated + # decorators, so let's not worry about it. + info_arg = inspect_validator(validator.func, mode=validator.info.mode, type='field') + val_type = 'with-info' if info_arg else 'no-info' + + schema = _VALIDATOR_F_MATCH[(validator.info.mode, val_type)](validator.func, schema) + return schema + + +def _validators_require_validate_default(validators: Iterable[Decorator[ValidatorDecoratorInfo]]) -> bool: + """In v1, if any of the validators for a field had `always=True`, the default value would be validated. + + This serves as an auxiliary function for re-implementing that logic, by looping over a provided + collection of (v1-style) ValidatorDecoratorInfo's and checking if any of them have `always=True`. + + We should be able to drop this function and the associated logic calling it once we drop support + for v1-style validator decorators. (Or we can extend it and keep it if we add something equivalent + to the v1-validator `always` kwarg to `field_validator`.) + """ + for validator in validators: + if validator.info.always: + return True + return False + + +def _convert_to_aliases( + alias: str | AliasChoices | AliasPath | None, +) -> str | list[str | int] | list[list[str | int]] | None: + if isinstance(alias, (AliasChoices, AliasPath)): + return alias.convert_to_aliases() + else: + return alias + + +def apply_model_validators( + schema: core_schema.CoreSchema, + validators: Iterable[Decorator[ModelValidatorDecoratorInfo]], + mode: Literal['inner', 'outer', 'all'], +) -> core_schema.CoreSchema: + """Apply model validators to a schema. + + If mode == 'inner', only "before" validators are applied + If mode == 'outer', validators other than "before" are applied + If mode == 'all', all validators are applied + + Args: + schema: The schema to apply validators on. + validators: An iterable of validators. + mode: The validator mode. + + Returns: + The updated schema. + """ + ref: str | None = schema.pop('ref', None) # type: ignore + for validator in validators: + if mode == 'inner' and validator.info.mode != 'before': + continue + if mode == 'outer' and validator.info.mode == 'before': + continue + info_arg = inspect_validator(validator.func, mode=validator.info.mode, type='model') + if validator.info.mode == 'wrap': + if info_arg: + schema = core_schema.with_info_wrap_validator_function(function=validator.func, schema=schema) + else: + schema = core_schema.no_info_wrap_validator_function(function=validator.func, schema=schema) + elif validator.info.mode == 'before': + if info_arg: + schema = core_schema.with_info_before_validator_function(function=validator.func, schema=schema) + else: + schema = core_schema.no_info_before_validator_function(function=validator.func, schema=schema) + else: + assert validator.info.mode == 'after' + if info_arg: + schema = core_schema.with_info_after_validator_function(function=validator.func, schema=schema) + else: + schema = core_schema.no_info_after_validator_function(function=validator.func, schema=schema) + if ref: + schema['ref'] = ref # type: ignore + return schema + + +def wrap_default(field_info: FieldInfo, schema: core_schema.CoreSchema) -> core_schema.CoreSchema: + """Wrap schema with default schema if default value or `default_factory` are available. + + Args: + field_info: The field info object. + schema: The schema to apply default on. + + Returns: + Updated schema by default value or `default_factory`. + """ + if field_info.default_factory: + return core_schema.with_default_schema( + schema, + default_factory=field_info.default_factory, + default_factory_takes_data=takes_validated_data_argument(field_info.default_factory), + validate_default=field_info.validate_default, + ) + elif field_info.default is not PydanticUndefined: + return core_schema.with_default_schema( + schema, default=field_info.default, validate_default=field_info.validate_default + ) + else: + return schema + + +def _extract_get_pydantic_json_schema(tp: Any) -> GetJsonSchemaFunction | None: + """Extract `__get_pydantic_json_schema__` from a type, handling the deprecated `__modify_schema__`.""" + js_modify_function = getattr(tp, '__get_pydantic_json_schema__', None) + + if hasattr(tp, '__modify_schema__'): + BaseModel = import_cached_base_model() + + has_custom_v2_modify_js_func = ( + js_modify_function is not None + and BaseModel.__get_pydantic_json_schema__.__func__ # type: ignore + not in (js_modify_function, getattr(js_modify_function, '__func__', None)) + ) + + if not has_custom_v2_modify_js_func: + cls_name = getattr(tp, '__name__', None) + raise PydanticUserError( + f'The `__modify_schema__` method is not supported in Pydantic v2. ' + f'Use `__get_pydantic_json_schema__` instead{f" in class `{cls_name}`" if cls_name else ""}.', + code='custom-json-schema', + ) + + if (origin := get_origin(tp)) is not None: + # Generic aliases proxy attribute access to the origin, *except* dunder attributes, + # such as `__get_pydantic_json_schema__`, hence the explicit check. + return _extract_get_pydantic_json_schema(origin) + + if js_modify_function is None: + return None + + return js_modify_function + + +def resolve_original_schema(schema: CoreSchema, definitions: _Definitions) -> CoreSchema | None: + if schema['type'] == 'definition-ref': + return definitions.get_schema_from_ref(schema['schema_ref']) + elif schema['type'] == 'definitions': + return schema['schema'] + else: + return schema + + +def _inlining_behavior( + def_ref: core_schema.DefinitionReferenceSchema, +) -> Literal['inline', 'keep', 'preserve_metadata']: + """Determine the inlining behavior of the `'definition-ref'` schema. + + - If no `'serialization'` schema and no metadata is attached, the schema can safely be inlined. + - If it has metadata but only related to the deferred discriminator application, it can be inlined + provided that such metadata is kept. + - Otherwise, the schema should not be inlined. Doing so would remove the `'serialization'` schema or metadata. + """ + if 'serialization' in def_ref: + return 'keep' + metadata = def_ref.get('metadata') + if not metadata: + return 'inline' + if len(metadata) == 1 and 'pydantic_internal_union_discriminator' in metadata: + return 'preserve_metadata' + return 'keep' + + +class _Definitions: + """Keeps track of references and definitions.""" + + _recursively_seen: set[str] + """A set of recursively seen references. + + When a referenceable type is encountered, the `get_schema_or_ref` context manager is + entered to compute the reference. If the type references itself by some way (e.g. for + a dataclass a Pydantic model, the class can be referenced as a field annotation), + entering the context manager again will yield a `'definition-ref'` schema that should + short-circuit the normal generation process, as the reference was already in this set. + """ + + _definitions: dict[str, core_schema.CoreSchema] + """A mapping of references to their corresponding schema. + + When a schema for a referenceable type is generated, it is stored in this mapping. If the + same type is encountered again, the reference is yielded by the `get_schema_or_ref` context + manager. + """ + + def __init__(self) -> None: + self._recursively_seen = set() + self._definitions = {} + + @contextmanager + def get_schema_or_ref(self, tp: Any, /) -> Generator[tuple[str, core_schema.DefinitionReferenceSchema | None]]: + """Get a definition for `tp` if one exists. + + If a definition exists, a tuple of `(ref_string, CoreSchema)` is returned. + If no definition exists yet, a tuple of `(ref_string, None)` is returned. + + Note that the returned `CoreSchema` will always be a `DefinitionReferenceSchema`, + not the actual definition itself. + + This should be called for any type that can be identified by reference. + This includes any recursive types. + + At present the following types can be named/recursive: + + - Pydantic model + - Pydantic and stdlib dataclasses + - Typed dictionaries + - Named tuples + - `TypeAliasType` instances + - Enums + """ + ref = get_type_ref(tp) + # return the reference if we're either (1) in a cycle or (2) it the reference was already encountered: + if ref in self._recursively_seen or ref in self._definitions: + yield (ref, core_schema.definition_reference_schema(ref)) + else: + self._recursively_seen.add(ref) + try: + yield (ref, None) + finally: + self._recursively_seen.discard(ref) + + def get_schema_from_ref(self, ref: str) -> CoreSchema | None: + """Resolve the schema from the given reference.""" + return self._definitions.get(ref) + + def create_definition_reference_schema(self, schema: CoreSchema) -> core_schema.DefinitionReferenceSchema: + """Store the schema as a definition and return a `'definition-reference'` schema pointing to it. + + The schema must have a reference attached to it. + """ + ref = schema['ref'] # pyright: ignore + self._definitions[ref] = schema + return core_schema.definition_reference_schema(ref) + + def unpack_definitions(self, schema: core_schema.DefinitionsSchema) -> CoreSchema: + """Store the definitions of the `'definitions'` core schema and return the inner core schema.""" + for def_schema in schema['definitions']: + self._definitions[def_schema['ref']] = def_schema # pyright: ignore + return schema['schema'] + + def finalize_schema(self, schema: CoreSchema) -> CoreSchema: + """Finalize the core schema. + + This traverses the core schema and referenced definitions, replaces `'definition-ref'` schemas + by the referenced definition if possible, and applies deferred discriminators. + """ + definitions = self._definitions + try: + gather_result = gather_schemas_for_cleaning( + schema, + definitions=definitions, + ) + except MissingDefinitionError as e: + raise InvalidSchemaError from e + + remaining_defs: dict[str, CoreSchema] = {} + + # Note: this logic doesn't play well when core schemas with deferred discriminator metadata + # and references are encountered. See the `test_deferred_discriminated_union_and_references()` test. + for ref, inlinable_def_ref in gather_result['collected_references'].items(): + if inlinable_def_ref is not None and (inlining_behavior := _inlining_behavior(inlinable_def_ref)) != 'keep': + if inlining_behavior == 'inline': + # `ref` was encountered, and only once: + # - `inlinable_def_ref` is a `'definition-ref'` schema and is guaranteed to be + # the only one. Transform it into the definition it points to. + # - Do not store the definition in the `remaining_defs`. + inlinable_def_ref.clear() # pyright: ignore[reportAttributeAccessIssue] + inlinable_def_ref.update(self._resolve_definition(ref, definitions)) # pyright: ignore + elif inlining_behavior == 'preserve_metadata': + # `ref` was encountered, and only once, but contains discriminator metadata. + # We will do the same thing as if `inlining_behavior` was `'inline'`, but make + # sure to keep the metadata for the deferred discriminator application logic below. + meta = inlinable_def_ref.pop('metadata') + inlinable_def_ref.clear() # pyright: ignore[reportAttributeAccessIssue] + inlinable_def_ref.update(self._resolve_definition(ref, definitions)) # pyright: ignore + inlinable_def_ref['metadata'] = meta + else: + # `ref` was encountered, at least two times (or only once, but with metadata or a serialization schema): + # - Do not inline the `'definition-ref'` schemas (they are not provided in the gather result anyway). + # - Store the definition in the `remaining_defs` + remaining_defs[ref] = self._resolve_definition(ref, definitions) + + for cs in gather_result['deferred_discriminator_schemas']: + discriminator: str | None = cs['metadata'].pop('pydantic_internal_union_discriminator', None) # pyright: ignore[reportTypedDictNotRequiredAccess] + if discriminator is None: + # This can happen in rare scenarios, when a deferred schema is present multiple times in the + # gather result (e.g. when using the `Sequence` type -- see `test_sequence_discriminated_union()`). + # In this case, a previous loop iteration applied the discriminator and so we can just skip it here. + continue + applied = _discriminated_union.apply_discriminator(cs.copy(), discriminator, remaining_defs) + # Mutate the schema directly to have the discriminator applied + cs.clear() # pyright: ignore[reportAttributeAccessIssue] + cs.update(applied) # pyright: ignore + + if remaining_defs: + schema = core_schema.definitions_schema(schema=schema, definitions=[*remaining_defs.values()]) + return schema + + def _resolve_definition(self, ref: str, definitions: dict[str, CoreSchema]) -> CoreSchema: + definition = definitions[ref] + if definition['type'] != 'definition-ref': + return definition + + # Some `'definition-ref'` schemas might act as "intermediate" references (e.g. when using + # a PEP 695 type alias (which is referenceable) that references another PEP 695 type alias): + visited: set[str] = set() + while definition['type'] == 'definition-ref' and _inlining_behavior(definition) == 'inline': + schema_ref = definition['schema_ref'] + if schema_ref in visited: + raise PydanticUserError( + f'{ref} contains a circular reference to itself.', code='circular-reference-schema' + ) + visited.add(schema_ref) + definition = definitions[schema_ref] + return {**definition, 'ref': ref} # pyright: ignore[reportReturnType] + + +class _FieldNameStack: + __slots__ = ('_stack',) + + def __init__(self) -> None: + self._stack: list[str] = [] + + @contextmanager + def push(self, field_name: str) -> Iterator[None]: + self._stack.append(field_name) + yield + self._stack.pop() + + def get(self) -> str | None: + if self._stack: + return self._stack[-1] + else: + return None + + +class _ModelTypeStack: + __slots__ = ('_stack',) + + def __init__(self) -> None: + self._stack: list[type] = [] + + @contextmanager + def push(self, type_obj: type) -> Iterator[None]: + self._stack.append(type_obj) + yield + self._stack.pop() + + def get(self) -> type | None: + if self._stack: + return self._stack[-1] + else: + return None diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_generics.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_generics.py new file mode 100644 index 0000000000000000000000000000000000000000..f3e927cb0697f162039ebf6371e1b4903d8417a5 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_generics.py @@ -0,0 +1,530 @@ +from __future__ import annotations + +import operator +import sys +import types +import typing +from collections import ChainMap +from collections.abc import Iterator, Mapping +from contextlib import contextmanager +from contextvars import ContextVar +from functools import reduce +from itertools import zip_longest +from types import prepare_class +from typing import TYPE_CHECKING, Annotated, Any, TypedDict, TypeVar, cast +from weakref import WeakValueDictionary + +import typing_extensions +from typing_inspection import typing_objects +from typing_inspection.introspection import is_union_origin + +from . import _typing_extra +from ._core_utils import get_type_ref +from ._forward_ref import PydanticRecursiveRef +from ._utils import all_identical, is_model_class + +if TYPE_CHECKING: + from ..main import BaseModel + +GenericTypesCacheKey = tuple[Any, Any, tuple[Any, ...]] + +# Note: We want to remove LimitedDict, but to do this, we'd need to improve the handling of generics caching. +# Right now, to handle recursive generics, we some types must remain cached for brief periods without references. +# By chaining the WeakValuesDict with a LimitedDict, we have a way to retain caching for all types with references, +# while also retaining a limited number of types even without references. This is generally enough to build +# specific recursive generic models without losing required items out of the cache. + +KT = TypeVar('KT') +VT = TypeVar('VT') +_LIMITED_DICT_SIZE = 100 + + +class LimitedDict(dict[KT, VT]): + def __init__(self, size_limit: int = _LIMITED_DICT_SIZE) -> None: + self.size_limit = size_limit + super().__init__() + + def __setitem__(self, key: KT, value: VT, /) -> None: + super().__setitem__(key, value) + if len(self) > self.size_limit: + excess = len(self) - self.size_limit + self.size_limit // 10 + to_remove = list(self.keys())[:excess] + for k in to_remove: + del self[k] + + +# weak dictionaries allow the dynamically created parametrized versions of generic models to get collected +# once they are no longer referenced by the caller. +GenericTypesCache = WeakValueDictionary[GenericTypesCacheKey, 'type[BaseModel]'] + +if TYPE_CHECKING: + + class DeepChainMap(ChainMap[KT, VT]): # type: ignore + ... + +else: + + class DeepChainMap(ChainMap): + """Variant of ChainMap that allows direct updates to inner scopes. + + Taken from https://docs.python.org/3/library/collections.html#collections.ChainMap, + with some light modifications for this use case. + """ + + def clear(self) -> None: + for mapping in self.maps: + mapping.clear() + + def __setitem__(self, key: KT, value: VT) -> None: + for mapping in self.maps: + mapping[key] = value + + def __delitem__(self, key: KT) -> None: + hit = False + for mapping in self.maps: + if key in mapping: + del mapping[key] + hit = True + if not hit: + raise KeyError(key) + + +# Despite the fact that LimitedDict _seems_ no longer necessary, I'm very nervous to actually remove it +# and discover later on that we need to re-add all this infrastructure... +# _GENERIC_TYPES_CACHE = DeepChainMap(GenericTypesCache(), LimitedDict()) + +_GENERIC_TYPES_CACHE = GenericTypesCache() + + +class PydanticGenericMetadata(TypedDict): + origin: type[BaseModel] | None # analogous to typing._GenericAlias.__origin__ + args: tuple[Any, ...] # analogous to typing._GenericAlias.__args__ + parameters: tuple[TypeVar, ...] # analogous to typing.Generic.__parameters__ + + +def create_generic_submodel( + model_name: str, origin: type[BaseModel], args: tuple[Any, ...], params: tuple[Any, ...] +) -> type[BaseModel]: + """Dynamically create a submodel of a provided (generic) BaseModel. + + This is used when producing concrete parametrizations of generic models. This function + only *creates* the new subclass; the schema/validators/serialization must be updated to + reflect a concrete parametrization elsewhere. + + Args: + model_name: The name of the newly created model. + origin: The base class for the new model to inherit from. + args: A tuple of generic metadata arguments. + params: A tuple of generic metadata parameters. + + Returns: + The created submodel. + """ + namespace: dict[str, Any] = {'__module__': origin.__module__} + bases = (origin,) + meta, ns, kwds = prepare_class(model_name, bases) + namespace.update(ns) + created_model = meta( + model_name, + bases, + namespace, + __pydantic_generic_metadata__={ + 'origin': origin, + 'args': args, + 'parameters': params, + }, + __pydantic_reset_parent_namespace__=False, + **kwds, + ) + + model_module, called_globally = _get_caller_frame_info(depth=3) + if called_globally: # create global reference and therefore allow pickling + object_by_reference = None + reference_name = model_name + reference_module_globals = sys.modules[created_model.__module__].__dict__ + while object_by_reference is not created_model: + object_by_reference = reference_module_globals.setdefault(reference_name, created_model) + reference_name += '_' + + return created_model + + +def _get_caller_frame_info(depth: int = 2) -> tuple[str | None, bool]: + """Used inside a function to check whether it was called globally. + + Args: + depth: The depth to get the frame. + + Returns: + A tuple contains `module_name` and `called_globally`. + + Raises: + RuntimeError: If the function is not called inside a function. + """ + try: + previous_caller_frame = sys._getframe(depth) + except ValueError as e: + raise RuntimeError('This function must be used inside another function') from e + except AttributeError: # sys module does not have _getframe function, so there's nothing we can do about it + return None, False + frame_globals = previous_caller_frame.f_globals + return frame_globals.get('__name__'), previous_caller_frame.f_locals is frame_globals + + +DictValues: type[Any] = {}.values().__class__ + + +def iter_contained_typevars(v: Any) -> Iterator[TypeVar]: + """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found. + + This is inspired as an alternative to directly accessing the `__parameters__` attribute of a GenericAlias, + since __parameters__ of (nested) generic BaseModel subclasses won't show up in that list. + """ + if isinstance(v, TypeVar): + yield v + elif is_model_class(v): + yield from v.__pydantic_generic_metadata__['parameters'] + elif isinstance(v, (DictValues, list)): + for var in v: + yield from iter_contained_typevars(var) + else: + args = get_args(v) + for arg in args: + yield from iter_contained_typevars(arg) + + +def get_args(v: Any) -> Any: + pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None) + if pydantic_generic_metadata: + return pydantic_generic_metadata.get('args') + return typing_extensions.get_args(v) + + +def get_origin(v: Any) -> Any: + pydantic_generic_metadata: PydanticGenericMetadata | None = getattr(v, '__pydantic_generic_metadata__', None) + if pydantic_generic_metadata: + return pydantic_generic_metadata.get('origin') + return typing_extensions.get_origin(v) + + +def get_standard_typevars_map(cls: Any) -> dict[TypeVar, Any] | None: + """Package a generic type's typevars and parametrization (if present) into a dictionary compatible with the + `replace_types` function. Specifically, this works with standard typing generics and typing._GenericAlias. + """ + origin = get_origin(cls) + if origin is None: + return None + if not hasattr(origin, '__parameters__'): + return None + + # In this case, we know that cls is a _GenericAlias, and origin is the generic type + # So it is safe to access cls.__args__ and origin.__parameters__ + args: tuple[Any, ...] = cls.__args__ # type: ignore + parameters: tuple[TypeVar, ...] = origin.__parameters__ + return dict(zip(parameters, args)) + + +def get_model_typevars_map(cls: type[BaseModel]) -> dict[TypeVar, Any]: + """Package a generic BaseModel's typevars and concrete parametrization (if present) into a dictionary compatible + with the `replace_types` function. + + Since BaseModel.__class_getitem__ does not produce a typing._GenericAlias, and the BaseModel generic info is + stored in the __pydantic_generic_metadata__ attribute, we need special handling here. + """ + # TODO: This could be unified with `get_standard_typevars_map` if we stored the generic metadata + # in the __origin__, __args__, and __parameters__ attributes of the model. + generic_metadata = cls.__pydantic_generic_metadata__ + origin = generic_metadata['origin'] + args = generic_metadata['args'] + if not args: + # No need to go into `iter_contained_typevars`: + return {} + return dict(zip(iter_contained_typevars(origin), args)) + + +def replace_types(type_: Any, type_map: Mapping[TypeVar, Any] | None) -> Any: + """Return type with all occurrences of `type_map` keys recursively replaced with their values. + + Args: + type_: The class or generic alias. + type_map: Mapping from `TypeVar` instance to concrete types. + + Returns: + A new type representing the basic structure of `type_` with all + `typevar_map` keys recursively replaced. + + Example: + ```python + from typing import Union + + from pydantic._internal._generics import replace_types + + replace_types(tuple[str, Union[list[str], float]], {str: int}) + #> tuple[int, Union[list[int], float]] + ``` + """ + if not type_map: + return type_ + + type_args = get_args(type_) + origin_type = get_origin(type_) + + if typing_objects.is_annotated(origin_type): + annotated_type, *annotations = type_args + annotated_type = replace_types(annotated_type, type_map) + # TODO remove parentheses when we drop support for Python 3.10: + return Annotated[(annotated_type, *annotations)] + + # Having type args is a good indicator that this is a typing special form + # instance or a generic alias of some sort. + if type_args: + resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args) + if all_identical(type_args, resolved_type_args): + # If all arguments are the same, there is no need to modify the + # type or create a new object at all + return type_ + + if ( + origin_type is not None + and isinstance(type_, _typing_extra.typing_base) + and not isinstance(origin_type, _typing_extra.typing_base) + and getattr(type_, '_name', None) is not None + ): + # In python < 3.9 generic aliases don't exist so any of these like `list`, + # `type` or `collections.abc.Callable` need to be translated. + # See: https://www.python.org/dev/peps/pep-0585 + origin_type = getattr(typing, type_._name) + assert origin_type is not None + + if is_union_origin(origin_type): + if any(typing_objects.is_any(arg) for arg in resolved_type_args): + # `Any | T` ~ `Any`: + resolved_type_args = (Any,) + # `Never | T` ~ `T`: + resolved_type_args = tuple( + arg + for arg in resolved_type_args + if not (typing_objects.is_noreturn(arg) or typing_objects.is_never(arg)) + ) + + # PEP-604 syntax (e.g. `list | str`) is represented with a types.UnionType object that does not + # implement `__getitem__()`. In Python 3.14+, `typing.Union` and `types.UnionType` are the same, + # and we instead rely on `typing.Union` as it implicitly converts string annotations to `ForwardRef` + # instances (this is to avoid type errors as per https://github.com/python/cpython/pull/105366). + # TODO remove type ignore comment when we drop support for Python 3.9 (https://github.com/microsoft/pyright/issues/11241): + if (3, 10) <= sys.version_info < (3, 14) and origin_type is types.UnionType: # pyright: ignore[reportAttributeAccessIssue] + return reduce(operator.or_, resolved_type_args) + # NotRequired[T] and Required[T] don't support tuple type resolved_type_args, hence the condition below + return origin_type[resolved_type_args[0] if len(resolved_type_args) == 1 else resolved_type_args] + + # We handle pydantic generic models separately as they don't have the same + # semantics as "typing" classes or generic aliases + + if not origin_type and is_model_class(type_): + parameters = type_.__pydantic_generic_metadata__['parameters'] + if not parameters: + return type_ + resolved_type_args = tuple(replace_types(t, type_map) for t in parameters) + if all_identical(parameters, resolved_type_args): + return type_ + return type_[resolved_type_args] + + # Handle special case for typehints that can have lists as arguments. + # `typing.Callable[[int, str], int]` is an example for this. + if isinstance(type_, list): + resolved_list = [replace_types(element, type_map) for element in type_] + if all_identical(type_, resolved_list): + return type_ + return resolved_list + + # If all else fails, we try to resolve the type directly and otherwise just + # return the input with no modifications. + return type_map.get(type_, type_) + + +def map_generic_model_arguments(cls: type[BaseModel], args: tuple[Any, ...]) -> dict[TypeVar, Any]: + """Return a mapping between the parameters of a generic model and the provided arguments during parameterization. + + Raises: + TypeError: If the number of arguments does not match the parameters (i.e. if providing too few or too many arguments). + + Example: + ```python {test="skip" lint="skip"} + class Model[T, U, V = int](BaseModel): ... + + map_generic_model_arguments(Model, (str, bytes)) + #> {T: str, U: bytes, V: int} + + map_generic_model_arguments(Model, (str,)) + #> TypeError: Too few arguments for ; actual 1, expected at least 2 + + map_generic_model_arguments(Model, (str, bytes, int, complex)) + #> TypeError: Too many arguments for ; actual 4, expected 3 + ``` + + Note: + This function is analogous to the private `typing._check_generic_specialization` function. + """ + parameters = cls.__pydantic_generic_metadata__['parameters'] + expected_len = len(parameters) + typevars_map: dict[TypeVar, Any] = {} + + _missing = object() + for parameter, argument in zip_longest(parameters, args, fillvalue=_missing): + if parameter is _missing: + raise TypeError(f'Too many arguments for {cls}; actual {len(args)}, expected {expected_len}') + + if argument is _missing: + param = cast(TypeVar, parameter) + try: + has_default = param.has_default() # pyright: ignore[reportAttributeAccessIssue] + except AttributeError: + # Happens if using `typing.TypeVar` (and not `typing_extensions`) on Python < 3.13. + has_default = False + if has_default: + # The default might refer to other type parameters. For an example, see: + # https://typing.python.org/en/latest/spec/generics.html#type-parameters-as-parameters-to-generics + typevars_map[param] = replace_types(param.__default__, typevars_map) # pyright: ignore[reportAttributeAccessIssue] + else: + expected_len -= sum(hasattr(p, 'has_default') and p.has_default() for p in parameters) # pyright: ignore[reportAttributeAccessIssue] + raise TypeError(f'Too few arguments for {cls}; actual {len(args)}, expected at least {expected_len}') + else: + param = cast(TypeVar, parameter) + typevars_map[param] = argument + + return typevars_map + + +_generic_recursion_cache: ContextVar[set[str] | None] = ContextVar('_generic_recursion_cache', default=None) + + +@contextmanager +def generic_recursion_self_type( + origin: type[BaseModel], args: tuple[Any, ...] +) -> Iterator[PydanticRecursiveRef | None]: + """This contextmanager should be placed around the recursive calls used to build a generic type, + and accept as arguments the generic origin type and the type arguments being passed to it. + + If the same origin and arguments are observed twice, it implies that a self-reference placeholder + can be used while building the core schema, and will produce a schema_ref that will be valid in the + final parent schema. + """ + previously_seen_type_refs = _generic_recursion_cache.get() + if previously_seen_type_refs is None: + previously_seen_type_refs = set() + token = _generic_recursion_cache.set(previously_seen_type_refs) + else: + token = None + + try: + type_ref = get_type_ref(origin, args_override=args) + if type_ref in previously_seen_type_refs: + self_type = PydanticRecursiveRef(type_ref=type_ref) + yield self_type + else: + previously_seen_type_refs.add(type_ref) + yield + previously_seen_type_refs.remove(type_ref) + finally: + if token: + _generic_recursion_cache.reset(token) + + +def recursively_defined_type_refs() -> set[str]: + visited = _generic_recursion_cache.get() + if not visited: + return set() # not in a generic recursion, so there are no types + + return visited.copy() # don't allow modifications + + +def get_cached_generic_type_early(parent: type[BaseModel], typevar_values: Any) -> type[BaseModel] | None: + """The use of a two-stage cache lookup approach was necessary to have the highest performance possible for + repeated calls to `__class_getitem__` on generic types (which may happen in tighter loops during runtime), + while still ensuring that certain alternative parametrizations ultimately resolve to the same type. + + As a concrete example, this approach was necessary to make Model[List[T]][int] equal to Model[List[int]]. + The approach could be modified to not use two different cache keys at different points, but the + _early_cache_key is optimized to be as quick to compute as possible (for repeated-access speed), and the + _late_cache_key is optimized to be as "correct" as possible, so that two types that will ultimately be the + same after resolving the type arguments will always produce cache hits. + + If we wanted to move to only using a single cache key per type, we would either need to always use the + slower/more computationally intensive logic associated with _late_cache_key, or would need to accept + that Model[List[T]][int] is a different type than Model[List[T]][int]. Because we rely on subclass relationships + during validation, I think it is worthwhile to ensure that types that are functionally equivalent are actually + equal. + """ + return _GENERIC_TYPES_CACHE.get(_early_cache_key(parent, typevar_values)) + + +def get_cached_generic_type_late( + parent: type[BaseModel], typevar_values: Any, origin: type[BaseModel], args: tuple[Any, ...] +) -> type[BaseModel] | None: + """See the docstring of `get_cached_generic_type_early` for more information about the two-stage cache lookup.""" + cached = _GENERIC_TYPES_CACHE.get(_late_cache_key(origin, args, typevar_values)) + if cached is not None: + set_cached_generic_type(parent, typevar_values, cached, origin, args) + return cached + + +def set_cached_generic_type( + parent: type[BaseModel], + typevar_values: tuple[Any, ...], + type_: type[BaseModel], + origin: type[BaseModel] | None = None, + args: tuple[Any, ...] | None = None, +) -> None: + """See the docstring of `get_cached_generic_type_early` for more information about why items are cached with + two different keys. + """ + _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values)] = type_ + if len(typevar_values) == 1: + _GENERIC_TYPES_CACHE[_early_cache_key(parent, typevar_values[0])] = type_ + if origin and args: + _GENERIC_TYPES_CACHE[_late_cache_key(origin, args, typevar_values)] = type_ + + +def _union_orderings_key(typevar_values: Any) -> Any: + """This is intended to help differentiate between Union types with the same arguments in different order. + + Thanks to caching internal to the `typing` module, it is not possible to distinguish between + List[Union[int, float]] and List[Union[float, int]] (and similarly for other "parent" origins besides List) + because `typing` considers Union[int, float] to be equal to Union[float, int]. + + However, you _can_ distinguish between (top-level) Union[int, float] vs. Union[float, int]. + Because we parse items as the first Union type that is successful, we get slightly more consistent behavior + if we make an effort to distinguish the ordering of items in a union. It would be best if we could _always_ + get the exact-correct order of items in the union, but that would require a change to the `typing` module itself. + (See https://github.com/python/cpython/issues/86483 for reference.) + """ + if isinstance(typevar_values, tuple): + return tuple(_union_orderings_key(value) for value in typevar_values) + elif typing_objects.is_union(typing_extensions.get_origin(typevar_values)): + return get_args(typevar_values) + else: + return () + + +def _early_cache_key(cls: type[BaseModel], typevar_values: Any) -> GenericTypesCacheKey: + """This is intended for minimal computational overhead during lookups of cached types. + + Note that this is overly simplistic, and it's possible that two different cls/typevar_values + inputs would ultimately result in the same type being created in BaseModel.__class_getitem__. + To handle this, we have a fallback _late_cache_key that is checked later if the _early_cache_key + lookup fails, and should result in a cache hit _precisely_ when the inputs to __class_getitem__ + would result in the same type. + """ + return cls, typevar_values, _union_orderings_key(typevar_values) + + +def _late_cache_key(origin: type[BaseModel], args: tuple[Any, ...], typevar_values: Any) -> GenericTypesCacheKey: + """This is intended for use later in the process of creating a new type, when we have more information + about the exact args that will be passed. If it turns out that a different set of inputs to + __class_getitem__ resulted in the same inputs to the generic type creation process, we can still + return the cached type, and update the cache with the _early_cache_key as well. + """ + # The _union_orderings_key is placed at the start here to ensure there cannot be a collision with an + # _early_cache_key, as that function will always produce a BaseModel subclass as the first item in the key, + # whereas this function will always produce a tuple as the first item in the key. + return _union_orderings_key(typevar_values), origin, args diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_git.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_git.py new file mode 100644 index 0000000000000000000000000000000000000000..96dcda28f801cd19989838b029f6f2b250dedc52 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_git.py @@ -0,0 +1,27 @@ +"""Git utilities, adopted from mypy's git utilities (https://github.com/python/mypy/blob/master/mypy/git.py).""" + +from __future__ import annotations + +import subprocess +from pathlib import Path + + +def is_git_repo(dir: Path) -> bool: + """Is the given directory version-controlled with git?""" + return dir.joinpath('.git').exists() + + +def have_git() -> bool: # pragma: no cover + """Can we run the git executable?""" + try: + subprocess.check_output(['git', '--help']) + return True + except subprocess.CalledProcessError: + return False + except OSError: + return False + + +def git_revision(dir: Path) -> str: + """Get the SHA-1 of the HEAD of a git repository.""" + return subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD'], cwd=dir).decode('utf-8').strip() diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_import_utils.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_import_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..638102f771dd674799255cc6ef69cacb2c184d13 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_import_utils.py @@ -0,0 +1,20 @@ +from functools import cache +from typing import TYPE_CHECKING + +if TYPE_CHECKING: + from pydantic import BaseModel + from pydantic.fields import FieldInfo + + +@cache +def import_cached_base_model() -> type['BaseModel']: + from pydantic import BaseModel + + return BaseModel + + +@cache +def import_cached_field_info() -> type['FieldInfo']: + from pydantic.fields import FieldInfo + + return FieldInfo diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_internal_dataclass.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_internal_dataclass.py new file mode 100644 index 0000000000000000000000000000000000000000..33e152cc8d178486ef016285855495a86d5991d8 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_internal_dataclass.py @@ -0,0 +1,7 @@ +import sys + +# `slots` is available on Python >= 3.10 +if sys.version_info >= (3, 10): + slots_true = {'slots': True} +else: + slots_true = {} diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_known_annotated_metadata.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_known_annotated_metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..5954dba7017edd9b55ddb0eb1831fa449a8edf7c --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_known_annotated_metadata.py @@ -0,0 +1,403 @@ +from __future__ import annotations + +from collections import defaultdict +from collections.abc import Iterable +from copy import copy +from functools import lru_cache, partial +from typing import TYPE_CHECKING, Any + +from pydantic_core import CoreSchema, PydanticCustomError, ValidationError, to_jsonable_python +from pydantic_core import core_schema as cs + +from ._fields import PydanticMetadata +from ._import_utils import import_cached_field_info + +if TYPE_CHECKING: + pass + +STRICT = {'strict'} +FAIL_FAST = {'fail_fast'} +LENGTH_CONSTRAINTS = {'min_length', 'max_length'} +INEQUALITY = {'le', 'ge', 'lt', 'gt'} +NUMERIC_CONSTRAINTS = {'multiple_of', *INEQUALITY} +ALLOW_INF_NAN = {'allow_inf_nan'} + +STR_CONSTRAINTS = { + *LENGTH_CONSTRAINTS, + *STRICT, + 'strip_whitespace', + 'to_lower', + 'to_upper', + 'pattern', + 'coerce_numbers_to_str', + 'ascii_only', +} +BYTES_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT} + +LIST_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST} +TUPLE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST} +SET_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT, *FAIL_FAST} +DICT_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT} +GENERATOR_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *STRICT} +SEQUENCE_CONSTRAINTS = {*LENGTH_CONSTRAINTS, *FAIL_FAST} + +FLOAT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT} +DECIMAL_CONSTRAINTS = {'max_digits', 'decimal_places', *FLOAT_CONSTRAINTS} +INT_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *ALLOW_INF_NAN, *STRICT} +BOOL_CONSTRAINTS = STRICT +UUID_CONSTRAINTS = STRICT + +DATE_TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT} +TIMEDELTA_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT} +TIME_CONSTRAINTS = {*NUMERIC_CONSTRAINTS, *STRICT} +LAX_OR_STRICT_CONSTRAINTS = STRICT +ENUM_CONSTRAINTS = STRICT +COMPLEX_CONSTRAINTS = STRICT + +UNION_CONSTRAINTS = {'union_mode'} +URL_CONSTRAINTS = { + 'max_length', + 'allowed_schemes', + 'host_required', + 'default_host', + 'default_port', + 'default_path', +} + +TEXT_SCHEMA_TYPES = ('str', 'bytes', 'url', 'multi-host-url') +SEQUENCE_SCHEMA_TYPES = ('list', 'tuple', 'set', 'frozenset', 'generator', *TEXT_SCHEMA_TYPES) +NUMERIC_SCHEMA_TYPES = ('float', 'int', 'date', 'time', 'timedelta', 'datetime') + +CONSTRAINTS_TO_ALLOWED_SCHEMAS: dict[str, set[str]] = defaultdict(set) + +constraint_schema_pairings: list[tuple[set[str], tuple[str, ...]]] = [ + (STR_CONSTRAINTS, TEXT_SCHEMA_TYPES), + (BYTES_CONSTRAINTS, ('bytes',)), + (LIST_CONSTRAINTS, ('list',)), + (TUPLE_CONSTRAINTS, ('tuple',)), + (SET_CONSTRAINTS, ('set', 'frozenset')), + (DICT_CONSTRAINTS, ('dict',)), + (GENERATOR_CONSTRAINTS, ('generator',)), + (FLOAT_CONSTRAINTS, ('float',)), + (INT_CONSTRAINTS, ('int',)), + (DATE_TIME_CONSTRAINTS, ('date', 'time', 'datetime', 'timedelta')), + # TODO: this is a bit redundant, we could probably avoid some of these + (STRICT, (*TEXT_SCHEMA_TYPES, *SEQUENCE_SCHEMA_TYPES, *NUMERIC_SCHEMA_TYPES, 'typed-dict', 'model')), + (UNION_CONSTRAINTS, ('union',)), + (URL_CONSTRAINTS, ('url', 'multi-host-url')), + (BOOL_CONSTRAINTS, ('bool',)), + (UUID_CONSTRAINTS, ('uuid',)), + (LAX_OR_STRICT_CONSTRAINTS, ('lax-or-strict',)), + (ENUM_CONSTRAINTS, ('enum',)), + (DECIMAL_CONSTRAINTS, ('decimal',)), + (COMPLEX_CONSTRAINTS, ('complex',)), +] + +for constraints, schemas in constraint_schema_pairings: + for c in constraints: + CONSTRAINTS_TO_ALLOWED_SCHEMAS[c].update(schemas) + + +def as_jsonable_value(v: Any) -> Any: + if type(v) not in (int, str, float, bytes, bool, type(None)): + return to_jsonable_python(v) + return v + + +def expand_grouped_metadata(annotations: Iterable[Any]) -> Iterable[Any]: + """Expand the annotations. + + Args: + annotations: An iterable of annotations. + + Returns: + An iterable of expanded annotations. + + Example: + ```python + from annotated_types import Ge, Len + + from pydantic._internal._known_annotated_metadata import expand_grouped_metadata + + print(list(expand_grouped_metadata([Ge(4), Len(5)]))) + #> [Ge(ge=4), MinLen(min_length=5)] + ``` + """ + import annotated_types as at + + FieldInfo = import_cached_field_info() + + for annotation in annotations: + if isinstance(annotation, at.GroupedMetadata): + yield from annotation + elif isinstance(annotation, FieldInfo): + yield from annotation.metadata + # this is a bit problematic in that it results in duplicate metadata + # all of our "consumers" can handle it, but it is not ideal + # we probably should split up FieldInfo into: + # - annotated types metadata + # - individual metadata known only to Pydantic + annotation = copy(annotation) + annotation.metadata = [] + yield annotation + else: + yield annotation + + +@lru_cache +def _get_at_to_constraint_map() -> dict[type, str]: + """Return a mapping of annotated types to constraints. + + Normally, we would define a mapping like this in the module scope, but we can't do that + because we don't permit module level imports of `annotated_types`, in an attempt to speed up + the import time of `pydantic`. We still only want to have this dictionary defined in one place, + so we use this function to cache the result. + """ + import annotated_types as at + + return { + at.Gt: 'gt', + at.Ge: 'ge', + at.Lt: 'lt', + at.Le: 'le', + at.MultipleOf: 'multiple_of', + at.MinLen: 'min_length', + at.MaxLen: 'max_length', + } + + +def apply_known_metadata(annotation: Any, schema: CoreSchema) -> CoreSchema | None: # noqa: C901 + """Apply `annotation` to `schema` if it is an annotation we know about (Gt, Le, etc.). + Otherwise return `None`. + + This does not handle all known annotations. If / when it does, it can always + return a CoreSchema and return the unmodified schema if the annotation should be ignored. + + Assumes that GroupedMetadata has already been expanded via `expand_grouped_metadata`. + + Args: + annotation: The annotation. + schema: The schema. + + Returns: + An updated schema with annotation if it is an annotation we know about, `None` otherwise. + + Raises: + RuntimeError: If a constraint can't be applied to a specific schema type. + ValueError: If an unknown constraint is encountered. + """ + import annotated_types as at + + from ._validators import NUMERIC_VALIDATOR_LOOKUP, forbid_inf_nan_check + + schema = schema.copy() + schema_update, other_metadata = collect_known_metadata([annotation]) + schema_type = schema['type'] + + chain_schema_constraints: set[str] = { + 'pattern', + 'strip_whitespace', + 'to_lower', + 'to_upper', + 'coerce_numbers_to_str', + 'ascii_only', + } + chain_schema_steps: list[CoreSchema] = [] + + for constraint, value in schema_update.items(): + if constraint not in CONSTRAINTS_TO_ALLOWED_SCHEMAS: + raise ValueError(f'Unknown constraint {constraint}') + allowed_schemas = CONSTRAINTS_TO_ALLOWED_SCHEMAS[constraint] + + # if it becomes necessary to handle more than one constraint + # in this recursive case with function-after or function-wrap, we should refactor + # this is a bit challenging because we sometimes want to apply constraints to the inner schema, + # whereas other times we want to wrap the existing schema with a new one that enforces a new constraint. + if schema_type in {'function-before', 'function-wrap', 'function-after'} and constraint == 'strict': + schema['schema'] = apply_known_metadata(annotation, schema['schema']) # type: ignore # schema is function schema + return schema + + # if we're allowed to apply constraint directly to the schema, like le to int, do that + if schema_type in allowed_schemas: + if constraint == 'union_mode' and schema_type == 'union': + schema['mode'] = value # type: ignore # schema is UnionSchema + else: + schema[constraint] = value + continue + + # else, apply a function after validator to the schema to enforce the corresponding constraint + if constraint in chain_schema_constraints: + + def _apply_constraint_with_incompatibility_info( + value: Any, handler: cs.ValidatorFunctionWrapHandler + ) -> Any: + try: + x = handler(value) + except ValidationError as ve: + # if the error is about the type, it's likely that the constraint is incompatible the type of the field + # for example, the following invalid schema wouldn't be caught during schema build, but rather at this point + # with a cryptic 'string_type' error coming from the string validator, + # that we'd rather express as a constraint incompatibility error (TypeError) + # Annotated[list[int], Field(pattern='abc')] + if 'type' in ve.errors()[0]['type']: + raise TypeError( + f"Unable to apply constraint '{constraint}' to supplied value {value} for schema of type '{schema_type}'" # noqa: B023 + ) + raise ve + return x + + chain_schema_steps.append( + cs.no_info_wrap_validator_function( + _apply_constraint_with_incompatibility_info, cs.str_schema(**{constraint: value}) + ) + ) + elif constraint in NUMERIC_VALIDATOR_LOOKUP: + if constraint in LENGTH_CONSTRAINTS: + inner_schema = schema + while inner_schema['type'] in {'function-before', 'function-wrap', 'function-after'}: + inner_schema = inner_schema['schema'] # type: ignore + inner_schema_type = inner_schema['type'] + if inner_schema_type == 'list' or ( + inner_schema_type == 'json-or-python' and inner_schema['json_schema']['type'] == 'list' # type: ignore + ): + js_constraint_key = 'minItems' if constraint == 'min_length' else 'maxItems' + else: + js_constraint_key = 'minLength' if constraint == 'min_length' else 'maxLength' + else: + js_constraint_key = constraint + + schema = cs.no_info_after_validator_function( + partial(NUMERIC_VALIDATOR_LOOKUP[constraint], **{constraint: value}), schema + ) + metadata = schema.get('metadata', {}) + if (existing_json_schema_updates := metadata.get('pydantic_js_updates')) is not None: + metadata['pydantic_js_updates'] = { + **existing_json_schema_updates, + **{js_constraint_key: as_jsonable_value(value)}, + } + else: + metadata['pydantic_js_updates'] = {js_constraint_key: as_jsonable_value(value)} + schema['metadata'] = metadata + elif constraint == 'allow_inf_nan' and value is False: + schema = cs.no_info_after_validator_function( + forbid_inf_nan_check, + schema, + ) + else: + # It's rare that we'd get here, but it's possible if we add a new constraint and forget to handle it + # Most constraint errors are caught at runtime during attempted application + raise RuntimeError(f"Unable to apply constraint '{constraint}' to schema of type '{schema_type}'") + + for annotation in other_metadata: + if (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): + constraint = at_to_constraint_map[annotation_type] + validator = NUMERIC_VALIDATOR_LOOKUP.get(constraint) + if validator is None: + raise ValueError(f'Unknown constraint {constraint}') + schema = cs.no_info_after_validator_function( + partial(validator, {constraint: getattr(annotation, constraint)}), schema + ) + continue + elif isinstance(annotation, (at.Predicate, at.Not)): + predicate_name = f'{annotation.func.__qualname__!r} ' if hasattr(annotation.func, '__qualname__') else '' + + # Note: B023 is ignored because even though we iterate over `other_metadata`, it is guaranteed + # to be of length 1. `apply_known_metadata()` is called from `GenerateSchema`, where annotations + # were already expanded via `expand_grouped_metadata()`. Confusing, but this falls into the annotations + # refactor. + if isinstance(annotation, at.Predicate): + + def val_func(v: Any) -> Any: + predicate_satisfied = annotation.func(v) # noqa: B023 + if not predicate_satisfied: + raise PydanticCustomError( + 'predicate_failed', + f'Predicate {predicate_name}failed', # pyright: ignore[reportArgumentType] # noqa: B023 + ) + return v + + else: + + def val_func(v: Any) -> Any: + predicate_satisfied = annotation.func(v) # noqa: B023 + if predicate_satisfied: + raise PydanticCustomError( + 'not_operation_failed', + f'Not of {predicate_name}failed', # pyright: ignore[reportArgumentType] # noqa: B023 + ) + return v + + schema = cs.no_info_after_validator_function(val_func, schema) + else: + # ignore any other unknown metadata + return None + + if chain_schema_steps: + chain_schema_steps = [schema] + chain_schema_steps + return cs.chain_schema(chain_schema_steps) + + return schema + + +def collect_known_metadata(annotations: Iterable[Any]) -> tuple[dict[str, Any], list[Any]]: + """Split `annotations` into known metadata and unknown annotations. + + Args: + annotations: An iterable of annotations. + + Returns: + A tuple contains a dict of known metadata and a list of unknown annotations. + + Example: + ```python + from annotated_types import Gt, Len + + from pydantic._internal._known_annotated_metadata import collect_known_metadata + + print(collect_known_metadata([Gt(1), Len(42), ...])) + #> ({'gt': 1, 'min_length': 42}, [Ellipsis]) + ``` + """ + annotations = expand_grouped_metadata(annotations) + + res: dict[str, Any] = {} + remaining: list[Any] = [] + + for annotation in annotations: + # isinstance(annotation, PydanticMetadata) also covers ._fields:_PydanticGeneralMetadata + if isinstance(annotation, PydanticMetadata): + res.update(annotation.__dict__) + # we don't use dataclasses.asdict because that recursively calls asdict on the field values + elif (annotation_type := type(annotation)) in (at_to_constraint_map := _get_at_to_constraint_map()): + constraint = at_to_constraint_map[annotation_type] + res[constraint] = getattr(annotation, constraint) + elif isinstance(annotation, type) and issubclass(annotation, PydanticMetadata): + # also support PydanticMetadata classes being used without initialisation, + # e.g. `Annotated[int, Strict]` as well as `Annotated[int, Strict()]` + res.update({k: v for k, v in vars(annotation).items() if not k.startswith('_')}) + else: + remaining.append(annotation) + # Nones can sneak in but pydantic-core will reject them + # it'd be nice to clean things up so we don't put in None (we probably don't _need_ to, it was just easier) + # but this is simple enough to kick that can down the road + res = {k: v for k, v in res.items() if v is not None} + return res, remaining + + +def check_metadata(metadata: dict[str, Any], allowed: Iterable[str], source_type: Any) -> None: + """A small utility function to validate that the given metadata can be applied to the target. + More than saving lines of code, this gives us a consistent error message for all of our internal implementations. + + Args: + metadata: A dict of metadata. + allowed: An iterable of allowed metadata. + source_type: The source type. + + Raises: + TypeError: If there is metadatas that can't be applied on source type. + """ + unknown = metadata.keys() - set(allowed) + if unknown: + raise TypeError( + f'The following constraints cannot be applied to {source_type!r}: {", ".join([f"{k!r}" for k in unknown])}' + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_mock_val_ser.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_mock_val_ser.py new file mode 100644 index 0000000000000000000000000000000000000000..9125ab32e5348b6e873c4b1939a887ebde35d97a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_mock_val_ser.py @@ -0,0 +1,228 @@ +from __future__ import annotations + +from collections.abc import Iterator, Mapping +from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, TypeVar, Union + +from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator + +from ..errors import PydanticErrorCodes, PydanticUserError +from ..plugin._schema_validator import PluggableSchemaValidator + +if TYPE_CHECKING: + from ..dataclasses import PydanticDataclass + from ..main import BaseModel + from ..type_adapter import TypeAdapter + + +ValSer = TypeVar('ValSer', bound=Union[SchemaValidator, PluggableSchemaValidator, SchemaSerializer]) +T = TypeVar('T') + + +class MockCoreSchema(Mapping[str, Any]): + """Mocker for `pydantic_core.CoreSchema` which optionally attempts to + rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails. + """ + + __slots__ = '_error_message', '_code', '_attempt_rebuild', '_built_memo' + + def __init__( + self, + error_message: str, + *, + code: PydanticErrorCodes, + attempt_rebuild: Callable[[], CoreSchema | None] | None = None, + ) -> None: + self._error_message = error_message + self._code: PydanticErrorCodes = code + self._attempt_rebuild = attempt_rebuild + self._built_memo: CoreSchema | None = None + + def __getitem__(self, key: str) -> Any: + return self._get_built().__getitem__(key) + + def __len__(self) -> int: + return self._get_built().__len__() + + def __iter__(self) -> Iterator[str]: + return self._get_built().__iter__() + + def _get_built(self) -> CoreSchema: + if self._built_memo is not None: + return self._built_memo + + if self._attempt_rebuild: + schema = self._attempt_rebuild() + if schema is not None: + self._built_memo = schema + return schema + raise PydanticUserError(self._error_message, code=self._code) + + def rebuild(self) -> CoreSchema | None: + self._built_memo = None + if self._attempt_rebuild: + schema = self._attempt_rebuild() + if schema is not None: + return schema + else: + raise PydanticUserError(self._error_message, code=self._code) + return None + + +class MockValSer(Generic[ValSer]): + """Mocker for `pydantic_core.SchemaValidator` or `pydantic_core.SchemaSerializer` which optionally attempts to + rebuild the thing it's mocking when one of its methods is accessed and raises an error if that fails. + """ + + __slots__ = '_error_message', '_code', '_val_or_ser', '_attempt_rebuild' + + def __init__( + self, + error_message: str, + *, + code: PydanticErrorCodes, + val_or_ser: Literal['validator', 'serializer'], + attempt_rebuild: Callable[[], ValSer | None] | None = None, + ) -> None: + self._error_message = error_message + self._val_or_ser = SchemaValidator if val_or_ser == 'validator' else SchemaSerializer + self._code: PydanticErrorCodes = code + self._attempt_rebuild = attempt_rebuild + + def __getattr__(self, item: str) -> None: + __tracebackhide__ = True + if self._attempt_rebuild: + val_ser = self._attempt_rebuild() + if val_ser is not None: + return getattr(val_ser, item) + + # raise an AttributeError if `item` doesn't exist + getattr(self._val_or_ser, item) + raise PydanticUserError(self._error_message, code=self._code) + + def rebuild(self) -> ValSer | None: + if self._attempt_rebuild: + val_ser = self._attempt_rebuild() + if val_ser is not None: + return val_ser + else: + raise PydanticUserError(self._error_message, code=self._code) + return None + + +def set_type_adapter_mocks(adapter: TypeAdapter) -> None: + """Set `core_schema`, `validator` and `serializer` to mock core types on a type adapter instance. + + Args: + adapter: The type adapter instance to set the mocks on + """ + type_repr = str(adapter._type) + undefined_type_error_message = ( + f'`TypeAdapter[{type_repr}]` is not fully defined; you should define `{type_repr}` and all referenced types,' + f' then call `.rebuild()` on the instance.' + ) + + def attempt_rebuild_fn(attr_fn: Callable[[TypeAdapter], T]) -> Callable[[], T | None]: + def handler() -> T | None: + if adapter.rebuild(raise_errors=False, _parent_namespace_depth=5) is not False: + return attr_fn(adapter) + return None + + return handler + + adapter.core_schema = MockCoreSchema( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.core_schema), + ) + adapter.validator = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='validator', + attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.validator), + ) + adapter.serializer = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='serializer', + attempt_rebuild=attempt_rebuild_fn(lambda ta: ta.serializer), + ) + + +def set_model_mocks(cls: type[BaseModel], undefined_name: str = 'all referenced types') -> None: + """Set `__pydantic_core_schema__`, `__pydantic_validator__` and `__pydantic_serializer__` to mock core types on a model. + + Args: + cls: The model class to set the mocks on + undefined_name: Name of the undefined thing, used in error messages + """ + undefined_type_error_message = ( + f'`{cls.__name__}` is not fully defined; you should define {undefined_name},' + f' then call `{cls.__name__}.model_rebuild()`.' + ) + + def attempt_rebuild_fn(attr_fn: Callable[[type[BaseModel]], T]) -> Callable[[], T | None]: + def handler() -> T | None: + if cls.model_rebuild(raise_errors=False, _parent_namespace_depth=5) is not False: + return attr_fn(cls) + return None + + return handler + + cls.__pydantic_core_schema__ = MockCoreSchema( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__), + ) + cls.__pydantic_validator__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='validator', + attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__), + ) + cls.__pydantic_serializer__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='serializer', + attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__), + ) + + +def set_dataclass_mocks(cls: type[PydanticDataclass], undefined_name: str = 'all referenced types') -> None: + """Set `__pydantic_validator__` and `__pydantic_serializer__` to `MockValSer`s on a dataclass. + + Args: + cls: The model class to set the mocks on + undefined_name: Name of the undefined thing, used in error messages + """ + from ..dataclasses import rebuild_dataclass + + undefined_type_error_message = ( + f'`{cls.__name__}` is not fully defined; you should define {undefined_name},' + f' then call `pydantic.dataclasses.rebuild_dataclass({cls.__name__})`.' + ) + + def attempt_rebuild_fn(attr_fn: Callable[[type[PydanticDataclass]], T]) -> Callable[[], T | None]: + def handler() -> T | None: + if rebuild_dataclass(cls, raise_errors=False, _parent_namespace_depth=5) is not False: + return attr_fn(cls) + return None + + return handler + + cls.__pydantic_core_schema__ = MockCoreSchema( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_core_schema__), + ) + cls.__pydantic_validator__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='validator', + attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_validator__), + ) + cls.__pydantic_serializer__ = MockValSer( # pyright: ignore[reportAttributeAccessIssue] + undefined_type_error_message, + code='class-not-fully-defined', + val_or_ser='serializer', + attempt_rebuild=attempt_rebuild_fn(lambda c: c.__pydantic_serializer__), + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_model_construction.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_model_construction.py new file mode 100644 index 0000000000000000000000000000000000000000..b7945442fb20d23ae16a2603130ffe566fb0989f --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_model_construction.py @@ -0,0 +1,868 @@ +"""Private logic for creating models.""" + +from __future__ import annotations as _annotations + +import operator +import sys +import typing +import warnings +import weakref +from abc import ABCMeta +from functools import cache, partial, wraps +from types import FunctionType +from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, cast + +from pydantic_core import PydanticUndefined, SchemaSerializer +from typing_extensions import TypeAliasType, dataclass_transform, deprecated, get_args, get_origin +from typing_inspection import typing_objects + +from ..errors import PydanticUndefinedAnnotation, PydanticUserError +from ..plugin._schema_validator import create_schema_validator +from ..warnings import GenericBeforeBaseModelWarning, PydanticDeprecatedSince20 +from ._config import ConfigWrapper +from ._decorators import DecoratorInfos, PydanticDescriptorProxy, get_attribute_from_bases, unwrap_wrapped_function +from ._fields import collect_model_fields, is_valid_field_name, is_valid_privateattr_name, rebuild_model_fields +from ._generate_schema import GenerateSchema, InvalidSchemaError +from ._generics import PydanticGenericMetadata, get_model_typevars_map +from ._import_utils import import_cached_base_model, import_cached_field_info +from ._mock_val_ser import set_model_mocks +from ._namespace_utils import NsResolver +from ._signature import generate_pydantic_signature +from ._typing_extra import ( + _make_forward_ref, + eval_type_backport, + is_classvar_annotation, + parent_frame_namespace, +) +from ._utils import LazyClassAttribute, SafeGetItemProxy + +if TYPE_CHECKING: + from ..fields import Field as PydanticModelField + from ..fields import FieldInfo, ModelPrivateAttr + from ..fields import PrivateAttr as PydanticModelPrivateAttr + from ..main import BaseModel + from ._fields import PydanticExtraInfo +else: + PydanticModelField = object() + PydanticModelPrivateAttr = object() + +object_setattr = object.__setattr__ + + +class _ModelNamespaceDict(dict): + """A dictionary subclass that intercepts attribute setting on model classes and + warns about overriding of decorators. + """ + + def __setitem__(self, k: str, v: object) -> None: + existing: Any = self.get(k, None) + if existing and v is not existing and isinstance(existing, PydanticDescriptorProxy): + warnings.warn( + f'`{k}` overrides an existing Pydantic `{existing.decorator_info.decorator_repr}` decorator', + stacklevel=2, + ) + + return super().__setitem__(k, v) + + +def NoInitField( + *, + init: Literal[False] = False, +) -> Any: + """Only for typing purposes. Used as default value of `__pydantic_fields_set__`, + `__pydantic_extra__`, `__pydantic_private__`, so they could be ignored when + synthesizing the `__init__` signature. + """ + + +# For ModelMetaclass.register(): +_T = TypeVar('_T') + + +@dataclass_transform(kw_only_default=True, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr, NoInitField)) +class ModelMetaclass(ABCMeta): + def __new__( + mcs, + cls_name: str, + bases: tuple[type[Any], ...], + namespace: dict[str, Any], + __pydantic_generic_metadata__: PydanticGenericMetadata | None = None, + __pydantic_reset_parent_namespace__: bool = True, + _create_model_module: str | None = None, + **kwargs: Any, + ) -> type: + """Metaclass for creating Pydantic models. + + Args: + cls_name: The name of the class to be created. + bases: The base classes of the class to be created. + namespace: The attribute dictionary of the class to be created. + __pydantic_generic_metadata__: Metadata for generic models. + __pydantic_reset_parent_namespace__: Reset parent namespace. + _create_model_module: The module of the class to be created, if created by `create_model`. + **kwargs: Catch-all for any other keyword arguments. + + Returns: + The new class created by the metaclass. + """ + # Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we rely on the fact + # that `BaseModel` itself won't have any bases, but any subclass of it will, to determine whether the `__new__` + # call we're in the middle of is for the `BaseModel` class. + if bases: + raw_annotations: dict[str, Any] + if sys.version_info >= (3, 14): + if ( + '__annotations__' in namespace + ): # `from __future__ import annotations` was used in the model's module + raw_annotations = namespace['__annotations__'] + else: + # See https://docs.python.org/3.14/library/annotationlib.html#using-annotations-in-a-metaclass: + from annotationlib import Format, call_annotate_function, get_annotate_from_class_namespace + + if annotate := get_annotate_from_class_namespace(namespace): + raw_annotations = call_annotate_function(annotate, format=Format.FORWARDREF) + else: + raw_annotations = {} + else: + raw_annotations = namespace.get('__annotations__', {}) + + base_field_names, class_vars, base_private_attributes = mcs._collect_bases_data(bases) + + config_wrapper = ConfigWrapper.for_model(bases, namespace, raw_annotations, kwargs) + namespace['model_config'] = config_wrapper.config_dict + private_attributes = inspect_namespace( + namespace, raw_annotations, config_wrapper.ignored_types, class_vars, base_field_names + ) + if private_attributes or base_private_attributes: + original_model_post_init = get_model_post_init(namespace, bases) + if original_model_post_init is not None: + # if there are private attributes and a model_post_init function, we handle both + + @wraps(original_model_post_init) + def wrapped_model_post_init(self: BaseModel, context: Any, /) -> None: + """We need to both initialize private attributes and call the user-defined model_post_init + method. + """ + init_private_attributes(self, context) + original_model_post_init(self, context) + + namespace['model_post_init'] = wrapped_model_post_init + else: + namespace['model_post_init'] = init_private_attributes + + namespace['__class_vars__'] = class_vars + namespace['__private_attributes__'] = {**base_private_attributes, **private_attributes} + + cls = cast('type[BaseModel]', super().__new__(mcs, cls_name, bases, namespace, **kwargs)) + BaseModel_ = import_cached_base_model() + + mro = cls.__mro__ + if Generic in mro and mro.index(Generic) < mro.index(BaseModel_): + warnings.warn( + GenericBeforeBaseModelWarning( + 'Classes should inherit from `BaseModel` before generic classes (e.g. `typing.Generic[T]`) ' + 'for pydantic generics to work properly.' + ), + stacklevel=2, + ) + + cls.__pydantic_custom_init__ = not getattr(cls.__init__, '__pydantic_base_init__', False) + cls.__pydantic_post_init__ = ( + None if cls.model_post_init is BaseModel_.model_post_init else 'model_post_init' + ) + + cls.__pydantic_setattr_handlers__ = {} + + cls.__pydantic_decorators__ = DecoratorInfos.build(cls, replace_wrapped_methods=True) + cls.__pydantic_decorators__.update_from_config(config_wrapper) + + # Use the getattr below to grab the __parameters__ from the `typing.Generic` parent class + if __pydantic_generic_metadata__: + cls.__pydantic_generic_metadata__ = __pydantic_generic_metadata__ + else: + parent_parameters = getattr(cls, '__pydantic_generic_metadata__', {}).get('parameters', ()) + parameters = getattr(cls, '__parameters__', None) or parent_parameters + if parameters and parent_parameters and not all(x in parameters for x in parent_parameters): + from ..root_model import RootModelRootType + + missing_parameters = tuple(x for x in parameters if x not in parent_parameters) + if RootModelRootType in parent_parameters and RootModelRootType not in parameters: + # This is a special case where the user has subclassed RootModel, but has not parameterized + # RootModel with the generic type identifiers being used. Ex: + # class MyModel(RootModel, Generic[T]): + # root: T + # Should instead just be: + # class MyModel(RootModel[T]): + # root: T + parameters_str = ', '.join([x.__name__ for x in missing_parameters]) + error_message = ( + f'{cls.__name__} is a subclass of `RootModel`, but does not include the generic type identifier(s) ' + f'{parameters_str} in its parameters. ' + f'You should parametrize RootModel directly, e.g., `class {cls.__name__}(RootModel[{parameters_str}]): ...`.' + ) + else: + combined_parameters = parent_parameters + missing_parameters + parameters_str = ', '.join([str(x) for x in combined_parameters]) + generic_type_label = f'typing.Generic[{parameters_str}]' + error_message = ( + f'All parameters must be present on typing.Generic;' + f' you should inherit from {generic_type_label}.' + ) + if Generic not in bases: # pragma: no cover + # We raise an error here not because it is desirable, but because some cases are mishandled. + # It would be nice to remove this error and still have things behave as expected, it's just + # challenging because we are using a custom `__class_getitem__` to parametrize generic models, + # and not returning a typing._GenericAlias from it. + bases_str = ', '.join([x.__name__ for x in bases] + [generic_type_label]) + error_message += ( + f' Note: `typing.Generic` must go last: `class {cls.__name__}({bases_str}): ...`)' + ) + raise TypeError(error_message) + + cls.__pydantic_generic_metadata__ = { + 'origin': None, + 'args': (), + 'parameters': parameters, + } + + cls.__pydantic_complete__ = False # Ensure this specific class gets completed + + # preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487 + # for attributes not in `new_namespace` (e.g. private attributes) + for name, obj in private_attributes.items(): + obj.__set_name__(cls, name) + + if __pydantic_reset_parent_namespace__: + cls.__pydantic_parent_namespace__ = build_lenient_weakvaluedict(parent_frame_namespace()) + parent_namespace: dict[str, Any] | None = getattr(cls, '__pydantic_parent_namespace__', None) + if isinstance(parent_namespace, dict): + parent_namespace = unpack_lenient_weakvaluedict(parent_namespace) + + ns_resolver = NsResolver(parent_namespace=parent_namespace) + + set_model_fields(cls, config_wrapper=config_wrapper, ns_resolver=ns_resolver) + + # This is also set in `complete_model_class()`, after schema gen because they are recreated. + # We set them here as well for backwards compatibility: + cls.__pydantic_computed_fields__ = { + k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items() + } + + if config_wrapper.defer_build: + set_model_mocks(cls) + else: + # Any operation that requires accessing the field infos instances should be put inside + # `complete_model_class()`: + complete_model_class( + cls, + config_wrapper, + ns_resolver, + raise_errors=False, + create_model_module=_create_model_module, + ) + + if config_wrapper.frozen and '__hash__' not in namespace: + set_default_hash_func(cls, bases) + + # using super(cls, cls) on the next line ensures we only call the parent class's __pydantic_init_subclass__ + # I believe the `type: ignore` is only necessary because mypy doesn't realize that this code branch is + # only hit for _proper_ subclasses of BaseModel + super(cls, cls).__pydantic_init_subclass__(**kwargs) # type: ignore[misc] + return cls + else: + # These are instance variables, but have been assigned to `NoInitField` to trick the type checker. + for instance_slot in '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__': + namespace.pop( + instance_slot, + None, # In case the metaclass is used with a class other than `BaseModel`. + ) + namespace.get('__annotations__', {}).clear() + return super().__new__(mcs, cls_name, bases, namespace, **kwargs) + + if not TYPE_CHECKING: # pragma: no branch + # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access + + def __getattr__(self, item: str) -> Any: + """This is necessary to keep attribute access working for class attribute access.""" + private_attributes = self.__dict__.get('__private_attributes__') + if private_attributes and item in private_attributes: + return private_attributes[item] + raise AttributeError(item) + + @classmethod + def __prepare__(cls, *args: Any, **kwargs: Any) -> dict[str, object]: + return _ModelNamespaceDict() + + # Due to performance and memory issues, in the ABCMeta.__subclasscheck__ implementation, we don't support + # registered virtual subclasses. See https://github.com/python/cpython/issues/92810#issuecomment-2762454345. + # This may change once CPython is fixed (possibly in 3.15), in which case we should conditionally + # define `register()`. + def register(self, subclass: type[_T]) -> type[_T]: + warnings.warn( + f"For performance reasons, virtual subclasses registered using '{self.__qualname__}.register()' " + "are not supported in 'isinstance()' and 'issubclass()' checks.", + stacklevel=2, + ) + return super().register(subclass) + + __instancecheck__ = type.__instancecheck__ # pyright: ignore[reportAssignmentType] + __subclasscheck__ = type.__subclasscheck__ # pyright: ignore[reportAssignmentType] + + @staticmethod + def _collect_bases_data(bases: tuple[type[Any], ...]) -> tuple[set[str], set[str], dict[str, ModelPrivateAttr]]: + BaseModel = import_cached_base_model() + + field_names: set[str] = set() + class_vars: set[str] = set() + private_attributes: dict[str, ModelPrivateAttr] = {} + for base in bases: + if issubclass(base, BaseModel) and base is not BaseModel: + # model_fields might not be defined yet in the case of generics, so we use getattr here: + field_names.update(getattr(base, '__pydantic_fields__', {}).keys()) + class_vars.update(base.__class_vars__) + private_attributes.update(base.__private_attributes__) + return field_names, class_vars, private_attributes + + @property + @deprecated( + 'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.', category=None + ) + def __fields__(self) -> dict[str, FieldInfo]: + warnings.warn( + 'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.', + PydanticDeprecatedSince20, + stacklevel=2, + ) + return getattr(self, '__pydantic_fields__', {}) + + @property + def __pydantic_fields_complete__(self) -> bool: + """Whether the fields were successfully collected (i.e. type hints were successfully resolved). + + This is a private attribute, not meant to be used outside Pydantic. + """ + if '__pydantic_fields__' not in self.__dict__: + return False + + field_infos = cast('dict[str, FieldInfo]', self.__pydantic_fields__) # pyright: ignore[reportAttributeAccessIssue] + + pydantic_extra_info = cast('PydanticExtraInfo | None', self.__pydantic_extra_info__) # pyright: ignore[reportAttributeAccessIssue] + if pydantic_extra_info is not None: + extra_complete = pydantic_extra_info.complete + else: + extra_complete = True + + return all(field_info._complete for field_info in field_infos.values()) and extra_complete + + def __dir__(self) -> list[str]: + attributes = list(super().__dir__()) + if '__fields__' in attributes: + attributes.remove('__fields__') + return attributes + + +def init_private_attributes(self: BaseModel, context: Any, /) -> None: + """This function is meant to behave like a BaseModel method to initialize private attributes. + + It takes context as an argument since that's what pydantic-core passes when calling it. + + Args: + self: The BaseModel instance. + context: The context. + """ + if getattr(self, '__pydantic_private__', None) is None: + pydantic_private = {} + for name, private_attr in self.__private_attributes__.items(): + # Avoid needlessly creating a new dict for the validated data: + if private_attr.default_factory_takes_validated_data: + default = private_attr.get_default( + call_default_factory=True, validated_data={**self.__dict__, **pydantic_private} + ) + else: + default = private_attr.get_default(call_default_factory=True) + if default is not PydanticUndefined: + pydantic_private[name] = default + object_setattr(self, '__pydantic_private__', pydantic_private) + + +def get_model_post_init(namespace: dict[str, Any], bases: tuple[type[Any], ...]) -> Callable[..., Any] | None: + """Get the `model_post_init` method from the namespace or the class bases, or `None` if not defined.""" + if 'model_post_init' in namespace: + return namespace['model_post_init'] + + BaseModel = import_cached_base_model() + + model_post_init = get_attribute_from_bases(bases, 'model_post_init') + if model_post_init is not BaseModel.model_post_init: + return model_post_init + + +def inspect_namespace( # noqa C901 + namespace: dict[str, Any], + raw_annotations: dict[str, Any], + ignored_types: tuple[type[Any], ...], + base_class_vars: set[str], + base_class_fields: set[str], +) -> dict[str, ModelPrivateAttr]: + """Iterate over the namespace and: + * gather private attributes + * check for items which look like fields but are not (e.g. have no annotation) and warn. + + Args: + namespace: The attribute dictionary of the class to be created. + raw_annotations: The (non-evaluated) annotations of the model. + ignored_types: A tuple of ignore types. + base_class_vars: A set of base class class variables. + base_class_fields: A set of base class fields. + + Returns: + A dict containing private attributes info. + + Raises: + TypeError: If there is a `__root__` field in model. + NameError: If private attribute name is invalid. + PydanticUserError: + - If a field does not have a type annotation. + - If a field on base class was overridden by a non-annotated attribute. + """ + from ..fields import ModelPrivateAttr, PrivateAttr + + FieldInfo = import_cached_field_info() + + all_ignored_types = ignored_types + default_ignored_types() + + private_attributes: dict[str, ModelPrivateAttr] = {} + + if '__root__' in raw_annotations or '__root__' in namespace: + raise TypeError("To define root models, use `pydantic.RootModel` rather than a field called '__root__'") + + ignored_names: set[str] = set() + for var_name, value in list(namespace.items()): + if var_name == 'model_config' or var_name == '__pydantic_extra__': + continue + elif ( + isinstance(value, type) + and value.__module__ == namespace['__module__'] + and '__qualname__' in namespace + and value.__qualname__.startswith(f'{namespace["__qualname__"]}.') + ): + # `value` is a nested type defined in this namespace; don't error + continue + elif isinstance(value, all_ignored_types) or value.__class__.__module__ == 'functools': + ignored_names.add(var_name) + continue + elif isinstance(value, ModelPrivateAttr): + if var_name.startswith('__'): + raise NameError( + 'Private attributes must not use dunder names;' + f' use a single underscore prefix instead of {var_name!r}.' + ) + elif is_valid_field_name(var_name): + raise NameError( + 'Private attributes must not use valid field names;' + f' use sunder names, e.g. {"_" + var_name!r} instead of {var_name!r}.' + ) + private_attributes[var_name] = value + del namespace[var_name] + elif isinstance(value, FieldInfo) and not is_valid_field_name(var_name): + suggested_name = var_name.lstrip('_') or 'my_field' # don't suggest '' for all-underscore name + raise NameError( + f'Fields must not use names with leading underscores;' + f' e.g., use {suggested_name!r} instead of {var_name!r}.' + ) + + elif var_name.startswith('__'): + continue + elif is_valid_privateattr_name(var_name): + if var_name not in raw_annotations or not is_classvar_annotation(raw_annotations[var_name]): + private_attributes[var_name] = cast(ModelPrivateAttr, PrivateAttr(default=value)) + del namespace[var_name] + elif var_name in base_class_vars: + continue + elif var_name not in raw_annotations: + if var_name in base_class_fields: + raise PydanticUserError( + f'Field {var_name!r} defined on a base class was overridden by a non-annotated attribute. ' + f'All field definitions, including overrides, require a type annotation.', + code='model-field-overridden', + ) + elif isinstance(value, FieldInfo): + raise PydanticUserError( + f'Field {var_name!r} requires a type annotation', code='model-field-missing-annotation' + ) + else: + raise PydanticUserError( + f'A non-annotated attribute was detected: `{var_name} = {value!r}`. All model fields require a ' + f'type annotation; if `{var_name}` is not meant to be a field, you may be able to resolve this ' + f"error by annotating it as a `ClassVar` or updating `model_config['ignored_types']`.", + code='model-field-missing-annotation', + ) + + for ann_name, ann_type in raw_annotations.items(): + if ( + is_valid_privateattr_name(ann_name) + and ann_name not in private_attributes + and ann_name not in ignored_names + # This condition can be a false negative when `ann_type` is stringified, + # but it is handled in most cases in `set_model_fields`: + and not is_classvar_annotation(ann_type) + and ann_type not in all_ignored_types + and getattr(ann_type, '__module__', None) != 'functools' + ): + if isinstance(ann_type, str): + # Walking up the frames to get the module namespace where the model is defined + # (as the model class wasn't created yet, we unfortunately can't use `cls.__module__`): + frame = sys._getframe(2) + if frame is not None: + try: + ann_type = eval_type_backport( + _make_forward_ref(ann_type, is_argument=False, is_class=True), + globalns=frame.f_globals, + localns=frame.f_locals, + ) + except (NameError, TypeError): + pass + + if typing_objects.is_annotated(get_origin(ann_type)): + _, *metadata = get_args(ann_type) + private_attr = next((v for v in metadata if isinstance(v, ModelPrivateAttr)), None) + if private_attr is not None: + private_attributes[ann_name] = private_attr + continue + private_attributes[ann_name] = PrivateAttr() + + return private_attributes + + +def set_default_hash_func(cls: type[BaseModel], bases: tuple[type[Any], ...]) -> None: + base_hash_func = get_attribute_from_bases(bases, '__hash__') + new_hash_func = make_hash_func(cls) + if base_hash_func in {None, object.__hash__} or getattr(base_hash_func, '__code__', None) == new_hash_func.__code__: + # If `__hash__` is some default, we generate a hash function. + # It will be `None` if not overridden from BaseModel. + # It may be `object.__hash__` if there is another + # parent class earlier in the bases which doesn't override `__hash__` (e.g. `typing.Generic`). + # It may be a value set by `set_default_hash_func` if `cls` is a subclass of another frozen model. + # In the last case we still need a new hash function to account for new `model_fields`. + cls.__hash__ = new_hash_func + + +def make_hash_func(cls: type[BaseModel]) -> Any: + getter = operator.itemgetter(*cls.__pydantic_fields__.keys()) if cls.__pydantic_fields__ else lambda _: 0 + + def hash_func(self: Any) -> int: + try: + return hash(getter(self.__dict__)) + except KeyError: + # In rare cases (such as when using the deprecated copy method), the __dict__ may not contain + # all model fields, which is how we can get here. + # getter(self.__dict__) is much faster than any 'safe' method that accounts for missing keys, + # and wrapping it in a `try` doesn't slow things down much in the common case. + return hash(getter(SafeGetItemProxy(self.__dict__))) + + return hash_func + + +def set_model_fields( + cls: type[BaseModel], + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver, +) -> None: + """Collect and set `cls.__pydantic_fields__` and `cls.__class_vars__`. + + Args: + cls: BaseModel or dataclass. + config_wrapper: The config wrapper instance. + ns_resolver: Namespace resolver to use when getting model annotations. + """ + typevars_map = get_model_typevars_map(cls) + fields, pydantic_extra_info, class_vars = collect_model_fields( + cls, config_wrapper, ns_resolver, typevars_map=typevars_map + ) + + cls.__pydantic_fields__ = fields + cls.__pydantic_extra_info__ = pydantic_extra_info + cls.__class_vars__.update(class_vars) + + for k in class_vars: + # Class vars should not be private attributes + # We remove them _here_ and not earlier because we rely on inspecting the class to determine its classvars, + # but private attributes are determined by inspecting the namespace _prior_ to class creation. + # In the case that a classvar with a leading-'_' is defined via a ForwardRef (e.g., when using + # `__future__.annotations`), we want to remove the private attribute which was detected _before_ we knew it + # evaluated to a classvar + + value = cls.__private_attributes__.pop(k, None) + if value is not None and value.default is not PydanticUndefined: + setattr(cls, k, value.default) + + +def complete_model_class( + cls: type[BaseModel], + config_wrapper: ConfigWrapper, + ns_resolver: NsResolver, + *, + raise_errors: bool = True, + call_on_complete_hook: bool = True, + create_model_module: str | None = None, + is_force_rebuild: bool = False, +) -> bool: + """Finish building a model class. + + This logic must be called after class has been created since validation functions must be bound + and `get_type_hints` requires a class object. + + Args: + cls: BaseModel or dataclass. + config_wrapper: The config wrapper instance. + ns_resolver: The namespace resolver instance to use during schema building. + raise_errors: Whether to raise errors. + call_on_complete_hook: Whether to call the `__pydantic_on_complete__` hook. + create_model_module: The module of the class to be created, if created by `create_model`. + is_force_rebuild: Whether the model is being force-rebuilt (if True, pre-built serializers and + validators are not used, to avoid stale references). + + Returns: + `True` if the model is successfully completed, else `False`. + + Raises: + PydanticUndefinedAnnotation: If PydanticUndefinedAnnotation occurs in __get_pydantic_core_schema__ + and `raise_errors=True`. + """ + typevars_map = get_model_typevars_map(cls) + + if not cls.__pydantic_fields_complete__: + # Note: when coming from `ModelMetaclass.__new__()`, this results in fields being built twice. + # We do so a second time here so that we can get the ``NameError`` for the specific undefined annotation. + # Alternatively, we could let `GenerateSchema()` raise the error, but there are cases where incomplete + # fields are inherited in `collect_model_fields()` and can actually have their annotation resolved in the + # generate schema process. As we want to avoid having `__pydantic_fields_complete__` set to `False` + # when `__pydantic_complete__` is `True`, we rebuild here: + try: + cls.__pydantic_fields__, cls.__pydantic_extra_info__ = rebuild_model_fields( + cls, + config_wrapper=config_wrapper, + ns_resolver=ns_resolver, + typevars_map=typevars_map, + ) + except NameError as e: + exc = PydanticUndefinedAnnotation.from_name_error(e) + set_model_mocks(cls, f'`{exc.name}`') + if raise_errors: + raise exc from e + + if not raise_errors and not cls.__pydantic_fields_complete__: + # No need to continue with schema gen, it is guaranteed to fail + return False + + assert cls.__pydantic_fields_complete__ + + gen_schema = GenerateSchema( + config_wrapper, + ns_resolver, + typevars_map, + ) + + try: + schema = gen_schema.generate_schema(cls) + except PydanticUndefinedAnnotation as e: + if raise_errors: + raise + set_model_mocks(cls, f'`{e.name}`') + return False + + core_config = config_wrapper.core_config(title=cls.__name__) + + try: + schema = gen_schema.clean_schema(schema) + except InvalidSchemaError: + set_model_mocks(cls) + return False + + # This needs to happen *after* model schema generation, as the return types + # of the properties are evaluated and the `ComputedFieldInfo` are recreated: + cls.__pydantic_computed_fields__ = {k: v.info for k, v in cls.__pydantic_decorators__.computed_fields.items()} + + set_deprecated_descriptors(cls) + + cls.__pydantic_core_schema__ = schema + + cls.__pydantic_validator__ = create_schema_validator( + schema, + cls, + create_model_module or cls.__module__, + cls.__qualname__, + 'create_model' if create_model_module else 'BaseModel', + core_config, + config_wrapper.plugin_settings, + _use_prebuilt=not is_force_rebuild, + ) + cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config, _use_prebuilt=not is_force_rebuild) + + # set __signature__ attr only for model class, but not for its instances + # (because instances can define `__call__`, and `inspect.signature` shouldn't + # use the `__signature__` attribute and instead generate from `__call__`). + cls.__signature__ = LazyClassAttribute( + '__signature__', + partial( + generate_pydantic_signature, + init=cls.__init__, + fields=cls.__pydantic_fields__, + validate_by_name=config_wrapper.validate_by_name, + extra=config_wrapper.extra, + ), + ) + + cls.__pydantic_complete__ = True + + if call_on_complete_hook: + cls.__pydantic_on_complete__() + + return True + + +def set_deprecated_descriptors(cls: type[BaseModel]) -> None: + """Set data descriptors on the class for deprecated fields.""" + for field, field_info in cls.__pydantic_fields__.items(): + if (msg := field_info.deprecation_message) is not None: + desc = _DeprecatedFieldDescriptor(msg) + desc.__set_name__(cls, field) + setattr(cls, field, desc) + + for field, computed_field_info in cls.__pydantic_computed_fields__.items(): + if ( + (msg := computed_field_info.deprecation_message) is not None + # Avoid having two warnings emitted: + and not hasattr(unwrap_wrapped_function(computed_field_info.wrapped_property), '__deprecated__') + ): + desc = _DeprecatedFieldDescriptor(msg, computed_field_info.wrapped_property) + desc.__set_name__(cls, field) + setattr(cls, field, desc) + + +class _DeprecatedFieldDescriptor: + """Read-only data descriptor used to emit a runtime deprecation warning before accessing a deprecated field. + + Attributes: + msg: The deprecation message to be emitted. + wrapped_property: The property instance if the deprecated field is a computed field, or `None`. + field_name: The name of the field being deprecated. + """ + + field_name: str + + def __init__(self, msg: str, wrapped_property: property | None = None) -> None: + self.msg = msg + self.wrapped_property = wrapped_property + + def __set_name__(self, cls: type[BaseModel], name: str) -> None: + self.field_name = name + + def __get__(self, obj: BaseModel | None, obj_type: type[BaseModel] | None = None) -> Any: + if obj is None: + if self.wrapped_property is not None: + return self.wrapped_property.__get__(None, obj_type) + raise AttributeError(self.field_name) + + warnings.warn(self.msg, DeprecationWarning, stacklevel=2) + + if self.wrapped_property is not None: + return self.wrapped_property.__get__(obj, obj_type) + return obj.__dict__[self.field_name] + + # Defined to make it a data descriptor and take precedence over the instance's dictionary. + # Note that it will not be called when setting a value on a model instance + # as `BaseModel.__setattr__` is defined and takes priority. + def __set__(self, obj: Any, value: Any) -> NoReturn: + raise AttributeError(self.field_name) + + +class _PydanticWeakRef: + """Wrapper for `weakref.ref` that enables `pickle` serialization. + + Cloudpickle fails to serialize weakref.ref objects due to an arcane error related to + to abstract base classes (`abc.ABC`). This class works around the issue by wrapping + `weakref.ref` instead of subclassing it. + + See https://github.com/pydantic/pydantic/issues/6763 for context. + + Semantics: + - If not pickled, behaves the same as a `weakref.ref`. + - If pickled along with the referenced object, the same `weakref.ref` behavior + will be maintained between them after unpickling. + - If pickled without the referenced object, after unpickling the underlying + reference will be cleared (`__call__` will always return `None`). + """ + + def __init__(self, obj: Any): + if obj is None: + # The object will be `None` upon deserialization if the serialized weakref + # had lost its underlying object. + self._wr = None + else: + self._wr = weakref.ref(obj) + + def __call__(self) -> Any: + if self._wr is None: + return None + else: + return self._wr() + + def __reduce__(self) -> tuple[Callable, tuple[weakref.ReferenceType | None]]: + return _PydanticWeakRef, (self(),) + + +def build_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None: + """Takes an input dictionary, and produces a new value that (invertibly) replaces the values with weakrefs. + + We can't just use a WeakValueDictionary because many types (including int, str, etc.) can't be stored as values + in a WeakValueDictionary. + + The `unpack_lenient_weakvaluedict` function can be used to reverse this operation. + """ + if d is None: + return None + result = {} + for k, v in d.items(): + try: + proxy = _PydanticWeakRef(v) + except TypeError: + proxy = v + result[k] = proxy + return result + + +def unpack_lenient_weakvaluedict(d: dict[str, Any] | None) -> dict[str, Any] | None: + """Inverts the transform performed by `build_lenient_weakvaluedict`.""" + if d is None: + return None + + result = {} + for k, v in d.items(): + if isinstance(v, _PydanticWeakRef): + v = v() + if v is not None: + result[k] = v + else: + result[k] = v + return result + + +@cache +def default_ignored_types() -> tuple[type[Any], ...]: + from ..fields import ComputedFieldInfo + + ignored_types = [ + FunctionType, + property, + classmethod, + staticmethod, + PydanticDescriptorProxy, + ComputedFieldInfo, + TypeAliasType, # from `typing_extensions` + ] + + if sys.version_info >= (3, 12): + ignored_types.append(typing.TypeAliasType) + + return tuple(ignored_types) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_namespace_utils.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_namespace_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..af0cddb03d6700b5f292c7231b32d9a92a8472eb --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_namespace_utils.py @@ -0,0 +1,293 @@ +from __future__ import annotations + +import sys +from collections.abc import Generator, Iterator, Mapping +from contextlib import contextmanager +from functools import cached_property +from typing import Any, Callable, NamedTuple, TypeVar + +from typing_extensions import ParamSpec, TypeAlias, TypeAliasType, TypeVarTuple + +GlobalsNamespace: TypeAlias = 'dict[str, Any]' +"""A global namespace. + +In most cases, this is a reference to the `__dict__` attribute of a module. +This namespace type is expected as the `globals` argument during annotations evaluation. +""" + +MappingNamespace: TypeAlias = Mapping[str, Any] +"""Any kind of namespace. + +In most cases, this is a local namespace (e.g. the `__dict__` attribute of a class, +the [`f_locals`][frame.f_locals] attribute of a frame object, when dealing with types +defined inside functions). +This namespace type is expected as the `locals` argument during annotations evaluation. +""" + +_TypeVarLike: TypeAlias = 'TypeVar | ParamSpec | TypeVarTuple' + + +class NamespacesTuple(NamedTuple): + """A tuple of globals and locals to be used during annotations evaluation. + + This datastructure is defined as a named tuple so that it can easily be unpacked: + + ```python {lint="skip" test="skip"} + def eval_type(typ: type[Any], ns: NamespacesTuple) -> None: + return eval(typ, *ns) + ``` + """ + + globals: GlobalsNamespace + """The namespace to be used as the `globals` argument during annotations evaluation.""" + + locals: MappingNamespace + """The namespace to be used as the `locals` argument during annotations evaluation.""" + + +def get_module_ns_of(obj: Any) -> dict[str, Any]: + """Get the namespace of the module where the object is defined. + + Caution: this function does not return a copy of the module namespace, so the result + should not be mutated. The burden of enforcing this is on the caller. + """ + module_name = getattr(obj, '__module__', None) + if module_name: + try: + return sys.modules[module_name].__dict__ + except KeyError: + # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363 + return {} + return {} + + +# Note that this class is almost identical to `collections.ChainMap`, but need to enforce +# immutable mappings here: +class LazyLocalNamespace(Mapping[str, Any]): + """A lazily evaluated mapping, to be used as the `locals` argument during annotations evaluation. + + While the [`eval`][eval] function expects a mapping as the `locals` argument, it only + performs `__getitem__` calls. The [`Mapping`][collections.abc.Mapping] abstract base class + is fully implemented only for type checking purposes. + + Args: + *namespaces: The namespaces to consider, in ascending order of priority. + + Example: + ```python {lint="skip" test="skip"} + ns = LazyLocalNamespace({'a': 1, 'b': 2}, {'a': 3}) + ns['a'] + #> 3 + ns['b'] + #> 2 + ``` + """ + + def __init__(self, *namespaces: MappingNamespace) -> None: + self._namespaces = namespaces + + @cached_property + def data(self) -> dict[str, Any]: + return {k: v for ns in self._namespaces for k, v in ns.items()} + + def __len__(self) -> int: + return len(self.data) + + def __getitem__(self, key: str) -> Any: + return self.data[key] + + def __contains__(self, key: object) -> bool: + return key in self.data + + def __iter__(self) -> Iterator[str]: + return iter(self.data) + + +def ns_for_function(obj: Callable[..., Any], parent_namespace: MappingNamespace | None = None) -> NamespacesTuple: + """Return the global and local namespaces to be used when evaluating annotations for the provided function. + + The global namespace will be the `__dict__` attribute of the module the function was defined in. + The local namespace will contain the `__type_params__` introduced by PEP 695. + + Args: + obj: The object to use when building namespaces. + parent_namespace: Optional namespace to be added with the lowest priority in the local namespace. + If the passed function is a method, the `parent_namespace` will be the namespace of the class + the method is defined in. Thus, we also fetch type `__type_params__` from there (i.e. the + class-scoped type variables). + """ + locals_list: list[MappingNamespace] = [] + if parent_namespace is not None: + locals_list.append(parent_namespace) + + # Get the `__type_params__` attribute introduced by PEP 695. + # Note that the `typing._eval_type` function expects type params to be + # passed as a separate argument. However, internally, `_eval_type` calls + # `ForwardRef._evaluate` which will merge type params with the localns, + # essentially mimicking what we do here. + type_params: tuple[_TypeVarLike, ...] = getattr(obj, '__type_params__', ()) + if parent_namespace is not None: + # We also fetch type params from the parent namespace. If present, it probably + # means the function was defined in a class. This is to support the following: + # https://github.com/python/cpython/issues/124089. + type_params += parent_namespace.get('__type_params__', ()) + + locals_list.append({t.__name__: t for t in type_params}) + + # What about short-circuiting to `obj.__globals__`? + globalns = get_module_ns_of(obj) + + return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list)) + + +class NsResolver: + """A class responsible for the namespaces resolving logic for annotations evaluation. + + This class handles the namespace logic when evaluating annotations mainly for class objects. + + It holds a stack of classes that are being inspected during the core schema building, + and the `types_namespace` property exposes the globals and locals to be used for + type annotation evaluation. Additionally -- if no class is present in the stack -- a + fallback globals and locals can be provided using the `namespaces_tuple` argument + (this is useful when generating a schema for a simple annotation, e.g. when using + `TypeAdapter`). + + The namespace creation logic is unfortunately flawed in some cases, for backwards + compatibility reasons and to better support valid edge cases. See the description + for the `parent_namespace` argument and the example for more details. + + Args: + namespaces_tuple: The default globals and locals to use if no class is present + on the stack. This can be useful when using the `GenerateSchema` class + with `TypeAdapter`, where the "type" being analyzed is a simple annotation. + parent_namespace: An optional parent namespace that will be added to the locals + with the lowest priority. For a given class defined in a function, the locals + of this function are usually used as the parent namespace: + + ```python {lint="skip" test="skip"} + from pydantic import BaseModel + + def func() -> None: + SomeType = int + + class Model(BaseModel): + f: 'SomeType' + + # when collecting fields, an namespace resolver instance will be created + # this way: + # ns_resolver = NsResolver(parent_namespace={'SomeType': SomeType}) + ``` + + For backwards compatibility reasons and to support valid edge cases, this parent + namespace will be used for *every* type being pushed to the stack. In the future, + we might want to be smarter by only doing so when the type being pushed is defined + in the same module as the parent namespace. + + Example: + ```python {lint="skip" test="skip"} + ns_resolver = NsResolver( + parent_namespace={'fallback': 1}, + ) + + class Sub: + m: 'Model' + + class Model: + some_local = 1 + sub: Sub + + ns_resolver = NsResolver() + + # This is roughly what happens when we build a core schema for `Model`: + with ns_resolver.push(Model): + ns_resolver.types_namespace + #> NamespacesTuple({'Sub': Sub}, {'Model': Model, 'some_local': 1}) + # First thing to notice here, the model being pushed is added to the locals. + # Because `NsResolver` is being used during the model definition, it is not + # yet added to the globals. This is useful when resolving self-referencing annotations. + + with ns_resolver.push(Sub): + ns_resolver.types_namespace + #> NamespacesTuple({'Sub': Sub}, {'Sub': Sub, 'Model': Model}) + # Second thing to notice: `Sub` is present in both the globals and locals. + # This is not an issue, just that as described above, the model being pushed + # is added to the locals, but it happens to be present in the globals as well + # because it is already defined. + # Third thing to notice: `Model` is also added in locals. This is a backwards + # compatibility workaround that allows for `Sub` to be able to resolve `'Model'` + # correctly (as otherwise models would have to be rebuilt even though this + # doesn't look necessary). + ``` + """ + + def __init__( + self, + namespaces_tuple: NamespacesTuple | None = None, + parent_namespace: MappingNamespace | None = None, + ) -> None: + self._base_ns_tuple = namespaces_tuple or NamespacesTuple({}, {}) + self._parent_ns = parent_namespace + self._types_stack: list[type[Any] | TypeAliasType] = [] + + @cached_property + def types_namespace(self) -> NamespacesTuple: + """The current global and local namespaces to be used for annotations evaluation.""" + if not self._types_stack: + # TODO: should we merge the parent namespace here? + # This is relevant for TypeAdapter, where there are no types on the stack, and we might + # need access to the parent_ns. Right now, we sidestep this in `type_adapter.py` by passing + # locals to both parent_ns and the base_ns_tuple, but this is a bit hacky. + # we might consider something like: + # if self._parent_ns is not None: + # # Hacky workarounds, see class docstring: + # # An optional parent namespace that will be added to the locals with the lowest priority + # locals_list: list[MappingNamespace] = [self._parent_ns, self._base_ns_tuple.locals] + # return NamespacesTuple(self._base_ns_tuple.globals, LazyLocalNamespace(*locals_list)) + return self._base_ns_tuple + + typ = self._types_stack[-1] + + globalns = get_module_ns_of(typ) + + locals_list: list[MappingNamespace] = [] + # Hacky workarounds, see class docstring: + # An optional parent namespace that will be added to the locals with the lowest priority + if self._parent_ns is not None: + locals_list.append(self._parent_ns) + if len(self._types_stack) > 1: + first_type = self._types_stack[0] + locals_list.append({first_type.__name__: first_type}) + + # Adding `__type_params__` *before* `vars(typ)`, as the latter takes priority + # (see https://github.com/python/cpython/pull/120272). + # TODO `typ.__type_params__` when we drop support for Python 3.11: + type_params: tuple[_TypeVarLike, ...] = getattr(typ, '__type_params__', ()) + if type_params: + # Adding `__type_params__` is mostly useful for generic classes defined using + # PEP 695 syntax *and* using forward annotations (see the example in + # https://github.com/python/cpython/issues/114053). For TypeAliasType instances, + # it is way less common, but still required if using a string annotation in the alias + # value, e.g. `type A[T] = 'T'` (which is not necessary in most cases). + locals_list.append({t.__name__: t for t in type_params}) + + # TypeAliasType instances don't have a `__dict__` attribute, so the check + # is necessary: + if hasattr(typ, '__dict__'): + locals_list.append(vars(typ)) + + # The `len(self._types_stack) > 1` check above prevents this from being added twice: + locals_list.append({typ.__name__: typ}) + + return NamespacesTuple(globalns, LazyLocalNamespace(*locals_list)) + + @contextmanager + def push(self, typ: type[Any] | TypeAliasType, /) -> Generator[None]: + """Push a type to the stack.""" + self._types_stack.append(typ) + # Reset the cached property: + self.__dict__.pop('types_namespace', None) + try: + yield + finally: + self._types_stack.pop() + self.__dict__.pop('types_namespace', None) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_repr.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_repr.py new file mode 100644 index 0000000000000000000000000000000000000000..7e80a9c83db0f142fd5ae577a4388aa8ad4d0444 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_repr.py @@ -0,0 +1,124 @@ +"""Tools to provide pretty/human-readable display of objects.""" + +from __future__ import annotations as _annotations + +import types +from collections.abc import Callable, Collection, Generator, Iterable +from typing import TYPE_CHECKING, Any, ForwardRef, cast + +import typing_extensions +from typing_extensions import TypeAlias +from typing_inspection import typing_objects +from typing_inspection.introspection import is_union_origin + +from . import _typing_extra + +if TYPE_CHECKING: + # TODO remove type error comments when we drop support for Python 3.9 + ReprArgs: TypeAlias = Iterable[tuple[str | None, Any]] # pyright: ignore[reportGeneralTypeIssues] + RichReprResult: TypeAlias = Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]] # pyright: ignore[reportGeneralTypeIssues] + + +class PlainRepr(str): + """String class where repr doesn't include quotes. Useful with Representation when you want to return a string + representation of something that is valid (or pseudo-valid) python. + """ + + def __repr__(self) -> str: + return str(self) + + +class Representation: + # Mixin to provide `__str__`, `__repr__`, and `__pretty__` and `__rich_repr__` methods. + # `__pretty__` is used by [devtools](https://python-devtools.helpmanual.io/). + # `__rich_repr__` is used by [rich](https://rich.readthedocs.io/en/stable/pretty.html). + # (this is not a docstring to avoid adding a docstring to classes which inherit from Representation) + + __slots__ = () + + def __repr_args__(self) -> ReprArgs: + """Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden. + + Can either return: + * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]` + * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]` + """ + attrs_names = cast(Collection[str], self.__slots__) + if not attrs_names and hasattr(self, '__dict__'): + attrs_names = self.__dict__.keys() + attrs = ((s, getattr(self, s)) for s in attrs_names) + return [(a, v if v is not self else self.__repr_recursion__(v)) for a, v in attrs if v is not None] + + def __repr_name__(self) -> str: + """Name of the instance's class, used in __repr__.""" + return self.__class__.__name__ + + def __repr_recursion__(self, object: Any) -> str: + """Returns the string representation of a recursive object.""" + # This is copied over from the stdlib `pprint` module: + return f'' + + def __repr_str__(self, join_str: str) -> str: + return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__()) + + def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any]: + """Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.""" + yield self.__repr_name__() + '(' + yield 1 + for name, value in self.__repr_args__(): + if name is not None: + yield name + '=' + yield fmt(value) + yield ',' + yield 0 + yield -1 + yield ')' + + def __rich_repr__(self) -> RichReprResult: + """Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.""" + for name, field_repr in self.__repr_args__(): + if name is None: + yield field_repr + else: + yield name, field_repr + + def __str__(self) -> str: + return self.__repr_str__(' ') + + def __repr__(self) -> str: + return f'{self.__repr_name__()}({self.__repr_str__(", ")})' + + +def display_as_type(obj: Any) -> str: + """Pretty representation of a type, should be as close as possible to the original type definition string. + + Takes some logic from `typing._type_repr`. + """ + if isinstance(obj, (types.FunctionType, types.BuiltinFunctionType)): + return obj.__name__ + elif obj is ...: + return '...' + elif isinstance(obj, Representation): + return repr(obj) + elif isinstance(obj, ForwardRef) or typing_objects.is_typealiastype(obj): + return str(obj) + + if not isinstance(obj, (_typing_extra.typing_base, _typing_extra.WithArgsTypes, type)): + obj = obj.__class__ + + if is_union_origin(typing_extensions.get_origin(obj)): + args = ', '.join(map(display_as_type, typing_extensions.get_args(obj))) + return f'Union[{args}]' + elif isinstance(obj, _typing_extra.WithArgsTypes): + if typing_objects.is_literal(typing_extensions.get_origin(obj)): + args = ', '.join(map(repr, typing_extensions.get_args(obj))) + else: + args = ', '.join(map(display_as_type, typing_extensions.get_args(obj))) + try: + return f'{obj.__qualname__}[{args}]' + except AttributeError: + return str(obj).replace('typing.', '').replace('typing_extensions.', '') # handles TypeAliasType in 3.12 + elif isinstance(obj, type): + return obj.__qualname__ + else: + return repr(obj).replace('typing.', '').replace('typing_extensions.', '') diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_schema_gather.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_schema_gather.py new file mode 100644 index 0000000000000000000000000000000000000000..8e07b20e70990287a874c8cf5bd932fb57d30d45 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_schema_gather.py @@ -0,0 +1,212 @@ +# pyright: reportTypedDictNotRequiredAccess=false, reportGeneralTypeIssues=false, reportArgumentType=false, reportAttributeAccessIssue=false +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import TypedDict + +from pydantic_core.core_schema import ( + ComputedField, + CoreSchema, + DefinitionReferenceSchema, + SerSchema, + iter_union_choices, +) +from typing_extensions import TypeAlias + +AllSchemas: TypeAlias = 'CoreSchema | SerSchema | ComputedField' + + +class GatherResult(TypedDict): + """Schema traversing result.""" + + collected_references: dict[str, DefinitionReferenceSchema | None] + """The collected definition references. + + If a definition reference schema can be inlined, it means that there is + only one in the whole core schema. As such, it is stored as the value. + Otherwise, the value is set to `None`. + """ + + deferred_discriminator_schemas: list[CoreSchema] + """The list of core schemas having the discriminator application deferred.""" + + +class MissingDefinitionError(LookupError): + """A reference was pointing to a non-existing core schema.""" + + def __init__(self, schema_reference: str, /) -> None: + self.schema_reference = schema_reference + + +@dataclass +class GatherContext: + """The current context used during core schema traversing. + + Context instances should only be used during schema traversing. + """ + + definitions: dict[str, CoreSchema] + """The available definitions.""" + + deferred_discriminator_schemas: list[CoreSchema] = field(init=False, default_factory=list) + """The list of core schemas having the discriminator application deferred. + + Internally, these core schemas have a specific key set in the core metadata dict. + """ + + collected_references: dict[str, DefinitionReferenceSchema | None] = field(init=False, default_factory=dict) + """The collected definition references. + + If a definition reference schema can be inlined, it means that there is + only one in the whole core schema. As such, it is stored as the value. + Otherwise, the value is set to `None`. + + During schema traversing, definition reference schemas can be added as candidates, or removed + (by setting the value to `None`). + """ + + +def traverse_metadata(schema: AllSchemas, ctx: GatherContext) -> None: + meta = schema.get('metadata') + if meta is not None and 'pydantic_internal_union_discriminator' in meta: + ctx.deferred_discriminator_schemas.append(schema) # pyright: ignore[reportArgumentType] + + +def traverse_definition_ref(def_ref_schema: DefinitionReferenceSchema, ctx: GatherContext) -> None: + schema_ref = def_ref_schema['schema_ref'] + + if schema_ref not in ctx.collected_references: + definition = ctx.definitions.get(schema_ref) + if definition is None: + raise MissingDefinitionError(schema_ref) + + # The `'definition-ref'` schema was only encountered once, make it + # a candidate to be inlined: + ctx.collected_references[schema_ref] = def_ref_schema + traverse_schema(definition, ctx) + if 'serialization' in def_ref_schema: + traverse_schema(def_ref_schema['serialization'], ctx) + traverse_metadata(def_ref_schema, ctx) + else: + # The `'definition-ref'` schema was already encountered, meaning + # the previously encountered schema (and this one) can't be inlined: + ctx.collected_references[schema_ref] = None + + +def traverse_schema(schema: AllSchemas, context: GatherContext) -> None: + # TODO When we drop 3.9, use a match statement to get better type checking and remove + # file-level type ignore. + # (the `'type'` could also be fetched in every `if/elif` statement, but this alters performance). + schema_type = schema['type'] + + if schema_type == 'definition-ref': + traverse_definition_ref(schema, context) + # `traverse_definition_ref` handles the possible serialization and metadata schemas: + return + elif schema_type == 'definitions': + traverse_schema(schema['schema'], context) + for definition in schema['definitions']: + traverse_schema(definition, context) + elif schema_type in {'list', 'set', 'frozenset', 'generator'}: + if 'items_schema' in schema: + traverse_schema(schema['items_schema'], context) + elif schema_type == 'tuple': + if 'items_schema' in schema: + for s in schema['items_schema']: + traverse_schema(s, context) + elif schema_type == 'dict': + if 'keys_schema' in schema: + traverse_schema(schema['keys_schema'], context) + if 'values_schema' in schema: + traverse_schema(schema['values_schema'], context) + elif schema_type == 'union': + for choice in iter_union_choices(schema): + traverse_schema(choice, context) + elif schema_type == 'tagged-union': + for v in schema['choices'].values(): + traverse_schema(v, context) + elif schema_type == 'chain': + for step in schema['steps']: + traverse_schema(step, context) + elif schema_type == 'lax-or-strict': + traverse_schema(schema['lax_schema'], context) + traverse_schema(schema['strict_schema'], context) + elif schema_type == 'json-or-python': + traverse_schema(schema['json_schema'], context) + traverse_schema(schema['python_schema'], context) + elif schema_type in {'model-fields', 'typed-dict'}: + if 'extras_schema' in schema: + traverse_schema(schema['extras_schema'], context) + if 'computed_fields' in schema: + for s in schema['computed_fields']: + traverse_schema(s, context) + for s in schema['fields'].values(): + traverse_schema(s, context) + elif schema_type == 'dataclass-args': + if 'computed_fields' in schema: + for s in schema['computed_fields']: + traverse_schema(s, context) + for s in schema['fields']: + traverse_schema(s, context) + elif schema_type == 'arguments': + for s in schema['arguments_schema']: + traverse_schema(s['schema'], context) + if 'var_args_schema' in schema: + traverse_schema(schema['var_args_schema'], context) + if 'var_kwargs_schema' in schema: + traverse_schema(schema['var_kwargs_schema'], context) + elif schema_type == 'arguments-v3': + for s in schema['arguments_schema']: + traverse_schema(s['schema'], context) + elif schema_type == 'call': + traverse_schema(schema['arguments_schema'], context) + if 'return_schema' in schema: + traverse_schema(schema['return_schema'], context) + elif schema_type == 'computed-field': + traverse_schema(schema['return_schema'], context) + elif schema_type == 'function-before': + if 'schema' in schema: + traverse_schema(schema['schema'], context) + if 'json_schema_input_schema' in schema: + traverse_schema(schema['json_schema_input_schema'], context) + elif schema_type == 'function-plain': + # TODO duplicate schema types for serializers and validators, needs to be deduplicated. + if 'return_schema' in schema: + traverse_schema(schema['return_schema'], context) + if 'json_schema_input_schema' in schema: + traverse_schema(schema['json_schema_input_schema'], context) + elif schema_type == 'function-wrap': + # TODO duplicate schema types for serializers and validators, needs to be deduplicated. + if 'return_schema' in schema: + traverse_schema(schema['return_schema'], context) + if 'schema' in schema: + traverse_schema(schema['schema'], context) + if 'json_schema_input_schema' in schema: + traverse_schema(schema['json_schema_input_schema'], context) + else: + if 'schema' in schema: + traverse_schema(schema['schema'], context) + + if 'serialization' in schema: + traverse_schema(schema['serialization'], context) + traverse_metadata(schema, context) + + +def gather_schemas_for_cleaning(schema: CoreSchema, definitions: dict[str, CoreSchema]) -> GatherResult: + """Traverse the core schema and definitions and return the necessary information for schema cleaning. + + During the core schema traversing, any `'definition-ref'` schema is: + + - Validated: the reference must point to an existing definition. If this is not the case, a + `MissingDefinitionError` exception is raised. + - Stored in the context: the actual reference is stored in the context. Depending on whether + the `'definition-ref'` schema is encountered more that once, the schema itself is also + saved in the context to be inlined (i.e. replaced by the definition it points to). + """ + context = GatherContext(definitions) + traverse_schema(schema, context) + + return { + 'collected_references': context.collected_references, + 'deferred_discriminator_schemas': context.deferred_discriminator_schemas, + } diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_schema_generation_shared.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_schema_generation_shared.py new file mode 100644 index 0000000000000000000000000000000000000000..b231a82e1969822695bb3680217b66e73c81d017 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_schema_generation_shared.py @@ -0,0 +1,125 @@ +"""Types and utility functions used by various other internal tools.""" + +from __future__ import annotations + +from typing import TYPE_CHECKING, Any, Callable, Literal + +from pydantic_core import core_schema + +from ..annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler + +if TYPE_CHECKING: + from ..json_schema import GenerateJsonSchema, JsonSchemaValue + from ._core_utils import CoreSchemaOrField + from ._generate_schema import GenerateSchema + from ._namespace_utils import NamespacesTuple + + GetJsonSchemaFunction = Callable[[CoreSchemaOrField, GetJsonSchemaHandler], JsonSchemaValue] + HandlerOverride = Callable[[CoreSchemaOrField], JsonSchemaValue] + + +class GenerateJsonSchemaHandler(GetJsonSchemaHandler): + """JsonSchemaHandler implementation that doesn't do ref unwrapping by default. + + This is used for any Annotated metadata so that we don't end up with conflicting + modifications to the definition schema. + + Used internally by Pydantic, please do not rely on this implementation. + See `GetJsonSchemaHandler` for the handler API. + """ + + def __init__(self, generate_json_schema: GenerateJsonSchema, handler_override: HandlerOverride | None) -> None: + self.generate_json_schema = generate_json_schema + self.handler = handler_override or generate_json_schema.generate_inner + self.mode = generate_json_schema.mode + + def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue: + return self.handler(core_schema) + + def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue) -> JsonSchemaValue: + """Resolves `$ref` in the json schema. + + This returns the input json schema if there is no `$ref` in json schema. + + Args: + maybe_ref_json_schema: The input json schema that may contains `$ref`. + + Returns: + Resolved json schema. + + Raises: + LookupError: If it can't find the definition for `$ref`. + """ + if '$ref' not in maybe_ref_json_schema: + return maybe_ref_json_schema + ref = maybe_ref_json_schema['$ref'] + json_schema = self.generate_json_schema.get_schema_from_definitions(ref) + if json_schema is None: + raise LookupError( + f'Could not find a ref for {ref}.' + ' Maybe you tried to call resolve_ref_schema from within a recursive model?' + ) + return json_schema + + +class CallbackGetCoreSchemaHandler(GetCoreSchemaHandler): + """Wrapper to use an arbitrary function as a `GetCoreSchemaHandler`. + + Used internally by Pydantic, please do not rely on this implementation. + See `GetCoreSchemaHandler` for the handler API. + """ + + def __init__( + self, + handler: Callable[[Any], core_schema.CoreSchema], + generate_schema: GenerateSchema, + ref_mode: Literal['to-def', 'unpack'] = 'to-def', + ) -> None: + self._handler = handler + self._generate_schema = generate_schema + self._ref_mode = ref_mode + + def __call__(self, source_type: Any, /) -> core_schema.CoreSchema: + schema = self._handler(source_type) + if self._ref_mode == 'to-def': + ref = schema.get('ref') + if ref is not None: + return self._generate_schema.defs.create_definition_reference_schema(schema) + return schema + else: # ref_mode = 'unpack' + return self.resolve_ref_schema(schema) + + def _get_types_namespace(self) -> NamespacesTuple: + return self._generate_schema._types_namespace + + def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema: + return self._generate_schema.generate_schema(source_type) + + @property + def field_name(self) -> str | None: + return self._generate_schema.field_name_stack.get() + + def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema) -> core_schema.CoreSchema: + """Resolves reference in the core schema. + + Args: + maybe_ref_schema: The input core schema that may contains reference. + + Returns: + Resolved core schema. + + Raises: + LookupError: If it can't find the definition for reference. + """ + if maybe_ref_schema['type'] == 'definition-ref': + ref = maybe_ref_schema['schema_ref'] + definition = self._generate_schema.defs.get_schema_from_ref(ref) + if definition is None: + raise LookupError( + f'Could not find a ref for {ref}.' + ' Maybe you tried to call resolve_ref_schema from within a recursive model?' + ) + return definition + elif maybe_ref_schema['type'] == 'definitions': + return self.resolve_ref_schema(maybe_ref_schema['schema']) + return maybe_ref_schema diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_serializers.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_serializers.py new file mode 100644 index 0000000000000000000000000000000000000000..a4058e009020e28658e13e30731b2eb29eb0d154 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_serializers.py @@ -0,0 +1,53 @@ +from __future__ import annotations + +import collections +import collections.abc +import typing +from typing import Any + +from pydantic_core import PydanticOmit, core_schema + +SEQUENCE_ORIGIN_MAP: dict[Any, Any] = { + typing.Deque: collections.deque, # noqa: UP006 + collections.deque: collections.deque, + list: list, + typing.List: list, # noqa: UP006 + tuple: tuple, + typing.Tuple: tuple, # noqa: UP006 + set: set, + typing.AbstractSet: set, + typing.Set: set, # noqa: UP006 + frozenset: frozenset, + typing.FrozenSet: frozenset, # noqa: UP006 + typing.Sequence: list, + typing.MutableSequence: list, + typing.MutableSet: set, + # this doesn't handle subclasses of these + # parametrized typing.Set creates one of these + collections.abc.MutableSet: set, + collections.abc.Set: frozenset, +} + + +def serialize_sequence_via_list( + v: Any, handler: core_schema.SerializerFunctionWrapHandler, info: core_schema.SerializationInfo +) -> Any: + items: list[Any] = [] + + mapped_origin = SEQUENCE_ORIGIN_MAP.get(type(v), None) + if mapped_origin is None: + # we shouldn't hit this branch, should probably add a serialization error or something + return v + + for index, item in enumerate(v): + try: + v = handler(item, index) + except PydanticOmit: # noqa: PERF203 + pass + else: + items.append(v) + + if info.mode_is_json(): + return items + else: + return mapped_origin(items) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_signature.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_signature.py new file mode 100644 index 0000000000000000000000000000000000000000..3b0c5ae8c40158a28a3b0cf21116c9695208076e --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_signature.py @@ -0,0 +1,189 @@ +from __future__ import annotations + +import dataclasses +from inspect import Parameter, Signature +from typing import TYPE_CHECKING, Any, Callable + +from pydantic_core import PydanticUndefined + +from ._typing_extra import signature_no_eval +from ._utils import is_valid_identifier + +if TYPE_CHECKING: + from ..config import ExtraValues + from ..fields import FieldInfo + + +# Copied over from stdlib dataclasses +class _HAS_DEFAULT_FACTORY_CLASS: + def __repr__(self): + return '' + + +_HAS_DEFAULT_FACTORY = _HAS_DEFAULT_FACTORY_CLASS() + + +def _field_name_for_signature(field_name: str, field_info: FieldInfo) -> str: + """Extract the correct name to use for the field when generating a signature. + + Assuming the field has a valid alias, this will return the alias. Otherwise, it will return the field name. + First priority is given to the alias, then the validation_alias, then the field name. + + Args: + field_name: The name of the field + field_info: The corresponding FieldInfo object. + + Returns: + The correct name to use when generating a signature. + """ + if isinstance(field_info.alias, str) and is_valid_identifier(field_info.alias): + return field_info.alias + if isinstance(field_info.validation_alias, str) and is_valid_identifier(field_info.validation_alias): + return field_info.validation_alias + + return field_name + + +def _process_param_defaults(param: Parameter) -> Parameter: + """Modify the signature for a parameter in a dataclass where the default value is a FieldInfo instance. + + Args: + param (Parameter): The parameter + + Returns: + Parameter: The custom processed parameter + """ + from ..fields import FieldInfo + + param_default = param.default + if isinstance(param_default, FieldInfo): + annotation = param.annotation + # Replace the annotation if appropriate + # inspect does "clever" things to show annotations as strings because we have + # `from __future__ import annotations` in main, we don't want that + if annotation == 'Any': + annotation = Any + + # Replace the field default + default = param_default.default + if default is PydanticUndefined: + if param_default.default_factory is None: + default = Signature.empty + else: + # this is used by dataclasses to indicate a factory exists: + default = dataclasses._HAS_DEFAULT_FACTORY # type: ignore + return param.replace( + annotation=annotation, name=_field_name_for_signature(param.name, param_default), default=default + ) + return param + + +def _generate_signature_parameters( # noqa: C901 (ignore complexity, could use a refactor) + init: Callable[..., None], + fields: dict[str, FieldInfo], + validate_by_name: bool, + extra: ExtraValues | None, +) -> dict[str, Parameter]: + """Generate a mapping of parameter names to Parameter objects for a pydantic BaseModel or dataclass.""" + from itertools import islice + + present_params = signature_no_eval(init).parameters.values() + merged_params: dict[str, Parameter] = {} + var_kw = None + use_var_kw = False + + for param in islice(present_params, 1, None): # skip self arg + # inspect does "clever" things to show annotations as strings because we have + # `from __future__ import annotations` in main, we don't want that + if fields.get(param.name): + # exclude params with init=False + if getattr(fields[param.name], 'init', True) is False: + continue + param = param.replace(name=_field_name_for_signature(param.name, fields[param.name])) + if param.annotation == 'Any': + param = param.replace(annotation=Any) + if param.kind is param.VAR_KEYWORD: + var_kw = param + continue + merged_params[param.name] = param + + if var_kw: # if custom init has no var_kw, fields which are not declared in it cannot be passed through + allow_names = validate_by_name + for field_name, field in fields.items(): + # when alias is a str it should be used for signature generation + param_name = _field_name_for_signature(field_name, field) + + if field_name in merged_params or param_name in merged_params: + continue + + if not is_valid_identifier(param_name): + if allow_names: + param_name = field_name + else: + use_var_kw = True + continue + + if field.is_required(): + default = Parameter.empty + elif field.default_factory is not None: + # Mimics stdlib dataclasses: + default = _HAS_DEFAULT_FACTORY + else: + default = field.default + merged_params[param_name] = Parameter( + param_name, + Parameter.KEYWORD_ONLY, + annotation=field.rebuild_annotation(), + default=default, + ) + + if extra == 'allow': + use_var_kw = True + + if var_kw and use_var_kw: + # Make sure the parameter for extra kwargs + # does not have the same name as a field + default_model_signature = [ + ('self', Parameter.POSITIONAL_ONLY), + ('data', Parameter.VAR_KEYWORD), + ] + if [(p.name, p.kind) for p in present_params] == default_model_signature: + # if this is the standard model signature, use extra_data as the extra args name + var_kw_name = 'extra_data' + else: + # else start from var_kw + var_kw_name = var_kw.name + + # generate a name that's definitely unique + while var_kw_name in fields: + var_kw_name += '_' + merged_params[var_kw_name] = var_kw.replace(name=var_kw_name) + + return merged_params + + +def generate_pydantic_signature( + init: Callable[..., None], + fields: dict[str, FieldInfo], + validate_by_name: bool, + extra: ExtraValues | None, + is_dataclass: bool = False, +) -> Signature: + """Generate signature for a pydantic BaseModel or dataclass. + + Args: + init: The class init. + fields: The model fields. + validate_by_name: The `validate_by_name` value of the config. + extra: The `extra` value of the config. + is_dataclass: Whether the model is a dataclass. + + Returns: + The dataclass/BaseModel subclass signature. + """ + merged_params = _generate_signature_parameters(init, fields, validate_by_name, extra) + + if is_dataclass: + merged_params = {k: _process_param_defaults(v) for k, v in merged_params.items()} + + return Signature(parameters=list(merged_params.values()), return_annotation=None) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_typing_extra.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_typing_extra.py new file mode 100644 index 0000000000000000000000000000000000000000..c217f1127d7c71ab47720263384b9564abe69a29 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_typing_extra.py @@ -0,0 +1,785 @@ +"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap Python's typing module.""" + +from __future__ import annotations + +import collections.abc +import re +import sys +import types +import typing +from functools import partial +from inspect import Signature, signature +from typing import TYPE_CHECKING, Any, Callable, cast + +import typing_extensions +from typing_extensions import deprecated, get_args, get_origin +from typing_inspection import typing_objects +from typing_inspection.introspection import is_union_origin + +from pydantic.version import version_short + +from ._namespace_utils import GlobalsNamespace, MappingNamespace, NsResolver, get_module_ns_of + +if sys.version_info < (3, 10): + NoneType = type(None) + EllipsisType = type(Ellipsis) +else: + from types import EllipsisType as EllipsisType + from types import NoneType as NoneType + +if sys.version_info >= (3, 14): + import annotationlib + +if TYPE_CHECKING: + from pydantic import BaseModel + from pydantic.fields import FieldInfo + +# As per https://typing-extensions.readthedocs.io/en/latest/#runtime-use-of-types, +# always check for both `typing` and `typing_extensions` variants of a typing construct. +# (this is implemented differently than the suggested approach in the `typing_extensions` +# docs for performance). + + +_t_annotated = typing.Annotated +_te_annotated = typing_extensions.Annotated + + +def is_annotated(tp: Any, /) -> bool: + """Return whether the provided argument is a `Annotated` special form. + + ```python {test="skip" lint="skip"} + is_annotated(Annotated[int, ...]) + #> True + ``` + """ + origin = get_origin(tp) + return origin is _t_annotated or origin is _te_annotated + + +def annotated_type(tp: Any, /) -> Any | None: + """Return the type of the `Annotated` special form, or `None`.""" + return tp.__origin__ if typing_objects.is_annotated(get_origin(tp)) else None + + +def unpack_type(tp: Any, /) -> Any | None: + """Return the type wrapped by the `Unpack` special form, or `None`.""" + return get_args(tp)[0] if typing_objects.is_unpack(get_origin(tp)) else None + + +def is_hashable(tp: Any, /) -> bool: + """Return whether the provided argument is the `Hashable` class. + + ```python {test="skip" lint="skip"} + is_hashable(Hashable) + #> True + ``` + """ + # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes, + # hence the second check: + return tp is collections.abc.Hashable or get_origin(tp) is collections.abc.Hashable + + +def is_callable(tp: Any, /) -> bool: + """Return whether the provided argument is a `Callable`, parametrized or not. + + ```python {test="skip" lint="skip"} + is_callable(Callable[[int], str]) + #> True + is_callable(typing.Callable) + #> True + is_callable(collections.abc.Callable) + #> True + ``` + """ + # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes, + # hence the second check: + return tp is collections.abc.Callable or get_origin(tp) is collections.abc.Callable + + +_classvar_re = re.compile(r'((\w+\.)?Annotated\[)?(\w+\.)?ClassVar\[') + + +def is_classvar_annotation(tp: Any, /) -> bool: + """Return whether the provided argument represents a class variable annotation. + + Although not explicitly stated by the typing specification, `ClassVar` can be used + inside `Annotated` and as such, this function checks for this specific scenario. + + Because this function is used to detect class variables before evaluating forward references + (or because evaluation failed), we also implement a naive regex match implementation. This is + required because class variables are inspected before fields are collected, so we try to be + as accurate as possible. + """ + if typing_objects.is_classvar(tp): + return True + + origin = get_origin(tp) + + if typing_objects.is_classvar(origin): + return True + + if typing_objects.is_annotated(origin): + annotated_type = tp.__origin__ + if typing_objects.is_classvar(annotated_type) or typing_objects.is_classvar(get_origin(annotated_type)): + return True + + str_ann: str | None = None + if isinstance(tp, typing.ForwardRef): + str_ann = tp.__forward_arg__ + if isinstance(tp, str): + str_ann = tp + + if str_ann is not None and _classvar_re.match(str_ann): + # stdlib dataclasses do something similar, although a bit more advanced + # (see `dataclass._is_type`). + return True + + return False + + +_t_final = typing.Final +_te_final = typing_extensions.Final + + +# TODO implement `is_finalvar_annotation` as Final can be wrapped with other special forms: +def is_finalvar(tp: Any, /) -> bool: + """Return whether the provided argument is a `Final` special form, parametrized or not. + + ```python {test="skip" lint="skip"} + is_finalvar(Final[int]) + #> True + is_finalvar(Final) + #> True + """ + # Final is not necessarily parametrized: + if tp is _t_final or tp is _te_final: + return True + origin = get_origin(tp) + return origin is _t_final or origin is _te_final + + +_NONE_TYPES: tuple[Any, ...] = (None, NoneType, typing.Literal[None], typing_extensions.Literal[None]) + + +def is_none_type(tp: Any, /) -> bool: + """Return whether the argument represents the `None` type as part of an annotation. + + ```python {test="skip" lint="skip"} + is_none_type(None) + #> True + is_none_type(NoneType) + #> True + is_none_type(Literal[None]) + #> True + is_none_type(type[None]) + #> False + """ + return tp in _NONE_TYPES + + +def is_namedtuple(tp: Any, /) -> bool: + """Return whether the provided argument is a named tuple class. + + The class can be created using `typing.NamedTuple` or `collections.namedtuple`. + Parametrized generic classes are *not* assumed to be named tuples. + """ + from ._utils import lenient_issubclass # circ. import + + return lenient_issubclass(tp, tuple) and hasattr(tp, '_fields') + + +# TODO In 2.12, delete this export. It is currently defined only to not break +# pydantic-settings which relies on it: +origin_is_union = is_union_origin + + +def is_generic_alias(tp: Any, /) -> bool: + return isinstance(tp, (types.GenericAlias, typing._GenericAlias)) # pyright: ignore[reportAttributeAccessIssue] + + +# TODO: Ideally, we should avoid relying on the private `typing` constructs: + +if sys.version_info < (3, 10): + WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias) # pyright: ignore[reportAttributeAccessIssue] +else: + WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias, types.UnionType) # pyright: ignore[reportAttributeAccessIssue] + + +# Similarly, we shouldn't rely on this `_Final` class, which is even more private than `_GenericAlias`: +typing_base: Any = typing._Final # pyright: ignore[reportAttributeAccessIssue] + + +### Annotation evaluations functions: + + +def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dict[str, Any] | None: + """Fetch the local namespace of the parent frame where this function is called. + + Using this function is mostly useful to resolve forward annotations pointing to members defined in a local namespace, + such as assignments inside a function. Using the standard library tools, it is currently not possible to resolve + such annotations: + + ```python {lint="skip" test="skip"} + from typing import get_type_hints + + def func() -> None: + Alias = int + + class C: + a: 'Alias' + + # Raises a `NameError: 'Alias' is not defined` + get_type_hints(C) + ``` + + Pydantic uses this function when a Pydantic model is being defined to fetch the parent frame locals. However, + this only allows us to fetch the parent frame namespace and not other parents (e.g. a model defined in a function, + itself defined in another function). Inspecting the next outer frames (using `f_back`) is not reliable enough + (see https://discuss.python.org/t/20659). + + Because this function is mostly used to better resolve forward annotations, nothing is returned if the parent frame's + code object is defined at the module level. In this case, the locals of the frame will be the same as the module + globals where the class is defined (see `_namespace_utils.get_module_ns_of`). However, if you still want to fetch + the module globals (e.g. when rebuilding a model, where the frame where the rebuild call is performed might contain + members that you want to use for forward annotations evaluation), you can use the `force` parameter. + + Args: + parent_depth: The depth at which to get the frame. Defaults to 2, meaning the parent frame where this function + is called will be used. + force: Whether to always return the frame locals, even if the frame's code object is defined at the module level. + + Returns: + The locals of the namespace, or `None` if it was skipped as per the described logic. + """ + frame = sys._getframe(parent_depth) + + if frame.f_code.co_name.startswith('`, + # and we need to skip this frame as it is irrelevant. + frame = cast(types.FrameType, frame.f_back) # guaranteed to not be `None` + + # note, we don't copy frame.f_locals here (or during the last return call), because we don't expect the namespace to be + # modified down the line if this becomes a problem, we could implement some sort of frozen mapping structure to enforce this. + if force: + return frame.f_locals + + # If either of the following conditions are true, the class is defined at the top module level. + # To better understand why we need both of these checks, see + # https://github.com/pydantic/pydantic/pull/10113#discussion_r1714981531. + if frame.f_back is None or frame.f_code.co_name == '': + return None + + return frame.f_locals + + +def _type_convert(arg: Any) -> Any: + """Convert `None` to `NoneType` and strings to `ForwardRef` instances. + + This is a backport of the private `typing._type_convert` function. When + evaluating a type, `ForwardRef._evaluate` ends up being called, and is + responsible for making this conversion. However, we still have to apply + it for the first argument passed to our type evaluation functions, similarly + to the `typing.get_type_hints` function. + """ + if arg is None: + return NoneType + if isinstance(arg, str): + # Like `typing.get_type_hints`, assume the arg can be in any context, + # hence the proper `is_argument` and `is_class` args: + return _make_forward_ref(arg, is_argument=False, is_class=True) + return arg + + +def safe_get_annotations(obj: Any) -> dict[str, Any]: + """Get the annotations for the provided object, accounting for potential deferred forward references. + + Starting with Python 3.14, accessing the `__annotations__` attribute might raise a `NameError` if + a referenced symbol isn't defined yet. In this case, we return the annotation in the *forward ref* + format. + """ + if sys.version_info >= (3, 14): + return annotationlib.get_annotations(obj, format=annotationlib.Format.FORWARDREF) + else: + # TODO just do getattr(obj, '__annotations__', {}) when dropping support for Python 3.9: + if isinstance(obj, type): + return obj.__dict__.get('__annotations__', {}) + else: + return getattr(obj, '__annotations__', {}) + + +def get_model_type_hints( + model_class: type[BaseModel], + *, + ns_resolver: NsResolver | None = None, +) -> dict[str, tuple[Any, bool]]: + """Collect annotations from a Pydantic model class, including those from parent classes. + + Args: + model_class: The Pydantic model class to inspect. + ns_resolver: A namespace resolver instance to use. Defaults to an empty instance. + + Returns: + A dictionary mapping annotation names to a two-tuple: the first element is the evaluated + type or the original annotation if a `NameError` occurred, the second element is a boolean + indicating if whether the evaluation succeeded. + """ + hints: dict[str, Any] | dict[str, tuple[Any, bool]] = {} + ns_resolver = ns_resolver or NsResolver() + + for base in reversed(model_class.__mro__[:-1]): + # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals + # from the ns_resolver, but we want to be able to know which specific field failed + # to evaluate: + ann = safe_get_annotations(base) + + if not ann: + continue + + with ns_resolver.push(base): + base_model_fields: dict[str, FieldInfo] | None = base.__dict__.get('__pydantic_fields__') + + for name, value in ann.items(): + if name.startswith('_'): + globalns, localns = ns_resolver.types_namespace + + # For private attributes, we only need the annotation to detect the `ClassVar` special form. + # For this reason, we still try to evaluate it, but we also catch any possible exception (on + # top of the `NameError`s caught in `try_eval_type`) that could happen so that users are free + # to use any kind of forward annotation for private fields (e.g. circular imports, new typing + # syntax, etc). + try: + hints[name] = try_eval_type(value, globalns, localns) + except Exception: + hints[name] = (value, False) + else: + if base_model_fields is not None and name in base_model_fields: + # Avoid unnecessarily evaluating annotations from parent models, as we'll end up + # copying the `FieldInfo` instance from it anyway if we need to. + # We use the `annotation` attribute here, but in reality could put anything here, + # As we are guaranteed to not make use of it: + hints[name] = (base_model_fields[name].annotation, True) + else: + globalns, localns = ns_resolver.types_namespace + + hints[name] = try_eval_type(value, globalns, localns) + + return hints + + +def get_cls_type_hints( + obj: type[Any], + *, + ns_resolver: NsResolver | None = None, +) -> dict[str, Any]: + """Collect annotations from a class, including those from parent classes. + + Args: + obj: The class to inspect. + ns_resolver: A namespace resolver instance to use. Defaults to an empty instance. + """ + hints: dict[str, Any] = {} + ns_resolver = ns_resolver or NsResolver() + + for base in reversed(obj.__mro__): + # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals + # from the ns_resolver, but we want to be able to know which specific field failed + # to evaluate: + ann = safe_get_annotations(base) + + if not ann: + continue + + with ns_resolver.push(base): + globalns, localns = ns_resolver.types_namespace + for name, value in ann.items(): + hints[name] = eval_type(value, globalns, localns) + return hints + + +def try_eval_type( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, +) -> tuple[Any, bool]: + """Try evaluating the annotation using the provided namespaces. + + Args: + value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance + of `str`, it will be converted to a `ForwardRef`. + localns: The global namespace to use during annotation evaluation. + globalns: The local namespace to use during annotation evaluation. + + Returns: + A two-tuple containing the possibly evaluated type and a boolean indicating + whether the evaluation succeeded or not. + """ + value = _type_convert(value) + + try: + return eval_type_backport(value, globalns, localns), True + except NameError: + return value, False + + +def eval_type( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, +) -> Any: + """Evaluate the annotation using the provided namespaces. + + Args: + value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance + of `str`, it will be converted to a `ForwardRef`. + localns: The global namespace to use during annotation evaluation. + globalns: The local namespace to use during annotation evaluation. + """ + value = _type_convert(value) + return eval_type_backport(value, globalns, localns) + + +@deprecated( + '`eval_type_lenient` is deprecated, use `try_eval_type` instead.', + category=None, +) +def eval_type_lenient( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, +) -> Any: + ev, _ = try_eval_type(value, globalns, localns) + return ev + + +def eval_type_backport( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + type_params: tuple[Any, ...] | None = None, +) -> Any: + """An enhanced version of `typing._eval_type` which will fall back to using the `eval_type_backport` + package if it's installed to let older Python versions use newer typing constructs. + + Specifically, this transforms `X | Y` into `typing.Union[X, Y]` and `list[X]` into `typing.List[X]` + (as well as all the types made generic in PEP 585) if the original syntax is not supported in the + current Python version. + + This function will also display a helpful error if the value passed fails to evaluate. + """ + try: + return _eval_type_backport(value, globalns, localns, type_params) + except TypeError as e: + if 'Unable to evaluate type annotation' in str(e): + raise + + # If it is a `TypeError` and value isn't a `ForwardRef`, it would have failed during annotation definition. + # Thus we assert here for type checking purposes: + assert isinstance(value, typing.ForwardRef) + + message = f'Unable to evaluate type annotation {value.__forward_arg__!r}.' + if sys.version_info >= (3, 11): + e.add_note(message) + raise + else: + raise TypeError(message) from e + except RecursionError as e: + # TODO ideally recursion errors should be checked in `eval_type` above, but `eval_type_backport` + # is used directly in some places. + message = ( + "If you made use of an implicit recursive type alias (e.g. `MyType = list['MyType']), " + 'consider using PEP 695 type aliases instead. For more details, refer to the documentation: ' + f'https://docs.pydantic.dev/{version_short()}/concepts/types/#named-recursive-types' + ) + if sys.version_info >= (3, 11): + e.add_note(message) + raise + else: + raise RecursionError(f'{e.args[0]}\n{message}') + + +def _eval_type_backport( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + type_params: tuple[Any, ...] | None = None, +) -> Any: + try: + return _eval_type(value, globalns, localns, type_params) + except TypeError as e: + if not (isinstance(value, typing.ForwardRef) and is_backport_fixable_error(e)): + raise + + try: + from eval_type_backport import eval_type_backport + except ImportError: + raise TypeError( + f'Unable to evaluate type annotation {value.__forward_arg__!r}. If you are making use ' + 'of the new typing syntax (unions using `|` since Python 3.10 or builtins subscripting ' + 'since Python 3.9), you should either replace the use of new syntax with the existing ' + '`typing` constructs or install the `eval_type_backport` package.' + ) from e + + return eval_type_backport( + value, + globalns, + localns, # pyright: ignore[reportArgumentType], waiting on a new `eval_type_backport` release. + try_default=False, + ) + + +def _eval_type( + value: Any, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + type_params: tuple[Any, ...] | None = None, +) -> Any: + if sys.version_info >= (3, 14): + # Starting in 3.14, `_eval_type()` does *not* apply `_type_convert()` + # anymore. This means the `None` -> `type(None)` conversion does not apply: + evaluated = typing._eval_type( # type: ignore + value, + globalns, + localns, + type_params=type_params, + # This is relevant when evaluating types from `TypedDict` classes, where string annotations + # are automatically converted to `ForwardRef` instances with a module set. In this case, + # Our `globalns` is irrelevant and we need to indicate `typing._eval_type()` that it should + # infer it from the `ForwardRef.__forward_module__` attribute instead (`typing.get_type_hints()` + # does the same). Note that this would probably be unnecessary if we properly iterated over the + # `__orig_bases__` for TypedDicts in `get_cls_type_hints()`: + prefer_fwd_module=True, + ) + if evaluated is None: + evaluated = type(None) + return evaluated + elif sys.version_info >= (3, 13): + return typing._eval_type( # type: ignore + value, globalns, localns, type_params=type_params + ) + else: + return typing._eval_type( # type: ignore + value, globalns, localns + ) + + +def is_backport_fixable_error(e: TypeError) -> bool: + msg = str(e) + + return sys.version_info < (3, 10) and msg.startswith('unsupported operand type(s) for |: ') + + +def signature_no_eval(f: Callable[..., Any]) -> Signature: + """Get the signature of a callable without evaluating any annotations.""" + if sys.version_info >= (3, 14): + from annotationlib import Format + + return signature(f, annotation_format=Format.FORWARDREF) + else: + return signature(f) + + +def get_function_type_hints( + function: Callable[..., Any], + *, + include_keys: set[str] | None = None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, +) -> dict[str, Any]: + """Return type hints for a function. + + This is similar to the `typing.get_type_hints` function, with a few differences: + - Support `functools.partial` by using the underlying `func` attribute. + - Do not wrap type annotation of a parameter with `Optional` if it has a default value of `None` + (related bug: https://github.com/python/cpython/issues/90353, only fixed in 3.11+). + """ + if isinstance(function, partial): + annotations = safe_get_annotations(function.func) + else: + annotations = safe_get_annotations(function) + + if globalns is None: + globalns = get_module_ns_of(function) + type_params: tuple[Any, ...] | None = None + if localns is None: + # If localns was specified, it is assumed to already contain type params. This is because + # Pydantic has more advanced logic to do so (see `_namespace_utils.ns_for_function`). + type_params = getattr(function, '__type_params__', ()) + + type_hints = {} + for name, value in annotations.items(): + if include_keys is not None and name not in include_keys: + continue + if value is None: + value = NoneType + elif isinstance(value, str): + value = _make_forward_ref(value) + + type_hints[name] = eval_type_backport(value, globalns, localns, type_params) + + return type_hints + + +# TODO use typing.ForwardRef directly when we stop supporting 3.9: +if sys.version_info < (3, 9, 8) or (3, 10) <= sys.version_info < (3, 10, 1): + + def _make_forward_ref( + arg: Any, + is_argument: bool = True, + *, + is_class: bool = False, + ) -> typing.ForwardRef: + """Wrapper for ForwardRef that accounts for the `is_class` argument missing in older versions. + The `module` argument is omitted as it breaks <3.9.8, =3.10.0 and isn't used in the calls below. + + See https://github.com/python/cpython/pull/28560 for some background. + The backport happened on 3.9.8, see: + https://github.com/pydantic/pydantic/discussions/6244#discussioncomment-6275458, + and on 3.10.1 for the 3.10 branch, see: + https://github.com/pydantic/pydantic/issues/6912 + + Implemented as EAFP with memory. + """ + return typing.ForwardRef(arg, is_argument) # pyright: ignore[reportCallIssue] + +else: + _make_forward_ref = typing.ForwardRef # pyright: ignore[reportAssignmentType] + + +if sys.version_info >= (3, 10): + get_type_hints = typing.get_type_hints + +else: + """ + For older versions of python, we have a custom implementation of `get_type_hints` which is a close as possible to + the implementation in CPython 3.10.8. + """ + + @typing.no_type_check + def get_type_hints( # noqa: C901 + obj: Any, + globalns: dict[str, Any] | None = None, + localns: dict[str, Any] | None = None, + include_extras: bool = False, + ) -> dict[str, Any]: # pragma: no cover + """Taken verbatim from python 3.10.8 unchanged, except: + * type annotations of the function definition above. + * prefixing `typing.` where appropriate + * Use `_make_forward_ref` instead of `typing.ForwardRef` to handle the `is_class` argument. + + https://github.com/python/cpython/blob/aaaf5174241496afca7ce4d4584570190ff972fe/Lib/typing.py#L1773-L1875 + + DO NOT CHANGE THIS METHOD UNLESS ABSOLUTELY NECESSARY. + ====================================================== + + Return type hints for an object. + + This is often the same as obj.__annotations__, but it handles + forward references encoded as string literals, adds Optional[t] if a + default value equal to None is set and recursively replaces all + 'Annotated[T, ...]' with 'T' (unless 'include_extras=True'). + + The argument may be a module, class, method, or function. The annotations + are returned as a dictionary. For classes, annotations include also + inherited members. + + TypeError is raised if the argument is not of a type that can contain + annotations, and an empty dictionary is returned if no annotations are + present. + + BEWARE -- the behavior of globalns and localns is counterintuitive + (unless you are familiar with how eval() and exec() work). The + search order is locals first, then globals. + + - If no dict arguments are passed, an attempt is made to use the + globals from obj (or the respective module's globals for classes), + and these are also used as the locals. If the object does not appear + to have globals, an empty dictionary is used. For classes, the search + order is globals first then locals. + + - If one dict argument is passed, it is used for both globals and + locals. + + - If two dict arguments are passed, they specify globals and + locals, respectively. + """ + if getattr(obj, '__no_type_check__', None): + return {} + # Classes require a special treatment. + if isinstance(obj, type): + hints = {} + for base in reversed(obj.__mro__): + if globalns is None: + base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {}) + else: + base_globals = globalns + ann = base.__dict__.get('__annotations__', {}) + if isinstance(ann, types.GetSetDescriptorType): + ann = {} + base_locals = dict(vars(base)) if localns is None else localns + if localns is None and globalns is None: + # This is surprising, but required. Before Python 3.10, + # get_type_hints only evaluated the globalns of + # a class. To maintain backwards compatibility, we reverse + # the globalns and localns order so that eval() looks into + # *base_globals* first rather than *base_locals*. + # This only affects ForwardRefs. + base_globals, base_locals = base_locals, base_globals + for name, value in ann.items(): + if value is None: + value = type(None) + if isinstance(value, str): + value = _make_forward_ref(value, is_argument=False, is_class=True) + + value = eval_type_backport(value, base_globals, base_locals) + hints[name] = value + if not include_extras and hasattr(typing, '_strip_annotations'): + return { + k: typing._strip_annotations(t) # type: ignore + for k, t in hints.items() + } + else: + return hints + + if globalns is None: + if isinstance(obj, types.ModuleType): + globalns = obj.__dict__ + else: + nsobj = obj + # Find globalns for the unwrapped object. + while hasattr(nsobj, '__wrapped__'): + nsobj = nsobj.__wrapped__ + globalns = getattr(nsobj, '__globals__', {}) + if localns is None: + localns = globalns + elif localns is None: + localns = globalns + hints = getattr(obj, '__annotations__', None) + if hints is None: + # Return empty annotations for something that _could_ have them. + if isinstance(obj, typing._allowed_types): # type: ignore + return {} + else: + raise TypeError(f'{obj!r} is not a module, class, method, or function.') + defaults = typing._get_defaults(obj) # type: ignore + hints = dict(hints) + for name, value in hints.items(): + if value is None: + value = type(None) + if isinstance(value, str): + # class-level forward refs were handled above, this must be either + # a module-level annotation or a function argument annotation + + value = _make_forward_ref( + value, + is_argument=not isinstance(obj, types.ModuleType), + is_class=False, + ) + value = eval_type_backport(value, globalns, localns) + if name in defaults and defaults[name] is None: + value = typing.Optional[value] + hints[name] = value + return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()} # type: ignore diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_utils.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..7a0346d8470c4a4827f785905a596e9e992ec741 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_utils.py @@ -0,0 +1,446 @@ +"""Bucket of reusable internal utilities. + +This should be reduced as much as possible with functions only used in one place, moved to that place. +""" + +from __future__ import annotations as _annotations + +import dataclasses +import keyword +import sys +import warnings +import weakref +from collections import OrderedDict, defaultdict, deque +from collections.abc import Callable, Iterable, Mapping +from collections.abc import Set as AbstractSet +from copy import deepcopy +from functools import cached_property +from inspect import Parameter +from itertools import zip_longest +from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType +from typing import TYPE_CHECKING, Any, Generic, TypeVar, overload + +from pydantic_core import MISSING, PydanticUndefined +from typing_extensions import TypeAlias, TypeGuard, deprecated + +from pydantic import PydanticDeprecatedSince211 + +from . import _repr, _typing_extra +from ._import_utils import import_cached_base_model + +if TYPE_CHECKING: + # TODO remove type error comments when we drop support for Python 3.9 + MappingIntStrAny: TypeAlias = Mapping[int, Any] | Mapping[str, Any] # pyright: ignore[reportGeneralTypeIssues] + AbstractSetIntStr: TypeAlias = AbstractSet[int] | AbstractSet[str] # pyright: ignore[reportGeneralTypeIssues] + from ..main import BaseModel + + +# these are types that are returned unchanged by deepcopy +IMMUTABLE_NON_COLLECTIONS_TYPES: set[type[Any]] = { + int, + float, + complex, + str, + bool, + bytes, + type, + _typing_extra.NoneType, + FunctionType, + BuiltinFunctionType, + LambdaType, + weakref.ref, + CodeType, + # note: including ModuleType will differ from behaviour of deepcopy by not producing error. + # It might be not a good idea in general, but considering that this function used only internally + # against default values of fields, this will allow to actually have a field with module as default value + ModuleType, + NotImplemented.__class__, + Ellipsis.__class__, +} + +# these are types that if empty, might be copied with simple copy() instead of deepcopy() +BUILTIN_COLLECTIONS: set[type[Any]] = { + list, + set, + tuple, + frozenset, + dict, + OrderedDict, + defaultdict, + deque, +} + + +def can_be_positional(param: Parameter) -> bool: + """Return whether the parameter accepts a positional argument. + + ```python {test="skip" lint="skip"} + def func(a, /, b, *, c): + pass + + params = inspect.signature(func).parameters + can_be_positional(params['a']) + #> True + can_be_positional(params['b']) + #> True + can_be_positional(params['c']) + #> False + ``` + """ + return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD) + + +def sequence_like(v: Any) -> bool: + return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque)) + + +def lenient_isinstance(o: Any, class_or_tuple: type[Any] | tuple[type[Any], ...] | None) -> bool: # pragma: no cover + try: + return isinstance(o, class_or_tuple) # type: ignore[arg-type] + except TypeError: + return False + + +def lenient_issubclass(cls: Any, class_or_tuple: Any) -> bool: # pragma: no cover + try: + return isinstance(cls, type) and issubclass(cls, class_or_tuple) + except TypeError: + if isinstance(cls, _typing_extra.WithArgsTypes): + return False + raise # pragma: no cover + + +def is_model_class(cls: Any) -> TypeGuard[type[BaseModel]]: + """Returns true if cls is a _proper_ subclass of BaseModel, and provides proper type-checking, + unlike raw calls to lenient_issubclass. + """ + BaseModel = import_cached_base_model() + + return lenient_issubclass(cls, BaseModel) and cls is not BaseModel + + +def is_valid_identifier(identifier: str) -> bool: + """Checks that a string is a valid identifier and not a Python keyword. + :param identifier: The identifier to test. + :return: True if the identifier is valid. + """ + return identifier.isidentifier() and not keyword.iskeyword(identifier) + + +KeyType = TypeVar('KeyType') + + +def deep_update(mapping: dict[KeyType, Any], *updating_mappings: dict[KeyType, Any]) -> dict[KeyType, Any]: + updated_mapping = mapping.copy() + for updating_mapping in updating_mappings: + for k, v in updating_mapping.items(): + if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict): + updated_mapping[k] = deep_update(updated_mapping[k], v) + else: + updated_mapping[k] = v + return updated_mapping + + +def update_not_none(mapping: dict[Any, Any], **update: Any) -> None: + mapping.update({k: v for k, v in update.items() if v is not None}) + + +T = TypeVar('T') + + +def unique_list( + input_list: list[T] | tuple[T, ...], + *, + name_factory: Callable[[T], str] = str, +) -> list[T]: + """Make a list unique while maintaining order. + We update the list if another one with the same name is set + (e.g. model validator overridden in subclass). + """ + result: list[T] = [] + result_names: list[str] = [] + for v in input_list: + v_name = name_factory(v) + if v_name not in result_names: + result_names.append(v_name) + result.append(v) + else: + result[result_names.index(v_name)] = v + + return result + + +class ValueItems(_repr.Representation): + """Class for more convenient calculation of excluded or included fields on values.""" + + __slots__ = ('_items', '_type') + + def __init__(self, value: Any, items: AbstractSetIntStr | MappingIntStrAny) -> None: + items = self._coerce_items(items) + + if isinstance(value, (list, tuple)): + items = self._normalize_indexes(items, len(value)) # type: ignore + + self._items: MappingIntStrAny = items # type: ignore + + def is_excluded(self, item: Any) -> bool: + """Check if item is fully excluded. + + :param item: key or index of a value + """ + return self.is_true(self._items.get(item)) + + def is_included(self, item: Any) -> bool: + """Check if value is contained in self._items. + + :param item: key or index of value + """ + return item in self._items + + def for_element(self, e: int | str) -> AbstractSetIntStr | MappingIntStrAny | None: + """:param e: key or index of element on value + :return: raw values for element if self._items is dict and contain needed element + """ + item = self._items.get(e) # type: ignore + return item if not self.is_true(item) else None + + def _normalize_indexes(self, items: MappingIntStrAny, v_length: int) -> dict[int | str, Any]: + """:param items: dict or set of indexes which will be normalized + :param v_length: length of sequence indexes of which will be + + >>> self._normalize_indexes({0: True, -2: True, -1: True}, 4) + {0: True, 2: True, 3: True} + >>> self._normalize_indexes({'__all__': True}, 4) + {0: True, 1: True, 2: True, 3: True} + """ + normalized_items: dict[int | str, Any] = {} + all_items = None + for i, v in items.items(): + if not (isinstance(v, Mapping) or isinstance(v, AbstractSet) or self.is_true(v)): + raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}') + if i == '__all__': + all_items = self._coerce_value(v) + continue + if not isinstance(i, int): + raise TypeError( + 'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: ' + 'expected integer keys or keyword "__all__"' + ) + normalized_i = v_length + i if i < 0 else i + normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i)) + + if not all_items: + return normalized_items + if self.is_true(all_items): + for i in range(v_length): + normalized_items.setdefault(i, ...) + return normalized_items + for i in range(v_length): + normalized_item = normalized_items.setdefault(i, {}) + if not self.is_true(normalized_item): + normalized_items[i] = self.merge(all_items, normalized_item) + return normalized_items + + @classmethod + def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any: + """Merge a `base` item with an `override` item. + + Both `base` and `override` are converted to dictionaries if possible. + Sets are converted to dictionaries with the sets entries as keys and + Ellipsis as values. + + Each key-value pair existing in `base` is merged with `override`, + while the rest of the key-value pairs are updated recursively with this function. + + Merging takes place based on the "union" of keys if `intersect` is + set to `False` (default) and on the intersection of keys if + `intersect` is set to `True`. + """ + override = cls._coerce_value(override) + base = cls._coerce_value(base) + if override is None: + return base + if cls.is_true(base) or base is None: + return override + if cls.is_true(override): + return base if intersect else override + + # intersection or union of keys while preserving ordering: + if intersect: + merge_keys = [k for k in base if k in override] + [k for k in override if k in base] + else: + merge_keys = list(base) + [k for k in override if k not in base] + + merged: dict[int | str, Any] = {} + for k in merge_keys: + merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect) + if merged_item is not None: + merged[k] = merged_item + + return merged + + @staticmethod + def _coerce_items(items: AbstractSetIntStr | MappingIntStrAny) -> MappingIntStrAny: + if isinstance(items, Mapping): + pass + elif isinstance(items, AbstractSet): + items = dict.fromkeys(items, ...) # type: ignore + else: + class_name = getattr(items, '__class__', '???') + raise TypeError(f'Unexpected type of exclude value {class_name}') + return items # type: ignore + + @classmethod + def _coerce_value(cls, value: Any) -> Any: + if value is None or cls.is_true(value): + return value + return cls._coerce_items(value) + + @staticmethod + def is_true(v: Any) -> bool: + return v is True or v is ... + + def __repr_args__(self) -> _repr.ReprArgs: + return [(None, self._items)] + + +if TYPE_CHECKING: + + def LazyClassAttribute(name: str, get_value: Callable[[], T]) -> T: ... + +else: + + class LazyClassAttribute: + """A descriptor exposing an attribute only accessible on a class (hidden from instances). + + The attribute is lazily computed and cached during the first access. + """ + + def __init__(self, name: str, get_value: Callable[[], Any]) -> None: + self.name = name + self.get_value = get_value + + @cached_property + def value(self) -> Any: + return self.get_value() + + def __get__(self, instance: Any, owner: type[Any]) -> None: + if instance is None: + return self.value + raise AttributeError(f'{self.name!r} attribute of {owner.__name__!r} is class-only') + + +Obj = TypeVar('Obj') + + +def smart_deepcopy(obj: Obj) -> Obj: + """Return type as is for immutable built-in types + Use obj.copy() for built-in empty collections + Use copy.deepcopy() for non-empty collections and unknown objects. + """ + if obj is MISSING or obj is PydanticUndefined: + return obj # pyright: ignore[reportReturnType] + obj_type = obj.__class__ + if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES: + return obj # fastest case: obj is immutable and not collection therefore will not be copied anyway + try: + if not obj and obj_type in BUILTIN_COLLECTIONS: + # faster way for empty collections, no need to copy its members + return obj if obj_type is tuple else obj.copy() # tuple doesn't have copy method # type: ignore + except (TypeError, ValueError, RuntimeError): + # do we really dare to catch ALL errors? Seems a bit risky + pass + + return deepcopy(obj) # slowest way when we actually might need a deepcopy + + +_SENTINEL = object() + + +def all_identical(left: Iterable[Any], right: Iterable[Any]) -> bool: + """Check that the items of `left` are the same objects as those in `right`. + + >>> a, b = object(), object() + >>> all_identical([a, b, a], [a, b, a]) + True + >>> all_identical([a, b, [a]], [a, b, [a]]) # new list object, while "equal" is not "identical" + False + """ + for left_item, right_item in zip_longest(left, right, fillvalue=_SENTINEL): + if left_item is not right_item: + return False + return True + + +def get_first_not_none(a: Any, b: Any) -> Any: + """Return the first argument if it is not `None`, otherwise return the second argument.""" + return a if a is not None else b + + +@dataclasses.dataclass(frozen=True) +class SafeGetItemProxy: + """Wrapper redirecting `__getitem__` to `get` with a sentinel value as default + + This makes is safe to use in `operator.itemgetter` when some keys may be missing + """ + + # Define __slots__manually for performances + # @dataclasses.dataclass() only support slots=True in python>=3.10 + __slots__ = ('wrapped',) + + wrapped: Mapping[str, Any] + + def __getitem__(self, key: str, /) -> Any: + return self.wrapped.get(key, _SENTINEL) + + # required to pass the object to operator.itemgetter() instances due to a quirk of typeshed + # https://github.com/python/mypy/issues/13713 + # https://github.com/python/typeshed/pull/8785 + # Since this is typing-only, hide it in a typing.TYPE_CHECKING block + if TYPE_CHECKING: + + def __contains__(self, key: str, /) -> bool: + return self.wrapped.__contains__(key) + + +_ModelT = TypeVar('_ModelT', bound='BaseModel') +_RT = TypeVar('_RT') + + +class deprecated_instance_property(Generic[_ModelT, _RT]): + """A decorator exposing the decorated class method as a property, with a warning on instance access. + + This decorator takes a class method defined on the `BaseModel` class and transforms it into + an attribute. The attribute can be accessed on both the class and instances of the class. If accessed + via an instance, a deprecation warning is emitted stating that instance access will be removed in V3. + """ + + def __init__(self, fget: Callable[[type[_ModelT]], _RT], /) -> None: + # Note: fget should be a classmethod: + self.fget = fget + + @overload + def __get__(self, instance: None, objtype: type[_ModelT]) -> _RT: ... + @overload + @deprecated( + 'Accessing this attribute on the instance is deprecated, and will be removed in Pydantic V3. ' + 'Instead, you should access this attribute from the model class.', + category=None, + ) + def __get__(self, instance: _ModelT, objtype: type[_ModelT]) -> _RT: ... + def __get__(self, instance: _ModelT | None, objtype: type[_ModelT]) -> _RT: + if instance is not None: + # fmt: off + attr_name = ( + self.fget.__name__ + if sys.version_info >= (3, 10) + else self.fget.__func__.__name__ # pyright: ignore[reportFunctionMemberAccess] + ) + # fmt: on + warnings.warn( + f'Accessing the {attr_name!r} attribute on the instance is deprecated. ' + 'Instead, you should access this attribute from the model class.', + category=PydanticDeprecatedSince211, + stacklevel=2, + ) + return self.fget.__get__(instance, objtype)() diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_validate_call.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_validate_call.py new file mode 100644 index 0000000000000000000000000000000000000000..49ccd4f2d321128dbabb02a2eef2616875da0b95 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_validate_call.py @@ -0,0 +1,141 @@ +from __future__ import annotations as _annotations + +import functools +import inspect +from collections.abc import Awaitable +from functools import partial +from typing import Any, Callable + +import pydantic_core + +from ..config import ConfigDict +from ..plugin._schema_validator import create_schema_validator +from ._config import ConfigWrapper +from ._generate_schema import GenerateSchema, ValidateCallSupportedTypes +from ._namespace_utils import MappingNamespace, NsResolver, ns_for_function +from ._typing_extra import signature_no_eval + + +def extract_function_name(func: ValidateCallSupportedTypes) -> str: + """Extract the name of a `ValidateCallSupportedTypes` object.""" + return f'partial({func.func.__name__})' if isinstance(func, functools.partial) else func.__name__ + + +def extract_function_qualname(func: ValidateCallSupportedTypes) -> str: + """Extract the qualname of a `ValidateCallSupportedTypes` object.""" + return f'partial({func.func.__qualname__})' if isinstance(func, functools.partial) else func.__qualname__ + + +def update_wrapper_attributes(wrapped: ValidateCallSupportedTypes, wrapper: Callable[..., Any]): + """Update the `wrapper` function with the attributes of the `wrapped` function. Return the updated function.""" + if inspect.iscoroutinefunction(wrapped): + + @functools.wraps(wrapped) + async def wrapper_function(*args, **kwargs): # type: ignore + return await wrapper(*args, **kwargs) + else: + + @functools.wraps(wrapped) + def wrapper_function(*args, **kwargs): + return wrapper(*args, **kwargs) + + # We need to manually update this because `partial` object has no `__name__` and `__qualname__`. + wrapper_function.__name__ = extract_function_name(wrapped) + wrapper_function.__qualname__ = extract_function_qualname(wrapped) + wrapper_function.raw_function = wrapped # type: ignore + + return wrapper_function + + +class ValidateCallWrapper: + """This is a wrapper around a function that validates the arguments passed to it, and optionally the return value.""" + + __slots__ = ( + 'function', + 'validate_return', + 'schema_type', + 'module', + 'qualname', + 'ns_resolver', + 'config_wrapper', + '__pydantic_complete__', + '__pydantic_validator__', + '__return_pydantic_validator__', + ) + + def __init__( + self, + function: ValidateCallSupportedTypes, + config: ConfigDict | None, + validate_return: bool, + parent_namespace: MappingNamespace | None, + ) -> None: + self.function = function + self.validate_return = validate_return + if isinstance(function, partial): + self.schema_type = function.func + self.module = function.func.__module__ + else: + self.schema_type = function + self.module = function.__module__ + self.qualname = extract_function_qualname(function) + + self.ns_resolver = NsResolver( + namespaces_tuple=ns_for_function(self.schema_type, parent_namespace=parent_namespace) + ) + self.config_wrapper = ConfigWrapper(config) + if not self.config_wrapper.defer_build: + self._create_validators() + else: + self.__pydantic_complete__ = False + + def _create_validators(self) -> None: + gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver) + schema = gen_schema.clean_schema(gen_schema.generate_schema(self.function)) + core_config = self.config_wrapper.core_config(title=self.qualname) + + self.__pydantic_validator__ = create_schema_validator( + schema, + self.schema_type, + self.module, + self.qualname, + 'validate_call', + core_config, + self.config_wrapper.plugin_settings, + ) + if self.validate_return: + signature = signature_no_eval(self.function) + return_type = signature.return_annotation if signature.return_annotation is not signature.empty else Any + gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver) + schema = gen_schema.clean_schema(gen_schema.generate_schema(return_type)) + validator = create_schema_validator( + schema, + self.schema_type, + self.module, + self.qualname, + 'validate_call', + core_config, + self.config_wrapper.plugin_settings, + ) + if inspect.iscoroutinefunction(self.function): + + async def return_val_wrapper(aw: Awaitable[Any]) -> None: + return validator.validate_python(await aw) + + self.__return_pydantic_validator__ = return_val_wrapper + else: + self.__return_pydantic_validator__ = validator.validate_python + else: + self.__return_pydantic_validator__ = None + + self.__pydantic_complete__ = True + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + if not self.__pydantic_complete__: + self._create_validators() + + res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs)) + if self.__return_pydantic_validator__: + return self.__return_pydantic_validator__(res) + else: + return res diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..3ad6c92790404b7b63cafed02e9dccb5c4fb7a3a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_internal/_validators.py @@ -0,0 +1,534 @@ +"""Validator functions for standard library types. + +Import of this module is deferred since it contains imports of many standard library modules. +""" + +from __future__ import annotations as _annotations + +import collections.abc +import math +import re +import typing +from collections.abc import Sequence +from decimal import Decimal +from fractions import Fraction +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from typing import Any, Callable, TypeVar, Union, cast +from zoneinfo import ZoneInfo, ZoneInfoNotFoundError + +import typing_extensions +from pydantic_core import PydanticCustomError, PydanticKnownError, core_schema +from typing_extensions import get_args, get_origin +from typing_inspection import typing_objects + +from pydantic._internal._import_utils import import_cached_field_info +from pydantic.errors import PydanticSchemaGenerationError + + +def sequence_validator( + input_value: Sequence[Any], + /, + validator: core_schema.ValidatorFunctionWrapHandler, +) -> Sequence[Any]: + """Validator for `Sequence` types, isinstance(v, Sequence) has already been called.""" + value_type = type(input_value) + + # We don't accept any plain string as a sequence + # Relevant issue: https://github.com/pydantic/pydantic/issues/5595 + if issubclass(value_type, (str, bytes)): + raise PydanticCustomError( + 'sequence_str', + "'{type_name}' instances are not allowed as a Sequence value", + {'type_name': value_type.__name__}, + ) + + # TODO: refactor sequence validation to validate with either a list or a tuple + # schema, depending on the type of the value. + # Additionally, we should be able to remove one of either this validator or the + # SequenceValidator in _std_types_schema.py (preferably this one, while porting over some logic). + # Effectively, a refactor for sequence validation is needed. + if value_type is tuple: + input_value = list(input_value) + + v_list = validator(input_value) + + # the rest of the logic is just re-creating the original type from `v_list` + if value_type is list: + return v_list + elif issubclass(value_type, range): + # return the list as we probably can't re-create the range + return v_list + elif value_type is tuple: + return tuple(v_list) + else: + # best guess at how to re-create the original type, more custom construction logic might be required + return value_type(v_list) # type: ignore[call-arg] + + +def import_string(value: Any) -> Any: + if isinstance(value, str): + try: + return _import_string_logic(value) + except ImportError as e: + raise PydanticCustomError('import_error', 'Invalid python path: {error}', {'error': str(e)}) from e + else: + # otherwise we just return the value and let the next validator do the rest of the work + return value + + +def _import_string_logic(dotted_path: str) -> Any: + """Inspired by uvicorn — dotted paths should include a colon before the final item if that item is not a module. + (This is necessary to distinguish between a submodule and an attribute when there is a conflict.). + + If the dotted path does not include a colon and the final item is not a valid module, importing as an attribute + rather than a submodule will be attempted automatically. + + So, for example, the following values of `dotted_path` result in the following returned values: + * 'collections': + * 'collections.abc': + * 'collections.abc:Mapping': + * `collections.abc.Mapping`: (though this is a bit slower than the previous line) + + An error will be raised under any of the following scenarios: + * `dotted_path` contains more than one colon (e.g., 'collections:abc:Mapping') + * the substring of `dotted_path` before the colon is not a valid module in the environment (e.g., '123:Mapping') + * the substring of `dotted_path` after the colon is not an attribute of the module (e.g., 'collections:abc123') + """ + from importlib import import_module + + components = dotted_path.strip().split(':') + if len(components) > 2: + raise ImportError(f"Import strings should have at most one ':'; received {dotted_path!r}") + attribute = None + if len(components) == 2: + attribute = components[1] + module_path = components[0] + if not module_path: + raise ImportError(f'Import strings should have a nonempty module name; received {dotted_path!r}') + + try: + module = import_module(module_path) + except ModuleNotFoundError: + if attribute is None and '.' in module_path: + # Try interpreting the final dotted segment as an attribute, not a submodule + maybe_module_path, maybe_attribute = module_path.rsplit('.', 1) + + try: + return _import_string_logic(f'{maybe_module_path}:{maybe_attribute}') + except ImportError: + pass + raise + + if attribute is not None: + try: + return getattr(module, attribute) + except AttributeError as e: + raise ImportError(f'cannot import name {attribute!r} from {module_path!r}') from e + else: + return module + + +def pattern_either_validator(input_value: Any, /) -> re.Pattern[Any]: + if isinstance(input_value, re.Pattern): + return input_value + elif isinstance(input_value, (str, bytes)): + # todo strict mode + return compile_pattern(input_value) # type: ignore + else: + raise PydanticCustomError('pattern_type', 'Input should be a valid pattern') + + +def pattern_str_validator(input_value: Any, /) -> re.Pattern[str]: + if isinstance(input_value, re.Pattern): + if isinstance(input_value.pattern, str): + return input_value + else: + raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern') + elif isinstance(input_value, str): + return compile_pattern(input_value) + elif isinstance(input_value, bytes): + raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern') + else: + raise PydanticCustomError('pattern_type', 'Input should be a valid pattern') + + +def pattern_bytes_validator(input_value: Any, /) -> re.Pattern[bytes]: + if isinstance(input_value, re.Pattern): + if isinstance(input_value.pattern, bytes): + return input_value + else: + raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern') + elif isinstance(input_value, bytes): + return compile_pattern(input_value) + elif isinstance(input_value, str): + raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern') + else: + raise PydanticCustomError('pattern_type', 'Input should be a valid pattern') + + +PatternType = TypeVar('PatternType', str, bytes) + + +def compile_pattern(pattern: PatternType) -> re.Pattern[PatternType]: + try: + return re.compile(pattern) + except re.error: + raise PydanticCustomError('pattern_regex', 'Input should be a valid regular expression') + + +def ip_v4_address_validator(input_value: Any, /) -> IPv4Address: + if isinstance(input_value, IPv4Address): + return input_value + + try: + return IPv4Address(input_value) + except ValueError: + raise PydanticCustomError('ip_v4_address', 'Input is not a valid IPv4 address') + + +def ip_v6_address_validator(input_value: Any, /) -> IPv6Address: + if isinstance(input_value, IPv6Address): + return input_value + + try: + return IPv6Address(input_value) + except ValueError: + raise PydanticCustomError('ip_v6_address', 'Input is not a valid IPv6 address') + + +def ip_v4_network_validator(input_value: Any, /) -> IPv4Network: + """Assume IPv4Network initialised with a default `strict` argument. + + See more: + https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network + """ + if isinstance(input_value, IPv4Network): + return input_value + + try: + return IPv4Network(input_value) + except ValueError: + raise PydanticCustomError('ip_v4_network', 'Input is not a valid IPv4 network') + + +def ip_v6_network_validator(input_value: Any, /) -> IPv6Network: + """Assume IPv6Network initialised with a default `strict` argument. + + See more: + https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network + """ + if isinstance(input_value, IPv6Network): + return input_value + + try: + return IPv6Network(input_value) + except ValueError: + raise PydanticCustomError('ip_v6_network', 'Input is not a valid IPv6 network') + + +def ip_v4_interface_validator(input_value: Any, /) -> IPv4Interface: + if isinstance(input_value, IPv4Interface): + return input_value + + try: + return IPv4Interface(input_value) + except ValueError: + raise PydanticCustomError('ip_v4_interface', 'Input is not a valid IPv4 interface') + + +def ip_v6_interface_validator(input_value: Any, /) -> IPv6Interface: + if isinstance(input_value, IPv6Interface): + return input_value + + try: + return IPv6Interface(input_value) + except ValueError: + raise PydanticCustomError('ip_v6_interface', 'Input is not a valid IPv6 interface') + + +def fraction_validator(input_value: Any, /) -> Fraction: + if isinstance(input_value, Fraction): + return input_value + + try: + return Fraction(input_value) + except ValueError: + raise PydanticCustomError('fraction_parsing', 'Input is not a valid fraction') + + +def forbid_inf_nan_check(x: Any) -> Any: + if not math.isfinite(x): + raise PydanticKnownError('finite_number') + return x + + +def _safe_repr(v: Any) -> int | float | str: + """The context argument for `PydanticKnownError` requires a number or str type, so we do a simple repr() coercion for types like timedelta. + + See tests/test_types.py::test_annotated_metadata_any_order for some context. + """ + if isinstance(v, (int, float, str)): + return v + return repr(v) + + +def greater_than_validator(x: Any, gt: Any) -> Any: + try: + if not (x > gt): + raise PydanticKnownError('greater_than', {'gt': _safe_repr(gt)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'gt' to supplied value {x}") + + +def greater_than_or_equal_validator(x: Any, ge: Any) -> Any: + try: + if not (x >= ge): + raise PydanticKnownError('greater_than_equal', {'ge': _safe_repr(ge)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'ge' to supplied value {x}") + + +def less_than_validator(x: Any, lt: Any) -> Any: + try: + if not (x < lt): + raise PydanticKnownError('less_than', {'lt': _safe_repr(lt)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'lt' to supplied value {x}") + + +def less_than_or_equal_validator(x: Any, le: Any) -> Any: + try: + if not (x <= le): + raise PydanticKnownError('less_than_equal', {'le': _safe_repr(le)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'le' to supplied value {x}") + + +def multiple_of_validator(x: Any, multiple_of: Any) -> Any: + try: + if x % multiple_of: + raise PydanticKnownError('multiple_of', {'multiple_of': _safe_repr(multiple_of)}) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'multiple_of' to supplied value {x}") + + +def min_length_validator(x: Any, min_length: Any) -> Any: + try: + if not (len(x) >= min_length): + raise PydanticKnownError( + 'too_short', {'field_type': 'Value', 'min_length': min_length, 'actual_length': len(x)} + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'min_length' to supplied value {x}") + + +def max_length_validator(x: Any, max_length: Any) -> Any: + try: + if len(x) > max_length: + raise PydanticKnownError( + 'too_long', + {'field_type': 'Value', 'max_length': max_length, 'actual_length': len(x)}, + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'max_length' to supplied value {x}") + + +def _extract_decimal_digits_info(decimal: Decimal) -> tuple[int, int]: + """Compute the total number of digits and decimal places for a given [`Decimal`][decimal.Decimal] instance. + + This function handles both normalized and non-normalized Decimal instances. + Example: Decimal('1.230') -> 4 digits, 3 decimal places + + Args: + decimal (Decimal): The decimal number to analyze. + + Returns: + tuple[int, int]: A tuple containing the number of decimal places and total digits. + + Though this could be divided into two separate functions, the logic is easier to follow if we couple the computation + of the number of decimals and digits together. + """ + try: + decimal_tuple = decimal.as_tuple() + + assert isinstance(decimal_tuple.exponent, int) + + exponent = decimal_tuple.exponent + num_digits = len(decimal_tuple.digits) + + if exponent >= 0: + # A positive exponent adds that many trailing zeros + # Ex: digit_tuple=(1, 2, 3), exponent=2 -> 12300 -> 0 decimal places, 5 digits + num_digits += exponent + decimal_places = 0 + else: + # If the absolute value of the negative exponent is larger than the + # number of digits, then it's the same as the number of digits, + # because it'll consume all the digits in digit_tuple and then + # add abs(exponent) - len(digit_tuple) leading zeros after the decimal point. + # Ex: digit_tuple=(1, 2, 3), exponent=-2 -> 1.23 -> 2 decimal places, 3 digits + # Ex: digit_tuple=(1, 2, 3), exponent=-4 -> 0.0123 -> 4 decimal places, 4 digits + decimal_places = abs(exponent) + num_digits = max(num_digits, decimal_places) + + return decimal_places, num_digits + except (AssertionError, AttributeError): + raise TypeError(f'Unable to extract decimal digits info from supplied value {decimal}') + + +def max_digits_validator(x: Any, max_digits: Any) -> Any: + try: + _, num_digits = _extract_decimal_digits_info(x) + _, normalized_num_digits = _extract_decimal_digits_info(x.normalize()) + if (num_digits > max_digits) and (normalized_num_digits > max_digits): + raise PydanticKnownError( + 'decimal_max_digits', + {'max_digits': max_digits}, + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'max_digits' to supplied value {x}") + + +def decimal_places_validator(x: Any, decimal_places: Any) -> Any: + try: + decimal_places_, _ = _extract_decimal_digits_info(x) + if decimal_places_ > decimal_places: + normalized_decimal_places, _ = _extract_decimal_digits_info(x.normalize()) + if normalized_decimal_places > decimal_places: + raise PydanticKnownError( + 'decimal_max_places', + {'decimal_places': decimal_places}, + ) + return x + except TypeError: + raise TypeError(f"Unable to apply constraint 'decimal_places' to supplied value {x}") + + +def deque_validator(input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler) -> collections.deque[Any]: + return collections.deque(handler(input_value), maxlen=getattr(input_value, 'maxlen', None)) + + +def defaultdict_validator( + input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler, default_default_factory: Callable[[], Any] +) -> collections.defaultdict[Any, Any]: + if isinstance(input_value, collections.defaultdict): + default_factory = input_value.default_factory + return collections.defaultdict(default_factory, handler(input_value)) + else: + return collections.defaultdict(default_default_factory, handler(input_value)) + + +def get_defaultdict_default_default_factory(values_source_type: Any) -> Callable[[], Any]: + FieldInfo = import_cached_field_info() + + values_type_origin = get_origin(values_source_type) + + def infer_default() -> Callable[[], Any]: + allowed_default_types: dict[Any, Any] = { + tuple: tuple, + collections.abc.Sequence: tuple, + collections.abc.MutableSequence: list, + list: list, + typing.Sequence: list, + set: set, + typing.MutableSet: set, + collections.abc.MutableSet: set, + collections.abc.Set: frozenset, + typing.MutableMapping: dict, + typing.Mapping: dict, + collections.abc.Mapping: dict, + collections.abc.MutableMapping: dict, + float: float, + int: int, + str: str, + bool: bool, + } + values_type = values_type_origin or values_source_type + instructions = 'set using `DefaultDict[..., Annotated[..., Field(default_factory=...)]]`' + if typing_objects.is_typevar(values_type): + + def type_var_default_factory() -> None: + raise RuntimeError( + 'Generic defaultdict cannot be used without a concrete value type or an' + ' explicit default factory, ' + instructions + ) + + return type_var_default_factory + elif values_type not in allowed_default_types: + # a somewhat subjective set of types that have reasonable default values + allowed_msg = ', '.join([t.__name__ for t in set(allowed_default_types.values())]) + raise PydanticSchemaGenerationError( + f'Unable to infer a default factory for keys of type {values_source_type}.' + f' Only {allowed_msg} are supported, other types require an explicit default factory' + ' ' + instructions + ) + return allowed_default_types[values_type] + + # Assume Annotated[..., Field(...)] + if typing_objects.is_annotated(values_type_origin): + field_info = next((v for v in get_args(values_source_type) if isinstance(v, FieldInfo)), None) + else: + field_info = None + if field_info and field_info.default_factory: + # Assume the default factory does not take any argument: + default_default_factory = cast(Callable[[], Any], field_info.default_factory) + else: + default_default_factory = infer_default() + return default_default_factory + + +def validate_str_is_valid_iana_tz(value: Any, /) -> ZoneInfo: + if isinstance(value, ZoneInfo): + return value + try: + return ZoneInfo(value) + except (ZoneInfoNotFoundError, ValueError, TypeError): + raise PydanticCustomError('zoneinfo_str', 'invalid timezone: {value}', {'value': value}) + + +NUMERIC_VALIDATOR_LOOKUP: dict[str, Callable] = { + 'gt': greater_than_validator, + 'ge': greater_than_or_equal_validator, + 'lt': less_than_validator, + 'le': less_than_or_equal_validator, + 'multiple_of': multiple_of_validator, + 'min_length': min_length_validator, + 'max_length': max_length_validator, + 'max_digits': max_digits_validator, + 'decimal_places': decimal_places_validator, +} + +IpType = Union[IPv4Address, IPv6Address, IPv4Network, IPv6Network, IPv4Interface, IPv6Interface] + +IP_VALIDATOR_LOOKUP: dict[type[IpType], Callable] = { + IPv4Address: ip_v4_address_validator, + IPv6Address: ip_v6_address_validator, + IPv4Network: ip_v4_network_validator, + IPv6Network: ip_v6_network_validator, + IPv4Interface: ip_v4_interface_validator, + IPv6Interface: ip_v6_interface_validator, +} + +MAPPING_ORIGIN_MAP: dict[Any, Any] = { + typing.DefaultDict: collections.defaultdict, # noqa: UP006 + collections.defaultdict: collections.defaultdict, + typing.OrderedDict: collections.OrderedDict, # noqa: UP006 + collections.OrderedDict: collections.OrderedDict, + typing_extensions.OrderedDict: collections.OrderedDict, + typing.Counter: collections.Counter, + collections.Counter: collections.Counter, + # this doesn't handle subclasses of these + typing.Mapping: dict, + typing.MutableMapping: dict, + # parametrized typing.{Mutable}Mapping creates one of these + collections.abc.Mapping: dict, + collections.abc.MutableMapping: dict, +} diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_migration.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_migration.py new file mode 100644 index 0000000000000000000000000000000000000000..b4ecd283a0ac53c55ff75e9a03669d03b2d0ea30 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/_migration.py @@ -0,0 +1,316 @@ +import sys +from typing import Any, Callable + +from pydantic.warnings import PydanticDeprecatedSince20 + +from .version import version_short + +MOVED_IN_V2 = { + 'pydantic.utils:version_info': 'pydantic.version:version_info', + 'pydantic.error_wrappers:ValidationError': 'pydantic:ValidationError', + 'pydantic.utils:to_camel': 'pydantic.alias_generators:to_pascal', + 'pydantic.utils:to_lower_camel': 'pydantic.alias_generators:to_camel', + 'pydantic:PyObject': 'pydantic.types:ImportString', + 'pydantic.types:PyObject': 'pydantic.types:ImportString', + 'pydantic.generics:GenericModel': 'pydantic.BaseModel', +} + +DEPRECATED_MOVED_IN_V2 = { + 'pydantic.tools:schema_of': 'pydantic.deprecated.tools:schema_of', + 'pydantic.tools:parse_obj_as': 'pydantic.deprecated.tools:parse_obj_as', + 'pydantic.tools:schema_json_of': 'pydantic.deprecated.tools:schema_json_of', + 'pydantic.json:pydantic_encoder': 'pydantic.deprecated.json:pydantic_encoder', + 'pydantic:validate_arguments': 'pydantic.deprecated.decorator:validate_arguments', + 'pydantic.json:custom_pydantic_encoder': 'pydantic.deprecated.json:custom_pydantic_encoder', + 'pydantic.json:timedelta_isoformat': 'pydantic.deprecated.json:timedelta_isoformat', + 'pydantic.decorator:validate_arguments': 'pydantic.deprecated.decorator:validate_arguments', + 'pydantic.class_validators:validator': 'pydantic.deprecated.class_validators:validator', + 'pydantic.class_validators:root_validator': 'pydantic.deprecated.class_validators:root_validator', + 'pydantic.config:BaseConfig': 'pydantic.deprecated.config:BaseConfig', + 'pydantic.config:Extra': 'pydantic.deprecated.config:Extra', +} + +REDIRECT_TO_V1 = { + f'pydantic.utils:{obj}': f'pydantic.v1.utils:{obj}' + for obj in ( + 'deep_update', + 'GetterDict', + 'lenient_issubclass', + 'lenient_isinstance', + 'is_valid_field', + 'update_not_none', + 'import_string', + 'Representation', + 'ROOT_KEY', + 'smart_deepcopy', + 'sequence_like', + ) +} + + +REMOVED_IN_V2 = { + 'pydantic:ConstrainedBytes', + 'pydantic:ConstrainedDate', + 'pydantic:ConstrainedDecimal', + 'pydantic:ConstrainedFloat', + 'pydantic:ConstrainedFrozenSet', + 'pydantic:ConstrainedInt', + 'pydantic:ConstrainedList', + 'pydantic:ConstrainedSet', + 'pydantic:ConstrainedStr', + 'pydantic:JsonWrapper', + 'pydantic:NoneBytes', + 'pydantic:NoneStr', + 'pydantic:NoneStrBytes', + 'pydantic:Protocol', + 'pydantic:Required', + 'pydantic:StrBytes', + 'pydantic:compiled', + 'pydantic.config:get_config', + 'pydantic.config:inherit_config', + 'pydantic.config:prepare_config', + 'pydantic:create_model_from_namedtuple', + 'pydantic:create_model_from_typeddict', + 'pydantic.dataclasses:create_pydantic_model_from_dataclass', + 'pydantic.dataclasses:make_dataclass_validator', + 'pydantic.dataclasses:set_validation', + 'pydantic.datetime_parse:parse_date', + 'pydantic.datetime_parse:parse_time', + 'pydantic.datetime_parse:parse_datetime', + 'pydantic.datetime_parse:parse_duration', + 'pydantic.error_wrappers:ErrorWrapper', + 'pydantic.errors:AnyStrMaxLengthError', + 'pydantic.errors:AnyStrMinLengthError', + 'pydantic.errors:ArbitraryTypeError', + 'pydantic.errors:BoolError', + 'pydantic.errors:BytesError', + 'pydantic.errors:CallableError', + 'pydantic.errors:ClassError', + 'pydantic.errors:ColorError', + 'pydantic.errors:ConfigError', + 'pydantic.errors:DataclassTypeError', + 'pydantic.errors:DateError', + 'pydantic.errors:DateNotInTheFutureError', + 'pydantic.errors:DateNotInThePastError', + 'pydantic.errors:DateTimeError', + 'pydantic.errors:DecimalError', + 'pydantic.errors:DecimalIsNotFiniteError', + 'pydantic.errors:DecimalMaxDigitsError', + 'pydantic.errors:DecimalMaxPlacesError', + 'pydantic.errors:DecimalWholeDigitsError', + 'pydantic.errors:DictError', + 'pydantic.errors:DurationError', + 'pydantic.errors:EmailError', + 'pydantic.errors:EnumError', + 'pydantic.errors:EnumMemberError', + 'pydantic.errors:ExtraError', + 'pydantic.errors:FloatError', + 'pydantic.errors:FrozenSetError', + 'pydantic.errors:FrozenSetMaxLengthError', + 'pydantic.errors:FrozenSetMinLengthError', + 'pydantic.errors:HashableError', + 'pydantic.errors:IPv4AddressError', + 'pydantic.errors:IPv4InterfaceError', + 'pydantic.errors:IPv4NetworkError', + 'pydantic.errors:IPv6AddressError', + 'pydantic.errors:IPv6InterfaceError', + 'pydantic.errors:IPv6NetworkError', + 'pydantic.errors:IPvAnyAddressError', + 'pydantic.errors:IPvAnyInterfaceError', + 'pydantic.errors:IPvAnyNetworkError', + 'pydantic.errors:IntEnumError', + 'pydantic.errors:IntegerError', + 'pydantic.errors:InvalidByteSize', + 'pydantic.errors:InvalidByteSizeUnit', + 'pydantic.errors:InvalidDiscriminator', + 'pydantic.errors:InvalidLengthForBrand', + 'pydantic.errors:JsonError', + 'pydantic.errors:JsonTypeError', + 'pydantic.errors:ListError', + 'pydantic.errors:ListMaxLengthError', + 'pydantic.errors:ListMinLengthError', + 'pydantic.errors:ListUniqueItemsError', + 'pydantic.errors:LuhnValidationError', + 'pydantic.errors:MissingDiscriminator', + 'pydantic.errors:MissingError', + 'pydantic.errors:NoneIsAllowedError', + 'pydantic.errors:NoneIsNotAllowedError', + 'pydantic.errors:NotDigitError', + 'pydantic.errors:NotNoneError', + 'pydantic.errors:NumberNotGeError', + 'pydantic.errors:NumberNotGtError', + 'pydantic.errors:NumberNotLeError', + 'pydantic.errors:NumberNotLtError', + 'pydantic.errors:NumberNotMultipleError', + 'pydantic.errors:PathError', + 'pydantic.errors:PathNotADirectoryError', + 'pydantic.errors:PathNotAFileError', + 'pydantic.errors:PathNotExistsError', + 'pydantic.errors:PatternError', + 'pydantic.errors:PyObjectError', + 'pydantic.errors:PydanticTypeError', + 'pydantic.errors:PydanticValueError', + 'pydantic.errors:SequenceError', + 'pydantic.errors:SetError', + 'pydantic.errors:SetMaxLengthError', + 'pydantic.errors:SetMinLengthError', + 'pydantic.errors:StrError', + 'pydantic.errors:StrRegexError', + 'pydantic.errors:StrictBoolError', + 'pydantic.errors:SubclassError', + 'pydantic.errors:TimeError', + 'pydantic.errors:TupleError', + 'pydantic.errors:TupleLengthError', + 'pydantic.errors:UUIDError', + 'pydantic.errors:UUIDVersionError', + 'pydantic.errors:UrlError', + 'pydantic.errors:UrlExtraError', + 'pydantic.errors:UrlHostError', + 'pydantic.errors:UrlHostTldError', + 'pydantic.errors:UrlPortError', + 'pydantic.errors:UrlSchemeError', + 'pydantic.errors:UrlSchemePermittedError', + 'pydantic.errors:UrlUserInfoError', + 'pydantic.errors:WrongConstantError', + 'pydantic.main:validate_model', + 'pydantic.networks:stricturl', + 'pydantic:parse_file_as', + 'pydantic:parse_raw_as', + 'pydantic:stricturl', + 'pydantic.tools:parse_file_as', + 'pydantic.tools:parse_raw_as', + 'pydantic.types:ConstrainedBytes', + 'pydantic.types:ConstrainedDate', + 'pydantic.types:ConstrainedDecimal', + 'pydantic.types:ConstrainedFloat', + 'pydantic.types:ConstrainedFrozenSet', + 'pydantic.types:ConstrainedInt', + 'pydantic.types:ConstrainedList', + 'pydantic.types:ConstrainedSet', + 'pydantic.types:ConstrainedStr', + 'pydantic.types:JsonWrapper', + 'pydantic.types:NoneBytes', + 'pydantic.types:NoneStr', + 'pydantic.types:NoneStrBytes', + 'pydantic.types:StrBytes', + 'pydantic.typing:evaluate_forwardref', + 'pydantic.typing:AbstractSetIntStr', + 'pydantic.typing:AnyCallable', + 'pydantic.typing:AnyClassMethod', + 'pydantic.typing:CallableGenerator', + 'pydantic.typing:DictAny', + 'pydantic.typing:DictIntStrAny', + 'pydantic.typing:DictStrAny', + 'pydantic.typing:IntStr', + 'pydantic.typing:ListStr', + 'pydantic.typing:MappingIntStrAny', + 'pydantic.typing:NoArgAnyCallable', + 'pydantic.typing:NoneType', + 'pydantic.typing:ReprArgs', + 'pydantic.typing:SetStr', + 'pydantic.typing:StrPath', + 'pydantic.typing:TupleGenerator', + 'pydantic.typing:WithArgsTypes', + 'pydantic.typing:all_literal_values', + 'pydantic.typing:display_as_type', + 'pydantic.typing:get_all_type_hints', + 'pydantic.typing:get_args', + 'pydantic.typing:get_origin', + 'pydantic.typing:get_sub_types', + 'pydantic.typing:is_callable_type', + 'pydantic.typing:is_classvar', + 'pydantic.typing:is_finalvar', + 'pydantic.typing:is_literal_type', + 'pydantic.typing:is_namedtuple', + 'pydantic.typing:is_new_type', + 'pydantic.typing:is_none_type', + 'pydantic.typing:is_typeddict', + 'pydantic.typing:is_typeddict_special', + 'pydantic.typing:is_union', + 'pydantic.typing:new_type_supertype', + 'pydantic.typing:resolve_annotations', + 'pydantic.typing:typing_base', + 'pydantic.typing:update_field_forward_refs', + 'pydantic.typing:update_model_forward_refs', + 'pydantic.utils:ClassAttribute', + 'pydantic.utils:DUNDER_ATTRIBUTES', + 'pydantic.utils:PyObjectStr', + 'pydantic.utils:ValueItems', + 'pydantic.utils:almost_equal_floats', + 'pydantic.utils:get_discriminator_alias_and_values', + 'pydantic.utils:get_model', + 'pydantic.utils:get_unique_discriminator_alias', + 'pydantic.utils:in_ipython', + 'pydantic.utils:is_valid_identifier', + 'pydantic.utils:path_type', + 'pydantic.utils:validate_field_name', + 'pydantic:validate_model', +} + + +def getattr_migration(module: str) -> Callable[[str], Any]: + """Implement PEP 562 for objects that were either moved or removed on the migration + to V2. + + Args: + module: The module name. + + Returns: + A callable that will raise an error if the object is not found. + """ + # This avoids circular import with errors.py. + from .errors import PydanticImportError + + def wrapper(name: str) -> object: + """Raise an error if the object is not found, or warn if it was moved. + + In case it was moved, it still returns the object. + + Args: + name: The object name. + + Returns: + The object. + """ + if name == '__path__': + raise AttributeError(f'module {module!r} has no attribute {name!r}') + + import warnings + + from ._internal._validators import import_string + + import_path = f'{module}:{name}' + if import_path in MOVED_IN_V2.keys(): + new_location = MOVED_IN_V2[import_path] + warnings.warn( + f'`{import_path}` has been moved to `{new_location}`.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return import_string(MOVED_IN_V2[import_path]) + if import_path in DEPRECATED_MOVED_IN_V2: + # skip the warning here because a deprecation warning will be raised elsewhere + return import_string(DEPRECATED_MOVED_IN_V2[import_path]) + if import_path in REDIRECT_TO_V1: + new_location = REDIRECT_TO_V1[import_path] + warnings.warn( + f'`{import_path}` has been removed. We are importing from `{new_location}` instead.' + 'See the migration guide for more details: https://docs.pydantic.dev/latest/migration/', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return import_string(REDIRECT_TO_V1[import_path]) + if import_path == 'pydantic:BaseSettings': + raise PydanticImportError( + '`BaseSettings` has been moved to the `pydantic-settings` package. ' + f'See https://docs.pydantic.dev/{version_short()}/migration/#basesettings-has-moved-to-pydantic-settings ' + 'for more details.' + ) + if import_path in REMOVED_IN_V2: + raise PydanticImportError(f'`{import_path}` has been removed in V2.') + globals: dict[str, Any] = sys.modules[module].__dict__ + if name in globals: + return globals[name] + raise AttributeError(f'module {module!r} has no attribute {name!r}') + + return wrapper diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/alias_generators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/alias_generators.py new file mode 100644 index 0000000000000000000000000000000000000000..0b7653f5825979edcd446d698d320bdb980b8e9e --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/alias_generators.py @@ -0,0 +1,62 @@ +"""Alias generators for converting between different capitalization conventions.""" + +import re + +__all__ = ('to_pascal', 'to_camel', 'to_snake') + +# TODO: in V3, change the argument names to be more descriptive +# Generally, don't only convert from snake_case, or name the functions +# more specifically like snake_to_camel. + + +def to_pascal(snake: str) -> str: + """Convert a snake_case string to PascalCase. + + Args: + snake: The string to convert. + + Returns: + The PascalCase string. + """ + camel = snake.title() + return re.sub('([0-9A-Za-z])_(?=[0-9A-Z])', lambda m: m.group(1), camel) + + +def to_camel(snake: str) -> str: + """Convert a snake_case string to camelCase. + + Args: + snake: The string to convert. + + Returns: + The converted camelCase string. + """ + # If the string is already in camelCase and does not contain a digit followed + # by a lowercase letter, return it as it is + if re.match('^[a-z]+[A-Za-z0-9]*$', snake) and not re.search(r'\d[a-z]', snake): + return snake + + camel = to_pascal(snake) + return re.sub('(^_*[A-Z])', lambda m: m.group(1).lower(), camel) + + +def to_snake(camel: str) -> str: + """Convert a PascalCase, camelCase, or kebab-case string to snake_case. + + Args: + camel: The string to convert. + + Returns: + The converted string in snake_case. + """ + # Handle the sequence of uppercase letters followed by a lowercase letter + snake = re.sub(r'([A-Z]+)([A-Z][a-z])', lambda m: f'{m.group(1)}_{m.group(2)}', camel) + # Insert an underscore between a lowercase letter and an uppercase letter + snake = re.sub(r'([a-z])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake) + # Insert an underscore between a digit and an uppercase letter + snake = re.sub(r'([0-9])([A-Z])', lambda m: f'{m.group(1)}_{m.group(2)}', snake) + # Insert an underscore between a lowercase letter and a digit + snake = re.sub(r'([a-z])([0-9])', lambda m: f'{m.group(1)}_{m.group(2)}', snake) + # Replace hyphens with underscores to handle kebab-case + snake = snake.replace('-', '_') + return snake.lower() diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/aliases.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/aliases.py new file mode 100644 index 0000000000000000000000000000000000000000..ac2273701b5a0ae7cb0d0171cd08a7bbf77a9178 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/aliases.py @@ -0,0 +1,135 @@ +"""Support for alias configurations.""" + +from __future__ import annotations + +import dataclasses +from typing import Any, Callable, Literal + +from pydantic_core import PydanticUndefined + +from ._internal import _internal_dataclass + +__all__ = ('AliasGenerator', 'AliasPath', 'AliasChoices') + + +@dataclasses.dataclass(**_internal_dataclass.slots_true) +class AliasPath: + """!!! abstract "Usage Documentation" + [`AliasPath` and `AliasChoices`](../concepts/alias.md#aliaspath-and-aliaschoices) + + A data class used by `validation_alias` as a convenience to create aliases. + + Attributes: + path: A list of string or integer aliases. + """ + + path: list[int | str] + + def __init__(self, first_arg: str, *args: str | int) -> None: + self.path = [first_arg] + list(args) + + def convert_to_aliases(self) -> list[str | int]: + """Converts arguments to a list of string or integer aliases. + + Returns: + The list of aliases. + """ + return self.path + + def search_dict_for_path(self, d: dict) -> Any: + """Searches a dictionary for the path specified by the alias. + + Returns: + The value at the specified path, or `PydanticUndefined` if the path is not found. + """ + v = d + for k in self.path: + if isinstance(v, str): + # disallow indexing into a str, like for AliasPath('x', 0) and x='abc' + return PydanticUndefined + try: + v = v[k] + except (KeyError, IndexError, TypeError): + return PydanticUndefined + return v + + +@dataclasses.dataclass(**_internal_dataclass.slots_true) +class AliasChoices: + """!!! abstract "Usage Documentation" + [`AliasPath` and `AliasChoices`](../concepts/alias.md#aliaspath-and-aliaschoices) + + A data class used by `validation_alias` as a convenience to create aliases. + + Attributes: + choices: A list containing a string or `AliasPath`. + """ + + choices: list[str | AliasPath] + + def __init__(self, first_choice: str | AliasPath, *choices: str | AliasPath) -> None: + self.choices = [first_choice] + list(choices) + + def convert_to_aliases(self) -> list[list[str | int]]: + """Converts arguments to a list of lists containing string or integer aliases. + + Returns: + The list of aliases. + """ + aliases: list[list[str | int]] = [] + for c in self.choices: + if isinstance(c, AliasPath): + aliases.append(c.convert_to_aliases()) + else: + aliases.append([c]) + return aliases + + +@dataclasses.dataclass(**_internal_dataclass.slots_true) +class AliasGenerator: + """!!! abstract "Usage Documentation" + [Using an `AliasGenerator`](../concepts/alias.md#using-an-aliasgenerator) + + A data class used by `alias_generator` as a convenience to create various aliases. + + Attributes: + alias: A callable that takes a field name and returns an alias for it. + validation_alias: A callable that takes a field name and returns a validation alias for it. + serialization_alias: A callable that takes a field name and returns a serialization alias for it. + """ + + alias: Callable[[str], str] | None = None + validation_alias: Callable[[str], str | AliasPath | AliasChoices] | None = None + serialization_alias: Callable[[str], str] | None = None + + def _generate_alias( + self, + alias_kind: Literal['alias', 'validation_alias', 'serialization_alias'], + allowed_types: tuple[type[str] | type[AliasPath] | type[AliasChoices], ...], + field_name: str, + ) -> str | AliasPath | AliasChoices | None: + """Generate an alias of the specified kind. Returns None if the alias generator is None. + + Raises: + TypeError: If the alias generator produces an invalid type. + """ + alias = None + if alias_generator := getattr(self, alias_kind): + alias = alias_generator(field_name) + if alias and not isinstance(alias, allowed_types): + raise TypeError( + f'Invalid `{alias_kind}` type. `{alias_kind}` generator must produce one of `{allowed_types}`' + ) + return alias + + def generate_aliases(self, field_name: str) -> tuple[str | None, str | AliasPath | AliasChoices | None, str | None]: + """Generate `alias`, `validation_alias`, and `serialization_alias` for a field. + + Returns: + A tuple of three aliases - validation, alias, and serialization. + """ + alias = self._generate_alias('alias', (str,), field_name) + validation_alias = self._generate_alias('validation_alias', (str, AliasChoices, AliasPath), field_name) + serialization_alias = self._generate_alias('serialization_alias', (str,), field_name) + + return alias, validation_alias, serialization_alias # type: ignore diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/annotated_handlers.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/annotated_handlers.py new file mode 100644 index 0000000000000000000000000000000000000000..d0cb5d3d4616f938aea2aae42985b4c166b500cc --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/annotated_handlers.py @@ -0,0 +1,122 @@ +"""Type annotations to use with `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__`.""" + +from __future__ import annotations as _annotations + +from typing import TYPE_CHECKING, Any, Union + +from pydantic_core import core_schema + +if TYPE_CHECKING: + from ._internal._namespace_utils import NamespacesTuple + from .json_schema import JsonSchemaMode, JsonSchemaValue + + CoreSchemaOrField = Union[ + core_schema.CoreSchema, + core_schema.ModelField, + core_schema.DataclassField, + core_schema.TypedDictField, + core_schema.ComputedField, + ] + +__all__ = 'GetJsonSchemaHandler', 'GetCoreSchemaHandler' + + +class GetJsonSchemaHandler: + """Handler to call into the next JSON schema generation function. + + Attributes: + mode: Json schema mode, can be `validation` or `serialization`. + """ + + mode: JsonSchemaMode + + def __call__(self, core_schema: CoreSchemaOrField, /) -> JsonSchemaValue: + """Call the inner handler and get the JsonSchemaValue it returns. + This will call the next JSON schema modifying function up until it calls + into `pydantic.json_schema.GenerateJsonSchema`, which will raise a + `pydantic.errors.PydanticInvalidForJsonSchema` error if it cannot generate + a JSON schema. + + Args: + core_schema: A `pydantic_core.core_schema.CoreSchema`. + + Returns: + JsonSchemaValue: The JSON schema generated by the inner JSON schema modify + functions. + """ + raise NotImplementedError + + def resolve_ref_schema(self, maybe_ref_json_schema: JsonSchemaValue, /) -> JsonSchemaValue: + """Get the real schema for a `{"$ref": ...}` schema. + If the schema given is not a `$ref` schema, it will be returned as is. + This means you don't have to check before calling this function. + + Args: + maybe_ref_json_schema: A JsonSchemaValue which may be a `$ref` schema. + + Raises: + LookupError: If the ref is not found. + + Returns: + JsonSchemaValue: A JsonSchemaValue that has no `$ref`. + """ + raise NotImplementedError + + +class GetCoreSchemaHandler: + """Handler to call into the next CoreSchema schema generation function.""" + + def __call__(self, source_type: Any, /) -> core_schema.CoreSchema: + """Call the inner handler and get the CoreSchema it returns. + This will call the next CoreSchema modifying function up until it calls + into Pydantic's internal schema generation machinery, which will raise a + `pydantic.errors.PydanticSchemaGenerationError` error if it cannot generate + a CoreSchema for the given source type. + + Args: + source_type: The input type. + + Returns: + CoreSchema: The `pydantic-core` CoreSchema generated. + """ + raise NotImplementedError + + def generate_schema(self, source_type: Any, /) -> core_schema.CoreSchema: + """Generate a schema unrelated to the current context. + Use this function if e.g. you are handling schema generation for a sequence + and want to generate a schema for its items. + Otherwise, you may end up doing something like applying a `min_length` constraint + that was intended for the sequence itself to its items! + + Args: + source_type: The input type. + + Returns: + CoreSchema: The `pydantic-core` CoreSchema generated. + """ + raise NotImplementedError + + def resolve_ref_schema(self, maybe_ref_schema: core_schema.CoreSchema, /) -> core_schema.CoreSchema: + """Get the real schema for a `definition-ref` schema. + If the schema given is not a `definition-ref` schema, it will be returned as is. + This means you don't have to check before calling this function. + + Args: + maybe_ref_schema: A `CoreSchema`, `ref`-based or not. + + Raises: + LookupError: If the `ref` is not found. + + Returns: + A concrete `CoreSchema`. + """ + raise NotImplementedError + + @property + def field_name(self) -> str | None: + """Get the name of the closest field to this validator.""" + raise NotImplementedError + + def _get_types_namespace(self) -> NamespacesTuple: + """Internal method used during type resolution for serializer annotations.""" + raise NotImplementedError diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/class_validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/class_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..9977150c92fcb083fcdfa632c9de3b5fa92470cb --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/class_validators.py @@ -0,0 +1,5 @@ +"""`class_validators` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/color.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/color.py new file mode 100644 index 0000000000000000000000000000000000000000..9a42d586fd71a786ba63a4d5ce55a0ed93d866b0 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/color.py @@ -0,0 +1,604 @@ +"""Color definitions are used as per the CSS3 +[CSS Color Module Level 3](http://www.w3.org/TR/css3-color/#svg-color) specification. + +A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`. + +In these cases the _last_ color when sorted alphabetically takes preferences, +eg. `Color((0, 255, 255)).as_named() == 'cyan'` because "cyan" comes after "aqua". + +Warning: Deprecated + The `Color` class is deprecated, use `pydantic_extra_types` instead. + See [`pydantic-extra-types.Color`](../usage/types/extra_types/color_types.md) + for more information. +""" + +import math +import re +from colorsys import hls_to_rgb, rgb_to_hls +from typing import Any, Callable, Optional, Union, cast + +from pydantic_core import CoreSchema, PydanticCustomError, core_schema +from typing_extensions import deprecated + +from ._internal import _repr +from ._internal._schema_generation_shared import GetJsonSchemaHandler as _GetJsonSchemaHandler +from .json_schema import JsonSchemaValue +from .warnings import PydanticDeprecatedSince20 + +ColorTuple = Union[tuple[int, int, int], tuple[int, int, int, float]] +ColorType = Union[ColorTuple, str] +HslColorTuple = Union[tuple[float, float, float], tuple[float, float, float, float]] + + +class RGBA: + """Internal use only as a representation of a color.""" + + __slots__ = 'r', 'g', 'b', 'alpha', '_tuple' + + def __init__(self, r: float, g: float, b: float, alpha: Optional[float]): + self.r = r + self.g = g + self.b = b + self.alpha = alpha + + self._tuple: tuple[float, float, float, Optional[float]] = (r, g, b, alpha) + + def __getitem__(self, item: Any) -> Any: + return self._tuple[item] + + +# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached +_r_255 = r'(\d{1,3}(?:\.\d+)?)' +_r_comma = r'\s*,\s*' +_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)' +_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?' +_r_sl = r'(\d{1,3}(?:\.\d+)?)%' +r_hex_short = r'\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*' +r_hex_long = r'\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*' +# CSS3 RGB examples: rgb(0, 0, 0), rgba(0, 0, 0, 0.5), rgba(0, 0, 0, 50%) +r_rgb = rf'\s*rgba?\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}(?:{_r_comma}{_r_alpha})?\s*\)\s*' +# CSS3 HSL examples: hsl(270, 60%, 50%), hsla(270, 60%, 50%, 0.5), hsla(270, 60%, 50%, 50%) +r_hsl = rf'\s*hsla?\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}(?:{_r_comma}{_r_alpha})?\s*\)\s*' +# CSS4 RGB examples: rgb(0 0 0), rgb(0 0 0 / 0.5), rgb(0 0 0 / 50%), rgba(0 0 0 / 50%) +r_rgb_v4_style = rf'\s*rgba?\(\s*{_r_255}\s+{_r_255}\s+{_r_255}(?:\s*/\s*{_r_alpha})?\s*\)\s*' +# CSS4 HSL examples: hsl(270 60% 50%), hsl(270 60% 50% / 0.5), hsl(270 60% 50% / 50%), hsla(270 60% 50% / 50%) +r_hsl_v4_style = rf'\s*hsla?\(\s*{_r_h}\s+{_r_sl}\s+{_r_sl}(?:\s*/\s*{_r_alpha})?\s*\)\s*' + +# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used +repeat_colors = {int(c * 2, 16) for c in '0123456789abcdef'} +rads = 2 * math.pi + + +@deprecated( + 'The `Color` class is deprecated, use `pydantic_extra_types` instead. ' + 'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_color/.', + category=PydanticDeprecatedSince20, +) +class Color(_repr.Representation): + """Represents a color.""" + + __slots__ = '_original', '_rgba' + + def __init__(self, value: ColorType) -> None: + self._rgba: RGBA + self._original: ColorType + if isinstance(value, (tuple, list)): + self._rgba = parse_tuple(value) + elif isinstance(value, str): + self._rgba = parse_str(value) + elif isinstance(value, Color): + self._rgba = value._rgba + value = value._original + else: + raise PydanticCustomError( + 'color_error', 'value is not a valid color: value must be a tuple, list or string' + ) + + # if we've got here value must be a valid color + self._original = value + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = {} + field_schema.update(type='string', format='color') + return field_schema + + def original(self) -> ColorType: + """Original value passed to `Color`.""" + return self._original + + def as_named(self, *, fallback: bool = False) -> str: + """Returns the name of the color if it can be found in `COLORS_BY_VALUE` dictionary, + otherwise returns the hexadecimal representation of the color or raises `ValueError`. + + Args: + fallback: If True, falls back to returning the hexadecimal representation of + the color instead of raising a ValueError when no named color is found. + + Returns: + The name of the color, or the hexadecimal representation of the color. + + Raises: + ValueError: When no named color is found and fallback is `False`. + """ + if self._rgba.alpha is None: + rgb = cast(tuple[int, int, int], self.as_rgb_tuple()) + try: + return COLORS_BY_VALUE[rgb] + except KeyError as e: + if fallback: + return self.as_hex() + else: + raise ValueError('no named color found, use fallback=True, as_hex() or as_rgb()') from e + else: + return self.as_hex() + + def as_hex(self) -> str: + """Returns the hexadecimal representation of the color. + + Hex string representing the color can be 3, 4, 6, or 8 characters depending on whether the string + a "short" representation of the color is possible and whether there's an alpha channel. + + Returns: + The hexadecimal representation of the color. + """ + values = [float_to_255(c) for c in self._rgba[:3]] + if self._rgba.alpha is not None: + values.append(float_to_255(self._rgba.alpha)) + + as_hex = ''.join(f'{v:02x}' for v in values) + if all(c in repeat_colors for c in values): + as_hex = ''.join(as_hex[c] for c in range(0, len(as_hex), 2)) + return '#' + as_hex + + def as_rgb(self) -> str: + """Color as an `rgb(, , )` or `rgba(, , , )` string.""" + if self._rgba.alpha is None: + return f'rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})' + else: + return ( + f'rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, ' + f'{round(self._alpha_float(), 2)})' + ) + + def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple: + """Returns the color as an RGB or RGBA tuple. + + Args: + alpha: Whether to include the alpha channel. There are three options for this input: + + - `None` (default): Include alpha only if it's set. (e.g. not `None`) + - `True`: Always include alpha. + - `False`: Always omit alpha. + + Returns: + A tuple that contains the values of the red, green, and blue channels in the range 0 to 255. + If alpha is included, it is in the range 0 to 1. + """ + r, g, b = (float_to_255(c) for c in self._rgba[:3]) + if alpha is None: + if self._rgba.alpha is None: + return r, g, b + else: + return r, g, b, self._alpha_float() + elif alpha: + return r, g, b, self._alpha_float() + else: + # alpha is False + return r, g, b + + def as_hsl(self) -> str: + """Color as an `hsl(, , )` or `hsl(, , , )` string.""" + if self._rgba.alpha is None: + h, s, li = self.as_hsl_tuple(alpha=False) # type: ignore + return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})' + else: + h, s, li, a = self.as_hsl_tuple(alpha=True) # type: ignore + return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})' + + def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple: + """Returns the color as an HSL or HSLA tuple. + + Args: + alpha: Whether to include the alpha channel. + + - `None` (default): Include the alpha channel only if it's set (e.g. not `None`). + - `True`: Always include alpha. + - `False`: Always omit alpha. + + Returns: + The color as a tuple of hue, saturation, lightness, and alpha (if included). + All elements are in the range 0 to 1. + + Note: + This is HSL as used in HTML and most other places, not HLS as used in Python's `colorsys`. + """ + h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b) # noqa: E741 + if alpha is None: + if self._rgba.alpha is None: + return h, s, l + else: + return h, s, l, self._alpha_float() + if alpha: + return h, s, l, self._alpha_float() + else: + # alpha is False + return h, s, l + + def _alpha_float(self) -> float: + return 1 if self._rgba.alpha is None else self._rgba.alpha + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: Callable[[Any], CoreSchema] + ) -> core_schema.CoreSchema: + return core_schema.with_info_plain_validator_function( + cls._validate, serialization=core_schema.to_string_ser_schema() + ) + + @classmethod + def _validate(cls, __input_value: Any, _: Any) -> 'Color': + return cls(__input_value) + + def __str__(self) -> str: + return self.as_named(fallback=True) + + def __repr_args__(self) -> '_repr.ReprArgs': + return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())] + + def __eq__(self, other: Any) -> bool: + return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple() + + def __hash__(self) -> int: + return hash(self.as_rgb_tuple()) + + +def parse_tuple(value: tuple[Any, ...]) -> RGBA: + """Parse a tuple or list to get RGBA values. + + Args: + value: A tuple or list. + + Returns: + An `RGBA` tuple parsed from the input tuple. + + Raises: + PydanticCustomError: If tuple is not valid. + """ + if len(value) == 3: + r, g, b = (parse_color_value(v) for v in value) + return RGBA(r, g, b, None) + elif len(value) == 4: + r, g, b = (parse_color_value(v) for v in value[:3]) + return RGBA(r, g, b, parse_float_alpha(value[3])) + else: + raise PydanticCustomError('color_error', 'value is not a valid color: tuples must have length 3 or 4') + + +def parse_str(value: str) -> RGBA: + """Parse a string representing a color to an RGBA tuple. + + Possible formats for the input string include: + + * named color, see `COLORS_BY_NAME` + * hex short eg. `fff` (prefix can be `#`, `0x` or nothing) + * hex long eg. `ffffff` (prefix can be `#`, `0x` or nothing) + * `rgb(, , )` + * `rgba(, , , )` + + Args: + value: A string representing a color. + + Returns: + An `RGBA` tuple parsed from the input string. + + Raises: + ValueError: If the input string cannot be parsed to an RGBA tuple. + """ + value_lower = value.lower() + try: + r, g, b = COLORS_BY_NAME[value_lower] + except KeyError: + pass + else: + return ints_to_rgba(r, g, b, None) + + m = re.fullmatch(r_hex_short, value_lower) + if m: + *rgb, a = m.groups() + r, g, b = (int(v * 2, 16) for v in rgb) + if a: + alpha: Optional[float] = int(a * 2, 16) / 255 + else: + alpha = None + return ints_to_rgba(r, g, b, alpha) + + m = re.fullmatch(r_hex_long, value_lower) + if m: + *rgb, a = m.groups() + r, g, b = (int(v, 16) for v in rgb) + if a: + alpha = int(a, 16) / 255 + else: + alpha = None + return ints_to_rgba(r, g, b, alpha) + + m = re.fullmatch(r_rgb, value_lower) or re.fullmatch(r_rgb_v4_style, value_lower) + if m: + return ints_to_rgba(*m.groups()) # type: ignore + + m = re.fullmatch(r_hsl, value_lower) or re.fullmatch(r_hsl_v4_style, value_lower) + if m: + return parse_hsl(*m.groups()) # type: ignore + + raise PydanticCustomError('color_error', 'value is not a valid color: string not recognised as a valid color') + + +def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float] = None) -> RGBA: + """Converts integer or string values for RGB color and an optional alpha value to an `RGBA` object. + + Args: + r: An integer or string representing the red color value. + g: An integer or string representing the green color value. + b: An integer or string representing the blue color value. + alpha: A float representing the alpha value. Defaults to None. + + Returns: + An instance of the `RGBA` class with the corresponding color and alpha values. + """ + return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha)) + + +def parse_color_value(value: Union[int, str], max_val: int = 255) -> float: + """Parse the color value provided and return a number between 0 and 1. + + Args: + value: An integer or string color value. + max_val: Maximum range value. Defaults to 255. + + Raises: + PydanticCustomError: If the value is not a valid color. + + Returns: + A number between 0 and 1. + """ + try: + color = float(value) + except ValueError: + raise PydanticCustomError('color_error', 'value is not a valid color: color values must be a valid number') + if 0 <= color <= max_val: + return color / max_val + else: + raise PydanticCustomError( + 'color_error', + 'value is not a valid color: color values must be in the range 0 to {max_val}', + {'max_val': max_val}, + ) + + +def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]: + """Parse an alpha value checking it's a valid float in the range 0 to 1. + + Args: + value: The input value to parse. + + Returns: + The parsed value as a float, or `None` if the value was None or equal 1. + + Raises: + PydanticCustomError: If the input value cannot be successfully parsed as a float in the expected range. + """ + if value is None: + return None + try: + if isinstance(value, str) and value.endswith('%'): + alpha = float(value[:-1]) / 100 + else: + alpha = float(value) + except ValueError: + raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be a valid float') + + if math.isclose(alpha, 1): + return None + elif 0 <= alpha <= 1: + return alpha + else: + raise PydanticCustomError('color_error', 'value is not a valid color: alpha values must be in the range 0 to 1') + + +def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA: + """Parse raw hue, saturation, lightness, and alpha values and convert to RGBA. + + Args: + h: The hue value. + h_units: The unit for hue value. + sat: The saturation value. + light: The lightness value. + alpha: Alpha value. + + Returns: + An instance of `RGBA`. + """ + s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100) + + h_value = float(h) + if h_units in {None, 'deg'}: + h_value = h_value % 360 / 360 + elif h_units == 'rad': + h_value = h_value % rads / rads + else: + # turns + h_value = h_value % 1 + + r, g, b = hls_to_rgb(h_value, l_value, s_value) + return RGBA(r, g, b, parse_float_alpha(alpha)) + + +def float_to_255(c: float) -> int: + """Converts a float value between 0 and 1 (inclusive) to an integer between 0 and 255 (inclusive). + + Args: + c: The float value to be converted. Must be between 0 and 1 (inclusive). + + Returns: + The integer equivalent of the given float value rounded to the nearest whole number. + + Raises: + ValueError: If the given float value is outside the acceptable range of 0 to 1 (inclusive). + """ + return int(round(c * 255)) + + +COLORS_BY_NAME = { + 'aliceblue': (240, 248, 255), + 'antiquewhite': (250, 235, 215), + 'aqua': (0, 255, 255), + 'aquamarine': (127, 255, 212), + 'azure': (240, 255, 255), + 'beige': (245, 245, 220), + 'bisque': (255, 228, 196), + 'black': (0, 0, 0), + 'blanchedalmond': (255, 235, 205), + 'blue': (0, 0, 255), + 'blueviolet': (138, 43, 226), + 'brown': (165, 42, 42), + 'burlywood': (222, 184, 135), + 'cadetblue': (95, 158, 160), + 'chartreuse': (127, 255, 0), + 'chocolate': (210, 105, 30), + 'coral': (255, 127, 80), + 'cornflowerblue': (100, 149, 237), + 'cornsilk': (255, 248, 220), + 'crimson': (220, 20, 60), + 'cyan': (0, 255, 255), + 'darkblue': (0, 0, 139), + 'darkcyan': (0, 139, 139), + 'darkgoldenrod': (184, 134, 11), + 'darkgray': (169, 169, 169), + 'darkgreen': (0, 100, 0), + 'darkgrey': (169, 169, 169), + 'darkkhaki': (189, 183, 107), + 'darkmagenta': (139, 0, 139), + 'darkolivegreen': (85, 107, 47), + 'darkorange': (255, 140, 0), + 'darkorchid': (153, 50, 204), + 'darkred': (139, 0, 0), + 'darksalmon': (233, 150, 122), + 'darkseagreen': (143, 188, 143), + 'darkslateblue': (72, 61, 139), + 'darkslategray': (47, 79, 79), + 'darkslategrey': (47, 79, 79), + 'darkturquoise': (0, 206, 209), + 'darkviolet': (148, 0, 211), + 'deeppink': (255, 20, 147), + 'deepskyblue': (0, 191, 255), + 'dimgray': (105, 105, 105), + 'dimgrey': (105, 105, 105), + 'dodgerblue': (30, 144, 255), + 'firebrick': (178, 34, 34), + 'floralwhite': (255, 250, 240), + 'forestgreen': (34, 139, 34), + 'fuchsia': (255, 0, 255), + 'gainsboro': (220, 220, 220), + 'ghostwhite': (248, 248, 255), + 'gold': (255, 215, 0), + 'goldenrod': (218, 165, 32), + 'gray': (128, 128, 128), + 'green': (0, 128, 0), + 'greenyellow': (173, 255, 47), + 'grey': (128, 128, 128), + 'honeydew': (240, 255, 240), + 'hotpink': (255, 105, 180), + 'indianred': (205, 92, 92), + 'indigo': (75, 0, 130), + 'ivory': (255, 255, 240), + 'khaki': (240, 230, 140), + 'lavender': (230, 230, 250), + 'lavenderblush': (255, 240, 245), + 'lawngreen': (124, 252, 0), + 'lemonchiffon': (255, 250, 205), + 'lightblue': (173, 216, 230), + 'lightcoral': (240, 128, 128), + 'lightcyan': (224, 255, 255), + 'lightgoldenrodyellow': (250, 250, 210), + 'lightgray': (211, 211, 211), + 'lightgreen': (144, 238, 144), + 'lightgrey': (211, 211, 211), + 'lightpink': (255, 182, 193), + 'lightsalmon': (255, 160, 122), + 'lightseagreen': (32, 178, 170), + 'lightskyblue': (135, 206, 250), + 'lightslategray': (119, 136, 153), + 'lightslategrey': (119, 136, 153), + 'lightsteelblue': (176, 196, 222), + 'lightyellow': (255, 255, 224), + 'lime': (0, 255, 0), + 'limegreen': (50, 205, 50), + 'linen': (250, 240, 230), + 'magenta': (255, 0, 255), + 'maroon': (128, 0, 0), + 'mediumaquamarine': (102, 205, 170), + 'mediumblue': (0, 0, 205), + 'mediumorchid': (186, 85, 211), + 'mediumpurple': (147, 112, 219), + 'mediumseagreen': (60, 179, 113), + 'mediumslateblue': (123, 104, 238), + 'mediumspringgreen': (0, 250, 154), + 'mediumturquoise': (72, 209, 204), + 'mediumvioletred': (199, 21, 133), + 'midnightblue': (25, 25, 112), + 'mintcream': (245, 255, 250), + 'mistyrose': (255, 228, 225), + 'moccasin': (255, 228, 181), + 'navajowhite': (255, 222, 173), + 'navy': (0, 0, 128), + 'oldlace': (253, 245, 230), + 'olive': (128, 128, 0), + 'olivedrab': (107, 142, 35), + 'orange': (255, 165, 0), + 'orangered': (255, 69, 0), + 'orchid': (218, 112, 214), + 'palegoldenrod': (238, 232, 170), + 'palegreen': (152, 251, 152), + 'paleturquoise': (175, 238, 238), + 'palevioletred': (219, 112, 147), + 'papayawhip': (255, 239, 213), + 'peachpuff': (255, 218, 185), + 'peru': (205, 133, 63), + 'pink': (255, 192, 203), + 'plum': (221, 160, 221), + 'powderblue': (176, 224, 230), + 'purple': (128, 0, 128), + 'red': (255, 0, 0), + 'rosybrown': (188, 143, 143), + 'royalblue': (65, 105, 225), + 'saddlebrown': (139, 69, 19), + 'salmon': (250, 128, 114), + 'sandybrown': (244, 164, 96), + 'seagreen': (46, 139, 87), + 'seashell': (255, 245, 238), + 'sienna': (160, 82, 45), + 'silver': (192, 192, 192), + 'skyblue': (135, 206, 235), + 'slateblue': (106, 90, 205), + 'slategray': (112, 128, 144), + 'slategrey': (112, 128, 144), + 'snow': (255, 250, 250), + 'springgreen': (0, 255, 127), + 'steelblue': (70, 130, 180), + 'tan': (210, 180, 140), + 'teal': (0, 128, 128), + 'thistle': (216, 191, 216), + 'tomato': (255, 99, 71), + 'turquoise': (64, 224, 208), + 'violet': (238, 130, 238), + 'wheat': (245, 222, 179), + 'white': (255, 255, 255), + 'whitesmoke': (245, 245, 245), + 'yellow': (255, 255, 0), + 'yellowgreen': (154, 205, 50), +} + +COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()} diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/config.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/config.py new file mode 100644 index 0000000000000000000000000000000000000000..6d895258c50e3d29fc556824f56c4216b00a1599 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/config.py @@ -0,0 +1,1296 @@ +"""Configuration for Pydantic models.""" + +from __future__ import annotations as _annotations + +import warnings +from re import Pattern +from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, Union, cast, overload + +from typing_extensions import TypeAlias, TypedDict, Unpack, deprecated + +from ._migration import getattr_migration +from .aliases import AliasGenerator +from .errors import PydanticUserError +from .warnings import PydanticDeprecatedSince211 + +if TYPE_CHECKING: + from ._internal._generate_schema import GenerateSchema as _GenerateSchema + from .fields import ComputedFieldInfo, FieldInfo + +__all__ = ('ConfigDict', 'with_config') + + +JsonValue: TypeAlias = Union[int, float, str, bool, None, list['JsonValue'], 'JsonDict'] +JsonDict: TypeAlias = dict[str, JsonValue] + +JsonEncoder = Callable[[Any], Any] + +JsonSchemaExtraCallable: TypeAlias = Union[ + Callable[[JsonDict], None], + Callable[[JsonDict, type[Any]], None], +] + +ExtraValues = Literal['allow', 'ignore', 'forbid'] + + +class ConfigDict(TypedDict, total=False): + """A TypedDict for configuring Pydantic behaviour.""" + + title: str | None + """The title for the generated JSON schema, defaults to the model's name""" + + model_title_generator: Callable[[type], str] | None + """A callable that takes a model class and returns the title for it. Defaults to `None`.""" + + field_title_generator: Callable[[str, FieldInfo | ComputedFieldInfo], str] | None + """A callable that takes a field's name and info and returns title for it. Defaults to `None`.""" + + str_to_lower: bool + """Whether to convert all characters to lowercase for str types. Defaults to `False`.""" + + str_to_upper: bool + """Whether to convert all characters to uppercase for str types. Defaults to `False`.""" + + str_strip_whitespace: bool + """Whether to strip leading and trailing whitespace for str types.""" + + str_min_length: int + """The minimum length for str types. Defaults to `None`.""" + + str_max_length: int | None + """The maximum length for str types. Defaults to `None`.""" + + extra: ExtraValues | None + ''' + Whether to ignore, allow, or forbid extra data during model initialization. Defaults to `'ignore'`. + + Three configuration values are available: + + - `'ignore'`: Providing extra data is ignored (the default): + ```python + from pydantic import BaseModel, ConfigDict + + class User(BaseModel): + model_config = ConfigDict(extra='ignore') # (1)! + + name: str + + user = User(name='John Doe', age=20) # (2)! + print(user) + #> name='John Doe' + ``` + + 1. This is the default behaviour. + 2. The `age` argument is ignored. + + - `'forbid'`: Providing extra data is not permitted, and a [`ValidationError`][pydantic_core.ValidationError] + will be raised if this is the case: + ```python + from pydantic import BaseModel, ConfigDict, ValidationError + + + class Model(BaseModel): + x: int + + model_config = ConfigDict(extra='forbid') + + + try: + Model(x=1, y='a') + except ValidationError as exc: + print(exc) + """ + 1 validation error for Model + y + Extra inputs are not permitted [type=extra_forbidden, input_value='a', input_type=str] + """ + ``` + + - `'allow'`: Providing extra data is allowed and stored in the `__pydantic_extra__` dictionary attribute: + ```python + from pydantic import BaseModel, ConfigDict + + + class Model(BaseModel): + x: int + + model_config = ConfigDict(extra='allow') + + + m = Model(x=1, y='a') + assert m.__pydantic_extra__ == {'y': 'a'} + ``` + By default, no validation will be applied to these extra items, but you can set a type for the values by overriding + the type annotation for `__pydantic_extra__`: + ```python + from pydantic import BaseModel, ConfigDict, Field, ValidationError + + + class Model(BaseModel): + __pydantic_extra__: dict[str, int] = Field(init=False) # (1)! + + x: int + + model_config = ConfigDict(extra='allow') + + + try: + Model(x=1, y='a') + except ValidationError as exc: + print(exc) + """ + 1 validation error for Model + y + Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str] + """ + + m = Model(x=1, y='2') + assert m.x == 1 + assert m.y == 2 + assert m.model_dump() == {'x': 1, 'y': 2} + assert m.__pydantic_extra__ == {'y': 2} + ``` + + 1. The `= Field(init=False)` does not have any effect at runtime, but prevents the `__pydantic_extra__` field from + being included as a parameter to the model's `__init__` method by type checkers. + + As well as specifying an `extra` configuration value on the model, you can also provide it as an argument to the validation methods. + This will override any `extra` configuration value set on the model: + ```python + from pydantic import BaseModel, ConfigDict, ValidationError + + class Model(BaseModel): + x: int + model_config = ConfigDict(extra="allow") + + try: + # Override model config and forbid extra fields just this time + Model.model_validate({"x": 1, "y": 2}, extra="forbid") + except ValidationError as exc: + print(exc) + """ + 1 validation error for Model + y + Extra inputs are not permitted [type=extra_forbidden, input_value=2, input_type=int] + """ + ``` + ''' + + frozen: bool + """ + Whether models are faux-immutable, i.e. whether `__setattr__` is allowed, and also generates + a `__hash__()` method for the model. This makes instances of the model potentially hashable if all the + attributes are hashable. Defaults to `False`. + + Note: + On V1, the inverse of this setting was called `allow_mutation`, and was `True` by default. + """ + + populate_by_name: bool + """ + Whether an aliased field may be populated by its name as given by the model + attribute, as well as the alias. Defaults to `False`. + + !!! warning + `populate_by_name` usage is not recommended in v2.11+ and will be deprecated in v3. + Instead, you should use the [`validate_by_name`][pydantic.config.ConfigDict.validate_by_name] configuration setting. + + When `validate_by_name=True` and `validate_by_alias=True`, this is strictly equivalent to the + previous behavior of `populate_by_name=True`. + + In v2.11, we also introduced a [`validate_by_alias`][pydantic.config.ConfigDict.validate_by_alias] setting that introduces more fine grained + control for validation behavior. + + Here's how you might go about using the new settings to achieve the same behavior: + + ```python + from pydantic import BaseModel, ConfigDict, Field + + class Model(BaseModel): + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) + + my_field: str = Field(alias='my_alias') # (1)! + + m = Model(my_alias='foo') # (2)! + print(m) + #> my_field='foo' + + m = Model(my_field='foo') # (3)! + print(m) + #> my_field='foo' + ``` + + 1. The field `'my_field'` has an alias `'my_alias'`. + 2. The model is populated by the alias `'my_alias'`. + 3. The model is populated by the attribute name `'my_field'`. + """ + + use_enum_values: bool + """ + Whether to populate models with the `value` property of enums, rather than the raw enum. + This may be useful if you want to serialize `model.model_dump()` later. Defaults to `False`. + + !!! note + If you have an `Optional[Enum]` value that you set a default for, you need to use `validate_default=True` + for said Field to ensure that the `use_enum_values` flag takes effect on the default, as extracting an + enum's value occurs during validation, not serialization. + + ```python + from enum import Enum + from typing import Optional + + from pydantic import BaseModel, ConfigDict, Field + + class SomeEnum(Enum): + FOO = 'foo' + BAR = 'bar' + BAZ = 'baz' + + class SomeModel(BaseModel): + model_config = ConfigDict(use_enum_values=True) + + some_enum: SomeEnum + another_enum: Optional[SomeEnum] = Field( + default=SomeEnum.FOO, validate_default=True + ) + + model1 = SomeModel(some_enum=SomeEnum.BAR) + print(model1.model_dump()) + #> {'some_enum': 'bar', 'another_enum': 'foo'} + + model2 = SomeModel(some_enum=SomeEnum.BAR, another_enum=SomeEnum.BAZ) + print(model2.model_dump()) + #> {'some_enum': 'bar', 'another_enum': 'baz'} + ``` + """ + + validate_assignment: bool + """ + Whether to validate the data when the model is changed. Defaults to `False`. + + The default behavior of Pydantic is to validate the data when the model is created. + + In case the user changes the data after the model is created, the model is _not_ revalidated. + + ```python + from pydantic import BaseModel + + class User(BaseModel): + name: str + + user = User(name='John Doe') # (1)! + print(user) + #> name='John Doe' + user.name = 123 # (1)! + print(user) + #> name=123 + ``` + + 1. The validation happens only when the model is created. + 2. The validation does not happen when the data is changed. + + In case you want to revalidate the model when the data is changed, you can use `validate_assignment=True`: + + ```python + from pydantic import BaseModel, ValidationError + + class User(BaseModel, validate_assignment=True): # (1)! + name: str + + user = User(name='John Doe') # (2)! + print(user) + #> name='John Doe' + try: + user.name = 123 # (3)! + except ValidationError as e: + print(e) + ''' + 1 validation error for User + name + Input should be a valid string [type=string_type, input_value=123, input_type=int] + ''' + ``` + + 1. You can either use class keyword arguments, or `model_config` to set `validate_assignment=True`. + 2. The validation happens when the model is created. + 3. The validation _also_ happens when the data is changed. + """ + + arbitrary_types_allowed: bool + """ + Whether arbitrary types are allowed for field types. Defaults to `False`. + + ```python + from pydantic import BaseModel, ConfigDict, ValidationError + + # This is not a pydantic model, it's an arbitrary class + class Pet: + def __init__(self, name: str): + self.name = name + + class Model(BaseModel): + model_config = ConfigDict(arbitrary_types_allowed=True) + + pet: Pet + owner: str + + pet = Pet(name='Hedwig') + # A simple check of instance type is used to validate the data + model = Model(owner='Harry', pet=pet) + print(model) + #> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry' + print(model.pet) + #> <__main__.Pet object at 0x0123456789ab> + print(model.pet.name) + #> Hedwig + print(type(model.pet)) + #> + try: + # If the value is not an instance of the type, it's invalid + Model(owner='Harry', pet='Hedwig') + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + pet + Input should be an instance of Pet [type=is_instance_of, input_value='Hedwig', input_type=str] + ''' + + # Nothing in the instance of the arbitrary type is checked + # Here name probably should have been a str, but it's not validated + pet2 = Pet(name=42) + model2 = Model(owner='Harry', pet=pet2) + print(model2) + #> pet=<__main__.Pet object at 0x0123456789ab> owner='Harry' + print(model2.pet) + #> <__main__.Pet object at 0x0123456789ab> + print(model2.pet.name) + #> 42 + print(type(model2.pet)) + #> + ``` + """ + + from_attributes: bool + """ + Whether to build models and look up discriminators of tagged unions using python object attributes. + """ + + loc_by_alias: bool + """Whether to use the actual key provided in the data (e.g. alias) for error `loc`s rather than the field's name. Defaults to `True`.""" + + alias_generator: Callable[[str], str] | AliasGenerator | None + """ + A callable that takes a field name and returns an alias for it + or an instance of [`AliasGenerator`][pydantic.aliases.AliasGenerator]. Defaults to `None`. + + When using a callable, the alias generator is used for both validation and serialization. + If you want to use different alias generators for validation and serialization, you can use + [`AliasGenerator`][pydantic.aliases.AliasGenerator] instead. + + If data source field names do not match your code style (e.g. CamelCase fields), + you can automatically generate aliases using `alias_generator`. Here's an example with + a basic callable: + + ```python + from pydantic import BaseModel, ConfigDict + from pydantic.alias_generators import to_pascal + + class Voice(BaseModel): + model_config = ConfigDict(alias_generator=to_pascal) + + name: str + language_code: str + + voice = Voice(Name='Filiz', LanguageCode='tr-TR') + print(voice.language_code) + #> tr-TR + print(voice.model_dump(by_alias=True)) + #> {'Name': 'Filiz', 'LanguageCode': 'tr-TR'} + ``` + + If you want to use different alias generators for validation and serialization, you can use + [`AliasGenerator`][pydantic.aliases.AliasGenerator]. + + ```python + from pydantic import AliasGenerator, BaseModel, ConfigDict + from pydantic.alias_generators import to_camel, to_pascal + + class Athlete(BaseModel): + first_name: str + last_name: str + sport: str + + model_config = ConfigDict( + alias_generator=AliasGenerator( + validation_alias=to_camel, + serialization_alias=to_pascal, + ) + ) + + athlete = Athlete(firstName='John', lastName='Doe', sport='track') + print(athlete.model_dump(by_alias=True)) + #> {'FirstName': 'John', 'LastName': 'Doe', 'Sport': 'track'} + ``` + + Note: + Pydantic offers three built-in alias generators: [`to_pascal`][pydantic.alias_generators.to_pascal], + [`to_camel`][pydantic.alias_generators.to_camel], and [`to_snake`][pydantic.alias_generators.to_snake]. + """ + + ignored_types: tuple[type, ...] + """A tuple of types that may occur as values of class attributes without annotations. This is + typically used for custom descriptors (classes that behave like `property`). If an attribute is set on a + class without an annotation and has a type that is not in this tuple (or otherwise recognized by + _pydantic_), an error will be raised. Defaults to `()`. + """ + + allow_inf_nan: bool + """Whether to allow infinity (`+inf` an `-inf`) and NaN values to float and decimal fields. Defaults to `True`.""" + + json_schema_extra: JsonDict | JsonSchemaExtraCallable | None + """A dict or callable to provide extra JSON schema properties. Defaults to `None`.""" + + json_encoders: dict[type[object], JsonEncoder] | None + """ + A `dict` of custom JSON encoders for specific types. Defaults to `None`. + + /// version-deprecated | v2 + This configuration option is a carryover from v1. We originally planned to remove it in v2 but didn't have a 1:1 replacement + so we are keeping it for now. It is still deprecated and will likely be removed in the future. + /// + """ + + # new in V2 + strict: bool + """ + Whether strict validation is applied to all fields on the model. + + By default, Pydantic attempts to coerce values to the correct type, when possible. + + There are situations in which you may want to disable this behavior, and instead raise an error if a value's type + does not match the field's type annotation. + + To configure strict mode for all fields on a model, you can set `strict=True` on the model. + + ```python + from pydantic import BaseModel, ConfigDict + + class Model(BaseModel): + model_config = ConfigDict(strict=True) + + name: str + age: int + ``` + + See [Strict Mode](../concepts/strict_mode.md) for more details. + + See the [Conversion Table](../concepts/conversion_table.md) for more details on how Pydantic converts data in both + strict and lax modes. + + /// version-added | v2 + /// + """ + # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never' + revalidate_instances: Literal['always', 'never', 'subclass-instances'] + """ + When and how to revalidate models and dataclasses during validation. Can be one of: + + - `'never'`: will *not* revalidate models and dataclasses during validation + - `'always'`: will revalidate models and dataclasses during validation + - `'subclass-instances'`: will revalidate models and dataclasses during validation if the instance is a + subclass of the model or dataclass + + The default is `'never'` (no revalidation). + + This configuration only affects *the current model* it is applied on, and does *not* propagate to the models + referenced in fields. + + ```python + from pydantic import BaseModel + + class User(BaseModel, revalidate_instances='never'): # (1)! + name: str + + class Transaction(BaseModel): + user: User + + my_user = User(name='John') + t = Transaction(user=my_user) + + my_user.name = 1 # (2)! + t = Transaction(user=my_user) # (3)! + print(t) + #> user=User(name=1) + ``` + + 1. This is the default behavior. + 2. The assignment is *not* validated, unless you set [`validate_assignment`][pydantic.ConfigDict.validate_assignment] in the configuration. + 3. Since `revalidate_instances` is set to `'never'`, the user instance is not revalidated. + + Here is an example demonstrating the behavior of `'subclass-instances'`: + + ```python + from pydantic import BaseModel + + class User(BaseModel, revalidate_instances='subclass-instances'): + name: str + + class SubUser(User): + age: int + + class Transaction(BaseModel): + user: User + + my_user = User(name='John') + my_user.name = 1 # (1)! + t = Transaction(user=my_user) # (2)! + print(t) + #> user=User(name=1) + + my_sub_user = SubUser(name='John', age=20) + t = Transaction(user=my_sub_user) + print(t) # (3)! + #> user=User(name='John') + ``` + + 1. The assignment is *not* validated, unless you set [`validate_assignment`][pydantic.ConfigDict.validate_assignment] in the configuration. + 2. Because `my_user` is a "direct" instance of `User`, it is *not* being revalidated. It would have been the case if + `revalidate_instances` was set to `'always'`. + 3. Because `my_sub_user` is an instance of a `User` subclass, it is being revalidated. In this case, Pydantic coerces `my_sub_user` to the defined + `User` class defined on `Transaction`. If one of its fields had an invalid value, a validation error would have been raised. + + /// version-added | v2 + /// + """ + + ser_json_timedelta: Literal['iso8601', 'float'] + """ + The format of JSON serialized timedeltas. Accepts the string values of `'iso8601'` and + `'float'`. Defaults to `'iso8601'`. + + - `'iso8601'` will serialize timedeltas to [ISO 8601 text format](https://en.wikipedia.org/wiki/ISO_8601#Durations). + - `'float'` will serialize timedeltas to the total number of seconds. + + /// version-changed | v2.12 + It is now recommended to use the [`ser_json_temporal`][pydantic.config.ConfigDict.ser_json_temporal] + setting. `ser_json_timedelta` will be deprecated in v3. + /// + """ + + ser_json_temporal: Literal['iso8601', 'seconds', 'milliseconds'] + """ + The format of JSON serialized temporal types from the [`datetime`][] module. This includes: + + - [`datetime.datetime`][] + - [`datetime.date`][] + - [`datetime.time`][] + - [`datetime.timedelta`][] + + Can be one of: + + - `'iso8601'` will serialize date-like types to [ISO 8601 text format](https://en.wikipedia.org/wiki/ISO_8601#Durations). + - `'milliseconds'` will serialize date-like types to a floating point number of milliseconds since the epoch. + - `'seconds'` will serialize date-like types to a floating point number of seconds since the epoch. + + Defaults to `'iso8601'`. + + /// version-added | v2.12 + This setting replaces [`ser_json_timedelta`][pydantic.config.ConfigDict.ser_json_timedelta], + which will be deprecated in v3. `ser_json_temporal` adds more configurability for the other temporal types. + /// + """ + + val_temporal_unit: Literal['seconds', 'milliseconds', 'infer'] + """ + The unit to assume for validating numeric input for datetime-like types ([`datetime.datetime`][] and [`datetime.date`][]). Can be one of: + + - `'seconds'` will validate date or time numeric inputs as seconds since the [epoch]. + - `'milliseconds'` will validate date or time numeric inputs as milliseconds since the [epoch]. + - `'infer'` will infer the unit from the string numeric input on unix time as: + + * seconds since the [epoch] if $-2^{10} <= v <= 2^{10}$ + * milliseconds since the [epoch] (if $v < -2^{10}$ or $v > 2^{10}$). + + Defaults to `'infer'`. + + /// version-added | v2.12 + /// + + [epoch]: https://en.wikipedia.org/wiki/Unix_time + """ + + ser_json_bytes: Literal['utf8', 'base64', 'hex'] + """ + The encoding of JSON serialized bytes. Defaults to `'utf8'`. + Set equal to `val_json_bytes` to get back an equal value after serialization round trip. + + - `'utf8'` will serialize bytes to UTF-8 strings. + - `'base64'` will serialize bytes to URL safe base64 strings. + - `'hex'` will serialize bytes to hexadecimal strings. + """ + + val_json_bytes: Literal['utf8', 'base64', 'hex'] + """ + /// version-added | v2.9 + /// + + The encoding of JSON serialized bytes to decode. Defaults to `'utf8'`. + Set equal to `ser_json_bytes` to get back an equal value after serialization round trip. + + - `'utf8'` will deserialize UTF-8 strings to bytes. + - `'base64'` will deserialize URL safe base64 strings to bytes. + - `'hex'` will deserialize hexadecimal strings to bytes. + """ + + ser_json_inf_nan: Literal['null', 'constants', 'strings'] + """ + The encoding of JSON serialized infinity and NaN float values. Defaults to `'null'`. + + - `'null'` will serialize infinity and NaN values as `null`. + - `'constants'` will serialize infinity and NaN values as `Infinity` and `NaN`. + - `'strings'` will serialize infinity as string `"Infinity"` and NaN as string `"NaN"`. + """ + + # whether to validate default values during validation, default False + validate_default: bool + """Whether to validate default values during validation. Defaults to `False`.""" + + validate_return: bool + """Whether to validate the return value from call validators. Defaults to `False`.""" + + protected_namespaces: tuple[str | Pattern[str], ...] + """ + A tuple of strings and/or regex patterns that prevent models from having fields with names that conflict with its existing members/methods. + + Strings are matched on a prefix basis. For instance, with `'dog'`, having a field named `'dog_name'` will be disallowed. + + Regex patterns are matched on the entire field name. For instance, with the pattern `'^dog$'`, having a field named `'dog'` will be disallowed, + but `'dog_name'` will be accepted. + + Defaults to `('model_validate', 'model_dump')`. This default is used to prevent collisions with the existing (and possibly future) + [validation](../concepts/models.md#validating-data) and [serialization](../concepts/serialization.md#serializing-data) methods. + + ```python + import warnings + + from pydantic import BaseModel + + warnings.filterwarnings('error') # Raise warnings as errors + + try: + + class Model(BaseModel): + model_dump_something: str + + except UserWarning as e: + print(e) + ''' + Field 'model_dump_something' in 'Model' conflicts with protected namespace 'model_dump'. + + You may be able to solve this by setting the 'protected_namespaces' configuration to ('model_validate',). + ''' + ``` + + You can customize this behavior using the `protected_namespaces` setting: + + ```python {test="skip"} + import re + import warnings + + from pydantic import BaseModel, ConfigDict + + with warnings.catch_warnings(record=True) as caught_warnings: + warnings.simplefilter('always') # Catch all warnings + + class Model(BaseModel): + safe_field: str + also_protect_field: str + protect_this: str + + model_config = ConfigDict( + protected_namespaces=( + 'protect_me_', + 'also_protect_', + re.compile('^protect_this$'), + ) + ) + + for warning in caught_warnings: + print(f'{warning.message}') + ''' + Field 'also_protect_field' in 'Model' conflicts with protected namespace 'also_protect_'. + You may be able to solve this by setting the 'protected_namespaces' configuration to ('protect_me_', re.compile('^protect_this$'))`. + + Field 'protect_this' in 'Model' conflicts with protected namespace 're.compile('^protect_this$')'. + You may be able to solve this by setting the 'protected_namespaces' configuration to ('protect_me_', 'also_protect_')`. + ''' + ``` + + While Pydantic will only emit a warning when an item is in a protected namespace but does not actually have a collision, + an error _is_ raised if there is an actual collision with an existing attribute: + + ```python + from pydantic import BaseModel, ConfigDict + + try: + + class Model(BaseModel): + model_validate: str + + model_config = ConfigDict(protected_namespaces=('model_',)) + + except ValueError as e: + print(e) + ''' + Field 'model_validate' conflicts with member > of protected namespace 'model_'. + ''' + ``` + + /// version-changed | v2.10 + The default protected namespaces was changed from `('model_',)` to `('model_validate', 'model_dump')`, to allow + for fields like `model_id`, `model_name` to be used. + /// + """ + + hide_input_in_errors: bool + """ + Whether to hide inputs when printing errors. Defaults to `False`. + + Pydantic shows the input value and type when it raises `ValidationError` during the validation. + + ```python + from pydantic import BaseModel, ValidationError + + class Model(BaseModel): + a: str + + try: + Model(a=123) + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + a + Input should be a valid string [type=string_type, input_value=123, input_type=int] + ''' + ``` + + You can hide the input value and type by setting the `hide_input_in_errors` config to `True`. + + ```python + from pydantic import BaseModel, ConfigDict, ValidationError + + class Model(BaseModel): + a: str + model_config = ConfigDict(hide_input_in_errors=True) + + try: + Model(a=123) + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + a + Input should be a valid string [type=string_type] + ''' + ``` + """ + + defer_build: bool + """ + Whether to defer model validator and serializer construction until the first model validation. Defaults to False. + + This can be useful to avoid the overhead of building models which are only + used nested within other models, or when you want to manually define type namespace via + [`Model.model_rebuild(_types_namespace=...)`][pydantic.BaseModel.model_rebuild]. + + /// version-changed | v2.10 + The setting also applies to [Pydantic dataclasses](../concepts/dataclasses.md) and [type adapters](../concepts/type_adapter.md). + /// + """ + + plugin_settings: dict[str, object] | None + """A `dict` of settings for plugins. Defaults to `None`.""" + + schema_generator: type[_GenerateSchema] | None + """ + The `GenerateSchema` class to use during core schema generation. + + /// version-deprecated | v2.10 + The `GenerateSchema` class is private and highly subject to change. + /// + """ + + json_schema_serialization_defaults_required: bool + """ + Whether fields with default values should be marked as required in the serialization schema. Defaults to `False`. + + This ensures that the serialization schema will reflect the fact a field with a default will always be present + when serializing the model, even though it is not required for validation. + + However, there are scenarios where this may be undesirable — in particular, if you want to share the schema + between validation and serialization, and don't mind fields with defaults being marked as not required during + serialization. See [#7209](https://github.com/pydantic/pydantic/issues/7209) for more details. + + ```python + from pydantic import BaseModel, ConfigDict + + class Model(BaseModel): + a: str = 'a' + + model_config = ConfigDict(json_schema_serialization_defaults_required=True) + + print(Model.model_json_schema(mode='validation')) + ''' + { + 'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}}, + 'title': 'Model', + 'type': 'object', + } + ''' + print(Model.model_json_schema(mode='serialization')) + ''' + { + 'properties': {'a': {'default': 'a', 'title': 'A', 'type': 'string'}}, + 'required': ['a'], + 'title': 'Model', + 'type': 'object', + } + ''' + ``` + + /// version-added | v2.4 + /// + """ + + json_schema_mode_override: Literal['validation', 'serialization', None] + """ + If not `None`, the specified mode will be used to generate the JSON schema regardless of what `mode` was passed to + the function call. Defaults to `None`. + + This provides a way to force the JSON schema generation to reflect a specific mode, e.g., to always use the + validation schema. + + It can be useful when using frameworks (such as FastAPI) that may generate different schemas for validation + and serialization that must both be referenced from the same schema; when this happens, we automatically append + `-Input` to the definition reference for the validation schema and `-Output` to the definition reference for the + serialization schema. By specifying a `json_schema_mode_override` though, this prevents the conflict between + the validation and serialization schemas (since both will use the specified schema), and so prevents the suffixes + from being added to the definition references. + + ```python + from pydantic import BaseModel, ConfigDict, Json + + class Model(BaseModel): + a: Json[int] # requires a string to validate, but will dump an int + + print(Model.model_json_schema(mode='serialization')) + ''' + { + 'properties': {'a': {'title': 'A', 'type': 'integer'}}, + 'required': ['a'], + 'title': 'Model', + 'type': 'object', + } + ''' + + class ForceInputModel(Model): + # the following ensures that even with mode='serialization', we + # will get the schema that would be generated for validation. + model_config = ConfigDict(json_schema_mode_override='validation') + + print(ForceInputModel.model_json_schema(mode='serialization')) + ''' + { + 'properties': { + 'a': { + 'contentMediaType': 'application/json', + 'contentSchema': {'type': 'integer'}, + 'title': 'A', + 'type': 'string', + } + }, + 'required': ['a'], + 'title': 'ForceInputModel', + 'type': 'object', + } + ''' + ``` + + /// version-added | v2.4 + /// + """ + + coerce_numbers_to_str: bool + """ + If `True`, enables automatic coercion of any `Number` type to `str` in "lax" (non-strict) mode. Defaults to `False`. + + Pydantic doesn't allow number types (`int`, `float`, `Decimal`) to be coerced as type `str` by default. + + ```python + from decimal import Decimal + + from pydantic import BaseModel, ConfigDict, ValidationError + + class Model(BaseModel): + value: str + + try: + print(Model(value=42)) + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + value + Input should be a valid string [type=string_type, input_value=42, input_type=int] + ''' + + class Model(BaseModel): + model_config = ConfigDict(coerce_numbers_to_str=True) + + value: str + + repr(Model(value=42).value) + #> "42" + repr(Model(value=42.13).value) + #> "42.13" + repr(Model(value=Decimal('42.13')).value) + #> "42.13" + ``` + """ + + regex_engine: Literal['rust-regex', 'python-re'] + """ + The regex engine to be used for pattern validation. + Defaults to `'rust-regex'`. + + - `'rust-regex'` uses the [`regex`](https://docs.rs/regex) Rust crate, + which is non-backtracking and therefore more DDoS resistant, but does not support all regex features. + - `'python-re'` use the [`re`][] module, which supports all regex features, but may be slower. + + !!! note + If you use a compiled regex pattern, the `'python-re'` engine will be used regardless of this setting. + This is so that flags such as [`re.IGNORECASE`][] are respected. + + ```python + from pydantic import BaseModel, ConfigDict, Field, ValidationError + + class Model(BaseModel): + model_config = ConfigDict(regex_engine='python-re') + + value: str = Field(pattern=r'^abc(?=def)') + + print(Model(value='abcdef').value) + #> abcdef + + try: + print(Model(value='abxyzcdef')) + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + value + String should match pattern '^abc(?=def)' [type=string_pattern_mismatch, input_value='abxyzcdef', input_type=str] + ''' + ``` + + /// version-added | v2.5 + /// + """ + + validation_error_cause: bool + """ + If `True`, Python exceptions that were part of a validation failure will be shown as an exception group as a cause. Can be useful for debugging. Defaults to `False`. + + Note: + Python 3.10 and older don't support exception groups natively. <=3.10, backport must be installed: `pip install exceptiongroup`. + + Note: + The structure of validation errors are likely to change in future Pydantic versions. Pydantic offers no guarantees about their structure. Should be used for visual traceback debugging only. + + /// version-added | v2.5 + /// + """ + + use_attribute_docstrings: bool + ''' + Whether docstrings of attributes (bare string literals immediately following the attribute declaration) + should be used for field descriptions. Defaults to `False`. + + ```python + from pydantic import BaseModel, ConfigDict, Field + + + class Model(BaseModel): + model_config = ConfigDict(use_attribute_docstrings=True) + + x: str + """ + Example of an attribute docstring + """ + + y: int = Field(description="Description in Field") + """ + Description in Field overrides attribute docstring + """ + + + print(Model.model_fields["x"].description) + # > Example of an attribute docstring + print(Model.model_fields["y"].description) + # > Description in Field + ``` + This requires the source code of the class to be available at runtime (and so won't work in the interactive interpreter shell). + + !!! warning "Usage with `TypedDict` and stdlib dataclasses" + Due to current limitations, attribute docstrings detection may not work as expected when using + [`TypedDict`][typing.TypedDict] and stdlib dataclasses, in particular when: + + - inheritance is being used. + - multiple classes have the same name in the same source file (unless Python 3.13 or greater is used). + + /// version-added | v2.7 + /// + ''' + + cache_strings: bool | Literal['all', 'keys', 'none'] + """ + Whether to cache strings to avoid constructing new Python objects. Defaults to True. + + Enabling this setting should significantly improve validation performance while increasing memory usage slightly. + + - `True` or `'all'` (the default): cache all strings + - `'keys'`: cache only dictionary keys + - `False` or `'none'`: no caching + + !!! note + `True` or `'all'` is required to cache strings during general validation because + validators don't know if they're in a key or a value. + + !!! tip + If repeated strings are rare, it's recommended to use `'keys'` or `'none'` to reduce memory usage, + as the performance difference is minimal if repeated strings are rare. + + /// version-added | v2.7 + /// + """ + + validate_by_alias: bool + """ + Whether an aliased field may be populated by its alias. Defaults to `True`. + + Here's an example of disabling validation by alias: + + ```py + from pydantic import BaseModel, ConfigDict, Field + + class Model(BaseModel): + model_config = ConfigDict(validate_by_name=True, validate_by_alias=False) + + my_field: str = Field(validation_alias='my_alias') # (1)! + + m = Model(my_field='foo') # (2)! + print(m) + #> my_field='foo' + ``` + + 1. The field `'my_field'` has an alias `'my_alias'`. + 2. The model can only be populated by the attribute name `'my_field'`. + + !!! warning + You cannot set both `validate_by_alias` and `validate_by_name` to `False`. + This would make it impossible to populate an attribute. + + See [usage errors](../errors/usage_errors.md#validate-by-alias-and-name-false) for an example. + + If you set `validate_by_alias` to `False`, under the hood, Pydantic dynamically sets + `validate_by_name` to `True` to ensure that validation can still occur. + + /// version-added | v2.11 + This setting was introduced in conjunction with [`validate_by_name`][pydantic.ConfigDict.validate_by_name] + to empower users with more fine grained validation control. + /// + """ + + validate_by_name: bool + """ + Whether an aliased field may be populated by its name as given by the model + attribute. Defaults to `False`. + + ```python + from pydantic import BaseModel, ConfigDict, Field + + class Model(BaseModel): + model_config = ConfigDict(validate_by_name=True, validate_by_alias=True) + + my_field: str = Field(validation_alias='my_alias') # (1)! + + m = Model(my_alias='foo') # (2)! + print(m) + #> my_field='foo' + + m = Model(my_field='foo') # (3)! + print(m) + #> my_field='foo' + ``` + + 1. The field `'my_field'` has an alias `'my_alias'`. + 2. The model is populated by the alias `'my_alias'`. + 3. The model is populated by the attribute name `'my_field'`. + + !!! warning + You cannot set both `validate_by_alias` and `validate_by_name` to `False`. + This would make it impossible to populate an attribute. + + See [usage errors](../errors/usage_errors.md#validate-by-alias-and-name-false) for an example. + + /// version-added | v2.11 + This setting was introduced in conjunction with [`validate_by_alias`][pydantic.ConfigDict.validate_by_alias] + to empower users with more fine grained validation control. It is an alternative to [`populate_by_name`][pydantic.ConfigDict.populate_by_name], + that enables validation by name **and** by alias. + /// + """ + + serialize_by_alias: bool + """ + Whether an aliased field should be serialized by its alias. Defaults to `False`. + + Note: In v2.11, `serialize_by_alias` was introduced to address the + [popular request](https://github.com/pydantic/pydantic/issues/8379) + for consistency with alias behavior for validation and serialization settings. + In v3, the default value is expected to change to `True` for consistency with the validation default. + + ```python + from pydantic import BaseModel, ConfigDict, Field + + class Model(BaseModel): + model_config = ConfigDict(serialize_by_alias=True) + + my_field: str = Field(serialization_alias='my_alias') # (1)! + + m = Model(my_field='foo') + print(m.model_dump()) # (2)! + #> {'my_alias': 'foo'} + ``` + + 1. The field `'my_field'` has an alias `'my_alias'`. + 2. The model is serialized using the alias `'my_alias'` for the `'my_field'` attribute. + + + /// version-added | v2.11 + This setting was introduced to address the [popular request](https://github.com/pydantic/pydantic/issues/8379) + for consistency with alias behavior for validation and serialization. + + In v3, the default value is expected to change to `True` for consistency with the validation default. + /// + """ + + url_preserve_empty_path: bool + """ + Whether to preserve empty URL paths when validating values for a URL type. Defaults to `False`. + + ```python + from pydantic import AnyUrl, BaseModel, ConfigDict + + class Model(BaseModel): + model_config = ConfigDict(url_preserve_empty_path=True) + + url: AnyUrl + + m = Model(url='http://example.com') + print(m.url) + #> http://example.com + ``` + + /// version-added | v2.12 + /// + """ + + polymorphic_serialization: bool + """ + Whether to use polymorphic serialization for subclasses of the model or Pydantic dataclass. Defaults to `False`. + """ + + +_TypeT = TypeVar('_TypeT', bound=type) + + +@overload +@deprecated('Passing `config` as a keyword argument is deprecated. Pass `config` as a positional argument instead.') +def with_config(*, config: ConfigDict) -> Callable[[_TypeT], _TypeT]: ... + + +@overload +def with_config(config: ConfigDict, /) -> Callable[[_TypeT], _TypeT]: ... + + +@overload +def with_config(**config: Unpack[ConfigDict]) -> Callable[[_TypeT], _TypeT]: ... + + +def with_config(config: ConfigDict | None = None, /, **kwargs: Any) -> Callable[[_TypeT], _TypeT]: + """!!! abstract "Usage Documentation" + [Configuration with other types](../concepts/config.md#configuration-on-other-supported-types) + + A convenience decorator to set a [Pydantic configuration](config.md) on a `TypedDict` or a `dataclass` from the standard library. + + Although the configuration can be set using the `__pydantic_config__` attribute, it does not play well with type checkers, + especially with `TypedDict`. + + !!! example "Usage" + + ```python + from typing_extensions import TypedDict + + from pydantic import ConfigDict, TypeAdapter, with_config + + @with_config(ConfigDict(str_to_lower=True)) + class TD(TypedDict): + x: str + + ta = TypeAdapter(TD) + + print(ta.validate_python({'x': 'ABC'})) + #> {'x': 'abc'} + ``` + + /// deprecated-removed | v2.11 v3 + Passing `config` as a keyword argument. + /// + + /// version-changed | v2.11 + Keyword arguments can be provided directly instead of a config dictionary. + /// + """ + if config is not None and kwargs: + raise ValueError('Cannot specify both `config` and keyword arguments') + + if len(kwargs) == 1 and (kwargs_conf := kwargs.get('config')) is not None: + warnings.warn( + 'Passing `config` as a keyword argument is deprecated. Pass `config` as a positional argument instead', + category=PydanticDeprecatedSince211, + stacklevel=2, + ) + final_config = cast(ConfigDict, kwargs_conf) + else: + final_config = config if config is not None else cast(ConfigDict, kwargs) + + def inner(class_: _TypeT, /) -> _TypeT: + # Ideally, we would check for `class_` to either be a `TypedDict` or a stdlib dataclass. + # However, the `@with_config` decorator can be applied *after* `@dataclass`. To avoid + # common mistakes, we at least check for `class_` to not be a Pydantic model. + from ._internal._utils import is_model_class + + if is_model_class(class_): + raise PydanticUserError( + f'Cannot use `with_config` on {class_.__name__} as it is a Pydantic model', + code='with-config-on-model', + ) + class_.__pydantic_config__ = final_config + return class_ + + return inner + + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/dataclasses.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/dataclasses.py new file mode 100644 index 0000000000000000000000000000000000000000..46401c310ee7d950d671368eb7e2899ed5b80ca1 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/dataclasses.py @@ -0,0 +1,413 @@ +"""Provide an enhanced dataclass that performs validation.""" + +from __future__ import annotations as _annotations + +import dataclasses +import functools +import sys +import types +from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, overload +from warnings import warn + +from typing_extensions import TypeGuard, dataclass_transform + +from ._internal import _config, _decorators, _mock_val_ser, _namespace_utils, _typing_extra +from ._internal import _dataclasses as _pydantic_dataclasses +from ._migration import getattr_migration +from .config import ConfigDict +from .errors import PydanticUserError +from .fields import Field, FieldInfo, PrivateAttr + +if TYPE_CHECKING: + from ._internal._dataclasses import PydanticDataclass + from ._internal._namespace_utils import MappingNamespace + +__all__ = 'dataclass', 'rebuild_dataclass' + +_T = TypeVar('_T') + +if sys.version_info >= (3, 10): + + @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) + @overload + def dataclass( + *, + init: Literal[False] = False, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: ConfigDict | type[object] | None = None, + validate_on_init: bool | None = None, + kw_only: bool = ..., + slots: bool = ..., + ) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore + ... + + @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) + @overload + def dataclass( + _cls: type[_T], # type: ignore + *, + init: Literal[False] = False, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool | None = None, + config: ConfigDict | type[object] | None = None, + validate_on_init: bool | None = None, + kw_only: bool = ..., + slots: bool = ..., + ) -> type[PydanticDataclass]: ... + +else: + + @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) + @overload + def dataclass( + *, + init: Literal[False] = False, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool | None = None, + config: ConfigDict | type[object] | None = None, + validate_on_init: bool | None = None, + ) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore + ... + + @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) + @overload + def dataclass( + _cls: type[_T], # type: ignore + *, + init: Literal[False] = False, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool | None = None, + config: ConfigDict | type[object] | None = None, + validate_on_init: bool | None = None, + ) -> type[PydanticDataclass]: ... + + +@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) +def dataclass( + _cls: type[_T] | None = None, + *, + init: Literal[False] = False, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool | None = None, + config: ConfigDict | type[object] | None = None, + validate_on_init: bool | None = None, + kw_only: bool = False, + slots: bool = False, +) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]: + """!!! abstract "Usage Documentation" + [`dataclasses`](../concepts/dataclasses.md) + + A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`, + but with added validation. + + This function should be used similarly to `dataclasses.dataclass`. + + Args: + _cls: The target `dataclass`. + init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to + `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its + own `__init__` function. + repr: A boolean indicating whether to include the field in the `__repr__` output. + eq: Determines if a `__eq__` method should be generated for the class. + order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`. + unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`. + frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its + attributes to be modified after it has been initialized. If not set, the value from the provided `config` argument will be used (and will default to `False` otherwise). + config: The Pydantic config to use for the `dataclass`. + validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses + are validated on init. + kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`. + slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of + new attributes after instantiation. + + Returns: + A decorator that accepts a class as its argument and returns a Pydantic `dataclass`. + + Raises: + AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`. + """ + assert init is False, 'pydantic.dataclasses.dataclass only supports init=False' + assert validate_on_init is not False, 'validate_on_init=False is no longer supported' + + if sys.version_info >= (3, 10): + kwargs = {'kw_only': kw_only, 'slots': slots} + else: + kwargs = {} + + def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]: + """Create a Pydantic dataclass from a regular dataclass. + + Args: + cls: The class to create the Pydantic dataclass from. + + Returns: + A Pydantic dataclass. + """ + from ._internal._utils import is_model_class + + if is_model_class(cls): + raise PydanticUserError( + f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model', + code='dataclass-on-model', + ) + + original_cls = cls + + # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base + # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about + has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__) + if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'): + warn( + f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. ' + f'The `config` specification from `dataclass` decorator will take priority.', + category=UserWarning, + stacklevel=2, + ) + + # if config is not explicitly provided, try to read it from the type + config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None) + config_wrapper = _config.ConfigWrapper(config_dict) + decorators = _decorators.DecoratorInfos.build(cls, replace_wrapped_methods=True) + decorators.update_from_config(config_wrapper) + + # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator + # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description, + # since dataclasses.dataclass will set this as the __doc__ + original_doc = cls.__doc__ + + if _pydantic_dataclasses.is_stdlib_dataclass(cls): + # Vanilla dataclasses include a default docstring (representing the class signature), + # which we don't want to preserve. + original_doc = None + + # We don't want to add validation to the existing std lib dataclass, so we will subclass it + # If the class is generic, we need to make sure the subclass also inherits from Generic + # with all the same parameters. + bases = (cls,) + if issubclass(cls, Generic): + generic_base = Generic[cls.__parameters__] # type: ignore + bases = bases + (generic_base,) + cls = types.new_class(cls.__name__, bases) + + # Respect frozen setting from dataclass constructor and fallback to config setting if not provided + if frozen is not None: + frozen_ = frozen + if config_wrapper.frozen: + # It's not recommended to define both, as the setting from the dataclass decorator will take priority. + warn( + f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.' + 'This is not recommended. The `frozen` specification on `dataclass` will take priority.', + category=UserWarning, + stacklevel=2, + ) + else: + frozen_ = config_wrapper.frozen or False + + # Make Pydantic's `Field()` function compatible with stdlib dataclasses. As we'll decorate + # `cls` with the stdlib `@dataclass` decorator first, there are two attributes, `kw_only` and + # `repr` that need to be understood *during* the stdlib creation. We do so in two steps: + + # 1. On the decorated class, wrap `Field()` assignment with `dataclass.field()`, with the + # two attributes set (done in `as_dataclass_field()`) + cls_anns = _typing_extra.safe_get_annotations(cls) + for field_name in cls_anns: + # We should look for assignments in `__dict__` instead, but for now we follow + # the same behavior as stdlib dataclasses (see https://github.com/python/cpython/issues/88609) + field_value = getattr(cls, field_name, None) + if isinstance(field_value, FieldInfo): + setattr(cls, field_name, _pydantic_dataclasses.as_dataclass_field(field_value)) + + # 2. For bases of `cls` that are stdlib dataclasses, we temporarily patch their fields + # (see the docstring of the context manager): + with _pydantic_dataclasses.patch_base_fields(cls): + cls = dataclasses.dataclass( # pyright: ignore[reportCallIssue] + cls, + # the value of init here doesn't affect anything except that it makes it easier to generate a signature + init=True, + repr=repr, + eq=eq, + order=order, + unsafe_hash=unsafe_hash, + frozen=frozen_, + **kwargs, + ) + + if config_wrapper.validate_assignment: + original_setattr = cls.__setattr__ + + @functools.wraps(cls.__setattr__) + def validated_setattr(instance: PydanticDataclass, name: str, value: Any, /) -> None: + if frozen_: + return original_setattr(instance, name, value) # pyright: ignore[reportCallIssue] + inst_cls = type(instance) + attr = getattr(inst_cls, name, None) + + if isinstance(attr, property): + attr.__set__(instance, value) + elif isinstance(attr, functools.cached_property): + instance.__dict__.__setitem__(name, value) + else: + inst_cls.__pydantic_validator__.validate_assignment(instance, name, value) + + cls.__setattr__ = validated_setattr.__get__(None, cls) # type: ignore + + if slots and not hasattr(cls, '__setstate__'): + # If slots is set, `pickle` (relied on by `copy.copy()`) will use + # `__setattr__()` to reconstruct the dataclass. However, the custom + # `__setattr__()` set above relies on `validate_assignment()`, which + # in turn expects all the field values to be already present on the + # instance, resulting in attribute errors. + # As such, we make use of `object.__setattr__()` instead. + # Note that we do so only if `__setstate__()` isn't already set (this is the + # case if on top of `slots`, `frozen` is used). + + # Taken from `dataclasses._dataclass_get/setstate()`: + def _dataclass_getstate(self: Any) -> list[Any]: + return [getattr(self, f.name) for f in dataclasses.fields(self)] + + def _dataclass_setstate(self: Any, state: list[Any]) -> None: + for field, value in zip(dataclasses.fields(self), state): + object.__setattr__(self, field.name, value) + + cls.__getstate__ = _dataclass_getstate # pyright: ignore[reportAttributeAccessIssue] + cls.__setstate__ = _dataclass_setstate # pyright: ignore[reportAttributeAccessIssue] + + # This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses. + # It should be set as early as possible: + cls.__is_pydantic_dataclass__ = True + cls.__pydantic_decorators__ = decorators # type: ignore + cls.__doc__ = original_doc + # Can be non-existent for dynamically created classes: + firstlineno = getattr(original_cls, '__firstlineno__', None) + cls.__module__ = original_cls.__module__ + if sys.version_info >= (3, 13) and firstlineno is not None: + # As per https://docs.python.org/3/reference/datamodel.html#type.__firstlineno__: + # Setting the `__module__` attribute removes the `__firstlineno__` item from the type’s dictionary. + original_cls.__firstlineno__ = firstlineno + cls.__firstlineno__ = firstlineno + cls.__qualname__ = original_cls.__qualname__ + cls.__pydantic_fields_complete__ = classmethod(_pydantic_fields_complete) + cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful. + # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models: + # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function), + # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models). + _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False) + return cls + + return create_dataclass if _cls is None else create_dataclass(_cls) + + +def _pydantic_fields_complete(cls: type[PydanticDataclass]) -> bool: + """Return whether the fields were successfully collected (i.e. type hints were successfully resolved). + + This is a private helper, not meant to be used outside Pydantic. + """ + return all(field_info._complete for field_info in cls.__pydantic_fields__.values()) + + +__getattr__ = getattr_migration(__name__) + +if sys.version_info < (3, 11): + # Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints + # Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable. + + def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn: + """This function does nothing but raise an error that is as similar as possible to what you'd get + if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just + to ensure typing._type_check does not error if the type hint evaluates to `InitVar[]`. + """ + raise TypeError("'InitVar' object is not callable") + + dataclasses.InitVar.__call__ = _call_initvar + + +def rebuild_dataclass( + cls: type[PydanticDataclass], + *, + force: bool = False, + raise_errors: bool = True, + _parent_namespace_depth: int = 2, + _types_namespace: MappingNamespace | None = None, +) -> bool | None: + """Try to rebuild the pydantic-core schema for the dataclass. + + This may be necessary when one of the annotations is a ForwardRef which could not be resolved during + the initial attempt to build the schema, and automatic rebuilding fails. + + This is analogous to `BaseModel.model_rebuild`. + + Args: + cls: The class to rebuild the pydantic-core schema for. + force: Whether to force the rebuilding of the schema, defaults to `False`. + raise_errors: Whether to raise errors, defaults to `True`. + _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. + _types_namespace: The types namespace, defaults to `None`. + + Returns: + Returns `None` if the schema is already "complete" and rebuilding was not required. + If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`. + """ + if not force and cls.__pydantic_complete__: + return None + + for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'): + if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer): + # Deleting the validator/serializer is necessary as otherwise they can get reused in + # pydantic-core. Same applies for the core schema that can be reused in schema generation. + delattr(cls, attr) + + cls.__pydantic_complete__ = False + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} + else: + rebuild_ns = {} + + ns_resolver = _namespace_utils.NsResolver( + parent_namespace=rebuild_ns, + ) + + return _pydantic_dataclasses.complete_dataclass( + cls, + _config.ConfigWrapper(cls.__pydantic_config__, check=False), + raise_errors=raise_errors, + ns_resolver=ns_resolver, + # We could provide a different config instead (with `'defer_build'` set to `True`) + # of this explicit `_force_build` argument, but because config can come from the + # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass` + # will overwrite `__pydantic_config__` with the provided config above: + _force_build=True, + ) + + +def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]: + """Whether a class is a pydantic dataclass. + + Args: + class_: The class. + + Returns: + `True` if the class is a pydantic dataclass, `False` otherwise. + """ + try: + return '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_) + except AttributeError: + return False diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/datetime_parse.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/datetime_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..53d52649e7620965ed194240a3becaa3ce3e3448 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/datetime_parse.py @@ -0,0 +1,5 @@ +"""The `datetime_parse` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/decorator.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/decorator.py new file mode 100644 index 0000000000000000000000000000000000000000..0d97560c1b791956726b04fd66740a947647aabe --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/decorator.py @@ -0,0 +1,5 @@ +"""The `decorator` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/class_validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/class_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..e6ba195064646af6b128c6be769790d8fa06c925 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/class_validators.py @@ -0,0 +1,256 @@ +"""Old `@validator` and `@root_validator` function validators from V1.""" + +from __future__ import annotations as _annotations + +from functools import partial, partialmethod +from types import FunctionType +from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar, Union, overload +from warnings import warn + +from typing_extensions import Protocol, TypeAlias, deprecated + +from .._internal import _decorators, _decorators_v1 +from ..errors import PydanticUserError +from ..warnings import PydanticDeprecatedSince20 + +_ALLOW_REUSE_WARNING_MESSAGE = '`allow_reuse` is deprecated and will be ignored; it should no longer be necessary' + + +if TYPE_CHECKING: + + class _OnlyValueValidatorClsMethod(Protocol): + def __call__(self, __cls: Any, __value: Any) -> Any: ... + + class _V1ValidatorWithValuesClsMethod(Protocol): + def __call__(self, __cls: Any, __value: Any, values: dict[str, Any]) -> Any: ... + + class _V1ValidatorWithValuesKwOnlyClsMethod(Protocol): + def __call__(self, __cls: Any, __value: Any, *, values: dict[str, Any]) -> Any: ... + + class _V1ValidatorWithKwargsClsMethod(Protocol): + def __call__(self, __cls: Any, **kwargs: Any) -> Any: ... + + class _V1ValidatorWithValuesAndKwargsClsMethod(Protocol): + def __call__(self, __cls: Any, values: dict[str, Any], **kwargs: Any) -> Any: ... + + class _V1RootValidatorClsMethod(Protocol): + def __call__( + self, __cls: Any, __values: _decorators_v1.RootValidatorValues + ) -> _decorators_v1.RootValidatorValues: ... + + V1Validator = Union[ + _OnlyValueValidatorClsMethod, + _V1ValidatorWithValuesClsMethod, + _V1ValidatorWithValuesKwOnlyClsMethod, + _V1ValidatorWithKwargsClsMethod, + _V1ValidatorWithValuesAndKwargsClsMethod, + _decorators_v1.V1ValidatorWithValues, + _decorators_v1.V1ValidatorWithValuesKwOnly, + _decorators_v1.V1ValidatorWithKwargs, + _decorators_v1.V1ValidatorWithValuesAndKwargs, + ] + + V1RootValidator = Union[ + _V1RootValidatorClsMethod, + _decorators_v1.V1RootValidatorFunction, + ] + + _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]] + + # Allow both a V1 (assumed pre=False) or V2 (assumed mode='after') validator + # We lie to type checkers and say we return the same thing we get + # but in reality we return a proxy object that _mostly_ behaves like the wrapped thing + _V1ValidatorType = TypeVar('_V1ValidatorType', V1Validator, _PartialClsOrStaticMethod) + _V1RootValidatorFunctionType = TypeVar( + '_V1RootValidatorFunctionType', + _decorators_v1.V1RootValidatorFunction, + _V1RootValidatorClsMethod, + _PartialClsOrStaticMethod, + ) +else: + # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 + # and https://youtrack.jetbrains.com/issue/PY-51428 + DeprecationWarning = PydanticDeprecatedSince20 + + +@deprecated( + 'Pydantic V1 style `@validator` validators are deprecated.' + ' You should migrate to Pydantic V2 style `@field_validator` validators,' + ' see the migration guide for more details', + category=None, +) +def validator( + __field: str, + *fields: str, + pre: bool = False, + each_item: bool = False, + always: bool = False, + check_fields: bool | None = None, + allow_reuse: bool = False, +) -> Callable[[_V1ValidatorType], _V1ValidatorType]: + """Decorate methods on the class indicating that they should be used to validate fields. + + Args: + __field (str): The first field the validator should be called on; this is separate + from `fields` to ensure an error is raised if you don't pass at least one. + *fields (str): Additional field(s) the validator should be called on. + pre (bool, optional): Whether this validator should be called before the standard + validators (else after). Defaults to False. + each_item (bool, optional): For complex objects (sets, lists etc.) whether to validate + individual elements rather than the whole object. Defaults to False. + always (bool, optional): Whether this method and other validators should be called even if + the value is missing. Defaults to False. + check_fields (bool | None, optional): Whether to check that the fields actually exist on the model. + Defaults to None. + allow_reuse (bool, optional): Whether to track and raise an error if another validator refers to + the decorated function. Defaults to False. + + Returns: + Callable: A decorator that can be used to decorate a + function to be used as a validator. + """ + warn( + 'Pydantic V1 style `@validator` validators are deprecated.' + ' You should migrate to Pydantic V2 style `@field_validator` validators,' + ' see the migration guide for more details', + DeprecationWarning, + stacklevel=2, + ) + + if allow_reuse is True: # pragma: no cover + warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning, stacklevel=2) + fields = __field, *fields + if isinstance(fields[0], FunctionType): + raise PydanticUserError( + '`@validator` should be used with fields and keyword arguments, not bare. ' + "E.g. usage should be `@validator('', ...)`", + code='decorator-missing-arguments', + ) + elif not all(isinstance(field, str) for field in fields): + raise PydanticUserError( + '`@validator` fields should be passed as separate string args. ' + "E.g. usage should be `@validator('', '', ...)`", + code='decorator-invalid-fields', + ) + + mode: Literal['before', 'after'] = 'before' if pre is True else 'after' + + def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]: + if _decorators.is_instance_method_from_sig(f): + raise PydanticUserError( + '`@validator` cannot be applied to instance methods', code='validator-instance-method' + ) + # auto apply the @classmethod decorator + f = _decorators.ensure_classmethod_based_on_signature(f) + wrap = _decorators_v1.make_generic_v1_field_validator + validator_wrapper_info = _decorators.ValidatorDecoratorInfo( + fields=fields, + mode=mode, + each_item=each_item, + always=always, + check_fields=check_fields, + ) + return _decorators.PydanticDescriptorProxy(f, validator_wrapper_info, shim=wrap) + + return dec # type: ignore[return-value] + + +@overload +def root_validator( + *, + # if you don't specify `pre` the default is `pre=False` + # which means you need to specify `skip_on_failure=True` + skip_on_failure: Literal[True], + allow_reuse: bool = ..., +) -> Callable[ + [_V1RootValidatorFunctionType], + _V1RootValidatorFunctionType, +]: ... + + +@overload +def root_validator( + *, + # if you specify `pre=True` then you don't need to specify + # `skip_on_failure`, in fact it is not allowed as an argument! + pre: Literal[True], + allow_reuse: bool = ..., +) -> Callable[ + [_V1RootValidatorFunctionType], + _V1RootValidatorFunctionType, +]: ... + + +@overload +def root_validator( + *, + # if you explicitly specify `pre=False` then you + # MUST specify `skip_on_failure=True` + pre: Literal[False], + skip_on_failure: Literal[True], + allow_reuse: bool = ..., +) -> Callable[ + [_V1RootValidatorFunctionType], + _V1RootValidatorFunctionType, +]: ... + + +@deprecated( + 'Pydantic V1 style `@root_validator` validators are deprecated.' + ' You should migrate to Pydantic V2 style `@model_validator` validators,' + ' see the migration guide for more details', + category=None, +) +def root_validator( + *__args, + pre: bool = False, + skip_on_failure: bool = False, + allow_reuse: bool = False, +) -> Any: + """Decorate methods on a model indicating that they should be used to validate (and perhaps + modify) data either before or after standard model parsing/validation is performed. + + Args: + pre (bool, optional): Whether this validator should be called before the standard + validators (else after). Defaults to False. + skip_on_failure (bool, optional): Whether to stop validation and return as soon as a + failure is encountered. Defaults to False. + allow_reuse (bool, optional): Whether to track and raise an error if another validator + refers to the decorated function. Defaults to False. + + Returns: + Any: A decorator that can be used to decorate a function to be used as a root_validator. + """ + warn( + 'Pydantic V1 style `@root_validator` validators are deprecated.' + ' You should migrate to Pydantic V2 style `@model_validator` validators,' + ' see the migration guide for more details', + DeprecationWarning, + stacklevel=2, + ) + + if __args: + # Ensure a nice error is raised if someone attempts to use the bare decorator + return root_validator()(*__args) # type: ignore + + if allow_reuse is True: # pragma: no cover + warn(_ALLOW_REUSE_WARNING_MESSAGE, DeprecationWarning, stacklevel=2) + mode: Literal['before', 'after'] = 'before' if pre is True else 'after' + if pre is False and skip_on_failure is not True: + raise PydanticUserError( + 'If you use `@root_validator` with pre=False (the default) you MUST specify `skip_on_failure=True`.' + ' Note that `@root_validator` is deprecated and should be replaced with `@model_validator`.', + code='root-validator-pre-skip', + ) + + wrap = partial(_decorators_v1.make_v1_generic_root_validator, pre=pre) + + def dec(f: Callable[..., Any] | classmethod[Any, Any, Any] | staticmethod[Any, Any]) -> Any: + if _decorators.is_instance_method_from_sig(f): + raise TypeError('`@root_validator` cannot be applied to instance methods') + # auto apply the @classmethod decorator + res = _decorators.ensure_classmethod_based_on_signature(f) + dec_info = _decorators.RootValidatorDecoratorInfo(mode=mode) + return _decorators.PydanticDescriptorProxy(res, dec_info, shim=wrap) + + return dec diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/config.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/config.py new file mode 100644 index 0000000000000000000000000000000000000000..bd4692ace9b4594799974d6ecf8d44357d1d02c5 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/config.py @@ -0,0 +1,72 @@ +from __future__ import annotations as _annotations + +import warnings +from typing import TYPE_CHECKING, Any, Literal + +from typing_extensions import deprecated + +from .._internal import _config +from ..warnings import PydanticDeprecatedSince20 + +if not TYPE_CHECKING: + # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 + # and https://youtrack.jetbrains.com/issue/PY-51428 + DeprecationWarning = PydanticDeprecatedSince20 + +__all__ = 'BaseConfig', 'Extra' + + +class _ConfigMetaclass(type): + def __getattr__(self, item: str) -> Any: + try: + obj = _config.config_defaults[item] + warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning) + return obj + except KeyError as exc: + raise AttributeError(f"type object '{self.__name__}' has no attribute {exc}") from exc + + +@deprecated('BaseConfig is deprecated. Use the `pydantic.ConfigDict` instead.', category=PydanticDeprecatedSince20) +class BaseConfig(metaclass=_ConfigMetaclass): + """This class is only retained for backwards compatibility. + + !!! Warning "Deprecated" + BaseConfig is deprecated. Use the [`pydantic.ConfigDict`][pydantic.ConfigDict] instead. + """ + + def __getattr__(self, item: str) -> Any: + try: + obj = super().__getattribute__(item) + warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning) + return obj + except AttributeError as exc: + try: + return getattr(type(self), item) + except AttributeError: + # re-raising changes the displayed text to reflect that `self` is not a type + raise AttributeError(str(exc)) from exc + + def __init_subclass__(cls, **kwargs: Any) -> None: + warnings.warn(_config.DEPRECATION_MESSAGE, DeprecationWarning) + return super().__init_subclass__(**kwargs) + + +class _ExtraMeta(type): + def __getattribute__(self, __name: str) -> Any: + # The @deprecated decorator accesses other attributes, so we only emit a warning for the expected ones + if __name in {'allow', 'ignore', 'forbid'}: + warnings.warn( + "`pydantic.config.Extra` is deprecated, use literal values instead (e.g. `extra='allow'`)", + DeprecationWarning, + stacklevel=2, + ) + return super().__getattribute__(__name) + + +@deprecated( + "Extra is deprecated. Use literal values instead (e.g. `extra='allow'`)", category=PydanticDeprecatedSince20 +) +class Extra(metaclass=_ExtraMeta): + allow: Literal['allow'] = 'allow' + ignore: Literal['ignore'] = 'ignore' + forbid: Literal['forbid'] = 'forbid' diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/copy_internals.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/copy_internals.py new file mode 100644 index 0000000000000000000000000000000000000000..0170dc08bc13d29ef4b23476877fc22dc81bdfff --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/copy_internals.py @@ -0,0 +1,224 @@ +from __future__ import annotations as _annotations + +import typing +from copy import deepcopy +from enum import Enum +from typing import Any + +import typing_extensions + +from .._internal import ( + _model_construction, + _typing_extra, + _utils, +) + +if typing.TYPE_CHECKING: + from .. import BaseModel + from .._internal._utils import AbstractSetIntStr, MappingIntStrAny + + AnyClassMethod = classmethod[Any, Any, Any] + TupleGenerator = typing.Generator[tuple[str, Any], None, None] + Model = typing.TypeVar('Model', bound='BaseModel') + # should be `set[int] | set[str] | dict[int, IncEx] | dict[str, IncEx] | None`, but mypy can't cope + IncEx: typing_extensions.TypeAlias = 'set[int] | set[str] | dict[int, Any] | dict[str, Any] | None' + +_object_setattr = _model_construction.object_setattr + + +def _iter( + self: BaseModel, + to_dict: bool = False, + by_alias: bool = False, + include: AbstractSetIntStr | MappingIntStrAny | None = None, + exclude: AbstractSetIntStr | MappingIntStrAny | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, +) -> TupleGenerator: + # Merge field set excludes with explicit exclude parameter with explicit overriding field set options. + # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case. + if exclude is not None: + exclude = _utils.ValueItems.merge( + {k: v.exclude for k, v in self.__pydantic_fields__.items() if v.exclude is not None}, exclude + ) + + if include is not None: + include = _utils.ValueItems.merge(dict.fromkeys(self.__pydantic_fields__, True), include, intersect=True) + + allowed_keys = _calculate_keys(self, include=include, exclude=exclude, exclude_unset=exclude_unset) # type: ignore + if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none): + # huge boost for plain _iter() + yield from self.__dict__.items() + if self.__pydantic_extra__: + yield from self.__pydantic_extra__.items() + return + + value_exclude = _utils.ValueItems(self, exclude) if exclude is not None else None + value_include = _utils.ValueItems(self, include) if include is not None else None + + if self.__pydantic_extra__ is None: + items = self.__dict__.items() + else: + items = list(self.__dict__.items()) + list(self.__pydantic_extra__.items()) + + for field_key, v in items: + if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None): + continue + + if exclude_defaults: + try: + field = self.__pydantic_fields__[field_key] + except KeyError: + pass + else: + if not field.is_required() and field.default == v: + continue + + if by_alias and field_key in self.__pydantic_fields__: + dict_key = self.__pydantic_fields__[field_key].alias or field_key + else: + dict_key = field_key + + if to_dict or value_include or value_exclude: + v = _get_value( + type(self), + v, + to_dict=to_dict, + by_alias=by_alias, + include=value_include and value_include.for_element(field_key), + exclude=value_exclude and value_exclude.for_element(field_key), + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + yield dict_key, v + + +def _copy_and_set_values( + self: Model, + values: dict[str, Any], + fields_set: set[str], + extra: dict[str, Any] | None = None, + private: dict[str, Any] | None = None, + *, + deep: bool, # UP006 +) -> Model: + if deep: + # chances of having empty dict here are quite low for using smart_deepcopy + values = deepcopy(values) + extra = deepcopy(extra) + private = deepcopy(private) + + cls = self.__class__ + m = cls.__new__(cls) + _object_setattr(m, '__dict__', values) + _object_setattr(m, '__pydantic_extra__', extra) + _object_setattr(m, '__pydantic_fields_set__', fields_set) + _object_setattr(m, '__pydantic_private__', private) + + return m + + +@typing.no_type_check +def _get_value( + cls: type[BaseModel], + v: Any, + to_dict: bool, + by_alias: bool, + include: AbstractSetIntStr | MappingIntStrAny | None, + exclude: AbstractSetIntStr | MappingIntStrAny | None, + exclude_unset: bool, + exclude_defaults: bool, + exclude_none: bool, +) -> Any: + from .. import BaseModel + + if isinstance(v, BaseModel): + if to_dict: + return v.model_dump( + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + include=include, # type: ignore + exclude=exclude, # type: ignore + exclude_none=exclude_none, + ) + else: + return v.copy(include=include, exclude=exclude) + + value_exclude = _utils.ValueItems(v, exclude) if exclude else None + value_include = _utils.ValueItems(v, include) if include else None + + if isinstance(v, dict): + return { + k_: _get_value( + cls, + v_, + to_dict=to_dict, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + include=value_include and value_include.for_element(k_), + exclude=value_exclude and value_exclude.for_element(k_), + exclude_none=exclude_none, + ) + for k_, v_ in v.items() + if (not value_exclude or not value_exclude.is_excluded(k_)) + and (not value_include or value_include.is_included(k_)) + } + + elif _utils.sequence_like(v): + seq_args = ( + _get_value( + cls, + v_, + to_dict=to_dict, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + include=value_include and value_include.for_element(i), + exclude=value_exclude and value_exclude.for_element(i), + exclude_none=exclude_none, + ) + for i, v_ in enumerate(v) + if (not value_exclude or not value_exclude.is_excluded(i)) + and (not value_include or value_include.is_included(i)) + ) + + return v.__class__(*seq_args) if _typing_extra.is_namedtuple(v.__class__) else v.__class__(seq_args) + + elif isinstance(v, Enum) and getattr(cls.model_config, 'use_enum_values', False): + return v.value + + else: + return v + + +def _calculate_keys( + self: BaseModel, + include: MappingIntStrAny | None, + exclude: MappingIntStrAny | None, + exclude_unset: bool, + update: dict[str, Any] | None = None, # noqa UP006 +) -> typing.AbstractSet[str] | None: + if include is None and exclude is None and exclude_unset is False: + return None + + keys: typing.AbstractSet[str] + if exclude_unset: + keys = self.__pydantic_fields_set__.copy() + else: + keys = set(self.__dict__.keys()) + keys = keys | (self.__pydantic_extra__ or {}).keys() + + if include is not None: + keys &= include.keys() + + if update: + keys -= update.keys() + + if exclude: + keys -= {k for k, v in exclude.items() if _utils.ValueItems.is_true(v)} + + return keys diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/decorator.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/decorator.py new file mode 100644 index 0000000000000000000000000000000000000000..e73ad209ae86b26d6fa892f1eba39cfc28980970 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/decorator.py @@ -0,0 +1,284 @@ +import warnings +from collections.abc import Mapping +from functools import wraps +from typing import TYPE_CHECKING, Any, Callable, Optional, TypeVar, Union, overload + +from typing_extensions import deprecated + +from .._internal import _config, _typing_extra +from ..alias_generators import to_pascal +from ..errors import PydanticUserError +from ..functional_validators import field_validator +from ..main import BaseModel, create_model +from ..warnings import PydanticDeprecatedSince20 + +if not TYPE_CHECKING: + # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 + # and https://youtrack.jetbrains.com/issue/PY-51428 + DeprecationWarning = PydanticDeprecatedSince20 + +__all__ = ('validate_arguments',) + +if TYPE_CHECKING: + AnyCallable = Callable[..., Any] + + AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable) + ConfigType = Union[None, type[Any], dict[str, Any]] + + +@overload +def validate_arguments( + func: None = None, *, config: 'ConfigType' = None +) -> Callable[['AnyCallableT'], 'AnyCallableT']: ... + + +@overload +def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT': ... + + +@deprecated( + 'The `validate_arguments` method is deprecated; use `validate_call` instead.', + category=None, +) +def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any: + """Decorator to validate the arguments passed to a function.""" + warnings.warn( + 'The `validate_arguments` method is deprecated; use `validate_call` instead.', + PydanticDeprecatedSince20, + stacklevel=2, + ) + + def validate(_func: 'AnyCallable') -> 'AnyCallable': + vd = ValidatedFunction(_func, config) + + @wraps(_func) + def wrapper_function(*args: Any, **kwargs: Any) -> Any: + return vd.call(*args, **kwargs) + + wrapper_function.vd = vd # type: ignore + wrapper_function.validate = vd.init_model_instance # type: ignore + wrapper_function.raw_function = vd.raw_function # type: ignore + wrapper_function.model = vd.model # type: ignore + return wrapper_function + + if func: + return validate(func) + else: + return validate + + +ALT_V_ARGS = 'v__args' +ALT_V_KWARGS = 'v__kwargs' +V_POSITIONAL_ONLY_NAME = 'v__positional_only' +V_DUPLICATE_KWARGS = 'v__duplicate_kwargs' + + +class ValidatedFunction: + def __init__(self, function: 'AnyCallable', config: 'ConfigType'): + from inspect import Parameter, signature + + parameters: Mapping[str, Parameter] = signature(function).parameters + + if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}: + raise PydanticUserError( + f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" ' + f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator', + code=None, + ) + + self.raw_function = function + self.arg_mapping: dict[int, str] = {} + self.positional_only_args: set[str] = set() + self.v_args_name = 'args' + self.v_kwargs_name = 'kwargs' + + type_hints = _typing_extra.get_type_hints(function, include_extras=True) + takes_args = False + takes_kwargs = False + fields: dict[str, tuple[Any, Any]] = {} + for i, (name, p) in enumerate(parameters.items()): + if p.annotation is p.empty: + annotation = Any + else: + annotation = type_hints[name] + + default = ... if p.default is p.empty else p.default + if p.kind == Parameter.POSITIONAL_ONLY: + self.arg_mapping[i] = name + fields[name] = annotation, default + fields[V_POSITIONAL_ONLY_NAME] = list[str], None + self.positional_only_args.add(name) + elif p.kind == Parameter.POSITIONAL_OR_KEYWORD: + self.arg_mapping[i] = name + fields[name] = annotation, default + fields[V_DUPLICATE_KWARGS] = list[str], None + elif p.kind == Parameter.KEYWORD_ONLY: + fields[name] = annotation, default + elif p.kind == Parameter.VAR_POSITIONAL: + self.v_args_name = name + fields[name] = tuple[annotation, ...], None + takes_args = True + else: + assert p.kind == Parameter.VAR_KEYWORD, p.kind + self.v_kwargs_name = name + fields[name] = dict[str, annotation], None + takes_kwargs = True + + # these checks avoid a clash between "args" and a field with that name + if not takes_args and self.v_args_name in fields: + self.v_args_name = ALT_V_ARGS + + # same with "kwargs" + if not takes_kwargs and self.v_kwargs_name in fields: + self.v_kwargs_name = ALT_V_KWARGS + + if not takes_args: + # we add the field so validation below can raise the correct exception + fields[self.v_args_name] = list[Any], None + + if not takes_kwargs: + # same with kwargs + fields[self.v_kwargs_name] = dict[Any, Any], None + + self.create_model(fields, takes_args, takes_kwargs, config) + + def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel: + values = self.build_values(args, kwargs) + return self.model(**values) + + def call(self, *args: Any, **kwargs: Any) -> Any: + m = self.init_model_instance(*args, **kwargs) + return self.execute(m) + + def build_values(self, args: tuple[Any, ...], kwargs: dict[str, Any]) -> dict[str, Any]: + values: dict[str, Any] = {} + if args: + arg_iter = enumerate(args) + while True: + try: + i, a = next(arg_iter) + except StopIteration: + break + arg_name = self.arg_mapping.get(i) + if arg_name is not None: + values[arg_name] = a + else: + values[self.v_args_name] = [a] + [a for _, a in arg_iter] + break + + var_kwargs: dict[str, Any] = {} + wrong_positional_args = [] + duplicate_kwargs = [] + fields_alias = [ + field.alias + for name, field in self.model.__pydantic_fields__.items() + if name not in (self.v_args_name, self.v_kwargs_name) + ] + non_var_fields = set(self.model.__pydantic_fields__) - {self.v_args_name, self.v_kwargs_name} + for k, v in kwargs.items(): + if k in non_var_fields or k in fields_alias: + if k in self.positional_only_args: + wrong_positional_args.append(k) + if k in values: + duplicate_kwargs.append(k) + values[k] = v + else: + var_kwargs[k] = v + + if var_kwargs: + values[self.v_kwargs_name] = var_kwargs + if wrong_positional_args: + values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args + if duplicate_kwargs: + values[V_DUPLICATE_KWARGS] = duplicate_kwargs + return values + + def execute(self, m: BaseModel) -> Any: + d = { + k: v + for k, v in m.__dict__.items() + if k in m.__pydantic_fields_set__ or m.__pydantic_fields__[k].default_factory + } + var_kwargs = d.pop(self.v_kwargs_name, {}) + + if self.v_args_name in d: + args_: list[Any] = [] + in_kwargs = False + kwargs = {} + for name, value in d.items(): + if in_kwargs: + kwargs[name] = value + elif name == self.v_args_name: + args_ += value + in_kwargs = True + else: + args_.append(value) + return self.raw_function(*args_, **kwargs, **var_kwargs) + elif self.positional_only_args: + args_ = [] + kwargs = {} + for name, value in d.items(): + if name in self.positional_only_args: + args_.append(value) + else: + kwargs[name] = value + return self.raw_function(*args_, **kwargs, **var_kwargs) + else: + return self.raw_function(**d, **var_kwargs) + + def create_model(self, fields: dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None: + pos_args = len(self.arg_mapping) + + config_wrapper = _config.ConfigWrapper(config) + + if config_wrapper.alias_generator: + raise PydanticUserError( + 'Setting the "alias_generator" property on custom Config for ' + '@validate_arguments is not yet supported, please remove.', + code=None, + ) + if config_wrapper.extra is None: + config_wrapper.config_dict['extra'] = 'forbid' + + class DecoratorBaseModel(BaseModel): + @field_validator(self.v_args_name, check_fields=False) + @classmethod + def check_args(cls, v: Optional[list[Any]]) -> Optional[list[Any]]: + if takes_args or v is None: + return v + + raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given') + + @field_validator(self.v_kwargs_name, check_fields=False) + @classmethod + def check_kwargs(cls, v: Optional[dict[str, Any]]) -> Optional[dict[str, Any]]: + if takes_kwargs or v is None: + return v + + plural = '' if len(v) == 1 else 's' + keys = ', '.join(map(repr, v.keys())) + raise TypeError(f'unexpected keyword argument{plural}: {keys}') + + @field_validator(V_POSITIONAL_ONLY_NAME, check_fields=False) + @classmethod + def check_positional_only(cls, v: Optional[list[str]]) -> None: + if v is None: + return + + plural = '' if len(v) == 1 else 's' + keys = ', '.join(map(repr, v)) + raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}') + + @field_validator(V_DUPLICATE_KWARGS, check_fields=False) + @classmethod + def check_duplicate_kwargs(cls, v: Optional[list[str]]) -> None: + if v is None: + return + + plural = '' if len(v) == 1 else 's' + keys = ', '.join(map(repr, v)) + raise TypeError(f'multiple values for argument{plural}: {keys}') + + model_config = config_wrapper.config_dict + + self.model = create_model(to_pascal(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/json.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/json.py new file mode 100644 index 0000000000000000000000000000000000000000..1e216a765d15d5dd9e379f9de4f8f91ab8063877 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/json.py @@ -0,0 +1,141 @@ +import datetime +import warnings +from collections import deque +from decimal import Decimal +from enum import Enum +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from pathlib import Path +from re import Pattern +from types import GeneratorType +from typing import TYPE_CHECKING, Any, Callable, Union +from uuid import UUID + +from typing_extensions import deprecated + +from .._internal._import_utils import import_cached_base_model +from ..color import Color +from ..networks import NameEmail +from ..types import SecretBytes, SecretStr +from ..warnings import PydanticDeprecatedSince20 + +if not TYPE_CHECKING: + # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 + # and https://youtrack.jetbrains.com/issue/PY-51428 + DeprecationWarning = PydanticDeprecatedSince20 + +__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat' + + +def isoformat(o: Union[datetime.date, datetime.time]) -> str: + return o.isoformat() + + +def decimal_encoder(dec_value: Decimal) -> Union[int, float]: + """Encodes a Decimal as int of there's no exponent, otherwise float. + + This is useful when we use ConstrainedDecimal to represent Numeric(x,0) + where a integer (but not int typed) is used. Encoding this as a float + results in failed round-tripping between encode and parse. + Our Id type is a prime example of this. + + >>> decimal_encoder(Decimal("1.0")) + 1.0 + + >>> decimal_encoder(Decimal("1")) + 1 + """ + exponent = dec_value.as_tuple().exponent + if isinstance(exponent, int) and exponent >= 0: + return int(dec_value) + else: + return float(dec_value) + + +ENCODERS_BY_TYPE: dict[type[Any], Callable[[Any], Any]] = { + bytes: lambda o: o.decode(), + Color: str, + datetime.date: isoformat, + datetime.datetime: isoformat, + datetime.time: isoformat, + datetime.timedelta: lambda td: td.total_seconds(), + Decimal: decimal_encoder, + Enum: lambda o: o.value, + frozenset: list, + deque: list, + GeneratorType: list, + IPv4Address: str, + IPv4Interface: str, + IPv4Network: str, + IPv6Address: str, + IPv6Interface: str, + IPv6Network: str, + NameEmail: str, + Path: str, + Pattern: lambda o: o.pattern, + SecretBytes: str, + SecretStr: str, + set: list, + UUID: str, +} + + +@deprecated( + '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.', + category=None, +) +def pydantic_encoder(obj: Any) -> Any: + warnings.warn( + '`pydantic_encoder` is deprecated, use `pydantic_core.to_jsonable_python` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from dataclasses import asdict, is_dataclass + + BaseModel = import_cached_base_model() + + if isinstance(obj, BaseModel): + return obj.model_dump() + elif is_dataclass(obj): + return asdict(obj) # type: ignore + + # Check the class type and its superclasses for a matching encoder + for base in obj.__class__.__mro__[:-1]: + try: + encoder = ENCODERS_BY_TYPE[base] + except KeyError: + continue + return encoder(obj) + else: # We have exited the for loop without finding a suitable encoder + raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable") + + +# TODO: Add a suggested migration path once there is a way to use custom encoders +@deprecated( + '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.', + category=None, +) +def custom_pydantic_encoder(type_encoders: dict[Any, Callable[[type[Any]], Any]], obj: Any) -> Any: + warnings.warn( + '`custom_pydantic_encoder` is deprecated, use `BaseModel.model_dump` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + # Check the class type and its superclasses for a matching encoder + for base in obj.__class__.__mro__[:-1]: + try: + encoder = type_encoders[base] + except KeyError: + continue + + return encoder(obj) + else: # We have exited the for loop without finding a suitable encoder + return pydantic_encoder(obj) + + +@deprecated('`timedelta_isoformat` is deprecated.', category=None) +def timedelta_isoformat(td: datetime.timedelta) -> str: + """ISO 8601 encoding for Python timedelta object.""" + warnings.warn('`timedelta_isoformat` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2) + minutes, seconds = divmod(td.seconds, 60) + hours, minutes = divmod(minutes, 60) + return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S' diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/parse.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/parse.py new file mode 100644 index 0000000000000000000000000000000000000000..2a92e62b7b2e8dab77cbe0c2dbb79c810af7f452 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/parse.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +import json +import pickle +import warnings +from enum import Enum +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable + +from typing_extensions import deprecated + +from ..warnings import PydanticDeprecatedSince20 + +if not TYPE_CHECKING: + # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 + # and https://youtrack.jetbrains.com/issue/PY-51428 + DeprecationWarning = PydanticDeprecatedSince20 + + +class Protocol(str, Enum): + json = 'json' + pickle = 'pickle' + + +@deprecated('`load_str_bytes` is deprecated.', category=None) +def load_str_bytes( + b: str | bytes, + *, + content_type: str | None = None, + encoding: str = 'utf8', + proto: Protocol | None = None, + allow_pickle: bool = False, + json_loads: Callable[[str], Any] = json.loads, +) -> Any: + warnings.warn('`load_str_bytes` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2) + if proto is None and content_type: + if content_type.endswith(('json', 'javascript')): + pass + elif allow_pickle and content_type.endswith('pickle'): + proto = Protocol.pickle + else: + raise TypeError(f'Unknown content-type: {content_type}') + + proto = proto or Protocol.json + + if proto == Protocol.json: + if isinstance(b, bytes): + b = b.decode(encoding) + return json_loads(b) # type: ignore + elif proto == Protocol.pickle: + if not allow_pickle: + raise RuntimeError('Trying to decode with pickle with allow_pickle=False') + bb = b if isinstance(b, bytes) else b.encode() # type: ignore + return pickle.loads(bb) + else: + raise TypeError(f'Unknown protocol: {proto}') + + +@deprecated('`load_file` is deprecated.', category=None) +def load_file( + path: str | Path, + *, + content_type: str | None = None, + encoding: str = 'utf8', + proto: Protocol | None = None, + allow_pickle: bool = False, + json_loads: Callable[[str], Any] = json.loads, +) -> Any: + warnings.warn('`load_file` is deprecated.', category=PydanticDeprecatedSince20, stacklevel=2) + path = Path(path) + b = path.read_bytes() + if content_type is None: + if path.suffix in ('.js', '.json'): + proto = Protocol.json + elif path.suffix == '.pkl': + proto = Protocol.pickle + + return load_str_bytes( + b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/tools.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..5ad7faef2ec69eaa737a58d3587770a612a510d8 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/deprecated/tools.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import json +import warnings +from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union + +from typing_extensions import deprecated + +from ..json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema +from ..type_adapter import TypeAdapter +from ..warnings import PydanticDeprecatedSince20 + +if not TYPE_CHECKING: + # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915 + # and https://youtrack.jetbrains.com/issue/PY-51428 + DeprecationWarning = PydanticDeprecatedSince20 + +__all__ = 'parse_obj_as', 'schema_of', 'schema_json_of' + +NameFactory = Union[str, Callable[[type[Any]], str]] + + +T = TypeVar('T') + + +@deprecated( + '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.', + category=None, +) +def parse_obj_as(type_: type[T], obj: Any, type_name: NameFactory | None = None) -> T: + warnings.warn( + '`parse_obj_as` is deprecated. Use `pydantic.TypeAdapter.validate_python` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + if type_name is not None: # pragma: no cover + warnings.warn( + 'The type_name parameter is deprecated. parse_obj_as no longer creates temporary models', + DeprecationWarning, + stacklevel=2, + ) + return TypeAdapter(type_).validate_python(obj) + + +@deprecated( + '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.', + category=None, +) +def schema_of( + type_: Any, + *, + title: NameFactory | None = None, + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, +) -> dict[str, Any]: + """Generate a JSON schema (as dict) for the passed model or dynamically generated one.""" + warnings.warn( + '`schema_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + res = TypeAdapter(type_).json_schema( + by_alias=by_alias, + schema_generator=schema_generator, + ref_template=ref_template, + ) + if title is not None: + if isinstance(title, str): + res['title'] = title + else: + warnings.warn( + 'Passing a callable for the `title` parameter is deprecated and no longer supported', + DeprecationWarning, + stacklevel=2, + ) + res['title'] = title(type_) + return res + + +@deprecated( + '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.', + category=None, +) +def schema_json_of( + type_: Any, + *, + title: NameFactory | None = None, + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + **dumps_kwargs: Any, +) -> str: + """Generate a JSON schema (as JSON) for the passed model or dynamically generated one.""" + warnings.warn( + '`schema_json_of` is deprecated. Use `pydantic.TypeAdapter.json_schema` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return json.dumps( + schema_of(type_, title=title, by_alias=by_alias, ref_template=ref_template, schema_generator=schema_generator), + **dumps_kwargs, + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/env_settings.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/env_settings.py new file mode 100644 index 0000000000000000000000000000000000000000..cd0b04e6a43c1bb3767dd136a19a9873fa547514 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/env_settings.py @@ -0,0 +1,5 @@ +"""The `env_settings` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/error_wrappers.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/error_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..2985419abf23f5e529af43883f1d365452e4190a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/error_wrappers.py @@ -0,0 +1,5 @@ +"""The `error_wrappers` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/errors.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..33851bded20b1125a3b55ecc03727692ed78e1db --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/errors.py @@ -0,0 +1,189 @@ +"""Pydantic-specific errors.""" + +from __future__ import annotations as _annotations + +import re +from typing import Any, ClassVar, Literal + +from typing_extensions import Self +from typing_inspection.introspection import Qualifier + +from pydantic._internal import _repr + +from ._migration import getattr_migration +from .version import version_short + +__all__ = ( + 'PydanticUserError', + 'PydanticUndefinedAnnotation', + 'PydanticImportError', + 'PydanticSchemaGenerationError', + 'PydanticInvalidForJsonSchema', + 'PydanticForbiddenQualifier', + 'PydanticErrorCodes', +) + +# We use this URL to allow for future flexibility about how we host the docs, while allowing for Pydantic +# code in the while with "old" URLs to still work. +# 'u' refers to "user errors" - e.g. errors caused by developers using pydantic, as opposed to validation errors. +DEV_ERROR_DOCS_URL = f'https://errors.pydantic.dev/{version_short()}/u/' +PydanticErrorCodes = Literal[ + 'class-not-fully-defined', + 'custom-json-schema', + 'decorator-invalid-fields', + 'decorator-missing-arguments', + 'decorator-missing-field', + 'discriminator-no-field', + 'discriminator-alias-type', + 'discriminator-needs-literal', + 'discriminator-alias', + 'discriminator-validator', + 'callable-discriminator-no-tag', + 'typed-dict-version', + 'model-field-overridden', + 'model-field-missing-annotation', + 'config-both', + 'removed-kwargs', + 'circular-reference-schema', + 'invalid-for-json-schema', + 'json-schema-already-used', + 'base-model-instantiated', + 'undefined-annotation', + 'schema-for-unknown-type', + 'import-error', + 'create-model-field-definitions', + 'validator-instance-method', + 'validator-input-type', + 'root-validator-pre-skip', + 'model-serializer-instance-method', + 'validator-field-config-info', + 'validator-v1-signature', + 'validator-signature', + 'field-serializer-signature', + 'model-serializer-signature', + 'multiple-field-serializers', + 'invalid-annotated-type', + 'type-adapter-config-unused', + 'root-model-extra', + 'unevaluable-type-annotation', + 'dataclass-init-false-extra-allow', + 'clashing-init-and-init-var', + 'model-config-invalid-field-name', + 'with-config-on-model', + 'dataclass-on-model', + 'validate-call-type', + 'unpack-typed-dict', + 'overlapping-unpack-typed-dict', + 'invalid-self-type', + 'validate-by-alias-and-name-false', +] + + +class PydanticErrorMixin: + """A mixin class for common functionality shared by all Pydantic-specific errors. + + Attributes: + message: A message describing the error. + code: An optional error code from PydanticErrorCodes enum. + """ + + def __init__(self, message: str, *, code: PydanticErrorCodes | None) -> None: + self.message = message + self.code = code + + def __str__(self) -> str: + if self.code is None: + return self.message + else: + return f'{self.message}\n\nFor further information visit {DEV_ERROR_DOCS_URL}{self.code}' + + +class PydanticUserError(PydanticErrorMixin, RuntimeError): + """An error raised due to incorrect use of Pydantic.""" + + +class PydanticUndefinedAnnotation(PydanticErrorMixin, NameError): + """A subclass of `NameError` raised when handling undefined annotations during `CoreSchema` generation. + + Attributes: + name: Name of the error. + message: Description of the error. + """ + + def __init__(self, name: str, message: str) -> None: + self.name = name + super().__init__(message=message, code='undefined-annotation') + + @classmethod + def from_name_error(cls, name_error: NameError) -> Self: + """Convert a `NameError` to a `PydanticUndefinedAnnotation` error. + + Args: + name_error: `NameError` to be converted. + + Returns: + Converted `PydanticUndefinedAnnotation` error. + """ + try: + name = name_error.name # type: ignore # python > 3.10 + except AttributeError: + name = re.search(r".*'(.+?)'", str(name_error)).group(1) # type: ignore[union-attr] + return cls(name=name, message=str(name_error)) + + +class PydanticImportError(PydanticErrorMixin, ImportError): + """An error raised when an import fails due to module changes between V1 and V2. + + Attributes: + message: Description of the error. + """ + + def __init__(self, message: str) -> None: + super().__init__(message, code='import-error') + + +class PydanticSchemaGenerationError(PydanticUserError): + """An error raised during failures to generate a `CoreSchema` for some type. + + Attributes: + message: Description of the error. + """ + + def __init__(self, message: str) -> None: + super().__init__(message, code='schema-for-unknown-type') + + +class PydanticInvalidForJsonSchema(PydanticUserError): + """An error raised during failures to generate a JSON schema for some `CoreSchema`. + + Attributes: + message: Description of the error. + """ + + def __init__(self, message: str) -> None: + super().__init__(message, code='invalid-for-json-schema') + + +class PydanticForbiddenQualifier(PydanticUserError): + """An error raised if a forbidden type qualifier is found in a type annotation.""" + + _qualifier_repr_map: ClassVar[dict[Qualifier, str]] = { + 'required': 'typing.Required', + 'not_required': 'typing.NotRequired', + 'read_only': 'typing.ReadOnly', + 'class_var': 'typing.ClassVar', + 'init_var': 'dataclasses.InitVar', + 'final': 'typing.Final', + } + + def __init__(self, qualifier: Qualifier, annotation: Any) -> None: + super().__init__( + message=( + f'The annotation {_repr.display_as_type(annotation)!r} contains the {self._qualifier_repr_map[qualifier]!r} ' + f'type qualifier, which is invalid in the context it is defined.' + ), + code=None, + ) + + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5b5add10921077da0928529592a75e7ada7ecee5 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/__init__.py @@ -0,0 +1 @@ +"""The "experimental" module of pydantic contains potential new features that are subject to change.""" diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/arguments_schema.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/arguments_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..af4a8f3be3242519b529668e321dd8393241f7ea --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/arguments_schema.py @@ -0,0 +1,44 @@ +"""Experimental module exposing a function to generate a core schema that validates callable arguments.""" + +from __future__ import annotations + +from collections.abc import Callable +from typing import Any, Literal + +from pydantic_core import CoreSchema + +from pydantic import ConfigDict +from pydantic._internal import _config, _generate_schema, _namespace_utils + + +def generate_arguments_schema( + func: Callable[..., Any], + schema_type: Literal['arguments', 'arguments-v3'] = 'arguments-v3', + parameters_callback: Callable[[int, str, Any], Literal['skip'] | None] | None = None, + config: ConfigDict | None = None, +) -> CoreSchema: + """Generate the schema for the arguments of a function. + + Args: + func: The function to generate the schema for. + schema_type: The type of schema to generate. + parameters_callback: A callable that will be invoked for each parameter. The callback + should take three required arguments: the index, the name and the type annotation + (or [`Parameter.empty`][inspect.Parameter.empty] if not annotated) of the parameter. + The callback can optionally return `'skip'`, so that the parameter gets excluded + from the resulting schema. + config: The configuration to use. + + Returns: + The generated schema. + """ + generate_schema = _generate_schema.GenerateSchema( + _config.ConfigWrapper(config), + ns_resolver=_namespace_utils.NsResolver(namespaces_tuple=_namespace_utils.ns_for_function(func)), + ) + + if schema_type == 'arguments': + schema = generate_schema._arguments_schema(func, parameters_callback) # pyright: ignore[reportArgumentType] + else: + schema = generate_schema._arguments_v3_schema(func, parameters_callback) # pyright: ignore[reportArgumentType] + return generate_schema.clean_schema(schema) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/missing_sentinel.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/missing_sentinel.py new file mode 100644 index 0000000000000000000000000000000000000000..3e7f820ceda3b8d984f01ad0aaa1e150f06beda3 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/missing_sentinel.py @@ -0,0 +1,5 @@ +"""Experimental module exposing a function a `MISSING` sentinel.""" + +from pydantic_core import MISSING + +__all__ = ('MISSING',) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/pipeline.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/pipeline.py new file mode 100644 index 0000000000000000000000000000000000000000..9ead658f06ff547613803ac852be85e643bfc584 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/experimental/pipeline.py @@ -0,0 +1,663 @@ +"""Experimental pipeline API functionality. Be careful with this API, it's subject to change.""" + +from __future__ import annotations + +import datetime +import operator +import re +import sys +from collections import deque +from collections.abc import Container +from dataclasses import dataclass +from decimal import Decimal +from functools import cached_property, partial +from re import Pattern +from typing import TYPE_CHECKING, Annotated, Any, Callable, Generic, Protocol, TypeVar, Union, overload + +import annotated_types + +if TYPE_CHECKING: + from pydantic import GetCoreSchemaHandler + +from pydantic_core import PydanticCustomError +from pydantic_core import core_schema as cs + +from pydantic import Strict +from pydantic._internal._internal_dataclass import slots_true as _slots_true + +if sys.version_info < (3, 10): + EllipsisType = type(Ellipsis) +else: + from types import EllipsisType + +__all__ = ['validate_as', 'validate_as_deferred', 'transform'] + +_slots_frozen = {**_slots_true, 'frozen': True} + + +@dataclass(**_slots_frozen) +class _ValidateAs: + tp: type[Any] + strict: bool = False + + +@dataclass +class _ValidateAsDefer: + func: Callable[[], type[Any]] + + @cached_property + def tp(self) -> type[Any]: + return self.func() + + +@dataclass(**_slots_frozen) +class _Transform: + func: Callable[[Any], Any] + + +@dataclass(**_slots_frozen) +class _PipelineOr: + left: _Pipeline[Any, Any] + right: _Pipeline[Any, Any] + + +@dataclass(**_slots_frozen) +class _PipelineAnd: + left: _Pipeline[Any, Any] + right: _Pipeline[Any, Any] + + +@dataclass(**_slots_frozen) +class _Eq: + value: Any + + +@dataclass(**_slots_frozen) +class _NotEq: + value: Any + + +@dataclass(**_slots_frozen) +class _In: + values: Container[Any] + + +@dataclass(**_slots_frozen) +class _NotIn: + values: Container[Any] + + +_ConstraintAnnotation = Union[ + annotated_types.Le, + annotated_types.Ge, + annotated_types.Lt, + annotated_types.Gt, + annotated_types.Len, + annotated_types.MultipleOf, + annotated_types.Timezone, + annotated_types.Interval, + annotated_types.Predicate, + # common predicates not included in annotated_types + _Eq, + _NotEq, + _In, + _NotIn, + # regular expressions + Pattern[str], +] + + +@dataclass(**_slots_frozen) +class _Constraint: + constraint: _ConstraintAnnotation + + +_Step = Union[_ValidateAs, _ValidateAsDefer, _Transform, _PipelineOr, _PipelineAnd, _Constraint] + +_InT = TypeVar('_InT') +_OutT = TypeVar('_OutT') +_NewOutT = TypeVar('_NewOutT') + + +class _FieldTypeMarker: + pass + + +# TODO: ultimately, make this public, see https://github.com/pydantic/pydantic/pull/9459#discussion_r1628197626 +# Also, make this frozen eventually, but that doesn't work right now because of the generic base +# Which attempts to modify __orig_base__ and such. +# We could go with a manual freeze, but that seems overkill for now. +@dataclass(**_slots_true) +class _Pipeline(Generic[_InT, _OutT]): + """Abstract representation of a chain of validation, transformation, and parsing steps.""" + + _steps: tuple[_Step, ...] + + def transform( + self, + func: Callable[[_OutT], _NewOutT], + ) -> _Pipeline[_InT, _NewOutT]: + """Transform the output of the previous step. + + If used as the first step in a pipeline, the type of the field is used. + That is, the transformation is applied to after the value is parsed to the field's type. + """ + return _Pipeline[_InT, _NewOutT](self._steps + (_Transform(func),)) + + @overload + def validate_as(self, tp: type[_NewOutT], *, strict: bool = False) -> _Pipeline[_InT, _NewOutT]: ... + + @overload + def validate_as( + self, + tp: ellipsis, # noqa: F821 # TODO: use `_typing_extra.EllipsisType` when we drop Py3.9 + *, + strict: bool = False, + ) -> _Pipeline[_InT, Any]: ... + + # TODO PEP 747: use TypeForm to properly type Annotated aliases (e.g. NewPath, FilePath). + # This fallback accepts any type expression but loses generic type inference. + @overload + def validate_as(self, tp: Any, *, strict: bool = ...) -> _Pipeline[_InT, Any]: ... + + def validate_as(self, tp: type[_NewOutT] | EllipsisType | Any, *, strict: bool = False) -> _Pipeline[_InT, Any]: # type: ignore + """Validate / parse the input into a new type. + + If no type is provided, the type of the field is used. + + Types are parsed in Pydantic's `lax` mode by default, + but you can enable `strict` mode by passing `strict=True`. + """ + if isinstance(tp, EllipsisType): + return _Pipeline[_InT, Any](self._steps + (_ValidateAs(_FieldTypeMarker, strict=strict),)) + return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAs(tp, strict=strict),)) + + def validate_as_deferred(self, func: Callable[[], type[_NewOutT]]) -> _Pipeline[_InT, _NewOutT]: + """Parse the input into a new type, deferring resolution of the type until the current class + is fully defined. + + This is useful when you need to reference the class in it's own type annotations. + """ + return _Pipeline[_InT, _NewOutT](self._steps + (_ValidateAsDefer(func),)) + + # constraints + @overload + def constrain(self: _Pipeline[_InT, _NewOutGe], constraint: annotated_types.Ge) -> _Pipeline[_InT, _NewOutGe]: ... + + @overload + def constrain(self: _Pipeline[_InT, _NewOutGt], constraint: annotated_types.Gt) -> _Pipeline[_InT, _NewOutGt]: ... + + @overload + def constrain(self: _Pipeline[_InT, _NewOutLe], constraint: annotated_types.Le) -> _Pipeline[_InT, _NewOutLe]: ... + + @overload + def constrain(self: _Pipeline[_InT, _NewOutLt], constraint: annotated_types.Lt) -> _Pipeline[_InT, _NewOutLt]: ... + + @overload + def constrain( + self: _Pipeline[_InT, _NewOutLen], constraint: annotated_types.Len + ) -> _Pipeline[_InT, _NewOutLen]: ... + + @overload + def constrain( + self: _Pipeline[_InT, _NewOutT], constraint: annotated_types.MultipleOf + ) -> _Pipeline[_InT, _NewOutT]: ... + + @overload + def constrain( + self: _Pipeline[_InT, _NewOutDatetime], constraint: annotated_types.Timezone + ) -> _Pipeline[_InT, _NewOutDatetime]: ... + + @overload + def constrain(self: _Pipeline[_InT, _OutT], constraint: annotated_types.Predicate) -> _Pipeline[_InT, _OutT]: ... + + @overload + def constrain( + self: _Pipeline[_InT, _NewOutInterval], constraint: annotated_types.Interval + ) -> _Pipeline[_InT, _NewOutInterval]: ... + + @overload + def constrain(self: _Pipeline[_InT, _OutT], constraint: _Eq) -> _Pipeline[_InT, _OutT]: ... + + @overload + def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotEq) -> _Pipeline[_InT, _OutT]: ... + + @overload + def constrain(self: _Pipeline[_InT, _OutT], constraint: _In) -> _Pipeline[_InT, _OutT]: ... + + @overload + def constrain(self: _Pipeline[_InT, _OutT], constraint: _NotIn) -> _Pipeline[_InT, _OutT]: ... + + @overload + def constrain(self: _Pipeline[_InT, _NewOutT], constraint: Pattern[str]) -> _Pipeline[_InT, _NewOutT]: ... + + def constrain(self, constraint: _ConstraintAnnotation) -> Any: + """Constrain a value to meet a certain condition. + + We support most conditions from `annotated_types`, as well as regular expressions. + + Most of the time you'll be calling a shortcut method like `gt`, `lt`, `len`, etc + so you don't need to call this directly. + """ + return _Pipeline[_InT, _OutT](self._steps + (_Constraint(constraint),)) + + def predicate(self: _Pipeline[_InT, _NewOutT], func: Callable[[_NewOutT], bool]) -> _Pipeline[_InT, _NewOutT]: + """Constrain a value to meet a certain predicate.""" + return self.constrain(annotated_types.Predicate(func)) + + def gt(self: _Pipeline[_InT, _NewOutGt], gt: _NewOutGt) -> _Pipeline[_InT, _NewOutGt]: + """Constrain a value to be greater than a certain value.""" + return self.constrain(annotated_types.Gt(gt)) + + def lt(self: _Pipeline[_InT, _NewOutLt], lt: _NewOutLt) -> _Pipeline[_InT, _NewOutLt]: + """Constrain a value to be less than a certain value.""" + return self.constrain(annotated_types.Lt(lt)) + + def ge(self: _Pipeline[_InT, _NewOutGe], ge: _NewOutGe) -> _Pipeline[_InT, _NewOutGe]: + """Constrain a value to be greater than or equal to a certain value.""" + return self.constrain(annotated_types.Ge(ge)) + + def le(self: _Pipeline[_InT, _NewOutLe], le: _NewOutLe) -> _Pipeline[_InT, _NewOutLe]: + """Constrain a value to be less than or equal to a certain value.""" + return self.constrain(annotated_types.Le(le)) + + def len(self: _Pipeline[_InT, _NewOutLen], min_len: int, max_len: int | None = None) -> _Pipeline[_InT, _NewOutLen]: + """Constrain a value to have a certain length.""" + return self.constrain(annotated_types.Len(min_len, max_len)) + + @overload + def multiple_of(self: _Pipeline[_InT, _NewOutDiv], multiple_of: _NewOutDiv) -> _Pipeline[_InT, _NewOutDiv]: ... + + @overload + def multiple_of(self: _Pipeline[_InT, _NewOutMod], multiple_of: _NewOutMod) -> _Pipeline[_InT, _NewOutMod]: ... + + def multiple_of(self: _Pipeline[_InT, Any], multiple_of: Any) -> _Pipeline[_InT, Any]: + """Constrain a value to be a multiple of a certain number.""" + return self.constrain(annotated_types.MultipleOf(multiple_of)) + + def eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]: + """Constrain a value to be equal to a certain value.""" + return self.constrain(_Eq(value)) + + def not_eq(self: _Pipeline[_InT, _OutT], value: _OutT) -> _Pipeline[_InT, _OutT]: + """Constrain a value to not be equal to a certain value.""" + return self.constrain(_NotEq(value)) + + def in_(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]: + """Constrain a value to be in a certain set.""" + return self.constrain(_In(values)) + + def not_in(self: _Pipeline[_InT, _OutT], values: Container[_OutT]) -> _Pipeline[_InT, _OutT]: + """Constrain a value to not be in a certain set.""" + return self.constrain(_NotIn(values)) + + # timezone methods + def datetime_tz_naive(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]: + return self.constrain(annotated_types.Timezone(None)) + + def datetime_tz_aware(self: _Pipeline[_InT, datetime.datetime]) -> _Pipeline[_InT, datetime.datetime]: + return self.constrain(annotated_types.Timezone(...)) + + def datetime_tz( + self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo + ) -> _Pipeline[_InT, datetime.datetime]: + return self.constrain(annotated_types.Timezone(tz)) # type: ignore + + def datetime_with_tz( + self: _Pipeline[_InT, datetime.datetime], tz: datetime.tzinfo | None + ) -> _Pipeline[_InT, datetime.datetime]: + return self.transform(partial(datetime.datetime.replace, tzinfo=tz)) + + # string methods + def str_lower(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]: + return self.transform(str.lower) + + def str_upper(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]: + return self.transform(str.upper) + + def str_title(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]: + return self.transform(str.title) + + def str_strip(self: _Pipeline[_InT, str]) -> _Pipeline[_InT, str]: + return self.transform(str.strip) + + def str_pattern(self: _Pipeline[_InT, str], pattern: str) -> _Pipeline[_InT, str]: + return self.constrain(re.compile(pattern)) + + def str_contains(self: _Pipeline[_InT, str], substring: str) -> _Pipeline[_InT, str]: + return self.predicate(lambda v: substring in v) + + def str_starts_with(self: _Pipeline[_InT, str], prefix: str) -> _Pipeline[_InT, str]: + return self.predicate(lambda v: v.startswith(prefix)) + + def str_ends_with(self: _Pipeline[_InT, str], suffix: str) -> _Pipeline[_InT, str]: + return self.predicate(lambda v: v.endswith(suffix)) + + # operators + def otherwise(self, other: _Pipeline[_OtherIn, _OtherOut]) -> _Pipeline[_InT | _OtherIn, _OutT | _OtherOut]: + """Combine two validation chains, returning the result of the first chain if it succeeds, and the second chain if it fails.""" + return _Pipeline((_PipelineOr(self, other),)) + + __or__ = otherwise + + def then(self, other: _Pipeline[_OutT, _OtherOut]) -> _Pipeline[_InT, _OtherOut]: + """Pipe the result of one validation chain into another.""" + return _Pipeline((_PipelineAnd(self, other),)) + + __and__ = then + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> cs.CoreSchema: + queue = deque(self._steps) + + s = None + + while queue: + step = queue.popleft() + s = _apply_step(step, s, handler, source_type) + + s = s or cs.any_schema() + return s + + def __supports_type__(self, _: _OutT) -> bool: + raise NotImplementedError + + +validate_as = _Pipeline[Any, Any](()).validate_as +validate_as_deferred = _Pipeline[Any, Any](()).validate_as_deferred +transform = _Pipeline[Any, Any]((_ValidateAs(_FieldTypeMarker),)).transform + + +def _check_func( + func: Callable[[Any], bool], predicate_err: str | Callable[[], str], s: cs.CoreSchema | None +) -> cs.CoreSchema: + def handler(v: Any) -> Any: + if func(v): + return v + raise ValueError(f'Expected {predicate_err if isinstance(predicate_err, str) else predicate_err()}') + + if s is None: + return cs.no_info_plain_validator_function(handler) + else: + return cs.no_info_after_validator_function(handler, s) + + +def _apply_step(step: _Step, s: cs.CoreSchema | None, handler: GetCoreSchemaHandler, source_type: Any) -> cs.CoreSchema: + if isinstance(step, _ValidateAs): + s = _apply_parse(s, step.tp, step.strict, handler, source_type) + elif isinstance(step, _ValidateAsDefer): + s = _apply_parse(s, step.tp, False, handler, source_type) + elif isinstance(step, _Transform): + s = _apply_transform(s, step.func, handler) + elif isinstance(step, _Constraint): + s = _apply_constraint(s, step.constraint) + elif isinstance(step, _PipelineOr): + s = cs.union_schema([handler(step.left), handler(step.right)]) + else: + assert isinstance(step, _PipelineAnd) + s = cs.chain_schema([handler(step.left), handler(step.right)]) + return s + + +def _apply_parse( + s: cs.CoreSchema | None, + tp: type[Any], + strict: bool, + handler: GetCoreSchemaHandler, + source_type: Any, +) -> cs.CoreSchema: + if tp is _FieldTypeMarker: + return cs.chain_schema([s, handler(source_type)]) if s else handler(source_type) + + if strict: + tp = Annotated[tp, Strict()] # type: ignore + + if s and s['type'] == 'any': + return handler(tp) + else: + return cs.chain_schema([s, handler(tp)]) if s else handler(tp) + + +def _apply_transform( + s: cs.CoreSchema | None, func: Callable[[Any], Any], handler: GetCoreSchemaHandler +) -> cs.CoreSchema: + if s is None: + return cs.no_info_plain_validator_function(func) + + if s['type'] == 'str': + if func is str.strip: + s = s.copy() + s['strip_whitespace'] = True + return s + elif func is str.lower: + s = s.copy() + s['to_lower'] = True + return s + elif func is str.upper: + s = s.copy() + s['to_upper'] = True + return s + + return cs.no_info_after_validator_function(func, s) + + +def _apply_constraint( # noqa: C901 + s: cs.CoreSchema | None, constraint: _ConstraintAnnotation +) -> cs.CoreSchema: + """Apply a single constraint to a schema.""" + if isinstance(constraint, annotated_types.Gt): + gt = constraint.gt + if s and s['type'] in {'int', 'float', 'decimal'}: + s = s.copy() + if s['type'] == 'int' and isinstance(gt, int): + s['gt'] = gt + elif s['type'] == 'float' and isinstance(gt, float): + s['gt'] = gt + elif s['type'] == 'decimal' and isinstance(gt, Decimal): + s['gt'] = gt + else: + + def check_gt(v: Any) -> bool: + return v > gt + + s = _check_func(check_gt, f'> {gt}', s) + elif isinstance(constraint, annotated_types.Ge): + ge = constraint.ge + if s and s['type'] in {'int', 'float', 'decimal'}: + s = s.copy() + if s['type'] == 'int' and isinstance(ge, int): + s['ge'] = ge + elif s['type'] == 'float' and isinstance(ge, float): + s['ge'] = ge + elif s['type'] == 'decimal' and isinstance(ge, Decimal): + s['ge'] = ge + + def check_ge(v: Any) -> bool: + return v >= ge + + s = _check_func(check_ge, f'>= {ge}', s) + elif isinstance(constraint, annotated_types.Lt): + lt = constraint.lt + if s and s['type'] in {'int', 'float', 'decimal'}: + s = s.copy() + if s['type'] == 'int' and isinstance(lt, int): + s['lt'] = lt + elif s['type'] == 'float' and isinstance(lt, float): + s['lt'] = lt + elif s['type'] == 'decimal' and isinstance(lt, Decimal): + s['lt'] = lt + + def check_lt(v: Any) -> bool: + return v < lt + + s = _check_func(check_lt, f'< {lt}', s) + elif isinstance(constraint, annotated_types.Le): + le = constraint.le + if s and s['type'] in {'int', 'float', 'decimal'}: + s = s.copy() + if s['type'] == 'int' and isinstance(le, int): + s['le'] = le + elif s['type'] == 'float' and isinstance(le, float): + s['le'] = le + elif s['type'] == 'decimal' and isinstance(le, Decimal): + s['le'] = le + + def check_le(v: Any) -> bool: + return v <= le + + s = _check_func(check_le, f'<= {le}', s) + elif isinstance(constraint, annotated_types.Len): + min_len = constraint.min_length + max_len = constraint.max_length + + if s and s['type'] in {'str', 'list', 'tuple', 'set', 'frozenset', 'dict'}: + assert ( + s['type'] == 'str' + or s['type'] == 'list' + or s['type'] == 'tuple' + or s['type'] == 'set' + or s['type'] == 'dict' + or s['type'] == 'frozenset' + ) + s = s.copy() + if min_len != 0: + s['min_length'] = min_len + if max_len is not None: + s['max_length'] = max_len + + def check_len(v: Any) -> bool: + if max_len is not None: + return (min_len <= len(v)) and (len(v) <= max_len) + return min_len <= len(v) + + s = _check_func(check_len, f'length >= {min_len} and length <= {max_len}', s) + elif isinstance(constraint, annotated_types.MultipleOf): + multiple_of = constraint.multiple_of + if s and s['type'] in {'int', 'float', 'decimal'}: + s = s.copy() + if s['type'] == 'int' and isinstance(multiple_of, int): + s['multiple_of'] = multiple_of + elif s['type'] == 'float' and isinstance(multiple_of, float): + s['multiple_of'] = multiple_of + elif s['type'] == 'decimal' and isinstance(multiple_of, Decimal): + s['multiple_of'] = multiple_of + + def check_multiple_of(v: Any) -> bool: + return v % multiple_of == 0 + + s = _check_func(check_multiple_of, f'% {multiple_of} == 0', s) + elif isinstance(constraint, annotated_types.Timezone): + tz = constraint.tz + + if tz is ...: + if s and s['type'] == 'datetime': + s = s.copy() + s['tz_constraint'] = 'aware' + else: + + def check_tz_aware(v: object) -> bool: + assert isinstance(v, datetime.datetime) + return v.tzinfo is not None + + s = _check_func(check_tz_aware, 'timezone aware', s) + elif tz is None: + if s and s['type'] == 'datetime': + s = s.copy() + s['tz_constraint'] = 'naive' + else: + + def check_tz_naive(v: object) -> bool: + assert isinstance(v, datetime.datetime) + return v.tzinfo is None + + s = _check_func(check_tz_naive, 'timezone naive', s) + else: + raise NotImplementedError('Constraining to a specific timezone is not yet supported') + elif isinstance(constraint, annotated_types.Interval): + if constraint.ge: + s = _apply_constraint(s, annotated_types.Ge(constraint.ge)) + if constraint.gt: + s = _apply_constraint(s, annotated_types.Gt(constraint.gt)) + if constraint.le: + s = _apply_constraint(s, annotated_types.Le(constraint.le)) + if constraint.lt: + s = _apply_constraint(s, annotated_types.Lt(constraint.lt)) + assert s is not None + elif isinstance(constraint, annotated_types.Predicate): + func = constraint.func + # Same logic as in `_known_annotated_metadata.apply_known_metadata()`: + predicate_name = f'{func.__qualname__!r} ' if hasattr(func, '__qualname__') else '' + + def predicate_func(v: Any) -> Any: + if not func(v): + raise PydanticCustomError( + 'predicate_failed', + f'Predicate {predicate_name}failed', # pyright: ignore[reportArgumentType] + ) + return v + + if s is None: + s = cs.no_info_plain_validator_function(predicate_func) + else: + s = cs.no_info_after_validator_function(predicate_func, s) + elif isinstance(constraint, _NotEq): + value = constraint.value + + def check_not_eq(v: Any) -> bool: + return operator.__ne__(v, value) + + s = _check_func(check_not_eq, f'!= {value}', s) + elif isinstance(constraint, _Eq): + value = constraint.value + + def check_eq(v: Any) -> bool: + return operator.__eq__(v, value) + + s = _check_func(check_eq, f'== {value}', s) + elif isinstance(constraint, _In): + values = constraint.values + + def check_in(v: Any) -> bool: + return operator.__contains__(values, v) + + s = _check_func(check_in, f'in {values}', s) + elif isinstance(constraint, _NotIn): + values = constraint.values + + def check_not_in(v: Any) -> bool: + return operator.__not__(operator.__contains__(values, v)) + + s = _check_func(check_not_in, f'not in {values}', s) + else: + assert isinstance(constraint, Pattern) + if s and s['type'] == 'str': + s = s.copy() + s['pattern'] = constraint.pattern + else: + + def check_pattern(v: object) -> bool: + assert isinstance(v, str) + return constraint.match(v) is not None + + s = _check_func(check_pattern, f'~ {constraint.pattern}', s) + return s + + +class _SupportsRange(annotated_types.SupportsLe, annotated_types.SupportsGe, Protocol): + pass + + +class _SupportsLen(Protocol): + def __len__(self) -> int: ... + + +_NewOutGt = TypeVar('_NewOutGt', bound=annotated_types.SupportsGt) +_NewOutGe = TypeVar('_NewOutGe', bound=annotated_types.SupportsGe) +_NewOutLt = TypeVar('_NewOutLt', bound=annotated_types.SupportsLt) +_NewOutLe = TypeVar('_NewOutLe', bound=annotated_types.SupportsLe) +_NewOutLen = TypeVar('_NewOutLen', bound=_SupportsLen) +_NewOutDiv = TypeVar('_NewOutDiv', bound=annotated_types.SupportsDiv) +_NewOutMod = TypeVar('_NewOutMod', bound=annotated_types.SupportsMod) +_NewOutDatetime = TypeVar('_NewOutDatetime', bound=datetime.datetime) +_NewOutInterval = TypeVar('_NewOutInterval', bound=_SupportsRange) +_OtherIn = TypeVar('_OtherIn') +_OtherOut = TypeVar('_OtherOut') diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/fields.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/fields.py new file mode 100644 index 0000000000000000000000000000000000000000..ede966fa8010c070fede69d8f048b618a1b38f17 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/fields.py @@ -0,0 +1,1892 @@ +"""Defining fields on models.""" + +from __future__ import annotations as _annotations + +import dataclasses +import inspect +import re +import sys +from collections.abc import Callable, Mapping +from copy import copy +from dataclasses import Field as DataclassField +from functools import cached_property +from typing import TYPE_CHECKING, Annotated, Any, ClassVar, Literal, TypeVar, final, overload +from warnings import warn + +import annotated_types +import typing_extensions +from pydantic_core import MISSING, PydanticUndefined +from typing_extensions import Self, TypeAlias, TypedDict, Unpack, deprecated +from typing_inspection import typing_objects +from typing_inspection.introspection import UNKNOWN, AnnotationSource, ForbiddenQualifier, Qualifier, inspect_annotation + +from . import types +from ._internal import _decorators, _fields, _generics, _internal_dataclass, _repr, _typing_extra, _utils +from ._internal._namespace_utils import GlobalsNamespace, MappingNamespace +from .aliases import AliasChoices, AliasGenerator, AliasPath +from .config import JsonDict +from .errors import PydanticForbiddenQualifier, PydanticUserError +from .json_schema import PydanticJsonSchemaWarning +from .warnings import PydanticDeprecatedSince20 + +if TYPE_CHECKING: + from ._internal._config import ConfigWrapper + from ._internal._repr import ReprArgs + + +__all__ = 'Field', 'FieldInfo', 'PrivateAttr', 'computed_field' + + +_Unset: Any = PydanticUndefined + +if sys.version_info >= (3, 13): + import warnings + + Deprecated: TypeAlias = warnings.deprecated | deprecated +else: + Deprecated: TypeAlias = deprecated + + +class _FromFieldInfoInputs(TypedDict, total=False): + """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.from_field`.""" + + # TODO PEP 747: use TypeForm: + annotation: type[Any] | None + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None + alias: str | None + alias_priority: int | None + validation_alias: str | AliasPath | AliasChoices | None + serialization_alias: str | None + title: str | None + field_title_generator: Callable[[str, FieldInfo], str] | None + description: str | None + examples: list[Any] | None + exclude: bool | None + exclude_if: Callable[[Any], bool] | None + gt: annotated_types.SupportsGt | None + ge: annotated_types.SupportsGe | None + lt: annotated_types.SupportsLt | None + le: annotated_types.SupportsLe | None + multiple_of: float | None + strict: bool | None + min_length: int | None + max_length: int | None + pattern: str | re.Pattern[str] | None + allow_inf_nan: bool | None + max_digits: int | None + decimal_places: int | None + union_mode: Literal['smart', 'left_to_right'] | None + discriminator: str | types.Discriminator | None + deprecated: Deprecated | str | bool | None + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None + frozen: bool | None + validate_default: bool | None + repr: bool + init: bool | None + init_var: bool | None + kw_only: bool | None + coerce_numbers_to_str: bool | None + fail_fast: bool | None + + +class _FieldInfoInputs(_FromFieldInfoInputs, total=False): + """This class exists solely to add type checking for the `**kwargs` in `FieldInfo.__init__`.""" + + default: Any + + +class _FieldInfoAsDict(TypedDict, closed=True): + # TODO PEP 747: use TypeForm: + annotation: Any + metadata: list[Any] + attributes: dict[str, Any] + + +@final +class FieldInfo(_repr.Representation): + """This class holds information about a field. + + `FieldInfo` is used for any field definition regardless of whether the [`Field()`][pydantic.fields.Field] + function is explicitly used. + + !!! warning + The `FieldInfo` class is meant to expose information about a field in a Pydantic model or dataclass. + `FieldInfo` instances shouldn't be instantiated directly, nor mutated. + + If you need to derive a new model from another one and are willing to alter `FieldInfo` instances, + refer to this [dynamic model example](../examples/dynamic_models.md). + + Attributes: + annotation: The type annotation of the field. + default: The default value of the field. + default_factory: A callable to generate the default value. The callable can either take 0 arguments + (in which case it is called as is) or a single argument containing the already validated data. + alias: The alias name of the field. + alias_priority: The priority of the field's alias. + validation_alias: The validation alias of the field. + serialization_alias: The serialization alias of the field. + title: The title of the field. + field_title_generator: A callable that takes a field name and returns title for it. + description: The description of the field. + examples: List of examples of the field. + exclude: Whether to exclude the field from the model serialization. + exclude_if: A callable that determines whether to exclude a field during serialization based on its value. + discriminator: Field name or Discriminator for discriminating the type in a tagged union. + deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport, + or a boolean. If `True`, a default deprecation message will be emitted when accessing the field. + json_schema_extra: A dict or callable to provide extra JSON schema properties. + frozen: Whether the field is frozen. + validate_default: Whether to validate the default value of the field. + repr: Whether to include the field in representation of the model. + init: Whether the field should be included in the constructor of the dataclass. + init_var: Whether the field should _only_ be included in the constructor of the dataclass, and not stored. + kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass. + metadata: The metadata list. Contains all the data that isn't expressed as direct `FieldInfo` attributes, including: + + * Type-specific constraints, such as `gt` or `min_length` (these are converted to metadata classes such as `annotated_types.Gt`). + * Any other arbitrary object used within [`Annotated`][typing.Annotated] metadata + (e.g. [custom types handlers](../concepts/types.md#as-an-annotation) or any object not recognized by Pydantic). + """ + + # TODO PEP 747: use TypeForm: + annotation: type[Any] | None + default: Any + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None + alias: str | None + alias_priority: int | None + validation_alias: str | AliasPath | AliasChoices | None + serialization_alias: str | None + title: str | None + field_title_generator: Callable[[str, FieldInfo], str] | None + description: str | None + examples: list[Any] | None + exclude: bool | None + exclude_if: Callable[[Any], bool] | None + discriminator: str | types.Discriminator | None + deprecated: Deprecated | str | bool | None + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None + frozen: bool | None + validate_default: bool | None + repr: bool + init: bool | None + init_var: bool | None + kw_only: bool | None + metadata: list[Any] + + __slots__ = ( + 'annotation', + 'default', + 'default_factory', + 'alias', + 'alias_priority', + 'validation_alias', + 'serialization_alias', + 'title', + 'field_title_generator', + 'description', + 'examples', + 'exclude', + 'exclude_if', + 'discriminator', + 'deprecated', + 'json_schema_extra', + 'frozen', + 'validate_default', + 'repr', + 'init', + 'init_var', + 'kw_only', + 'metadata', + '_attributes_set', + '_qualifiers', + '_complete', + '_original_assignment', + '_original_annotation', + '_final', + ) + + # used to convert kwargs to metadata/constraints, + # None has a special meaning - these items are collected into a `PydanticGeneralMetadata` + metadata_lookup: ClassVar[dict[str, Callable[[Any], Any] | None]] = { + 'strict': types.Strict, + 'gt': annotated_types.Gt, + 'ge': annotated_types.Ge, + 'lt': annotated_types.Lt, + 'le': annotated_types.Le, + 'multiple_of': annotated_types.MultipleOf, + 'min_length': annotated_types.MinLen, + 'max_length': annotated_types.MaxLen, + 'pattern': None, + 'allow_inf_nan': None, + 'max_digits': None, + 'decimal_places': None, + 'union_mode': None, + 'coerce_numbers_to_str': None, + 'fail_fast': types.FailFast, + } + + def __init__(self, **kwargs: Unpack[_FieldInfoInputs]) -> None: + """This class should generally not be initialized directly; instead, use the `pydantic.fields.Field` function + or one of the constructor classmethods. + + See the signature of `pydantic.fields.Field` for more details about the expected arguments. + """ + # Tracking the explicitly set attributes is necessary to correctly merge `Field()` functions + # (e.g. with `Annotated[int, Field(alias='a'), Field(alias=None)]`, even though `None` is the default value, + # we need to track that `alias=None` was explicitly set): + self._attributes_set = {k: v for k, v in kwargs.items() if v is not _Unset and k not in self.metadata_lookup} + kwargs = {k: _DefaultValues.get(k) if v is _Unset else v for k, v in kwargs.items()} # type: ignore + self.annotation = kwargs.get('annotation') + + # Note: in theory, the second `pop()` arguments are not required below, as defaults are already set from `_DefaultsValues`. + default = kwargs.pop('default', PydanticUndefined) + if default is Ellipsis: + self.default = PydanticUndefined + self._attributes_set.pop('default', None) + else: + self.default = default + + self.default_factory = kwargs.pop('default_factory', None) + + if self.default is not PydanticUndefined and self.default_factory is not None: + raise TypeError('cannot specify both default and default_factory') + + self.alias = kwargs.pop('alias', None) + self.validation_alias = kwargs.pop('validation_alias', None) + self.serialization_alias = kwargs.pop('serialization_alias', None) + alias_is_set = any(alias is not None for alias in (self.alias, self.validation_alias, self.serialization_alias)) + self.alias_priority = kwargs.pop('alias_priority', None) or 2 if alias_is_set else None + self.title = kwargs.pop('title', None) + self.field_title_generator = kwargs.pop('field_title_generator', None) + self.description = kwargs.pop('description', None) + self.examples = kwargs.pop('examples', None) + self.exclude = kwargs.pop('exclude', None) + self.exclude_if = kwargs.pop('exclude_if', None) + self.discriminator = kwargs.pop('discriminator', None) + # For compatibility with FastAPI<=0.110.0, we preserve the existing value if it is not overridden + self.deprecated = kwargs.pop('deprecated', getattr(self, 'deprecated', None)) + self.repr = kwargs.pop('repr', True) + self.json_schema_extra = kwargs.pop('json_schema_extra', None) + self.validate_default = kwargs.pop('validate_default', None) + self.frozen = kwargs.pop('frozen', None) + # currently only used on dataclasses + self.init = kwargs.pop('init', None) + self.init_var = kwargs.pop('init_var', None) + self.kw_only = kwargs.pop('kw_only', None) + + self.metadata = self._collect_metadata(kwargs) # type: ignore + + # Private attributes: + self._qualifiers: set[Qualifier] = set() + # Used to rebuild FieldInfo instances: + self._complete = True + self._original_annotation: Any = PydanticUndefined + self._original_assignment: Any = PydanticUndefined + # Used to track whether the `FieldInfo` instance represents the data about a field (and is exposed in `model_fields`/`__pydantic_fields__`), + # or if it is the result of the `Field()` function being used as metadata in an `Annotated` type/as an assignment + # (not an ideal pattern, see https://github.com/pydantic/pydantic/issues/11122): + self._final = False + + @staticmethod + def from_field(default: Any = PydanticUndefined, **kwargs: Unpack[_FromFieldInfoInputs]) -> FieldInfo: + """Create a new `FieldInfo` object with the `Field` function. + + Args: + default: The default value for the field. Defaults to Undefined. + **kwargs: Additional arguments dictionary. + + Raises: + TypeError: If 'annotation' is passed as a keyword argument. + + Returns: + A new FieldInfo object with the given parameters. + + Example: + This is how you can create a field with default value like this: + + ```python + import pydantic + + class MyModel(pydantic.BaseModel): + foo: int = pydantic.Field(4) + ``` + """ + if 'annotation' in kwargs: + raise TypeError('"annotation" is not permitted as a Field keyword argument') + return FieldInfo(default=default, **kwargs) + + @staticmethod + def from_annotation(annotation: type[Any], *, _source: AnnotationSource = AnnotationSource.ANY) -> FieldInfo: + """Creates a `FieldInfo` instance from a bare annotation. + + This function is used internally to create a `FieldInfo` from a bare annotation like this: + + ```python + import pydantic + + class MyModel(pydantic.BaseModel): + foo: int # <-- like this + ``` + + We also account for the case where the annotation can be an instance of `Annotated` and where + one of the (not first) arguments in `Annotated` is an instance of `FieldInfo`, e.g.: + + ```python + from typing import Annotated + + import annotated_types + + import pydantic + + class MyModel(pydantic.BaseModel): + foo: Annotated[int, annotated_types.Gt(42)] + bar: Annotated[int, pydantic.Field(gt=42)] + ``` + + Args: + annotation: An annotation object. + + Returns: + An instance of the field metadata. + """ + try: + inspected_ann = inspect_annotation( + annotation, + annotation_source=_source, + unpack_type_aliases='skip', + ) + except ForbiddenQualifier as e: + raise PydanticForbiddenQualifier(e.qualifier, annotation) + + # TODO check for classvar and error? + + # No assigned value, this happens when using a bare `Final` qualifier (also for other + # qualifiers, but they shouldn't appear here). In this case we infer the type as `Any` + # because we don't have any assigned value. + type_expr: Any = Any if inspected_ann.type is UNKNOWN else inspected_ann.type + final = 'final' in inspected_ann.qualifiers + metadata = inspected_ann.metadata + + attr_overrides = {'annotation': type_expr} + if final: + attr_overrides['frozen'] = True + field_info = FieldInfo._construct(metadata, **attr_overrides) + field_info._qualifiers = inspected_ann.qualifiers + field_info._final = True + return field_info + + @staticmethod + def from_annotated_attribute( + annotation: type[Any], default: Any, *, _source: AnnotationSource = AnnotationSource.ANY + ) -> FieldInfo: + """Create `FieldInfo` from an annotation with a default value. + + This is used in cases like the following: + + ```python + from typing import Annotated + + import annotated_types + + import pydantic + + class MyModel(pydantic.BaseModel): + foo: int = 4 # <-- like this + bar: Annotated[int, annotated_types.Gt(4)] = 4 # <-- or this + spam: Annotated[int, pydantic.Field(gt=4)] = 4 # <-- or this + ``` + + Args: + annotation: The type annotation of the field. + default: The default value of the field. + + Returns: + A field object with the passed values. + """ + if annotation is not MISSING and annotation is default: + raise PydanticUserError( + 'Error when building FieldInfo from annotated attribute. ' + "Make sure you don't have any field name clashing with a type annotation.", + code='unevaluable-type-annotation', + ) + + try: + inspected_ann = inspect_annotation( + annotation, + annotation_source=_source, + unpack_type_aliases='skip', + ) + except ForbiddenQualifier as e: + raise PydanticForbiddenQualifier(e.qualifier, annotation) + + # TODO check for classvar and error? + + # TODO infer from the default, this can be done in v3 once we treat final fields with + # a default as proper fields and not class variables: + type_expr: Any = Any if inspected_ann.type is UNKNOWN else inspected_ann.type + final = 'final' in inspected_ann.qualifiers + metadata = inspected_ann.metadata + + # HACK 1: the order in which the metadata is merged is inconsistent; we need to prepend + # metadata from the assignment at the beginning of the metadata. Changing this is only + # possible in v3 (at least). See https://github.com/pydantic/pydantic/issues/10507 + prepend_metadata: list[Any] | None = None + attr_overrides = {'annotation': type_expr} + if final: + attr_overrides['frozen'] = True + + # HACK 2: FastAPI is subclassing `FieldInfo` and historically expected the actual + # instance's type to be preserved when constructing new models with its subclasses as assignments. + # This code is never reached by Pydantic itself, and in an ideal world this shouldn't be necessary. + if not metadata and isinstance(default, FieldInfo) and type(default) is not FieldInfo: + field_info = default._copy() + field_info._attributes_set.update(attr_overrides) + for k, v in attr_overrides.items(): + setattr(field_info, k, v) + return field_info + + if isinstance(default, FieldInfo): + default_copy = default._copy() # Copy unnecessary when we remove HACK 1. + prepend_metadata = default_copy.metadata + default_copy.metadata = [] + metadata = metadata + [default_copy] + if 'init_var' in inspected_ann.qualifiers: + # Only relevant for dataclasses, when `f: InitVar[] = Field(...)` + # is used: + attr_overrides['init_var'] = True + elif isinstance(default, dataclasses.Field): + from_field = FieldInfo._from_dataclass_field(default) + prepend_metadata = from_field.metadata # Unnecessary when we remove HACK 1. + from_field.metadata = [] + metadata = metadata + [from_field] + if 'init_var' in inspected_ann.qualifiers: + attr_overrides['init_var'] = True + if (init := getattr(default, 'init', None)) is not None: + attr_overrides['init'] = init + if (kw_only := getattr(default, 'kw_only', None)) is not None: + attr_overrides['kw_only'] = kw_only + else: + # `default` is the actual default value + attr_overrides['default'] = default + + field_info = FieldInfo._construct( + prepend_metadata + metadata if prepend_metadata is not None else metadata, **attr_overrides + ) + field_info._qualifiers = inspected_ann.qualifiers + field_info._final = True + return field_info + + @classmethod + def _construct(cls, metadata: list[Any], **attr_overrides: Any) -> Self: + """Construct the final `FieldInfo` instance, by merging the possibly existing `FieldInfo` instances from the metadata. + + With the following example: + + ```python {test="skip" lint="skip"} + class Model(BaseModel): + f: Annotated[int, Gt(1), Field(description='desc', lt=2)] + ``` + + `metadata` refers to the metadata elements of the `Annotated` form. This metadata is iterated over from left to right: + + - If the element is a `Field()` function (which is itself a `FieldInfo` instance), the field attributes (such as + `description`) are saved to be set on the final `FieldInfo` instance. + On the other hand, some kwargs (such as `lt`) are stored as `metadata` (see `FieldInfo.__init__()`, calling + `FieldInfo._collect_metadata()`). In this case, the final metadata list is extended with the one from this instance. + - Else, the element is considered as a single metadata object, and is appended to the final metadata list. + + Args: + metadata: The list of metadata elements to merge together. If the `FieldInfo` instance to be constructed is for + a field with an assigned `Field()`, this `Field()` assignment should be added as the last element of the + provided metadata. + **attr_overrides: Extra attributes that should be set on the final merged `FieldInfo` instance. + + Returns: + The final merged `FieldInfo` instance. + """ + merged_metadata: list[Any] = [] + merged_kwargs: dict[str, Any] = {} + + for meta in metadata: + if isinstance(meta, FieldInfo): + merged_metadata.extend(meta.metadata) + + new_js_extra: JsonDict | None = None + current_js_extra = meta.json_schema_extra + if current_js_extra is not None and 'json_schema_extra' in merged_kwargs: + # We need to merge `json_schema_extra`'s: + existing_js_extra = merged_kwargs['json_schema_extra'] + if isinstance(existing_js_extra, dict): + if isinstance(current_js_extra, dict): + new_js_extra = { + **existing_js_extra, + **current_js_extra, + } + elif callable(current_js_extra): + warn( + 'Composing `dict` and `callable` type `json_schema_extra` is not supported. ' + 'The `callable` type is being ignored. ' + "If you'd like support for this behavior, please open an issue on pydantic.", + UserWarning, + ) + elif callable(existing_js_extra) and isinstance(current_js_extra, dict): + warn( + 'Composing `dict` and `callable` type `json_schema_extra` is not supported. ' + 'The `callable` type is being ignored. ' + "If you'd like support for this behavior, please open an issue on pydantic.", + UserWarning, + ) + + # HACK: It is common for users to define "make model partial" (or similar) utilities, that + # convert all model fields to be optional (i.e. have a default value). To do so, they mutate + # each `FieldInfo` instance from `model_fields` to set a `default`, and use `create_model()` + # with `Annotated[ | None, mutated_field_info]`` as an annotation. However, such + # mutations (by doing simple assignments) are only accidentally working, because we also + # need to track attributes explicitly set in `_attributes_set` (relying on default values for + # each attribute is *not* enough, for instance with `Annotated[int, Field(alias='a'), Field(alias=None)]` + # the resulting `FieldInfo` should have `alias=None`). + # To mitigate this, we add a special case when a "final" `FieldInfo` instance (that is an instance coming + # from `model_fields`) is used in annotated metadata (or assignment). In this case, we assume *all* attributes + # were explicitly set, and as such we use all of them (and this will correctly pick up the mutations). + # In theory, this shouldn't really be supported, you are only supposed to use the `Field()` function, not + # a `FieldInfo` instance directly (granted, `Field()` returns a `FieldInfo`, see + # https://github.com/pydantic/pydantic/issues/11122): + if meta._final: + merged_kwargs.update({attr: getattr(meta, attr) for attr in _Attrs}) + else: + merged_kwargs.update(meta._attributes_set) + + if new_js_extra is not None: + merged_kwargs['json_schema_extra'] = new_js_extra + elif typing_objects.is_deprecated(meta): + merged_kwargs['deprecated'] = meta + else: + merged_metadata.append(meta) + + merged_kwargs.update(attr_overrides) + merged_field_info = cls(**merged_kwargs) + merged_field_info.metadata = merged_metadata + return merged_field_info + + @staticmethod + @typing_extensions.deprecated( + "The 'merge_field_infos()' method is deprecated and will be removed in a future version. " + 'If you relied on this method, please open an issue in the Pydantic issue tracker.', + category=None, + ) + def merge_field_infos(*field_infos: FieldInfo, **overrides: Any) -> FieldInfo: + """Merge `FieldInfo` instances keeping only explicitly set attributes. + + Later `FieldInfo` instances override earlier ones. + + Returns: + FieldInfo: A merged FieldInfo instance. + """ + if len(field_infos) == 1: + # No merging necessary, but we still need to make a copy and apply the overrides + field_info = field_infos[0]._copy() + field_info._attributes_set.update(overrides) + + default_override = overrides.pop('default', PydanticUndefined) + if default_override is Ellipsis: + default_override = PydanticUndefined + if default_override is not PydanticUndefined: + field_info.default = default_override + + for k, v in overrides.items(): + setattr(field_info, k, v) + return field_info # type: ignore + + merged_field_info_kwargs: dict[str, Any] = {} + metadata = {} + for field_info in field_infos: + attributes_set = field_info._attributes_set.copy() + + try: + json_schema_extra = attributes_set.pop('json_schema_extra') + existing_json_schema_extra = merged_field_info_kwargs.get('json_schema_extra') + + if existing_json_schema_extra is None: + merged_field_info_kwargs['json_schema_extra'] = json_schema_extra + if isinstance(existing_json_schema_extra, dict): + if isinstance(json_schema_extra, dict): + merged_field_info_kwargs['json_schema_extra'] = { + **existing_json_schema_extra, + **json_schema_extra, + } + if callable(json_schema_extra): + warn( + 'Composing `dict` and `callable` type `json_schema_extra` is not supported.' + 'The `callable` type is being ignored.' + "If you'd like support for this behavior, please open an issue on pydantic.", + PydanticJsonSchemaWarning, + ) + elif callable(json_schema_extra): + # if ever there's a case of a callable, we'll just keep the last json schema extra spec + merged_field_info_kwargs['json_schema_extra'] = json_schema_extra + except KeyError: + pass + + # later FieldInfo instances override everything except json_schema_extra from earlier FieldInfo instances + merged_field_info_kwargs.update(attributes_set) + + for x in field_info.metadata: + if not isinstance(x, FieldInfo): + metadata[type(x)] = x + + merged_field_info_kwargs.update(overrides) + field_info = FieldInfo(**merged_field_info_kwargs) + field_info.metadata = list(metadata.values()) + return field_info + + @staticmethod + def _from_dataclass_field(dc_field: DataclassField[Any]) -> FieldInfo: + """Return a new `FieldInfo` instance from a `dataclasses.Field` instance. + + Args: + dc_field: The `dataclasses.Field` instance to convert. + + Returns: + The corresponding `FieldInfo` instance. + + Raises: + TypeError: If any of the `FieldInfo` kwargs does not match the `dataclass.Field` kwargs. + """ + default = dc_field.default + if default is dataclasses.MISSING: + default = _Unset + + if dc_field.default_factory is dataclasses.MISSING: + default_factory = _Unset + else: + default_factory = dc_field.default_factory + + # use the `Field` function so in correct kwargs raise the correct `TypeError` + dc_field_metadata = {k: v for k, v in dc_field.metadata.items() if k in _FIELD_ARG_NAMES} + if sys.version_info >= (3, 14) and dc_field.doc is not None: + dc_field_metadata['description'] = dc_field.doc + return Field(default=default, default_factory=default_factory, repr=dc_field.repr, **dc_field_metadata) # pyright: ignore[reportCallIssue] + + @staticmethod + def _collect_metadata(kwargs: dict[str, Any]) -> list[Any]: + """Collect annotations from kwargs. + + Args: + kwargs: Keyword arguments passed to the function. + + Returns: + A list of metadata objects - a combination of `annotated_types.BaseMetadata` and + `PydanticMetadata`. + """ + metadata: list[Any] = [] + general_metadata = {} + for key, value in list(kwargs.items()): + try: + marker = FieldInfo.metadata_lookup[key] + except KeyError: + continue + + del kwargs[key] + if value is not None: + if marker is None: + general_metadata[key] = value + else: + metadata.append(marker(value)) + if general_metadata: + metadata.append(_fields.pydantic_general_metadata(**general_metadata)) + return metadata + + @property + def deprecation_message(self) -> str | None: + """The deprecation message to be emitted, or `None` if not set.""" + if self.deprecated is None: + return None + if isinstance(self.deprecated, bool): + return 'deprecated' if self.deprecated else None + return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message + + @property + def default_factory_takes_validated_data(self) -> bool | None: + """Whether the provided default factory callable has a validated data parameter. + + Returns `None` if no default factory is set. + """ + if self.default_factory is not None: + return _fields.takes_validated_data_argument(self.default_factory) + + @overload + def get_default( + self, *, call_default_factory: Literal[True], validated_data: dict[str, Any] | None = None + ) -> Any: ... + + @overload + def get_default(self, *, call_default_factory: Literal[False] = ...) -> Any: ... + + def get_default(self, *, call_default_factory: bool = False, validated_data: dict[str, Any] | None = None) -> Any: + """Get the default value. + + We expose an option for whether to call the default_factory (if present), as calling it may + result in side effects that we want to avoid. However, there are times when it really should + be called (namely, when instantiating a model via `model_construct`). + + Args: + call_default_factory: Whether to call the default factory or not. + validated_data: The already validated data to be passed to the default factory. + + Returns: + The default value, calling the default factory if requested or `PydanticUndefined` if not set. + """ + return _fields.resolve_default_value( + default=self.default, + default_factory=self.default_factory, + validated_data=validated_data, + call_default_factory=call_default_factory, + ) + + def is_required(self) -> bool: + """Check if the field is required (i.e., does not have a default value or factory). + + Returns: + `True` if the field is required, `False` otherwise. + """ + return self.default is PydanticUndefined and self.default_factory is None + + def rebuild_annotation(self) -> Any: + """Attempts to rebuild the original annotation for use in function signatures. + + If metadata is present, it adds it to the original annotation using + `Annotated`. Otherwise, it returns the original annotation as-is. + + Note that because the metadata has been flattened, the original annotation + may not be reconstructed exactly as originally provided, e.g. if the original + type had unrecognized annotations, or was annotated with a call to `pydantic.Field`. + + Returns: + The rebuilt annotation. + """ + if not self.metadata: + return self.annotation + else: + # Annotated arguments must be a tuple + return Annotated[(self.annotation, *self.metadata)] # type: ignore + + def apply_typevars_map( + self, + typevars_map: Mapping[TypeVar, Any] | None, + globalns: GlobalsNamespace | None = None, + localns: MappingNamespace | None = None, + ) -> None: + """Apply a `typevars_map` to the annotation. + + This method is used when analyzing parametrized generic types to replace typevars with their concrete types. + + This method applies the `typevars_map` to the annotation in place. + + Args: + typevars_map: A dictionary mapping type variables to their concrete types. + globalns: The globals namespace to use during type annotation evaluation. + localns: The locals namespace to use during type annotation evaluation. + + See Also: + pydantic._internal._generics.replace_types is used for replacing the typevars with + their concrete types. + """ + annotation = _generics.replace_types(self.annotation, typevars_map) + annotation, evaluated = _typing_extra.try_eval_type(annotation, globalns, localns) + self.annotation = annotation + if not evaluated: + self._complete = False + self._original_annotation = self.annotation + + def asdict(self) -> _FieldInfoAsDict: + """Return a dictionary representation of the `FieldInfo` instance. + + The returned value is a dictionary with three items: + + * `annotation`: The type annotation of the field. + * `metadata`: The metadata list. + * `attributes`: A mapping of the remaining `FieldInfo` attributes to their values (e.g. `alias`, `title`). + """ + return { + 'annotation': self.annotation, + 'metadata': self.metadata, + 'attributes': {attr: getattr(self, attr) for attr in _Attrs}, + } + + def _copy(self) -> Self: + """Return a copy of the `FieldInfo` instance.""" + # Note: we can't define a custom `__copy__()`, as `FieldInfo` is being subclassed + # by some third-party libraries with extra attributes defined (and as `FieldInfo` + # is slotted, we can't make a copy of the `__dict__`). + if type(self) is FieldInfo: + # Fast-path if the instance isn't a subclass (`copy.copy()` relies on pickling which is slower): + copied = FieldInfo.__new__(FieldInfo) + for attr_name in FieldInfo.__slots__: + setattr(copied, attr_name, getattr(self, attr_name)) + else: + copied = copy(self) + + for attr_name in ('metadata', '_attributes_set', '_qualifiers'): + # Apply "deep-copy" behavior on collections attributes: + setattr(copied, attr_name, getattr(copied, attr_name).copy()) + + return copied # pyright: ignore[reportReturnType] + + def __repr_args__(self) -> ReprArgs: + yield 'annotation', _repr.PlainRepr(_repr.display_as_type(self.annotation)) + yield 'required', self.is_required() + + for s in self.__slots__: + # TODO: properly make use of the protocol (https://rich.readthedocs.io/en/stable/pretty.html#rich-repr-protocol) + # By yielding a three-tuple: + if s in ( + 'annotation', + '_attributes_set', + '_qualifiers', + '_complete', + '_original_assignment', + '_original_annotation', + '_final', + ): + continue + elif s == 'metadata' and not self.metadata: + continue + elif s == 'repr' and self.repr is True: + continue + if s == 'frozen' and self.frozen is False: + continue + if s == 'validation_alias' and self.validation_alias == self.alias: + continue + if s == 'serialization_alias' and self.serialization_alias == self.alias: + continue + if s == 'default' and self.default is not PydanticUndefined: + yield 'default', self.default + elif s == 'default_factory' and self.default_factory is not None: + yield 'default_factory', _repr.PlainRepr(_repr.display_as_type(self.default_factory)) + else: + value = getattr(self, s) + if value is not None and value is not PydanticUndefined: + yield s, value + + +class _EmptyKwargs(TypedDict): + """This class exists solely to ensure that type checking warns about passing `**extra` in `Field`.""" + + +_Attrs = { + 'default': ..., + 'default_factory': None, + 'alias': None, + 'alias_priority': None, + 'validation_alias': None, + 'serialization_alias': None, + 'title': None, + 'field_title_generator': None, + 'description': None, + 'examples': None, + 'exclude': None, + 'exclude_if': None, + 'discriminator': None, + 'deprecated': None, + 'json_schema_extra': None, + 'frozen': None, + 'validate_default': None, + 'repr': True, + 'init': None, + 'init_var': None, + 'kw_only': None, +} + +_DefaultValues = { + **_Attrs, + 'kw_only': None, + 'pattern': None, + 'strict': None, + 'gt': None, + 'ge': None, + 'lt': None, + 'le': None, + 'multiple_of': None, + 'allow_inf_nan': None, + 'max_digits': None, + 'decimal_places': None, + 'min_length': None, + 'max_length': None, + 'coerce_numbers_to_str': None, +} + + +_T = TypeVar('_T') + + +# NOTE: Actual return type is 'FieldInfo', but we want to help type checkers +# to understand the magic that happens at runtime with the following overloads: +@overload # type hint the return value as `Any` to avoid type checking regressions when using `...`. +def Field( + default: ellipsis, # noqa: F821 # TODO: use `_typing_extra.EllipsisType` when we drop Py3.9 + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: ... +@overload # `default` argument set, validate_default=True (no type checking on the default value) +def Field( + default: Any, + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: Literal[True], + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: ... +@overload # `default` argument set, validate_default=False or unset +def Field( + default: _T, + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + # NOTE: to get proper type checking on `exclude_if`'s argument, we could use `_T` instead of `Any`. However, + # this requires (at least for pyright) adding an additional overload where `exclude_if` is required (otherwise + # `a: int = Field(default_factory=str)` results in a false negative). + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: Literal[False] = ..., + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> _T: ... +@overload # `default_factory` argument set, validate_default=True (no type checking on the default value) +def Field( # pyright: ignore[reportOverlappingOverload] + *, + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any], + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: Literal[True], + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: ... +@overload # `default_factory` argument set, validate_default=False or unset +def Field( + *, + default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T], + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + # NOTE: to get proper type checking on `exclude_if`'s argument, we could use `_T` instead of `Any`. However, + # this requires (at least for pyright) adding an additional overload where `exclude_if` is required (otherwise + # `a: int = Field(default_factory=str)` results in a false negative). + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: Literal[False] | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> _T: ... +@overload +def Field( # No default set + *, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: ... +def Field( # noqa: C901 + default: Any = PydanticUndefined, + *, + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = _Unset, + alias: str | None = _Unset, + alias_priority: int | None = _Unset, + validation_alias: str | AliasPath | AliasChoices | None = _Unset, + serialization_alias: str | None = _Unset, + title: str | None = _Unset, + field_title_generator: Callable[[str, FieldInfo], str] | None = _Unset, + description: str | None = _Unset, + examples: list[Any] | None = _Unset, + exclude: bool | None = _Unset, + exclude_if: Callable[[Any], bool] | None = _Unset, + discriminator: str | types.Discriminator | None = _Unset, + deprecated: Deprecated | str | bool | None = _Unset, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = _Unset, + frozen: bool | None = _Unset, + validate_default: bool | None = _Unset, + repr: bool = _Unset, + init: bool | None = _Unset, + init_var: bool | None = _Unset, + kw_only: bool | None = _Unset, + pattern: str | re.Pattern[str] | None = _Unset, + strict: bool | None = _Unset, + coerce_numbers_to_str: bool | None = _Unset, + gt: annotated_types.SupportsGt | None = _Unset, + ge: annotated_types.SupportsGe | None = _Unset, + lt: annotated_types.SupportsLt | None = _Unset, + le: annotated_types.SupportsLe | None = _Unset, + multiple_of: float | None = _Unset, + allow_inf_nan: bool | None = _Unset, + max_digits: int | None = _Unset, + decimal_places: int | None = _Unset, + min_length: int | None = _Unset, + max_length: int | None = _Unset, + union_mode: Literal['smart', 'left_to_right'] = _Unset, + fail_fast: bool | None = _Unset, + **extra: Unpack[_EmptyKwargs], +) -> Any: + """!!! abstract "Usage Documentation" + [Fields](../concepts/fields.md) + + Create a field for objects that can be configured. + + Used to provide extra information about a field, either for the model schema or complex validation. Some arguments + apply only to number fields (`int`, `float`, `Decimal`) and some apply only to `str`. + + Note: + - Any `_Unset` objects will be replaced by the corresponding value defined in the `_DefaultValues` dictionary. If a key for the `_Unset` object is not found in the `_DefaultValues` dictionary, it will default to `None` + + Args: + default: Default value if the field is not set. + default_factory: A callable to generate the default value. The callable can either take 0 arguments + (in which case it is called as is) or a single argument containing the already validated data. + alias: The name to use for the attribute when validating or serializing by alias. + This is often used for things like converting between snake and camel case. + alias_priority: Priority of the alias. This affects whether an alias generator is used. + validation_alias: Like `alias`, but only affects validation, not serialization. + serialization_alias: Like `alias`, but only affects serialization, not validation. + title: Human-readable title. + field_title_generator: A callable that takes a field name and returns title for it. + description: Human-readable description. + examples: Example values for this field. + exclude: Whether to exclude the field from the model serialization. + exclude_if: A callable that determines whether to exclude a field during serialization based on its value. + discriminator: Field name or Discriminator for discriminating the type in a tagged union. + deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport, + or a boolean. If `True`, a default deprecation message will be emitted when accessing the field. + json_schema_extra: A dict or callable to provide extra JSON schema properties. + frozen: Whether the field is frozen. If true, attempts to change the value on an instance will raise an error. + validate_default: If `True`, apply validation to the default value every time you create an instance. + Otherwise, for performance reasons, the default value of the field is trusted and not validated. + repr: A boolean indicating whether to include the field in the `__repr__` output. + init: Whether the field should be included in the constructor of the dataclass. + (Only applies to dataclasses.) + init_var: Whether the field should _only_ be included in the constructor of the dataclass. + (Only applies to dataclasses.) + kw_only: Whether the field should be a keyword-only argument in the constructor of the dataclass. + (Only applies to dataclasses.) + coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode). + strict: If `True`, strict validation is applied to the field. + See [Strict Mode](../concepts/strict_mode.md) for details. + gt: Greater than. If set, value must be greater than this. Only applicable to numbers. + ge: Greater than or equal. If set, value must be greater than or equal to this. Only applicable to numbers. + lt: Less than. If set, value must be less than this. Only applicable to numbers. + le: Less than or equal. If set, value must be less than or equal to this. Only applicable to numbers. + multiple_of: Value must be a multiple of this. Only applicable to numbers. + min_length: Minimum length for iterables. + max_length: Maximum length for iterables. + pattern: Pattern for strings (a regular expression). + allow_inf_nan: Allow `inf`, `-inf`, `nan`. Only applicable to float and [`Decimal`][decimal.Decimal] numbers. + max_digits: Maximum number of allow digits for strings. + decimal_places: Maximum number of decimal places allowed for numbers. + union_mode: The strategy to apply when validating a union. Can be `smart` (the default), or `left_to_right`. + See [Union Mode](../concepts/unions.md#union-modes) for details. + fail_fast: If `True`, validation will stop on the first error. If `False`, all validation errors will be collected. + This option can be applied only to iterable types (list, tuple, set, and frozenset). + extra: (Deprecated) Extra fields that will be included in the JSON schema. + + !!! warning Deprecated + The `extra` kwargs is deprecated. Use `json_schema_extra` instead. + + Returns: + A new [`FieldInfo`][pydantic.fields.FieldInfo]. The return annotation is `Any` so `Field` can be used on + type-annotated fields without causing a type error. + """ + # Check deprecated and removed params from V1. This logic should eventually be removed. + const = extra.pop('const', None) # type: ignore + if const is not None: + raise PydanticUserError('`const` is removed, use `Literal` instead', code='removed-kwargs') + + min_items = extra.pop('min_items', None) # type: ignore + if min_items is not None: + warn( + '`min_items` is deprecated and will be removed, use `min_length` instead', + PydanticDeprecatedSince20, + stacklevel=2, + ) + if min_length in (None, _Unset): + min_length = min_items # type: ignore + + max_items = extra.pop('max_items', None) # type: ignore + if max_items is not None: + warn( + '`max_items` is deprecated and will be removed, use `max_length` instead', + PydanticDeprecatedSince20, + stacklevel=2, + ) + if max_length in (None, _Unset): + max_length = max_items # type: ignore + + unique_items = extra.pop('unique_items', None) # type: ignore + if unique_items is not None: + raise PydanticUserError( + ( + '`unique_items` is removed, use `Set` instead' + '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)' + ), + code='removed-kwargs', + ) + + allow_mutation = extra.pop('allow_mutation', None) # type: ignore + if allow_mutation is not None: + warn( + '`allow_mutation` is deprecated and will be removed. use `frozen` instead', + PydanticDeprecatedSince20, + stacklevel=2, + ) + if allow_mutation is False: + frozen = True + + regex = extra.pop('regex', None) # type: ignore + if regex is not None: + raise PydanticUserError('`regex` is removed. use `pattern` instead', code='removed-kwargs') + + if extra: + warn( + 'Using extra keyword arguments on `Field` is deprecated and will be removed.' + ' Use `json_schema_extra` instead.' + f' (Extra keys: {", ".join(k.__repr__() for k in extra.keys())})', + PydanticDeprecatedSince20, + stacklevel=2, + ) + if not json_schema_extra or json_schema_extra is _Unset: + json_schema_extra = extra # type: ignore + + if ( + validation_alias + and validation_alias is not _Unset + and not isinstance(validation_alias, (str, AliasChoices, AliasPath)) + ): + raise TypeError('Invalid `validation_alias` type. it should be `str`, `AliasChoices`, or `AliasPath`') + + if serialization_alias in (_Unset, None) and isinstance(alias, str): + serialization_alias = alias + + if validation_alias in (_Unset, None): + validation_alias = alias + + include = extra.pop('include', None) # type: ignore + if include is not None: + warn( + '`include` is deprecated and does nothing. It will be removed, use `exclude` instead', + PydanticDeprecatedSince20, + stacklevel=2, + ) + + return FieldInfo.from_field( + default, + default_factory=default_factory, + alias=alias, + alias_priority=alias_priority, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + title=title, + field_title_generator=field_title_generator, + description=description, + examples=examples, + exclude=exclude, + exclude_if=exclude_if, + discriminator=discriminator, + deprecated=deprecated, + json_schema_extra=json_schema_extra, + frozen=frozen, + pattern=pattern, + validate_default=validate_default, + repr=repr, + init=init, + init_var=init_var, + kw_only=kw_only, + coerce_numbers_to_str=coerce_numbers_to_str, + strict=strict, + gt=gt, + ge=ge, + lt=lt, + le=le, + multiple_of=multiple_of, + min_length=min_length, + max_length=max_length, + allow_inf_nan=allow_inf_nan, + max_digits=max_digits, + decimal_places=decimal_places, + union_mode=union_mode, + fail_fast=fail_fast, + ) + + +_FIELD_ARG_NAMES = set(inspect.signature(Field).parameters) +_FIELD_ARG_NAMES.remove('extra') # do not include the varkwargs parameter + + +class ModelPrivateAttr(_repr.Representation): + """A descriptor for private attributes in class models. + + !!! warning + You generally shouldn't be creating `ModelPrivateAttr` instances directly, instead use + `pydantic.fields.PrivateAttr`. (This is similar to `FieldInfo` vs. `Field`.) + + Attributes: + default: The default value of the attribute if not provided. + default_factory: A callable to generate the default value. The callable can either take 0 arguments + (in which case it is called as is) or a single argument containing the validated data (the model's + [`__dict__`][object.__dict__]) and the already initialized private attributes. + """ + + __slots__ = ('default', 'default_factory') + + def __init__( + self, + default: Any = PydanticUndefined, + *, + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = None, + ) -> None: + if default is Ellipsis: + self.default = PydanticUndefined + else: + self.default = default + self.default_factory = default_factory + + if not TYPE_CHECKING: + # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access + + def __getattr__(self, item: str) -> Any: + """This function improves compatibility with custom descriptors by ensuring delegation happens + as expected when the default value of a private attribute is a descriptor. + """ + if item in {'__get__', '__set__', '__delete__'}: + if hasattr(self.default, item): + return getattr(self.default, item) + raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') + + def __set_name__(self, cls: type[Any], name: str) -> None: + """Preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487.""" + default = self.default + if default is PydanticUndefined: + return + set_name = getattr(default, '__set_name__', None) + if callable(set_name): + set_name(cls, name) + + @property + def default_factory_takes_validated_data(self) -> bool | None: + """Whether the provided default factory callable has a validated data parameter. + + Returns `None` if no default factory is set. + """ + if self.default_factory is not None: + return _fields.takes_validated_data_argument(self.default_factory) + + @overload + def get_default( + self, *, call_default_factory: Literal[True], validated_data: dict[str, Any] | None = None + ) -> Any: ... + + @overload + def get_default(self, *, call_default_factory: Literal[False] = ...) -> Any: ... + + def get_default(self, *, call_default_factory: bool = False, validated_data: dict[str, Any] | None = None) -> Any: + """Get the default value. + + We expose an option for whether to call the default_factory (if present), as calling it may + result in side effects that we want to avoid. However, there are times when it really should + be called (namely, when instantiating a model via `model_construct`). + + Args: + call_default_factory: Whether to call the default factory or not. + validated_data: The already validated data to be passed to the default factory. + + Returns: + The default value, calling the default factory if requested or `None` if not set. + """ + return _fields.resolve_default_value( + default=self.default, + default_factory=self.default_factory, + validated_data=validated_data, + call_default_factory=call_default_factory, + ) + + def __eq__(self, other: Any) -> bool: + return isinstance(other, self.__class__) and (self.default, self.default_factory) == ( + other.default, + other.default_factory, + ) + + +# NOTE: Actual return type is 'ModelPrivateAttr', but we want to help type checkers +# to understand the magic that happens at runtime. +@overload # `default` argument set +def PrivateAttr( + default: _T, + *, + init: Literal[False] = False, +) -> _T: ... +@overload # `default_factory` argument set +def PrivateAttr( + *, + default_factory: Callable[[], _T] | Callable[[dict[str, Any]], _T], + init: Literal[False] = False, +) -> _T: ... +@overload # No default set +def PrivateAttr( + *, + init: Literal[False] = False, +) -> Any: ... +def PrivateAttr( + default: Any = PydanticUndefined, + *, + default_factory: Callable[[], Any] | Callable[[dict[str, Any]], Any] | None = None, + init: Literal[False] = False, +) -> Any: + """!!! abstract "Usage Documentation" + [Private Model Attributes](../concepts/models.md#private-model-attributes) + + Indicates that an attribute is intended for private use and not handled during normal validation/serialization. + + Private attributes are not validated by Pydantic, so it's up to you to ensure they are used in a type-safe manner. + + Private attributes are stored in `__private_attributes__` on the model. + + Args: + default: The attribute's default value. Defaults to Undefined. + default_factory: A callable to generate the default value. The callable can either take 0 arguments + (in which case it is called as is) or a single argument containing the validated data (the model's + [`__dict__`][object.__dict__]) and the already initialized private attributes. + If both `default` and `default_factory` are set, an error will be raised. + init: Whether the attribute should be included in the constructor of the dataclass. Always `False`. + + Returns: + An instance of [`ModelPrivateAttr`][pydantic.fields.ModelPrivateAttr] class. + + Raises: + ValueError: If both `default` and `default_factory` are set. + """ + if default is not PydanticUndefined and default_factory is not None: + raise TypeError('cannot specify both default and default_factory') + + return ModelPrivateAttr( + default, + default_factory=default_factory, + ) + + +@dataclasses.dataclass(**_internal_dataclass.slots_true) +class ComputedFieldInfo: + """A container for data from `@computed_field` so that we can access it while building the pydantic-core schema. + + Attributes: + decorator_repr: A class variable representing the decorator string, '@computed_field'. + wrapped_property: The wrapped computed field property. + return_type: The type of the computed field property's return value. + alias: The alias of the property to be used during serialization. + alias_priority: The priority of the alias. This affects whether an alias generator is used. + title: Title of the computed field to include in the serialization JSON schema. + field_title_generator: A callable that takes a field name and returns title for it. + description: Description of the computed field to include in the serialization JSON schema. + deprecated: A deprecation message, an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport, + or a boolean. If `True`, a default deprecation message will be emitted when accessing the field. + examples: Example values of the computed field to include in the serialization JSON schema. + json_schema_extra: A dict or callable to provide extra JSON schema properties. + repr: A boolean indicating whether to include the field in the __repr__ output. + """ + + decorator_repr: ClassVar[str] = '@computed_field' + wrapped_property: property + return_type: Any + alias: str | None + alias_priority: int | None + exclude_if: Callable[[Any], bool] | None + title: str | None + field_title_generator: Callable[[str, ComputedFieldInfo], str] | None + description: str | None + deprecated: Deprecated | str | bool | None + examples: list[Any] | None + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None + repr: bool + # NOTE: if you add a new field, add it to the `__copy__()` implementation. + + def __copy__(self) -> Self: + return type(self)( + wrapped_property=self.wrapped_property, + return_type=self.return_type, + alias=self.alias, + alias_priority=self.alias_priority, + exclude_if=self.exclude_if, + title=self.title, + field_title_generator=self.field_title_generator, + description=self.description, + deprecated=self.deprecated, + examples=self.examples.copy() if isinstance(self.examples, list) else self.examples, + json_schema_extra=self.json_schema_extra.copy() + if isinstance(self.json_schema_extra, dict) + else self.json_schema_extra, + repr=self.repr, + ) + + @property + def deprecation_message(self) -> str | None: + """The deprecation message to be emitted, or `None` if not set.""" + if self.deprecated is None: + return None + if isinstance(self.deprecated, bool): + return 'deprecated' if self.deprecated else None + return self.deprecated if isinstance(self.deprecated, str) else self.deprecated.message + + def _update_from_config(self, config_wrapper: ConfigWrapper, name: str) -> None: + """Update the instance from the configuration set on the class this computed field belongs to.""" + title_generator = self.field_title_generator or config_wrapper.field_title_generator + if title_generator is not None and self.title is None: + self.title = title_generator(name, self) + if config_wrapper.alias_generator is not None: + self._apply_alias_generator(config_wrapper.alias_generator, name) + + def _apply_alias_generator(self, alias_generator: Callable[[str], str] | AliasGenerator, name: str) -> None: + """Apply an alias generator to aliases if appropriate. + + Args: + alias_generator: A callable that takes a string and returns a string, or an `AliasGenerator` instance. + name: The name of the computed field from which to generate the alias. + """ + # Apply an alias_generator if + # 1. An alias is not specified + # 2. An alias is specified, but the priority is <= 1 + + if self.alias_priority is None or self.alias_priority <= 1 or self.alias is None: + alias, _, serialization_alias = None, None, None + + if isinstance(alias_generator, AliasGenerator): + alias, _, serialization_alias = alias_generator.generate_aliases(name) + elif callable(alias_generator): + alias = alias_generator(name) + + # if priority is not set, we set to 1 + # which supports the case where the alias_generator from a child class is used + # to generate an alias for a field in a parent class + if self.alias_priority is None or self.alias_priority <= 1: + self.alias_priority = 1 + + # if the priority is 1, then we set the aliases to the generated alias + # note that we use the serialization_alias with priority over alias, as computed_field + # aliases are used for serialization only (not validation) + if self.alias_priority == 1: + self.alias = _utils.get_first_not_none(serialization_alias, alias) + + +def _wrapped_property_is_private(property_: cached_property | property) -> bool: # type: ignore + """Returns true if provided property is private, False otherwise.""" + wrapped_name: str = '' + + if isinstance(property_, property): + wrapped_name = getattr(property_.fget, '__name__', '') + elif isinstance(property_, cached_property): # type: ignore + wrapped_name = getattr(property_.func, '__name__', '') # type: ignore + + return wrapped_name.startswith('_') and not wrapped_name.startswith('__') + + +# this should really be `property[T], cached_property[T]` but property is not generic unlike cached_property +# See https://github.com/python/typing/issues/985 and linked issues +PropertyT = TypeVar('PropertyT') + + +@overload +def computed_field(func: PropertyT, /) -> PropertyT: ... + + +@overload +def computed_field( + *, + alias: str | None = None, + alias_priority: int | None = None, + exclude_if: Callable[[Any], bool] | None = None, + title: str | None = None, + field_title_generator: Callable[[str, ComputedFieldInfo], str] | None = None, + description: str | None = None, + deprecated: Deprecated | str | bool | None = None, + examples: list[Any] | None = None, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = None, + repr: bool = True, + return_type: Any = PydanticUndefined, +) -> Callable[[PropertyT], PropertyT]: ... + + +def computed_field( + func: PropertyT | None = None, + /, + *, + alias: str | None = None, + alias_priority: int | None = None, + exclude_if: Callable[[Any], bool] | None = None, + title: str | None = None, + field_title_generator: Callable[[str, ComputedFieldInfo], str] | None = None, + description: str | None = None, + deprecated: Deprecated | str | bool | None = None, + examples: list[Any] | None = None, + json_schema_extra: JsonDict | Callable[[JsonDict], None] | None = None, + repr: bool | None = None, + return_type: Any = PydanticUndefined, +) -> PropertyT | Callable[[PropertyT], PropertyT]: + """!!! abstract "Usage Documentation" + [The `computed_field` decorator](../concepts/fields.md#the-computed_field-decorator) + + Decorator to include `property` and `cached_property` when serializing models or dataclasses. + + This is useful for fields that are computed from other fields, or for fields that are expensive to compute and should be cached. + + ```python + from pydantic import BaseModel, computed_field + + class Rectangle(BaseModel): + width: int + length: int + + @computed_field + @property + def area(self) -> int: + return self.width * self.length + + print(Rectangle(width=3, length=2).model_dump()) + #> {'width': 3, 'length': 2, 'area': 6} + ``` + + If applied to functions not yet decorated with `@property` or `@cached_property`, the function is + automatically wrapped with `property`. Although this is more concise, you will lose IntelliSense in your IDE, + and confuse static type checkers, thus explicit use of `@property` is recommended. + + !!! warning "Mypy Warning" + Even with the `@property` or `@cached_property` applied to your function before `@computed_field`, + mypy may throw a `Decorated property not supported` error. + See [mypy issue #1362](https://github.com/python/mypy/issues/1362), for more information. + To avoid this error message, add `# type: ignore[prop-decorator]` to the `@computed_field` line. + + [pyright](https://github.com/microsoft/pyright) supports `@computed_field` without error. + + ```python + import random + + from pydantic import BaseModel, computed_field + + class Square(BaseModel): + width: float + + @computed_field + def area(self) -> float: # converted to a `property` by `computed_field` + return round(self.width**2, 2) + + @area.setter + def area(self, new_area: float) -> None: + self.width = new_area**0.5 + + @computed_field(alias='the magic number', repr=False) + def random_number(self) -> int: + return random.randint(0, 1_000) + + square = Square(width=1.3) + + # `random_number` does not appear in representation + print(repr(square)) + #> Square(width=1.3, area=1.69) + + print(square.random_number) + #> 3 + + square.area = 4 + + print(square.model_dump_json(by_alias=True)) + #> {"width":2.0,"area":4.0,"the magic number":3} + ``` + + !!! warning "Overriding with `computed_field`" + You can't override a field from a parent class with a `computed_field` in the child class. + `mypy` complains about this behavior if allowed, and `dataclasses` doesn't allow this pattern either. + See the example below: + + ```python + from pydantic import BaseModel, computed_field + + class Parent(BaseModel): + a: str + + try: + + class Child(Parent): + @computed_field + @property + def a(self) -> str: + return 'new a' + + except TypeError as e: + print(e) + ''' + Field 'a' of class 'Child' overrides symbol of same name in a parent class. This override with a computed_field is incompatible. + ''' + ``` + + Private properties decorated with `@computed_field` have `repr=False` by default. + + ```python + from functools import cached_property + + from pydantic import BaseModel, computed_field + + class Model(BaseModel): + foo: int + + @computed_field + @cached_property + def _private_cached_property(self) -> int: + return -self.foo + + @computed_field + @property + def _private_property(self) -> int: + return -self.foo + + m = Model(foo=1) + print(repr(m)) + #> Model(foo=1) + ``` + + Args: + func: the function to wrap. + alias: alias to use when serializing this computed field, only used when `by_alias=True` + alias_priority: priority of the alias. This affects whether an alias generator is used + exclude_if: A callable that determines whether to exclude this computed field during serialization based on its value. + title: Title to use when including this computed field in JSON Schema + field_title_generator: A callable that takes a field name and returns title for it. + description: Description to use when including this computed field in JSON Schema, defaults to the function's + docstring + deprecated: A deprecation message (or an instance of `warnings.deprecated` or the `typing_extensions.deprecated` backport). + to be emitted when accessing the field. Or a boolean. This will automatically be set if the property is decorated with the + `deprecated` decorator. + examples: Example values to use when including this computed field in JSON Schema + json_schema_extra: A dict or callable to provide extra JSON schema properties. + repr: whether to include this computed field in model repr. + Default is `False` for private properties and `True` for public properties. + return_type: optional return for serialization logic to expect when serializing to JSON, if included + this must be correct, otherwise a `TypeError` is raised. + If you don't include a return type Any is used, which does runtime introspection to handle arbitrary + objects. + + Returns: + A proxy wrapper for the property. + """ + + def dec(f: Any) -> Any: + nonlocal description, deprecated, return_type, alias_priority + unwrapped = _decorators.unwrap_wrapped_function(f) + + if description is None and unwrapped.__doc__: + description = inspect.cleandoc(unwrapped.__doc__) + + if deprecated is None and hasattr(unwrapped, '__deprecated__'): + deprecated = unwrapped.__deprecated__ + + # if the function isn't already decorated with `@property` (or another descriptor), then we wrap it now + f = _decorators.ensure_property(f) + alias_priority = (alias_priority or 2) if alias is not None else None + + if repr is None: + repr_: bool = not _wrapped_property_is_private(property_=f) + else: + repr_ = repr + + dec_info = ComputedFieldInfo( + f, + return_type, + alias, + alias_priority, + exclude_if, + title, + field_title_generator, + description, + deprecated, + examples, + json_schema_extra, + repr_, + ) + return _decorators.PydanticDescriptorProxy(f, dec_info) + + if func is None: + return dec + else: + return dec(func) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/functional_serializers.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/functional_serializers.py new file mode 100644 index 0000000000000000000000000000000000000000..ed6333694a99892db37497f6ccc587ecd72fe1aa --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/functional_serializers.py @@ -0,0 +1,470 @@ +"""This module contains related classes and functions for serialization.""" + +from __future__ import annotations + +import dataclasses +from functools import partial, partialmethod +from typing import TYPE_CHECKING, Annotated, Any, Callable, Literal, TypeVar, overload + +from pydantic_core import PydanticUndefined, core_schema +from pydantic_core.core_schema import SerializationInfo, SerializerFunctionWrapHandler, WhenUsed +from typing_extensions import TypeAlias + +from . import PydanticUndefinedAnnotation +from ._internal import _decorators, _internal_dataclass +from .annotated_handlers import GetCoreSchemaHandler +from .errors import PydanticUserError + + +@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True) +class PlainSerializer: + """Plain serializers use a function to modify the output of serialization. + + This is particularly helpful when you want to customize the serialization for annotated types. + Consider an input of `list`, which will be serialized into a space-delimited string. + + ```python + from typing import Annotated + + from pydantic import BaseModel, PlainSerializer + + CustomStr = Annotated[ + list, PlainSerializer(lambda x: ' '.join(x), return_type=str) + ] + + class StudentModel(BaseModel): + courses: CustomStr + + student = StudentModel(courses=['Math', 'Chemistry', 'English']) + print(student.model_dump()) + #> {'courses': 'Math Chemistry English'} + ``` + + Attributes: + func: The serializer function. + return_type: The return type for the function. If omitted it will be inferred from the type annotation. + when_used: Determines when this serializer should be used. Accepts a string with values `'always'`, + `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'. + """ + + func: core_schema.SerializerFunction + return_type: Any = PydanticUndefined + when_used: WhenUsed = 'always' + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + """Gets the Pydantic core schema. + + Args: + source_type: The source type. + handler: The `GetCoreSchemaHandler` instance. + + Returns: + The Pydantic core schema. + """ + schema = handler(source_type) + if self.return_type is not PydanticUndefined: + return_type = self.return_type + else: + try: + # Do not pass in globals as the function could be defined in a different module. + # Instead, let `get_callable_return_type` infer the globals to use, but still pass + # in locals that may contain a parent/rebuild namespace: + return_type = _decorators.get_callable_return_type( + self.func, + localns=handler._get_types_namespace().locals, + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type) + schema['serialization'] = core_schema.plain_serializer_function_ser_schema( + function=self.func, + info_arg=_decorators.inspect_annotated_serializer(self.func, 'plain'), + return_schema=return_schema, + when_used=self.when_used, + ) + return schema + + +@dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True) +class WrapSerializer: + """Wrap serializers receive the raw inputs along with a handler function that applies the standard serialization + logic, and can modify the resulting value before returning it as the final output of serialization. + + For example, here's a scenario in which a wrap serializer transforms timezones to UTC **and** utilizes the existing `datetime` serialization logic. + + ```python + from datetime import datetime, timezone + from typing import Annotated, Any + + from pydantic import BaseModel, WrapSerializer + + class EventDatetime(BaseModel): + start: datetime + end: datetime + + def convert_to_utc(value: Any, handler, info) -> dict[str, datetime]: + # Note that `handler` can actually help serialize the `value` for + # further custom serialization in case it's a subclass. + partial_result = handler(value, info) + if info.mode == 'json': + return { + k: datetime.fromisoformat(v).astimezone(timezone.utc) + for k, v in partial_result.items() + } + return {k: v.astimezone(timezone.utc) for k, v in partial_result.items()} + + UTCEventDatetime = Annotated[EventDatetime, WrapSerializer(convert_to_utc)] + + class EventModel(BaseModel): + event_datetime: UTCEventDatetime + + dt = EventDatetime( + start='2024-01-01T07:00:00-08:00', end='2024-01-03T20:00:00+06:00' + ) + event = EventModel(event_datetime=dt) + print(event.model_dump()) + ''' + { + 'event_datetime': { + 'start': datetime.datetime( + 2024, 1, 1, 15, 0, tzinfo=datetime.timezone.utc + ), + 'end': datetime.datetime( + 2024, 1, 3, 14, 0, tzinfo=datetime.timezone.utc + ), + } + } + ''' + + print(event.model_dump_json()) + ''' + {"event_datetime":{"start":"2024-01-01T15:00:00Z","end":"2024-01-03T14:00:00Z"}} + ''' + ``` + + Attributes: + func: The serializer function to be wrapped. + return_type: The return type for the function. If omitted it will be inferred from the type annotation. + when_used: Determines when this serializer should be used. Accepts a string with values `'always'`, + `'unless-none'`, `'json'`, and `'json-unless-none'`. Defaults to 'always'. + """ + + func: core_schema.WrapSerializerFunction + return_type: Any = PydanticUndefined + when_used: WhenUsed = 'always' + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + """This method is used to get the Pydantic core schema of the class. + + Args: + source_type: Source type. + handler: Core schema handler. + + Returns: + The generated core schema of the class. + """ + schema = handler(source_type) + if self.return_type is not PydanticUndefined: + return_type = self.return_type + else: + try: + # Do not pass in globals as the function could be defined in a different module. + # Instead, let `get_callable_return_type` infer the globals to use, but still pass + # in locals that may contain a parent/rebuild namespace: + return_type = _decorators.get_callable_return_type( + self.func, + localns=handler._get_types_namespace().locals, + ) + except NameError as e: + raise PydanticUndefinedAnnotation.from_name_error(e) from e + + return_schema = None if return_type is PydanticUndefined else handler.generate_schema(return_type) + schema['serialization'] = core_schema.wrap_serializer_function_ser_schema( + function=self.func, + info_arg=_decorators.inspect_annotated_serializer(self.func, 'wrap'), + return_schema=return_schema, + when_used=self.when_used, + ) + return schema + + +if TYPE_CHECKING: + _Partial: TypeAlias = 'partial[Any] | partialmethod[Any]' + + FieldPlainSerializer: TypeAlias = 'core_schema.SerializerFunction | _Partial' + """A field serializer method or function in `plain` mode.""" + + FieldWrapSerializer: TypeAlias = 'core_schema.WrapSerializerFunction | _Partial' + """A field serializer method or function in `wrap` mode.""" + + FieldSerializer: TypeAlias = 'FieldPlainSerializer | FieldWrapSerializer' + """A field serializer method or function.""" + + _FieldPlainSerializerT = TypeVar('_FieldPlainSerializerT', bound=FieldPlainSerializer) + _FieldWrapSerializerT = TypeVar('_FieldWrapSerializerT', bound=FieldWrapSerializer) + + +@overload +def field_serializer( + field: str, + /, + *fields: str, + mode: Literal['wrap'], + return_type: Any = ..., + when_used: WhenUsed = ..., + check_fields: bool | None = ..., +) -> Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT]: ... + + +@overload +def field_serializer( + field: str, + /, + *fields: str, + mode: Literal['plain'] = ..., + return_type: Any = ..., + when_used: WhenUsed = ..., + check_fields: bool | None = ..., +) -> Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT]: ... + + +def field_serializer( # noqa: D417 + field: str, + /, + *fields: str, + mode: Literal['plain', 'wrap'] = 'plain', + # TODO PEP 747 (grep for 'return_type' on the whole code base): + return_type: Any = PydanticUndefined, + when_used: WhenUsed = 'always', + check_fields: bool | None = None, +) -> ( + Callable[[_FieldWrapSerializerT], _FieldWrapSerializerT] + | Callable[[_FieldPlainSerializerT], _FieldPlainSerializerT] +): + """Decorator that enables custom field serialization. + + In the below example, a field of type `set` is used to mitigate duplication. A `field_serializer` is used to serialize the data as a sorted list. + + ```python + from pydantic import BaseModel, field_serializer + + class StudentModel(BaseModel): + name: str = 'Jane' + courses: set[str] + + @field_serializer('courses', when_used='json') + def serialize_courses_in_order(self, courses: set[str]): + return sorted(courses) + + student = StudentModel(courses={'Math', 'Chemistry', 'English'}) + print(student.model_dump_json()) + #> {"name":"Jane","courses":["Chemistry","English","Math"]} + ``` + + See [the usage documentation](../concepts/serialization.md#serializers) for more information. + + Four signatures are supported for the decorated serializer: + + - `(self, value: Any, info: FieldSerializationInfo)` + - `(self, value: Any, nxt: SerializerFunctionWrapHandler, info: FieldSerializationInfo)` + - `(value: Any, info: SerializationInfo)` + - `(value: Any, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)` + + Args: + *fields: The field names the serializer should apply to. + mode: The serialization mode. + + - `plain` means the function will be called instead of the default serialization logic, + - `wrap` means the function will be called with an argument to optionally call the + default serialization logic. + return_type: Optional return type for the function, if omitted it will be inferred from the type annotation. + when_used: Determines the serializer will be used for serialization. + check_fields: Whether to check that the fields actually exist on the model. + + Raises: + PydanticUserError: + - If the decorator is used without any arguments (at least one field name must be provided). + - If the provided field names are not strings. + """ + if callable(field) or isinstance(field, classmethod): + raise PydanticUserError( + 'The `@field_serializer` decorator cannot be used without arguments, at least one field must be provided. ' + "For example: `@field_serializer('', ...)`.", + code='decorator-missing-arguments', + ) + + fields = field, *fields + if not all(isinstance(field, str) for field in fields): + raise PydanticUserError( + 'The provided field names to the `@field_serializer` decorator should be strings. ' + "For example: `@field_serializer('', '', ...).`", + code='decorator-invalid-fields', + ) + + def dec(f: FieldSerializer) -> _decorators.PydanticDescriptorProxy[Any]: + dec_info = _decorators.FieldSerializerDecoratorInfo( + fields=fields, + mode=mode, + return_type=return_type, + when_used=when_used, + check_fields=check_fields, + ) + return _decorators.PydanticDescriptorProxy(f, dec_info) # pyright: ignore[reportArgumentType] + + return dec # pyright: ignore[reportReturnType] + + +if TYPE_CHECKING: + # The first argument in the following callables represent the `self` type: + + ModelPlainSerializerWithInfo: TypeAlias = Callable[[Any, SerializationInfo[Any]], Any] + """A model serializer method with the `info` argument, in `plain` mode.""" + + ModelPlainSerializerWithoutInfo: TypeAlias = Callable[[Any], Any] + """A model serializer method without the `info` argument, in `plain` mode.""" + + ModelPlainSerializer: TypeAlias = 'ModelPlainSerializerWithInfo | ModelPlainSerializerWithoutInfo' + """A model serializer method in `plain` mode.""" + + ModelWrapSerializerWithInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo[Any]], Any] + """A model serializer method with the `info` argument, in `wrap` mode.""" + + ModelWrapSerializerWithoutInfo: TypeAlias = Callable[[Any, SerializerFunctionWrapHandler], Any] + """A model serializer method without the `info` argument, in `wrap` mode.""" + + ModelWrapSerializer: TypeAlias = 'ModelWrapSerializerWithInfo | ModelWrapSerializerWithoutInfo' + """A model serializer method in `wrap` mode.""" + + ModelSerializer: TypeAlias = 'ModelPlainSerializer | ModelWrapSerializer' + + _ModelPlainSerializerT = TypeVar('_ModelPlainSerializerT', bound=ModelPlainSerializer) + _ModelWrapSerializerT = TypeVar('_ModelWrapSerializerT', bound=ModelWrapSerializer) + + +@overload +def model_serializer(f: _ModelPlainSerializerT, /) -> _ModelPlainSerializerT: ... + + +@overload +def model_serializer( + *, mode: Literal['wrap'], when_used: WhenUsed = 'always', return_type: Any = ... +) -> Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT]: ... + + +@overload +def model_serializer( + *, + mode: Literal['plain'] = ..., + when_used: WhenUsed = 'always', + return_type: Any = ..., +) -> Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT]: ... + + +def model_serializer( + f: _ModelPlainSerializerT | _ModelWrapSerializerT | None = None, + /, + *, + mode: Literal['plain', 'wrap'] = 'plain', + when_used: WhenUsed = 'always', + return_type: Any = PydanticUndefined, +) -> ( + _ModelPlainSerializerT + | Callable[[_ModelWrapSerializerT], _ModelWrapSerializerT] + | Callable[[_ModelPlainSerializerT], _ModelPlainSerializerT] +): + """Decorator that enables custom model serialization. + + This is useful when a model need to be serialized in a customized manner, allowing for flexibility beyond just specific fields. + + An example would be to serialize temperature to the same temperature scale, such as degrees Celsius. + + ```python + from typing import Literal + + from pydantic import BaseModel, model_serializer + + class TemperatureModel(BaseModel): + unit: Literal['C', 'F'] + value: int + + @model_serializer() + def serialize_model(self): + if self.unit == 'F': + return {'unit': 'C', 'value': int((self.value - 32) / 1.8)} + return {'unit': self.unit, 'value': self.value} + + temperature = TemperatureModel(unit='F', value=212) + print(temperature.model_dump()) + #> {'unit': 'C', 'value': 100} + ``` + + Two signatures are supported for `mode='plain'`, which is the default: + + - `(self)` + - `(self, info: SerializationInfo)` + + And two other signatures for `mode='wrap'`: + + - `(self, nxt: SerializerFunctionWrapHandler)` + - `(self, nxt: SerializerFunctionWrapHandler, info: SerializationInfo)` + + See [the usage documentation](../concepts/serialization.md#serializers) for more information. + + Args: + f: The function to be decorated. + mode: The serialization mode. + + - `'plain'` means the function will be called instead of the default serialization logic + - `'wrap'` means the function will be called with an argument to optionally call the default + serialization logic. + when_used: Determines when this serializer should be used. + return_type: The return type for the function. If omitted it will be inferred from the type annotation. + + Returns: + The decorator function. + """ + + def dec(f: ModelSerializer) -> _decorators.PydanticDescriptorProxy[Any]: + dec_info = _decorators.ModelSerializerDecoratorInfo(mode=mode, return_type=return_type, when_used=when_used) + return _decorators.PydanticDescriptorProxy(f, dec_info) + + if f is None: + return dec # pyright: ignore[reportReturnType] + else: + return dec(f) # pyright: ignore[reportReturnType] + + +AnyType = TypeVar('AnyType') + + +if TYPE_CHECKING: + SerializeAsAny = Annotated[AnyType, ...] # SerializeAsAny[list[str]] will be treated by type checkers as list[str] + """Annotation used to mark a type as having duck-typing serialization behavior. + + See [usage documentation](../concepts/serialization.md#serializing-with-duck-typing) for more details. + """ +else: + + @dataclasses.dataclass(**_internal_dataclass.slots_true) + class SerializeAsAny: + """Annotation used to mark a type as having duck-typing serialization behavior. + + See [usage documentation](../concepts/serialization.md#serializing-with-duck-typing) for more details. + """ + + def __class_getitem__(cls, item: Any) -> Any: + return Annotated[item, SerializeAsAny()] + + def __get_pydantic_core_schema__( + self, source_type: Any, handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + schema = handler(source_type) + schema_to_update = schema + while schema_to_update['type'] == 'definitions': + schema_to_update = schema_to_update.copy() + schema_to_update = schema_to_update['schema'] + schema_to_update['serialization'] = core_schema.simple_ser_schema('any') + return schema + + __hash__ = object.__hash__ diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/functional_validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/functional_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..558e99c1e7a09e3c894d769c96cb28e25dc537ca --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/functional_validators.py @@ -0,0 +1,889 @@ +"""This module contains related classes and functions for validation.""" + +from __future__ import annotations as _annotations + +import dataclasses +import sys +import warnings +from functools import partialmethod +from typing import TYPE_CHECKING, Annotated, Any, Callable, Literal, TypeVar, Union, cast, overload + +from pydantic_core import PydanticUndefined, core_schema +from typing_extensions import Self, TypeAlias + +from ._internal import _decorators, _generics, _internal_dataclass +from .annotated_handlers import GetCoreSchemaHandler +from .errors import PydanticUserError +from .version import version_short +from .warnings import ArbitraryTypeWarning, PydanticDeprecatedSince212 + +if sys.version_info < (3, 11): + from typing_extensions import Protocol +else: + from typing import Protocol + +_inspect_validator = _decorators.inspect_validator + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class AfterValidator: + """!!! abstract "Usage Documentation" + [field *after* validators](../concepts/validators.md#field-after-validator) + + A metadata class that indicates that a validation should be applied **after** the inner validation logic. + + Attributes: + func: The validator function. + + Example: + ```python + from typing import Annotated + + from pydantic import AfterValidator, BaseModel, ValidationError + + MyInt = Annotated[int, AfterValidator(lambda v: v + 1)] + + class Model(BaseModel): + a: MyInt + + print(Model(a=1).a) + #> 2 + + try: + Model(a='a') + except ValidationError as e: + print(e.json(indent=2)) + ''' + [ + { + "type": "int_parsing", + "loc": [ + "a" + ], + "msg": "Input should be a valid integer, unable to parse string as an integer", + "input": "a", + "url": "https://errors.pydantic.dev/2/v/int_parsing" + } + ] + ''' + ``` + """ + + func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source_type) + info_arg = _inspect_validator(self.func, mode='after', type='field') + if info_arg: + func = cast(core_schema.WithInfoValidatorFunction, self.func) + return core_schema.with_info_after_validator_function(func, schema=schema) + else: + func = cast(core_schema.NoInfoValidatorFunction, self.func) + return core_schema.no_info_after_validator_function(func, schema=schema) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls(func=decorator.func) + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class BeforeValidator: + """!!! abstract "Usage Documentation" + [field *before* validators](../concepts/validators.md#field-before-validator) + + A metadata class that indicates that a validation should be applied **before** the inner validation logic. + + Attributes: + func: The validator function. + json_schema_input_type: The input type used to generate the appropriate + JSON Schema (in validation mode). The actual input type is `Any`. + + Example: + ```python + from typing import Annotated + + from pydantic import BaseModel, BeforeValidator + + MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)] + + class Model(BaseModel): + a: MyInt + + print(Model(a=1).a) + #> 2 + + try: + Model(a='a') + except TypeError as e: + print(e) + #> can only concatenate str (not "int") to str + ``` + """ + + func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction + json_schema_input_type: Any = PydanticUndefined + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source_type) + input_schema = ( + None + if self.json_schema_input_type is PydanticUndefined + else handler.generate_schema(self.json_schema_input_type) + ) + + info_arg = _inspect_validator(self.func, mode='before', type='field') + if info_arg: + func = cast(core_schema.WithInfoValidatorFunction, self.func) + return core_schema.with_info_before_validator_function( + func, + schema=schema, + json_schema_input_schema=input_schema, + ) + else: + func = cast(core_schema.NoInfoValidatorFunction, self.func) + return core_schema.no_info_before_validator_function( + func, schema=schema, json_schema_input_schema=input_schema + ) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls( + func=decorator.func, + json_schema_input_type=decorator.info.json_schema_input_type, + ) + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class PlainValidator: + """!!! abstract "Usage Documentation" + [field *plain* validators](../concepts/validators.md#field-plain-validator) + + A metadata class that indicates that a validation should be applied **instead** of the inner validation logic. + + !!! note + Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`. + You can now use the `json_schema_input_type` argument to specify the input type of the function + to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details. + + Attributes: + func: The validator function. + json_schema_input_type: The input type used to generate the appropriate + JSON Schema (in validation mode). The actual input type is `Any`. + + Example: + ```python + from typing import Annotated, Union + + from pydantic import BaseModel, PlainValidator + + def validate(v: object) -> int: + if not isinstance(v, (int, str)): + raise ValueError(f'Expected int or str, got {type(v)}') + + return int(v) + 1 + + MyInt = Annotated[ + int, + PlainValidator(validate, json_schema_input_type=Union[str, int]), # (1)! + ] + + class Model(BaseModel): + a: MyInt + + print(Model(a='1').a) + #> 2 + + print(Model(a=1).a) + #> 2 + ``` + + 1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema + generator that in validation mode, the input type for the `a` field can be either a [`str`][] or an [`int`][]. + """ + + func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction + json_schema_input_type: Any = Any + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + # Note that for some valid uses of PlainValidator, it is not possible to generate a core schema for the + # source_type, so calling `handler(source_type)` will error, which prevents us from generating a proper + # serialization schema. To work around this for use cases that will not involve serialization, we simply + # catch any PydanticSchemaGenerationError that may be raised while attempting to build the serialization schema + # and abort any attempts to handle special serialization. + from pydantic import PydanticSchemaGenerationError + + try: + schema = handler(source_type) + # TODO if `schema['serialization']` is one of `'include-exclude-dict/sequence', + # schema validation will fail. That's why we use 'type ignore' comments below. + serialization = schema.get( + 'serialization', + core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), + schema=schema, + return_schema=handler.generate_schema(source_type), + ), + ) + except PydanticSchemaGenerationError: + serialization = None + + input_schema = handler.generate_schema(self.json_schema_input_type) + + info_arg = _inspect_validator(self.func, mode='plain', type='field') + if info_arg: + func = cast(core_schema.WithInfoValidatorFunction, self.func) + return core_schema.with_info_plain_validator_function( + func, + serialization=serialization, # pyright: ignore[reportArgumentType] + json_schema_input_schema=input_schema, + ) + else: + func = cast(core_schema.NoInfoValidatorFunction, self.func) + return core_schema.no_info_plain_validator_function( + func, + serialization=serialization, # pyright: ignore[reportArgumentType] + json_schema_input_schema=input_schema, + ) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls( + func=decorator.func, + json_schema_input_type=decorator.info.json_schema_input_type, + ) + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class WrapValidator: + """!!! abstract "Usage Documentation" + [field *wrap* validators](../concepts/validators.md#field-wrap-validator) + + A metadata class that indicates that a validation should be applied **around** the inner validation logic. + + Attributes: + func: The validator function. + json_schema_input_type: The input type used to generate the appropriate + JSON Schema (in validation mode). The actual input type is `Any`. + + ```python + from datetime import datetime + from typing import Annotated + + from pydantic import BaseModel, ValidationError, WrapValidator + + def validate_timestamp(v, handler): + if v == 'now': + # we don't want to bother with further validation, just return the new value + return datetime.now() + try: + return handler(v) + except ValidationError: + # validation failed, in this case we want to return a default value + return datetime(2000, 1, 1) + + MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)] + + class Model(BaseModel): + a: MyTimestamp + + print(Model(a='now').a) + #> 2032-01-02 03:04:05.000006 + print(Model(a='invalid').a) + #> 2000-01-01 00:00:00 + ``` + """ + + func: core_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunction + json_schema_input_type: Any = PydanticUndefined + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source_type) + input_schema = ( + None + if self.json_schema_input_type is PydanticUndefined + else handler.generate_schema(self.json_schema_input_type) + ) + + info_arg = _inspect_validator(self.func, mode='wrap', type='field') + if info_arg: + func = cast(core_schema.WithInfoWrapValidatorFunction, self.func) + return core_schema.with_info_wrap_validator_function( + func, + schema=schema, + json_schema_input_schema=input_schema, + ) + else: + func = cast(core_schema.NoInfoWrapValidatorFunction, self.func) + return core_schema.no_info_wrap_validator_function( + func, + schema=schema, + json_schema_input_schema=input_schema, + ) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls( + func=decorator.func, + json_schema_input_type=decorator.info.json_schema_input_type, + ) + + +if TYPE_CHECKING: + + class _OnlyValueValidatorClsMethod(Protocol): + def __call__(self, cls: Any, value: Any, /) -> Any: ... + + class _V2ValidatorClsMethod(Protocol): + def __call__(self, cls: Any, value: Any, info: core_schema.ValidationInfo[Any], /) -> Any: ... + + class _OnlyValueWrapValidatorClsMethod(Protocol): + def __call__(self, cls: Any, value: Any, handler: core_schema.ValidatorFunctionWrapHandler, /) -> Any: ... + + class _V2WrapValidatorClsMethod(Protocol): + def __call__( + self, + cls: Any, + value: Any, + handler: core_schema.ValidatorFunctionWrapHandler, + info: core_schema.ValidationInfo[Any], + /, + ) -> Any: ... + + _V2Validator = Union[ + _V2ValidatorClsMethod, + core_schema.WithInfoValidatorFunction, + _OnlyValueValidatorClsMethod, + core_schema.NoInfoValidatorFunction, + ] + + _V2WrapValidator = Union[ + _V2WrapValidatorClsMethod, + core_schema.WithInfoWrapValidatorFunction, + _OnlyValueWrapValidatorClsMethod, + core_schema.NoInfoWrapValidatorFunction, + ] + + _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]] + + _V2BeforeAfterOrPlainValidatorType = TypeVar( + '_V2BeforeAfterOrPlainValidatorType', + bound=Union[_V2Validator, _PartialClsOrStaticMethod], + ) + _V2WrapValidatorType = TypeVar('_V2WrapValidatorType', bound=Union[_V2WrapValidator, _PartialClsOrStaticMethod]) + +FieldValidatorModes: TypeAlias = Literal['before', 'after', 'wrap', 'plain'] + + +@overload +def field_validator( + field: str, + /, + *fields: str, + mode: Literal['wrap'], + check_fields: bool | None = ..., + json_schema_input_type: Any = ..., +) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]: ... + + +@overload +def field_validator( + field: str, + /, + *fields: str, + mode: Literal['before', 'plain'], + check_fields: bool | None = ..., + json_schema_input_type: Any = ..., +) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ... + + +@overload +def field_validator( + field: str, + /, + *fields: str, + mode: Literal['after'] = ..., + check_fields: bool | None = ..., +) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ... + + +def field_validator( # noqa: D417 + field: str, + /, + *fields: str, + mode: FieldValidatorModes = 'after', + check_fields: bool | None = None, + json_schema_input_type: Any = PydanticUndefined, +) -> Callable[[Any], Any]: + """!!! abstract "Usage Documentation" + [field validators](../concepts/validators.md#field-validators) + + Decorate methods on the class indicating that they should be used to validate fields. + + Example usage: + ```python + from typing import Any + + from pydantic import ( + BaseModel, + ValidationError, + field_validator, + ) + + class Model(BaseModel): + a: str + + @field_validator('a') + @classmethod + def ensure_foobar(cls, v: Any): + if 'foobar' not in v: + raise ValueError('"foobar" not found in a') + return v + + print(repr(Model(a='this is foobar good'))) + #> Model(a='this is foobar good') + + try: + Model(a='snap') + except ValidationError as exc_info: + print(exc_info) + ''' + 1 validation error for Model + a + Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str] + ''' + ``` + + For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators). + + Args: + *fields: The field names the validator should apply to. + mode: Specifies whether to validate the fields before or after validation. + check_fields: Whether to check that the fields actually exist on the model. + json_schema_input_type: The input type of the function. This is only used to generate + the appropriate JSON Schema (in validation mode) and can only specified + when `mode` is either `'before'`, `'plain'` or `'wrap'`. + + Raises: + PydanticUserError: + - If the decorator is used without any arguments (at least one field name must be provided). + - If the provided field names are not strings. + - If `json_schema_input_type` is provided with an unsupported `mode`. + - If the decorator is applied to an instance method. + """ + if callable(field) or isinstance(field, classmethod): + raise PydanticUserError( + 'The `@field_validator` decorator cannot be used without arguments, at least one field must be provided. ' + "For example: `@field_validator('', ...)`.", + code='decorator-missing-arguments', + ) + + if mode not in ('before', 'plain', 'wrap') and json_schema_input_type is not PydanticUndefined: + raise PydanticUserError( + f"`json_schema_input_type` can't be used when mode is set to {mode!r}", + code='validator-input-type', + ) + + if json_schema_input_type is PydanticUndefined and mode == 'plain': + json_schema_input_type = Any + + fields = field, *fields + if not all(isinstance(field, str) for field in fields): + raise PydanticUserError( + 'The provided field names to the `@field_validator` decorator should be strings. ' + "For example: `@field_validator('', '', ...).`", + code='decorator-invalid-fields', + ) + + def dec( + f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any], + ) -> _decorators.PydanticDescriptorProxy[Any]: + if _decorators.is_instance_method_from_sig(f): + raise PydanticUserError( + 'The `@field_validator` decorator cannot be applied to instance methods', + code='validator-instance-method', + ) + + # auto apply the @classmethod decorator + f = _decorators.ensure_classmethod_based_on_signature(f) + + dec_info = _decorators.FieldValidatorDecoratorInfo( + fields=fields, mode=mode, check_fields=check_fields, json_schema_input_type=json_schema_input_type + ) + return _decorators.PydanticDescriptorProxy(f, dec_info) + + return dec + + +_ModelType = TypeVar('_ModelType') +_ModelTypeCo = TypeVar('_ModelTypeCo', covariant=True) + + +class ModelWrapValidatorHandler(core_schema.ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]): + """`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`.""" + + def __call__( # noqa: D102 + self, + value: Any, + outer_location: str | int | None = None, + /, + ) -> _ModelTypeCo: # pragma: no cover + ... + + +class ModelWrapValidatorWithoutInfo(Protocol[_ModelType]): + """A `@model_validator` decorated function signature. + This is used when `mode='wrap'` and the function does not have info argument. + """ + + def __call__( # noqa: D102 + self, + cls: type[_ModelType], + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + handler: ModelWrapValidatorHandler[_ModelType], + /, + ) -> _ModelType: ... + + +class ModelWrapValidator(Protocol[_ModelType]): + """A `@model_validator` decorated function signature. This is used when `mode='wrap'`.""" + + def __call__( # noqa: D102 + self, + cls: type[_ModelType], + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + handler: ModelWrapValidatorHandler[_ModelType], + info: core_schema.ValidationInfo, + /, + ) -> _ModelType: ... + + +class FreeModelBeforeValidatorWithoutInfo(Protocol): + """A `@model_validator` decorated function signature. + This is used when `mode='before'` and the function does not have info argument. + """ + + def __call__( # noqa: D102 + self, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + /, + ) -> Any: ... + + +class ModelBeforeValidatorWithoutInfo(Protocol): + """A `@model_validator` decorated function signature. + This is used when `mode='before'` and the function does not have info argument. + """ + + def __call__( # noqa: D102 + self, + cls: Any, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + /, + ) -> Any: ... + + +class FreeModelBeforeValidator(Protocol): + """A `@model_validator` decorated function signature. This is used when `mode='before'`.""" + + def __call__( # noqa: D102 + self, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + info: core_schema.ValidationInfo[Any], + /, + ) -> Any: ... + + +class ModelBeforeValidator(Protocol): + """A `@model_validator` decorated function signature. This is used when `mode='before'`.""" + + def __call__( # noqa: D102 + self, + cls: Any, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + info: core_schema.ValidationInfo[Any], + /, + ) -> Any: ... + + +ModelAfterValidatorWithoutInfo = Callable[[_ModelType], _ModelType] +"""A `@model_validator` decorated function signature. This is used when `mode='after'` and the function does not +have info argument. +""" + +ModelAfterValidator = Callable[[_ModelType, core_schema.ValidationInfo[Any]], _ModelType] +"""A `@model_validator` decorated function signature. This is used when `mode='after'`.""" + +_AnyModelWrapValidator = Union[ModelWrapValidator[_ModelType], ModelWrapValidatorWithoutInfo[_ModelType]] +_AnyModelBeforeValidator = Union[ + FreeModelBeforeValidator, ModelBeforeValidator, FreeModelBeforeValidatorWithoutInfo, ModelBeforeValidatorWithoutInfo +] +_AnyModelAfterValidator = Union[ModelAfterValidator[_ModelType], ModelAfterValidatorWithoutInfo[_ModelType]] + + +@overload +def model_validator( + *, + mode: Literal['wrap'], +) -> Callable[ + [_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo] +]: ... + + +@overload +def model_validator( + *, + mode: Literal['before'], +) -> Callable[ + [_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo] +]: ... + + +@overload +def model_validator( + *, + mode: Literal['after'], +) -> Callable[ + [_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo] +]: ... + + +def model_validator( + *, + mode: Literal['wrap', 'before', 'after'], +) -> Any: + """!!! abstract "Usage Documentation" + [Model Validators](../concepts/validators.md#model-validators) + + Decorate model methods for validation purposes. + + Example usage: + ```python + from typing_extensions import Self + + from pydantic import BaseModel, ValidationError, model_validator + + class Square(BaseModel): + width: float + height: float + + @model_validator(mode='after') + def verify_square(self) -> Self: + if self.width != self.height: + raise ValueError('width and height do not match') + return self + + s = Square(width=1, height=1) + print(repr(s)) + #> Square(width=1.0, height=1.0) + + try: + Square(width=1, height=2) + except ValidationError as e: + print(e) + ''' + 1 validation error for Square + Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict] + ''' + ``` + + For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators). + + Args: + mode: A required string literal that specifies the validation mode. + It can be one of the following: 'wrap', 'before', or 'after'. + + Returns: + A decorator that can be used to decorate a function to be used as a model validator. + """ + + def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]: + # auto apply the @classmethod decorator. NOTE: in V3, do not apply the conversion for 'after' validators: + f = _decorators.ensure_classmethod_based_on_signature(f) + if mode == 'after' and isinstance(f, classmethod): + warnings.warn( + category=PydanticDeprecatedSince212, + message=( + "Using `@model_validator` with mode='after' on a classmethod is deprecated. Instead, use an instance method. " + f'See the documentation at https://docs.pydantic.dev/{version_short()}/concepts/validators/#model-after-validator.' + ), + stacklevel=2, + ) + + dec_info = _decorators.ModelValidatorDecoratorInfo(mode=mode) + return _decorators.PydanticDescriptorProxy(f, dec_info) + + return dec + + +AnyType = TypeVar('AnyType') + + +if TYPE_CHECKING: + # If we add configurable attributes to IsInstance, we'd probably need to stop hiding it from type checkers like this + InstanceOf = Annotated[AnyType, ...] # `IsInstance[Sequence]` will be recognized by type checkers as `Sequence` + +else: + + @dataclasses.dataclass(**_internal_dataclass.slots_true) + class InstanceOf: + '''Generic type for annotating a type that is an instance of a given class. + + Example: + ```python + from pydantic import BaseModel, InstanceOf + + class Foo: + ... + + class Bar(BaseModel): + foo: InstanceOf[Foo] + + Bar(foo=Foo()) + try: + Bar(foo=42) + except ValidationError as e: + print(e) + """ + [ + │ { + │ │ 'type': 'is_instance_of', + │ │ 'loc': ('foo',), + │ │ 'msg': 'Input should be an instance of Foo', + │ │ 'input': 42, + │ │ 'ctx': {'class': 'Foo'}, + │ │ 'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of' + │ } + ] + """ + ``` + ''' + + @classmethod + def __class_getitem__(cls, item: AnyType) -> AnyType: + return Annotated[item, cls()] + + @classmethod + def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + from pydantic._internal._generate_schema import GENERATE_SCHEMA_ERRORS + + # use the generic _origin_ as the second argument to isinstance when appropriate + instance_of_schema = core_schema.is_instance_schema(_generics.get_origin(source) or source) + + try: + # Try to generate the "standard" schema, which will be used when loading from JSON + original_schema = handler(source) + except GENERATE_SCHEMA_ERRORS: + # If that fails, just produce a schema that can validate from python + return instance_of_schema + else: + # Use the "original" approach to serialization + instance_of_schema['serialization'] = core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), schema=original_schema + ) + return core_schema.json_or_python_schema(python_schema=instance_of_schema, json_schema=original_schema) + + __hash__ = object.__hash__ + + +if TYPE_CHECKING: + SkipValidation = Annotated[AnyType, ...] # SkipValidation[list[str]] will be treated by type checkers as list[str] +else: + + @dataclasses.dataclass(**_internal_dataclass.slots_true) + class SkipValidation: + """If this is applied as an annotation (e.g., via `x: Annotated[int, SkipValidation]`), validation will be + skipped. You can also use `SkipValidation[int]` as a shorthand for `Annotated[int, SkipValidation]`. + + This can be useful if you want to use a type annotation for documentation/IDE/type-checking purposes, + and know that it is safe to skip validation for one or more of the fields. + + Because this converts the validation schema to `any_schema`, subsequent annotation-applied transformations + may not have the expected effects. Therefore, when used, this annotation should generally be the final + annotation applied to a type. + """ + + def __class_getitem__(cls, item: Any) -> Any: + return Annotated[item, SkipValidation()] + + @classmethod + def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + with warnings.catch_warnings(): + warnings.simplefilter('ignore', ArbitraryTypeWarning) + original_schema = handler(source) + metadata = {'pydantic_js_annotation_functions': [lambda _c, h: h(original_schema)]} + return core_schema.any_schema( + metadata=metadata, + serialization=core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), schema=original_schema + ), + ) + + __hash__ = object.__hash__ + + +_FromTypeT = TypeVar('_FromTypeT') + + +class ValidateAs: + """A helper class to validate a custom type from a type that is natively supported by Pydantic. + + Args: + from_type: The type natively supported by Pydantic to use to perform validation. + instantiation_hook: A callable taking the validated type as an argument, and returning + the populated custom type. + + Example: + ```python {lint="skip"} + from typing import Annotated + + from pydantic import BaseModel, TypeAdapter, ValidateAs + + class MyCls: + def __init__(self, a: int) -> None: + self.a = a + + def __repr__(self) -> str: + return f"MyCls(a={self.a})" + + class Model(BaseModel): + a: int + + + ta = TypeAdapter( + Annotated[MyCls, ValidateAs(Model, lambda v: MyCls(a=v.a))] + ) + + print(ta.validate_python({'a': 1})) + #> MyCls(a=1) + ``` + """ + + # TODO: make use of PEP 747 + def __init__(self, from_type: type[_FromTypeT], /, instantiation_hook: Callable[[_FromTypeT], Any]) -> None: + self.from_type = from_type + self.instantiation_hook = instantiation_hook + + def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(self.from_type) + return core_schema.no_info_after_validator_function( + self.instantiation_hook, + schema=schema, + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/generics.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/generics.py new file mode 100644 index 0000000000000000000000000000000000000000..3f1070d08f3ac5bd554794401734eb349373ab8e --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/generics.py @@ -0,0 +1,5 @@ +"""The `generics` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/json.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/json.py new file mode 100644 index 0000000000000000000000000000000000000000..bcaff9f57958b8c0938255fd8f937293c5850cbd --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/json.py @@ -0,0 +1,5 @@ +"""The `json` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/json_schema.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/json_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..296547074e86c9a87ec7225b4d7117c30863ac5d --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/json_schema.py @@ -0,0 +1,2914 @@ +"""!!! abstract "Usage Documentation" + [JSON Schema](../concepts/json_schema.md) + +The `json_schema` module contains classes and functions to allow the way [JSON Schema](https://json-schema.org/) +is generated to be customized. + +In general you shouldn't need to use this module directly; instead, you can use +[`BaseModel.model_json_schema`][pydantic.BaseModel.model_json_schema] and +[`TypeAdapter.json_schema`][pydantic.TypeAdapter.json_schema]. +""" + +from __future__ import annotations as _annotations + +import collections.abc +import dataclasses +import inspect +import math +import os +import re +import warnings +from collections import Counter, defaultdict +from collections.abc import Hashable, Iterable, Sequence +from copy import deepcopy +from enum import Enum +from re import Pattern +from typing import ( + TYPE_CHECKING, + Annotated, + Any, + Callable, + Literal, + NewType, + TypeVar, + Union, + cast, + overload, +) + +import pydantic_core +from pydantic_core import MISSING, CoreSchema, PydanticOmit, core_schema, to_jsonable_python +from pydantic_core.core_schema import ComputedField +from typing_extensions import TypeAlias, assert_never, deprecated, final +from typing_inspection.introspection import get_literal_values + +from pydantic.warnings import PydanticDeprecatedSince26, PydanticDeprecatedSince29 + +from ._internal import ( + _config, + _core_metadata, + _core_utils, + _decorators, + _internal_dataclass, + _mock_val_ser, + _schema_generation_shared, + _typing_extra, +) +from .annotated_handlers import GetJsonSchemaHandler +from .config import JsonDict, JsonValue +from .errors import PydanticInvalidForJsonSchema, PydanticSchemaGenerationError, PydanticUserError + +if TYPE_CHECKING: + from . import ConfigDict + from ._internal._core_utils import CoreSchemaField, CoreSchemaOrField + from ._internal._dataclasses import PydanticDataclass + from ._internal._schema_generation_shared import GetJsonSchemaFunction + from .main import BaseModel + + +CoreSchemaOrFieldType = Literal[core_schema.CoreSchemaType, core_schema.CoreSchemaFieldType] +""" +A type alias for defined schema types that represents a union of +`core_schema.CoreSchemaType` and +`core_schema.CoreSchemaFieldType`. +""" + +JsonSchemaValue = dict[str, Any] +""" +A type alias for a JSON schema value. This is a dictionary of string keys to arbitrary JSON values. +""" + +JsonSchemaMode = Literal['validation', 'serialization'] +""" +A type alias that represents the mode of a JSON schema; either 'validation' or 'serialization'. + +For some types, the inputs to validation differ from the outputs of serialization. For example, +computed fields will only be present when serializing, and should not be provided when +validating. This flag provides a way to indicate whether you want the JSON schema required +for validation inputs, or that will be matched by serialization outputs. +""" + +_MODE_TITLE_MAPPING: dict[JsonSchemaMode, str] = {'validation': 'Input', 'serialization': 'Output'} + + +JsonSchemaWarningKind = Literal['skipped-choice', 'non-serializable-default', 'skipped-discriminator'] +""" +A type alias representing the kinds of warnings that can be emitted during JSON schema generation. + +See [`GenerateJsonSchema.render_warning_message`][pydantic.json_schema.GenerateJsonSchema.render_warning_message] +for more details. +""" + + +class PydanticJsonSchemaWarning(UserWarning): + """This class is used to emit warnings produced during JSON schema generation. + See the [`GenerateJsonSchema.emit_warning`][pydantic.json_schema.GenerateJsonSchema.emit_warning] and + [`GenerateJsonSchema.render_warning_message`][pydantic.json_schema.GenerateJsonSchema.render_warning_message] + methods for more details; these can be overridden to control warning behavior. + """ + + +NoDefault = object() +"""A sentinel value used to indicate that no default value should be used when generating a JSON Schema +for a core schema with a default value. +""" + + +# ##### JSON Schema Generation ##### +DEFAULT_REF_TEMPLATE = '#/$defs/{model}' +"""The default format string used to generate reference names.""" + +# There are three types of references relevant to building JSON schemas: +# 1. core_schema "ref" values; these are not exposed as part of the JSON schema +# * these might look like the fully qualified path of a model, its id, or something similar +CoreRef = NewType('CoreRef', str) +# 2. keys of the "definitions" object that will eventually go into the JSON schema +# * by default, these look like "MyModel", though may change in the presence of collisions +# * eventually, we may want to make it easier to modify the way these names are generated +DefsRef = NewType('DefsRef', str) +# 3. the values corresponding to the "$ref" key in the schema +# * By default, these look like "#/$defs/MyModel", as in {"$ref": "#/$defs/MyModel"} +JsonRef = NewType('JsonRef', str) + +CoreModeRef = tuple[CoreRef, JsonSchemaMode] +JsonSchemaKeyT = TypeVar('JsonSchemaKeyT', bound=Hashable) + +_PRIMITIVE_JSON_SCHEMA_TYPES = ('string', 'boolean', 'null', 'integer', 'number') + + +@dataclasses.dataclass(**_internal_dataclass.slots_true) +class _DefinitionsRemapping: + defs_remapping: dict[DefsRef, DefsRef] + json_remapping: dict[JsonRef, JsonRef] + + @staticmethod + def from_prioritized_choices( + prioritized_choices: dict[DefsRef, list[DefsRef]], + defs_to_json: dict[DefsRef, JsonRef], + definitions: dict[DefsRef, JsonSchemaValue], + ) -> _DefinitionsRemapping: + """ + This function should produce a remapping that replaces complex DefsRef with the simpler ones from the + prioritized_choices such that applying the name remapping would result in an equivalent JSON schema. + """ + # We need to iteratively simplify the definitions until we reach a fixed point. + # The reason for this is that outer definitions may reference inner definitions that get simplified + # into an equivalent reference, and the outer definitions won't be equivalent until we've simplified + # the inner definitions. + copied_definitions = deepcopy(definitions) + definitions_schema = {'$defs': copied_definitions} + for _iter in range(100): # prevent an infinite loop in the case of a bug, 100 iterations should be enough + # For every possible remapped DefsRef, collect all schemas that DefsRef might be used for: + schemas_for_alternatives: dict[DefsRef, list[JsonSchemaValue]] = defaultdict(list) + for defs_ref in copied_definitions: + alternatives = prioritized_choices[defs_ref] + for alternative in alternatives: + schemas_for_alternatives[alternative].append(copied_definitions[defs_ref]) + + # Deduplicate the schemas for each alternative; the idea is that we only want to remap to a new DefsRef + # if it introduces no ambiguity, i.e., there is only one distinct schema for that DefsRef. + for defs_ref in schemas_for_alternatives: + schemas_for_alternatives[defs_ref] = _deduplicate_schemas(schemas_for_alternatives[defs_ref]) + + # Build the remapping + defs_remapping: dict[DefsRef, DefsRef] = {} + json_remapping: dict[JsonRef, JsonRef] = {} + for original_defs_ref in definitions: + alternatives = prioritized_choices[original_defs_ref] + # Pick the first alternative that has only one schema, since that means there is no collision + remapped_defs_ref = next(x for x in alternatives if len(schemas_for_alternatives[x]) == 1) + defs_remapping[original_defs_ref] = remapped_defs_ref + + # Map all alternatives after the remapped one to the remapped one + # This ensures that intermediate simplifications are also remapped + remapped_index = alternatives.index(remapped_defs_ref) + for alt in alternatives[remapped_index:]: + json_remapping[defs_to_json[alt]] = defs_to_json[remapped_defs_ref] + remapping = _DefinitionsRemapping(defs_remapping, json_remapping) + new_definitions_schema = remapping.remap_json_schema({'$defs': copied_definitions}) + if definitions_schema == new_definitions_schema: + # We've reached the fixed point + return remapping + definitions_schema = new_definitions_schema + + raise PydanticInvalidForJsonSchema('Failed to simplify the JSON schema definitions') + + def remap_defs_ref(self, ref: DefsRef) -> DefsRef: + return self.defs_remapping.get(ref, ref) + + def remap_json_ref(self, ref: JsonRef) -> JsonRef: + return self.json_remapping.get(ref, ref) + + def remap_json_schema(self, schema: Any) -> Any: + """ + Recursively update the JSON schema replacing all $refs + """ + if isinstance(schema, str): + # Note: this may not really be a JsonRef; we rely on having no collisions between JsonRefs and other strings + return self.remap_json_ref(JsonRef(schema)) + elif isinstance(schema, list): + return [self.remap_json_schema(item) for item in schema] + elif isinstance(schema, dict): + for key, value in schema.items(): + if key == '$ref' and isinstance(value, str): + schema['$ref'] = self.remap_json_ref(JsonRef(value)) + elif key == '$defs': + schema['$defs'] = { + self.remap_defs_ref(DefsRef(key)): self.remap_json_schema(value) + for key, value in schema['$defs'].items() + } + else: + schema[key] = self.remap_json_schema(value) + return schema + + +class GenerateJsonSchema: + """!!! abstract "Usage Documentation" + [Customizing the JSON Schema Generation Process](../concepts/json_schema.md#customizing-the-json-schema-generation-process) + + A class for generating JSON schemas. + + This class generates JSON schemas based on configured parameters. The default schema dialect + is [https://json-schema.org/draft/2020-12/schema](https://json-schema.org/draft/2020-12/schema). + The class uses `by_alias` to configure how fields with + multiple names are handled and `ref_template` to format reference names. + + Attributes: + schema_dialect: The JSON schema dialect used to generate the schema. See + [Declaring a Dialect](https://json-schema.org/understanding-json-schema/reference/schema.html#id4) + in the JSON Schema documentation for more information about dialects. + ignored_warning_kinds: Warnings to ignore when generating the schema. `self.render_warning_message` will + do nothing if its argument `kind` is in `ignored_warning_kinds`; + this value can be modified on subclasses to easily control which warnings are emitted. + by_alias: Whether to use field aliases when generating the schema. + ref_template: The format string used when generating reference names. + core_to_json_refs: A mapping of core refs to JSON refs. + core_to_defs_refs: A mapping of core refs to definition refs. + defs_to_core_refs: A mapping of definition refs to core refs. + json_to_defs_refs: A mapping of JSON refs to definition refs. + definitions: Definitions in the schema. + + Args: + by_alias: Whether to use field aliases in the generated schemas. + ref_template: The format string to use when generating reference names. + union_format: The format to use when combining schemas from unions together. Can be one of: + + - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf) + keyword to combine schemas (the default). + - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type) + keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive + type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to + `any_of`. + + Raises: + JsonSchemaError: If the instance of the class is inadvertently reused after generating a schema. + """ + + schema_dialect = 'https://json-schema.org/draft/2020-12/schema' + + # `self.render_warning_message` will do nothing if its argument `kind` is in `ignored_warning_kinds`; + # this value can be modified on subclasses to easily control which warnings are emitted + ignored_warning_kinds: set[JsonSchemaWarningKind] = {'skipped-choice'} + + def __init__( + self, + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + union_format: Literal['any_of', 'primitive_type_array'] = 'any_of', + ) -> None: + self.by_alias = by_alias + self.ref_template = ref_template + self.union_format: Literal['any_of', 'primitive_type_array'] = union_format + + self.core_to_json_refs: dict[CoreModeRef, JsonRef] = {} + self.core_to_defs_refs: dict[CoreModeRef, DefsRef] = {} + self.defs_to_core_refs: dict[DefsRef, CoreModeRef] = {} + self.json_to_defs_refs: dict[JsonRef, DefsRef] = {} + + self.definitions: dict[DefsRef, JsonSchemaValue] = {} + self._config_wrapper_stack = _config.ConfigWrapperStack(_config.ConfigWrapper({})) + + self._mode: JsonSchemaMode = 'validation' + + # The following includes a mapping of a fully-unique defs ref choice to a list of preferred + # alternatives, which are generally simpler, such as only including the class name. + # At the end of schema generation, we use these to produce a JSON schema with more human-readable + # definitions, which would also work better in a generated OpenAPI client, etc. + self._prioritized_defsref_choices: dict[DefsRef, list[DefsRef]] = {} + self._collision_counter: dict[str, int] = defaultdict(int) + self._collision_index: dict[str, int] = {} + + self._schema_type_to_method = self.build_schema_type_to_method() + + # When we encounter definitions we need to try to build them immediately + # so that they are available schemas that reference them + # But it's possible that CoreSchema was never going to be used + # (e.g. because the CoreSchema that references short circuits is JSON schema generation without needing + # the reference) so instead of failing altogether if we can't build a definition we + # store the error raised and re-throw it if we end up needing that def + self._core_defs_invalid_for_json_schema: dict[DefsRef, PydanticInvalidForJsonSchema] = {} + + # This changes to True after generating a schema, to prevent issues caused by accidental reuse + # of a single instance of a schema generator + self._used = False + + @property + def _config(self) -> _config.ConfigWrapper: + return self._config_wrapper_stack.tail + + @property + def mode(self) -> JsonSchemaMode: + if self._config.json_schema_mode_override is not None: + return self._config.json_schema_mode_override + else: + return self._mode + + def build_schema_type_to_method( + self, + ) -> dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]]: + """Builds a dictionary mapping fields to methods for generating JSON schemas. + + Returns: + A dictionary containing the mapping of `CoreSchemaOrFieldType` to a handler method. + + Raises: + TypeError: If no method has been defined for generating a JSON schema for a given pydantic core schema type. + """ + mapping: dict[CoreSchemaOrFieldType, Callable[[CoreSchemaOrField], JsonSchemaValue]] = {} + core_schema_types: list[CoreSchemaOrFieldType] = list(get_literal_values(CoreSchemaOrFieldType)) + for key in core_schema_types: + method_name = f'{key.replace("-", "_")}_schema' + try: + mapping[key] = getattr(self, method_name) + except AttributeError as e: # pragma: no cover + if os.getenv('PYDANTIC_PRIVATE_ALLOW_UNHANDLED_SCHEMA_TYPES'): + continue + raise TypeError( + f'No method for generating JsonSchema for core_schema.type={key!r} ' + f'(expected: {type(self).__name__}.{method_name})' + ) from e + return mapping + + def generate_definitions( + self, inputs: Sequence[tuple[JsonSchemaKeyT, JsonSchemaMode, core_schema.CoreSchema]] + ) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], dict[DefsRef, JsonSchemaValue]]: + """Generates JSON schema definitions from a list of core schemas, pairing the generated definitions with a + mapping that links the input keys to the definition references. + + Args: + inputs: A sequence of tuples, where: + + - The first element is a JSON schema key type. + - The second element is the JSON mode: either 'validation' or 'serialization'. + - The third element is a core schema. + + Returns: + A tuple where: + + - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and + whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have + JsonRef references to definitions that are defined in the second returned element.) + - The second element is a dictionary whose keys are definition references for the JSON schemas + from the first returned element, and whose values are the actual JSON schema definitions. + + Raises: + PydanticUserError: Raised if the JSON schema generator has already been used to generate a JSON schema. + """ + if self._used: + raise PydanticUserError( + 'This JSON schema generator has already been used to generate a JSON schema. ' + f'You must create a new instance of {type(self).__name__} to generate a new JSON schema.', + code='json-schema-already-used', + ) + + for _, mode, schema in inputs: + self._mode = mode + self.generate_inner(schema) + + definitions_remapping = self._build_definitions_remapping() + + json_schemas_map: dict[tuple[JsonSchemaKeyT, JsonSchemaMode], DefsRef] = {} + for key, mode, schema in inputs: + self._mode = mode + json_schema = self.generate_inner(schema) + json_schemas_map[(key, mode)] = definitions_remapping.remap_json_schema(json_schema) + + json_schema = {'$defs': self.definitions} + json_schema = definitions_remapping.remap_json_schema(json_schema) + self._used = True + return json_schemas_map, self.sort(json_schema['$defs']) # type: ignore + + def generate(self, schema: CoreSchema, mode: JsonSchemaMode = 'validation') -> JsonSchemaValue: + """Generates a JSON schema for a specified schema in a specified mode. + + Args: + schema: A Pydantic model. + mode: The mode in which to generate the schema. Defaults to 'validation'. + + Returns: + A JSON schema representing the specified schema. + + Raises: + PydanticUserError: If the JSON schema generator has already been used to generate a JSON schema. + """ + self._mode = mode + if self._used: + raise PydanticUserError( + 'This JSON schema generator has already been used to generate a JSON schema. ' + f'You must create a new instance of {type(self).__name__} to generate a new JSON schema.', + code='json-schema-already-used', + ) + + json_schema: JsonSchemaValue = self.generate_inner(schema) + json_ref_counts = self.get_json_ref_counts(json_schema) + + ref = cast(JsonRef, json_schema.get('$ref')) + while ref is not None: # may need to unpack multiple levels + ref_json_schema = self.get_schema_from_definitions(ref) + if json_ref_counts[ref] == 1 and ref_json_schema is not None and len(json_schema) == 1: + # "Unpack" the ref since this is the only reference and there are no sibling keys + json_schema = ref_json_schema.copy() # copy to prevent recursive dict reference + json_ref_counts[ref] -= 1 + ref = cast(JsonRef, json_schema.get('$ref')) + ref = None + + self._garbage_collect_definitions(json_schema) + definitions_remapping = self._build_definitions_remapping() + + if self.definitions: + json_schema['$defs'] = self.definitions + + json_schema = definitions_remapping.remap_json_schema(json_schema) + + # For now, we will not set the $schema key. However, if desired, this can be easily added by overriding + # this method and adding the following line after a call to super().generate(schema): + # json_schema['$schema'] = self.schema_dialect + + self._used = True + return self.sort(json_schema) + + def generate_inner(self, schema: CoreSchemaOrField) -> JsonSchemaValue: # noqa: C901 + """Generates a JSON schema for a given core schema. + + Args: + schema: The given core schema. + + Returns: + The generated JSON schema. + + TODO: the nested function definitions here seem like bad practice, I'd like to unpack these + in a future PR. It'd be great if we could shorten the call stack a bit for JSON schema generation, + and I think there's potential for that here. + """ + # If a schema with the same CoreRef has been handled, just return a reference to it + # Note that this assumes that it will _never_ be the case that the same CoreRef is used + # on types that should have different JSON schemas + if 'ref' in schema: + core_ref = CoreRef(schema['ref']) # type: ignore[typeddict-item] + core_mode_ref = (core_ref, self.mode) + if core_mode_ref in self.core_to_defs_refs and self.core_to_defs_refs[core_mode_ref] in self.definitions: + return {'$ref': self.core_to_json_refs[core_mode_ref]} + + def populate_defs(core_schema: CoreSchema, json_schema: JsonSchemaValue) -> JsonSchemaValue: + if 'ref' in core_schema: + core_ref = CoreRef(core_schema['ref']) # type: ignore[typeddict-item] + defs_ref, ref_json_schema = self.get_cache_defs_ref_schema(core_ref) + json_ref = JsonRef(ref_json_schema['$ref']) + # Replace the schema if it's not a reference to itself + # What we want to avoid is having the def be just a ref to itself + # which is what would happen if we blindly assigned any + if json_schema.get('$ref', None) != json_ref: + self.definitions[defs_ref] = json_schema + self._core_defs_invalid_for_json_schema.pop(defs_ref, None) + json_schema = ref_json_schema + return json_schema + + def handler_func(schema_or_field: CoreSchemaOrField) -> JsonSchemaValue: + """Generate a JSON schema based on the input schema. + + Args: + schema_or_field: The core schema to generate a JSON schema from. + + Returns: + The generated JSON schema. + + Raises: + TypeError: If an unexpected schema type is encountered. + """ + # Generate the core-schema-type-specific bits of the schema generation: + json_schema: JsonSchemaValue | None = None + if self.mode == 'serialization' and 'serialization' in schema_or_field: + # In this case, we skip the JSON Schema generation of the schema + # and use the `'serialization'` schema instead (canonical example: + # `Annotated[int, PlainSerializer(str)]`). + ser_schema = schema_or_field['serialization'] # type: ignore + json_schema = self.ser_schema(ser_schema) + + # It might be that the 'serialization'` is skipped depending on `when_used`. + # This is only relevant for `nullable` schemas though, so we special case here. + if ( + json_schema is not None + and ser_schema.get('when_used') in ('unless-none', 'json-unless-none') + and schema_or_field['type'] == 'nullable' + ): + json_schema = self.get_union_of_schemas([{'type': 'null'}, json_schema]) + if json_schema is None: + if _core_utils.is_core_schema(schema_or_field) or _core_utils.is_core_schema_field(schema_or_field): + generate_for_schema_type = self._schema_type_to_method[schema_or_field['type']] + json_schema = generate_for_schema_type(schema_or_field) + else: + raise TypeError(f'Unexpected schema type: schema={schema_or_field}') + return json_schema + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, handler_func) + + metadata = cast(_core_metadata.CoreMetadata, schema.get('metadata', {})) + + # TODO: I dislike that we have to wrap these basic dict updates in callables, is there any way around this? + + if js_updates := metadata.get('pydantic_js_updates'): + + def js_updates_handler_func( + schema_or_field: CoreSchemaOrField, + current_handler: GetJsonSchemaHandler = current_handler, + ) -> JsonSchemaValue: + json_schema = {**current_handler(schema_or_field), **js_updates} + return json_schema + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, js_updates_handler_func) + + if js_extra := metadata.get('pydantic_js_extra'): + + def js_extra_handler_func( + schema_or_field: CoreSchemaOrField, + current_handler: GetJsonSchemaHandler = current_handler, + ) -> JsonSchemaValue: + json_schema = current_handler(schema_or_field) + if isinstance(js_extra, dict): + json_schema.update(to_jsonable_python(js_extra)) + elif callable(js_extra): + # similar to typing issue in _update_class_schema when we're working with callable js extra + js_extra(json_schema) # type: ignore + return json_schema + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, js_extra_handler_func) + + for js_modify_function in metadata.get('pydantic_js_functions', ()): + + def new_handler_func( + schema_or_field: CoreSchemaOrField, + current_handler: GetJsonSchemaHandler = current_handler, + js_modify_function: GetJsonSchemaFunction = js_modify_function, + ) -> JsonSchemaValue: + json_schema = js_modify_function(schema_or_field, current_handler) + if _core_utils.is_core_schema(schema_or_field): + json_schema = populate_defs(schema_or_field, json_schema) + original_schema = current_handler.resolve_ref_schema(json_schema) + ref = json_schema.pop('$ref', None) + if ref and json_schema: + original_schema.update(json_schema) + return original_schema + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func) + + for js_modify_function in metadata.get('pydantic_js_annotation_functions', ()): + + def new_handler_func( + schema_or_field: CoreSchemaOrField, + current_handler: GetJsonSchemaHandler = current_handler, + js_modify_function: GetJsonSchemaFunction = js_modify_function, + ) -> JsonSchemaValue: + return js_modify_function(schema_or_field, current_handler) + + current_handler = _schema_generation_shared.GenerateJsonSchemaHandler(self, new_handler_func) + + json_schema = current_handler(schema) + if _core_utils.is_core_schema(schema): + json_schema = populate_defs(schema, json_schema) + return json_schema + + def sort(self, value: JsonSchemaValue, parent_key: str | None = None) -> JsonSchemaValue: + """Override this method to customize the sorting of the JSON schema (e.g., don't sort at all, sort all keys unconditionally, etc.) + + By default, alphabetically sort the keys in the JSON schema, skipping the 'properties' and 'default' keys to preserve field definition order. + This sort is recursive, so it will sort all nested dictionaries as well. + """ + sorted_dict: dict[str, JsonSchemaValue] = {} + keys = value.keys() + if parent_key not in ('properties', 'default'): + keys = sorted(keys) + for key in keys: + sorted_dict[key] = self._sort_recursive(value[key], parent_key=key) + return sorted_dict + + def _sort_recursive(self, value: Any, parent_key: str | None = None) -> Any: + """Recursively sort a JSON schema value.""" + if isinstance(value, dict): + sorted_dict: dict[str, JsonSchemaValue] = {} + keys = value.keys() + if parent_key not in ('properties', 'default'): + keys = sorted(keys) + for key in keys: + sorted_dict[key] = self._sort_recursive(value[key], parent_key=key) + return sorted_dict + elif isinstance(value, list): + sorted_list: list[JsonSchemaValue] = [self._sort_recursive(item, parent_key) for item in value] + return sorted_list + else: + return value + + # ### Schema generation methods + + def invalid_schema(self, schema: core_schema.InvalidSchema) -> JsonSchemaValue: + """Placeholder - should never be called.""" + + raise RuntimeError('Cannot generate schema for invalid_schema. This is a bug! Please report it.') + + def any_schema(self, schema: core_schema.AnySchema) -> JsonSchemaValue: + """Generates a JSON schema that matches any value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {} + + def none_schema(self, schema: core_schema.NoneSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches `None`. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'null'} + + def bool_schema(self, schema: core_schema.BoolSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a bool value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'boolean'} + + def int_schema(self, schema: core_schema.IntSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches an int value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema: dict[str, Any] = {'type': 'integer'} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.numeric) + json_schema = {k: v for k, v in json_schema.items() if v not in {math.inf, -math.inf}} + return json_schema + + def float_schema(self, schema: core_schema.FloatSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a float value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema: dict[str, Any] = {'type': 'number'} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.numeric) + json_schema = {k: v for k, v in json_schema.items() if v not in {math.inf, -math.inf}} + return json_schema + + def decimal_schema(self, schema: core_schema.DecimalSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a decimal value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + + def get_decimal_pattern(schema: core_schema.DecimalSchema) -> str: + max_digits = schema.get('max_digits') + decimal_places = schema.get('decimal_places') + + pattern = ( + r'^(?!^[-+.]*$)[+-]?0*' # check it is not empty string and not one or sequence of ".+-" characters. + ) + + # Case 1: Both max_digits and decimal_places are set + if max_digits is not None and decimal_places is not None: + integer_places = max(0, max_digits - decimal_places) + pattern += ( + rf'(?:' + rf'\d{{0,{integer_places}}}' + rf'|' + rf'(?=[\d.]{{1,{max_digits + 1}}}0*$)' + rf'\d{{0,{integer_places}}}\.\d{{0,{decimal_places}}}0*$' + rf')' + ) + + # Case 2: Only max_digits is set + elif max_digits is not None and decimal_places is None: + pattern += ( + rf'(?:' + rf'\d{{0,{max_digits}}}' + rf'|' + rf'(?=[\d.]{{1,{max_digits + 1}}}0*$)' + rf'\d*\.\d*0*$' + rf')' + ) + + # Case 3: Only decimal_places is set + elif max_digits is None and decimal_places is not None: + pattern += rf'\d*\.?\d{{0,{decimal_places}}}0*$' + + # Case 4: Both are None (no restrictions) + else: + pattern += r'\d*\.?\d*$' # look for arbitrary integer or decimal + + return pattern + + json_schema = self.str_schema(core_schema.str_schema(pattern=get_decimal_pattern(schema))) + if self.mode == 'validation': + multiple_of = schema.get('multiple_of') + le = schema.get('le') + ge = schema.get('ge') + lt = schema.get('lt') + gt = schema.get('gt') + json_schema = { + 'anyOf': [ + self.float_schema( + core_schema.float_schema( + allow_inf_nan=schema.get('allow_inf_nan'), + multiple_of=None if multiple_of is None else float(multiple_of), + le=None if le is None else float(le), + ge=None if ge is None else float(ge), + lt=None if lt is None else float(lt), + gt=None if gt is None else float(gt), + ) + ), + json_schema, + ], + } + return json_schema + + def str_schema(self, schema: core_schema.StringSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a string value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema = {'type': 'string'} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.string) + if isinstance(json_schema.get('pattern'), Pattern): + # TODO: should we add regex flags to the pattern? + json_schema['pattern'] = json_schema.get('pattern').pattern # type: ignore + return json_schema + + def bytes_schema(self, schema: core_schema.BytesSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a bytes value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema = {'type': 'string', 'format': 'base64url' if self._config.ser_json_bytes == 'base64' else 'binary'} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.bytes) + return json_schema + + def date_schema(self, schema: core_schema.DateSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a date value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'string', 'format': 'date'} + + def time_schema(self, schema: core_schema.TimeSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a time value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'string', 'format': 'time'} + + def datetime_schema(self, schema: core_schema.DatetimeSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a datetime value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'string', 'format': 'date-time'} + + def timedelta_schema(self, schema: core_schema.TimedeltaSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a timedelta value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + if self._config.ser_json_timedelta == 'float': + return {'type': 'number'} + return {'type': 'string', 'format': 'duration'} + + def literal_schema(self, schema: core_schema.LiteralSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a literal value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + expected = [to_jsonable_python(v.value if isinstance(v, Enum) else v) for v in schema['expected']] + + result: dict[str, Any] = {} + if len(expected) == 1: + result['const'] = expected[0] + else: + result['enum'] = expected + + types = {type(e) for e in expected} + if types == {str}: + result['type'] = 'string' + elif types == {int}: + result['type'] = 'integer' + elif types == {float}: + result['type'] = 'number' + elif types == {bool}: + result['type'] = 'boolean' + elif types == {list}: + result['type'] = 'array' + elif types == {type(None)}: + result['type'] = 'null' + return result + + def missing_sentinel_schema(self, schema: core_schema.MissingSentinelSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches the `MISSING` sentinel value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + raise PydanticOmit + + def enum_schema(self, schema: core_schema.EnumSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches an Enum value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + enum_type = schema['cls'] + description = None if not enum_type.__doc__ else inspect.cleandoc(enum_type.__doc__) + if ( + description == 'An enumeration.' + ): # This is the default value provided by enum.EnumMeta.__new__; don't use it + description = None + result: dict[str, Any] = {'title': enum_type.__name__, 'description': description} + result = {k: v for k, v in result.items() if v is not None} + + expected = [to_jsonable_python(v.value) for v in schema['members']] + + result['enum'] = expected + + types = {type(e) for e in expected} + if isinstance(enum_type, str) or types == {str}: + result['type'] = 'string' + elif isinstance(enum_type, int) or types == {int}: + result['type'] = 'integer' + elif isinstance(enum_type, float) or types == {float}: + result['type'] = 'number' + elif types == {bool}: + result['type'] = 'boolean' + elif types == {list}: + result['type'] = 'array' + + return result + + def is_instance_schema(self, schema: core_schema.IsInstanceSchema) -> JsonSchemaValue: + """Handles JSON schema generation for a core schema that checks if a value is an instance of a class. + + Unless overridden in a subclass, this raises an error. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.handle_invalid_for_json_schema(schema, f'core_schema.IsInstanceSchema ({schema["cls"]})') + + def is_subclass_schema(self, schema: core_schema.IsSubclassSchema) -> JsonSchemaValue: + """Handles JSON schema generation for a core schema that checks if a value is a subclass of a class. + + For backwards compatibility with v1, this does not raise an error, but can be overridden to change this. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + # Note: This is for compatibility with V1; you can override if you want different behavior. + return {} + + def callable_schema(self, schema: core_schema.CallableSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a callable value. + + Unless overridden in a subclass, this raises an error. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.handle_invalid_for_json_schema(schema, 'core_schema.CallableSchema') + + def list_schema(self, schema: core_schema.ListSchema) -> JsonSchemaValue: + """Returns a schema that matches a list schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema']) + json_schema = {'type': 'array', 'items': items_schema} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.array) + return json_schema + + @deprecated('`tuple_positional_schema` is deprecated. Use `tuple_schema` instead.', category=None) + @final + def tuple_positional_schema(self, schema: core_schema.TupleSchema) -> JsonSchemaValue: + """Replaced by `tuple_schema`.""" + warnings.warn( + '`tuple_positional_schema` is deprecated. Use `tuple_schema` instead.', + PydanticDeprecatedSince26, + stacklevel=2, + ) + return self.tuple_schema(schema) + + @deprecated('`tuple_variable_schema` is deprecated. Use `tuple_schema` instead.', category=None) + @final + def tuple_variable_schema(self, schema: core_schema.TupleSchema) -> JsonSchemaValue: + """Replaced by `tuple_schema`.""" + warnings.warn( + '`tuple_variable_schema` is deprecated. Use `tuple_schema` instead.', + PydanticDeprecatedSince26, + stacklevel=2, + ) + return self.tuple_schema(schema) + + def tuple_schema(self, schema: core_schema.TupleSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a tuple schema e.g. `tuple[int, + str, bool]` or `tuple[int, ...]`. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema: JsonSchemaValue = {'type': 'array'} + if 'variadic_item_index' in schema: + variadic_item_index = schema['variadic_item_index'] + if variadic_item_index > 0: + json_schema['minItems'] = variadic_item_index + json_schema['prefixItems'] = [ + self.generate_inner(item) for item in schema['items_schema'][:variadic_item_index] + ] + if variadic_item_index + 1 == len(schema['items_schema']): + # if the variadic item is the last item, then represent it faithfully + json_schema['items'] = self.generate_inner(schema['items_schema'][variadic_item_index]) + else: + # otherwise, 'items' represents the schema for the variadic + # item plus the suffix, so just allow anything for simplicity + # for now + json_schema['items'] = True + else: + prefixItems = [self.generate_inner(item) for item in schema['items_schema']] + if prefixItems: + json_schema['prefixItems'] = prefixItems + json_schema['minItems'] = len(prefixItems) + json_schema['maxItems'] = len(prefixItems) + self.update_with_validations(json_schema, schema, self.ValidationsMapping.array) + return json_schema + + def set_schema(self, schema: core_schema.SetSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a set schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self._common_set_schema(schema) + + def frozenset_schema(self, schema: core_schema.FrozenSetSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a frozenset schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self._common_set_schema(schema) + + def _common_set_schema(self, schema: core_schema.SetSchema | core_schema.FrozenSetSchema) -> JsonSchemaValue: + items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema']) + json_schema = {'type': 'array', 'uniqueItems': True, 'items': items_schema} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.array) + return json_schema + + def generator_schema(self, schema: core_schema.GeneratorSchema) -> JsonSchemaValue: + """Returns a JSON schema that represents the provided GeneratorSchema. + + Args: + schema: The schema. + + Returns: + The generated JSON schema. + """ + items_schema = {} if 'items_schema' not in schema else self.generate_inner(schema['items_schema']) + json_schema = {'type': 'array', 'items': items_schema} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.array) + return json_schema + + def dict_schema(self, schema: core_schema.DictSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a dict schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema: JsonSchemaValue = {'type': 'object'} + + keys_schema = self.generate_inner(schema['keys_schema']).copy() if 'keys_schema' in schema else {} + if '$ref' not in keys_schema: + keys_pattern = keys_schema.pop('pattern', None) + # Don't give a title to patternProperties/propertyNames: + keys_schema.pop('title', None) + else: + # Here, we assume that if the keys schema is a definition reference, + # it can't be a simple string core schema (and thus no pattern can exist). + # However, this is only in practice (in theory, a definition reference core + # schema could be generated for a simple string schema). + # Note that we avoid calling `self.resolve_ref_schema`, as it might not exist yet. + keys_pattern = None + + values_schema = self.generate_inner(schema['values_schema']).copy() if 'values_schema' in schema else {} + # don't give a title to additionalProperties: + values_schema.pop('title', None) + + if values_schema or keys_pattern is not None: + if keys_pattern is None: + json_schema['additionalProperties'] = values_schema + else: + json_schema['patternProperties'] = {keys_pattern: values_schema} + else: # for `dict[str, Any]`, we allow any key and any value, since `str` is the default key type + json_schema['additionalProperties'] = True + + if ( + # The len check indicates that constraints are probably present: + (keys_schema.get('type') == 'string' and len(keys_schema) > 1) + # If this is a definition reference schema, it most likely has constraints: + or '$ref' in keys_schema + ): + keys_schema.pop('type', None) + json_schema['propertyNames'] = keys_schema + + self.update_with_validations(json_schema, schema, self.ValidationsMapping.object) + return json_schema + + def function_before_schema(self, schema: core_schema.BeforeValidatorFunctionSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a function-before schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + if self.mode == 'validation' and (input_schema := schema.get('json_schema_input_schema')): + return self.generate_inner(input_schema) + + return self.generate_inner(schema['schema']) + + def function_after_schema(self, schema: core_schema.AfterValidatorFunctionSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a function-after schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['schema']) + + def function_plain_schema(self, schema: core_schema.PlainValidatorFunctionSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a function-plain schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + if self.mode == 'validation' and (input_schema := schema.get('json_schema_input_schema')): + return self.generate_inner(input_schema) + + return self.handle_invalid_for_json_schema( + schema, f'core_schema.PlainValidatorFunctionSchema ({schema["function"]})' + ) + + def function_wrap_schema(self, schema: core_schema.WrapValidatorFunctionSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a function-wrap schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + if self.mode == 'validation' and (input_schema := schema.get('json_schema_input_schema')): + return self.generate_inner(input_schema) + + return self.generate_inner(schema['schema']) + + def default_schema(self, schema: core_schema.WithDefaultSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema with a default value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema = self.generate_inner(schema['schema']) + + default = self.get_default_value(schema) + if default is NoDefault or default is MISSING: + return json_schema + + # we reflect the application of custom plain, no-info serializers to defaults for + # JSON Schemas viewed in serialization mode: + # TODO: improvements along with https://github.com/pydantic/pydantic/issues/8208 + if self.mode == 'serialization': + # `_get_ser_schema_for_default_value()` is used to unpack potentially nested validator schemas: + ser_schema = _get_ser_schema_for_default_value(schema['schema']) + if ( + ser_schema is not None + and (ser_func := ser_schema.get('function')) + and not (default is None and ser_schema.get('when_used') in ('unless-none', 'json-unless-none')) + ): + try: + default = ser_func(default) # type: ignore + except Exception: + # It might be that the provided default needs to be validated (read: parsed) first + # (assuming `validate_default` is enabled). However, we can't perform + # such validation during JSON Schema generation so we don't support + # this pattern for now. + # (One example is when using `foo: ByteSize = '1MB'`, which validates and + # serializes as an int. In this case, `ser_func` is `int` and `int('1MB')` fails). + self.emit_warning( + 'non-serializable-default', + f'Unable to serialize value {default!r} with the plain serializer; excluding default from JSON schema', + ) + return json_schema + + # Sort set/frozenset defaults to ensure deterministic JSON schema generation + # We only sort if len > 1 because sets of size 0 or 1 are already deterministic + if isinstance(default, collections.abc.Set) and len(default) > 1: + try: + default = sorted(default) + except TypeError: # pragma: no cover + # If items aren't comparable (e.g. mixed types), we can't sort them. + pass + + try: + encoded_default = self.encode_default(default) + except pydantic_core.PydanticSerializationError: + self.emit_warning( + 'non-serializable-default', + f'Default value {default} is not JSON serializable; excluding default from JSON schema', + ) + # Return the inner schema, as though there was no default + return json_schema + + json_schema['default'] = encoded_default + return json_schema + + def get_default_value(self, schema: core_schema.WithDefaultSchema) -> Any: + """Get the default value to be used when generating a JSON Schema for a core schema with a default. + + The default implementation is to use the statically defined default value. This method can be overridden + if you want to make use of the default factory. + + Args: + schema: The `'with-default'` core schema. + + Returns: + The default value to use, or [`NoDefault`][pydantic.json_schema.NoDefault] if no default + value is available. + """ + return schema.get('default', NoDefault) + + def nullable_schema(self, schema: core_schema.NullableSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that allows null values. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + null_schema = {'type': 'null'} + inner_json_schema = self.generate_inner(schema['schema']) + + if inner_json_schema == null_schema: + return null_schema + else: + return self.get_union_of_schemas([inner_json_schema, null_schema]) + + def union_schema(self, schema: core_schema.UnionSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that allows values matching any of the given schemas. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + generated: list[JsonSchemaValue] = [] + + for choice in core_schema.iter_union_choices(schema): + try: + generated.append(self.generate_inner(choice)) + except PydanticOmit: # noqa: PERF203 + continue + except PydanticInvalidForJsonSchema as exc: + self.emit_warning('skipped-choice', exc.message) + if len(generated) == 1: + return generated[0] + return self.get_union_of_schemas(generated) + + def get_union_of_schemas(self, schemas: list[JsonSchemaValue]) -> JsonSchemaValue: + """Returns the JSON Schema representation for the union of the provided JSON Schemas. + + The result depends on the configured `'union_format'`. + + Args: + schemas: The list of JSON Schemas to be included in the union. + + Returns: + The JSON Schema representing the union of schemas. + """ + if self.union_format == 'primitive_type_array': + types: list[str] = [] + for schema in schemas: + schema_types: list[str] | str | None = schema.get('type') + if schema_types is None: + # No type, meaning it can be a ref or an empty schema. + break + if not isinstance(schema_types, list): + schema_types = [schema_types] + if not all(t in _PRIMITIVE_JSON_SCHEMA_TYPES for t in schema_types): + break + if len(schema) != 1: + # We only want to include types that don't have any constraints. For instance, + # if `schemas = [{'type': 'string', 'maxLength': 3}, {'type': 'string', 'minLength': 5}]`, + # we don't want to produce `{'type': 'string', 'maxLength': 3, 'minLength': 5}`. + # Same if we have some metadata (e.g. `title`) on a specific union member, we want to preserve it. + break + + types.extend(schema_types) + else: + # If we got there, all the schemas where valid to be used with the `'primitive_type_array` format + return {'type': list(dict.fromkeys(types))} + + return self.get_flattened_anyof(schemas) + + def tagged_union_schema(self, schema: core_schema.TaggedUnionSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that allows values matching any of the given schemas, where + the schemas are tagged with a discriminator field that indicates which schema should be used to validate + the value. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + generated: dict[str, JsonSchemaValue] = {} + for k, v in schema['choices'].items(): + if isinstance(k, Enum): + k = k.value + try: + # Use str(k) since keys must be strings for json; while not technically correct, + # it's the closest that can be represented in valid JSON + generated[str(k)] = self.generate_inner(v).copy() + except PydanticOmit: + continue + except PydanticInvalidForJsonSchema as exc: + self.emit_warning('skipped-choice', exc.message) + + one_of_choices = _deduplicate_schemas(generated.values()) + json_schema: JsonSchemaValue = {'oneOf': one_of_choices} + + # This reflects the v1 behavior; TODO: we should make it possible to exclude OpenAPI stuff from the JSON schema + openapi_discriminator = self._extract_discriminator(schema, one_of_choices) + if openapi_discriminator is not None: + json_schema['discriminator'] = { + 'propertyName': openapi_discriminator, + 'mapping': {k: v.get('$ref', v) for k, v in generated.items()}, + } + + return json_schema + + def _extract_discriminator( + self, schema: core_schema.TaggedUnionSchema, one_of_choices: list[JsonDict] + ) -> str | None: + """Extract a compatible OpenAPI discriminator from the schema and one_of choices that end up in the final + schema.""" + openapi_discriminator: str | None = None + + if isinstance(schema['discriminator'], str): + return schema['discriminator'] + + if isinstance(schema['discriminator'], list): + # If the discriminator is a single item list containing a string, that is equivalent to the string case + if len(schema['discriminator']) == 1 and isinstance(schema['discriminator'][0], str): + return schema['discriminator'][0] + # When an alias is used that is different from the field name, the discriminator will be a list of single + # str lists, one for the attribute and one for the actual alias. The logic here will work even if there is + # more than one possible attribute, and looks for whether a single alias choice is present as a documented + # property on all choices. If so, that property will be used as the OpenAPI discriminator. + for alias_path in schema['discriminator']: + if not isinstance(alias_path, list): + break # this means that the discriminator is not a list of alias paths + if len(alias_path) != 1: + continue # this means that the "alias" does not represent a single field + alias = alias_path[0] + if not isinstance(alias, str): + continue # this means that the "alias" does not represent a field + alias_is_present_on_all_choices = True + for choice in one_of_choices: + try: + choice = self.resolve_ref_schema(choice) + except RuntimeError as exc: + # TODO: fixme - this is a workaround for the fact that we can't always resolve refs + # for tagged union choices at this point in the schema gen process, we might need to do + # another pass at the end like we do for core schemas + self.emit_warning('skipped-discriminator', str(exc)) + choice = {} + properties = choice.get('properties', {}) + if not isinstance(properties, dict) or alias not in properties: + alias_is_present_on_all_choices = False + break + if alias_is_present_on_all_choices: + openapi_discriminator = alias + break + return openapi_discriminator + + def chain_schema(self, schema: core_schema.ChainSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a core_schema.ChainSchema. + + When generating a schema for validation, we return the validation JSON schema for the first step in the chain. + For serialization, we return the serialization JSON schema for the last step in the chain. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + step_index = 0 if self.mode == 'validation' else -1 # use first step for validation, last for serialization + return self.generate_inner(schema['steps'][step_index]) + + def lax_or_strict_schema(self, schema: core_schema.LaxOrStrictSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that allows values matching either the lax schema or the + strict schema. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + # TODO: Need to read the default value off of model config or whatever + use_strict = schema.get('strict', False) # TODO: replace this default False + # If your JSON schema fails to generate it is probably + # because one of the following two branches failed. + if use_strict: + return self.generate_inner(schema['strict_schema']) + else: + return self.generate_inner(schema['lax_schema']) + + def json_or_python_schema(self, schema: core_schema.JsonOrPythonSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that allows values matching either the JSON schema or the + Python schema. + + The JSON schema is used instead of the Python schema. If you want to use the Python schema, you should override + this method. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['json_schema']) + + def typed_dict_schema(self, schema: core_schema.TypedDictSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a typed dict. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + total = schema.get('total', True) + named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [ + (name, self.field_is_required(field, total), field) + for name, field in schema['fields'].items() + if self.field_is_present(field) + ] + if self.mode == 'serialization': + named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', []))) + cls = schema.get('cls') + config = _get_typed_dict_config(cls) + with self._config_wrapper_stack.push(config): + json_schema = self._named_required_fields_schema(named_required_fields) + + # There's some duplication between `extra_behavior` and + # the config's `extra`/core config's `extra_fields_behavior`. + # However, it is common to manually create TypedDictSchemas, + # where you don't necessarily have a class. + # At runtime, `extra_behavior` takes priority over the config + # for validation, so follow the same for the JSON Schema: + if 'extras_schema' in schema and schema['extras_schema'] != core_schema.any_schema(): + allow_additional_props = self.generate_inner(schema['extras_schema']) + else: + allow_additional_props = True + + if schema.get('extra_behavior') == 'forbid': + json_schema['additionalProperties'] = False + elif schema.get('extra_behavior') == 'allow': + json_schema['additionalProperties'] = allow_additional_props + + if cls is not None: + # `_update_class_schema()` will not override + # `additionalProperties` if already present: + self._update_class_schema(json_schema, cls, config) + elif 'additionalProperties' not in json_schema: + extra = schema.get('config', {}).get('extra_fields_behavior') + if extra == 'forbid': + json_schema['additionalProperties'] = False + elif extra == 'allow': + json_schema['additionalProperties'] = allow_additional_props + + return json_schema + + @staticmethod + def _name_required_computed_fields( + computed_fields: list[ComputedField], + ) -> list[tuple[str, bool, core_schema.ComputedField]]: + return [(field['property_name'], True, field) for field in computed_fields] + + def _named_required_fields_schema( + self, named_required_fields: Sequence[tuple[str, bool, CoreSchemaField]] + ) -> JsonSchemaValue: + properties: dict[str, JsonSchemaValue] = {} + required_fields: list[str] = [] + for name, required, field in named_required_fields: + if self.by_alias: + name = self._get_alias_name(field, name) + try: + field_json_schema = self.generate_inner(field).copy() + except PydanticOmit: + continue + if 'title' not in field_json_schema and self.field_title_should_be_set(field): + title = self.get_title_from_name(name) + field_json_schema['title'] = title + field_json_schema = self.handle_ref_overrides(field_json_schema) + properties[name] = field_json_schema + if required: + required_fields.append(name) + + json_schema = {'type': 'object', 'properties': properties} + if required_fields: + json_schema['required'] = required_fields + return json_schema + + def _get_alias_name(self, field: CoreSchemaField, name: str) -> str: + if field['type'] == 'computed-field': + alias: Any = field.get('alias', name) + elif self.mode == 'validation': + alias = field.get('validation_alias', name) + else: + alias = field.get('serialization_alias', name) + if isinstance(alias, str): + name = alias + elif isinstance(alias, list): + alias = cast('list[str] | str', alias) + for path in alias: + if isinstance(path, list) and len(path) == 1 and isinstance(path[0], str): + # Use the first valid single-item string path; the code that constructs the alias array + # should ensure the first such item is what belongs in the JSON schema + name = path[0] + break + else: + assert_never(alias) + return name + + def typed_dict_field_schema(self, schema: core_schema.TypedDictField) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a typed dict field. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['schema']) + + def dataclass_field_schema(self, schema: core_schema.DataclassField) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a dataclass field. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['schema']) + + def model_field_schema(self, schema: core_schema.ModelField) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a model field. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['schema']) + + def computed_field_schema(self, schema: core_schema.ComputedField) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a computed field. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['return_schema']) + + def model_schema(self, schema: core_schema.ModelSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a model. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + # We do not use schema['model'].model_json_schema() here + # because it could lead to inconsistent refs handling, etc. + cls = cast('type[BaseModel]', schema['cls']) + config = cls.model_config + + with self._config_wrapper_stack.push(config): + json_schema = self.generate_inner(schema['schema']) + + self._update_class_schema(json_schema, cls, config) + + return json_schema + + def _update_class_schema(self, json_schema: JsonSchemaValue, cls: type[Any], config: ConfigDict) -> None: + """Update json_schema with the following, extracted from `config` and `cls`: + + * title + * description + * additional properties + * json_schema_extra + * deprecated + + Done in place, hence there's no return value as the original json_schema is mutated. + No ref resolving is involved here, as that's not appropriate for simple updates. + """ + from ._internal._dataclasses import is_stdlib_dataclass + from .main import BaseModel + from .root_model import RootModel + + if (config_title := config.get('title')) is not None: + json_schema.setdefault('title', config_title) + elif model_title_generator := config.get('model_title_generator'): + title = model_title_generator(cls) + if not isinstance(title, str): + raise TypeError(f'model_title_generator {model_title_generator} must return str, not {title.__class__}') + json_schema.setdefault('title', title) + if 'title' not in json_schema: + json_schema['title'] = cls.__name__ + + # BaseModel and dataclasses; don't use cls.__doc__ as it will contain the verbose class signature by default + if cls is BaseModel: + docstring = None + elif is_stdlib_dataclass(cls): # For Pydantic dataclasses, we already handle this at class creation + # The `dataclass` module generates a `__doc__` based on the `inspect.signature()` + # result, which we don't want to use as a description. Such `__doc__` startswith + # `cls.__name__(`, which could lead to mistakenly discarding it if for some reason + # an explicitly set class docstring follows the same pattern, but this is unlikely + # to happen. + doc = cls.__doc__ + docstring = None if doc is None or doc.startswith(f'{cls.__name__}(') else doc + else: + docstring = cls.__doc__ + + if docstring: + json_schema.setdefault('description', inspect.cleandoc(docstring)) + elif issubclass(cls, RootModel) and (root_description := cls.__pydantic_fields__['root'].description): + json_schema.setdefault('description', root_description) + + extra = config.get('extra') + if 'additionalProperties' not in json_schema: # This check is particularly important for `typed_dict_schema()` + if extra == 'allow': + json_schema['additionalProperties'] = True + elif extra == 'forbid': + json_schema['additionalProperties'] = False + + json_schema_extra = config.get('json_schema_extra') + if issubclass(cls, BaseModel) and cls.__pydantic_root_model__: + root_json_schema_extra = cls.model_fields['root'].json_schema_extra + if json_schema_extra and root_json_schema_extra: + raise ValueError( + '"model_config[\'json_schema_extra\']" and "Field.json_schema_extra" on "RootModel.root"' + ' field must not be set simultaneously' + ) + if root_json_schema_extra: + json_schema_extra = root_json_schema_extra + + if isinstance(json_schema_extra, (staticmethod, classmethod)): + # In older versions of python, this is necessary to ensure staticmethod/classmethods are callable + json_schema_extra = json_schema_extra.__get__(cls) + + if isinstance(json_schema_extra, dict): + json_schema.update(json_schema_extra) + elif callable(json_schema_extra): + if len(_typing_extra.signature_no_eval(json_schema_extra).parameters) > 1: + json_schema_extra = cast(Callable[[JsonDict, type[Any]], None], json_schema_extra) + json_schema_extra(json_schema, cls) + else: + json_schema_extra = cast(Callable[[JsonDict], None], json_schema_extra) + json_schema_extra(json_schema) + elif json_schema_extra is not None: + raise ValueError( + f"model_config['json_schema_extra']={json_schema_extra} should be a dict, callable, or None" + ) + + if hasattr(cls, '__deprecated__'): + json_schema['deprecated'] = True + + def resolve_ref_schema(self, json_schema: JsonSchemaValue) -> JsonSchemaValue: + """Resolve a JsonSchemaValue to the non-ref schema if it is a $ref schema. + + Args: + json_schema: The schema to resolve. + + Returns: + The resolved schema. + + Raises: + RuntimeError: If the schema reference can't be found in definitions. + """ + while '$ref' in json_schema: + ref = json_schema['$ref'] + schema_to_update = self.get_schema_from_definitions(JsonRef(ref)) + if schema_to_update is None: + raise RuntimeError(f'Cannot update undefined schema for $ref={ref}') + json_schema = schema_to_update + return json_schema + + def model_fields_schema(self, schema: core_schema.ModelFieldsSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a model's fields. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [ + (name, self.field_is_required(field, total=True), field) + for name, field in schema['fields'].items() + if self.field_is_present(field) + ] + if self.mode == 'serialization': + named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', []))) + json_schema = self._named_required_fields_schema(named_required_fields) + extras_schema = schema.get('extras_schema', None) + if extras_schema is not None: + schema_to_update = self.resolve_ref_schema(json_schema) + schema_to_update['additionalProperties'] = self.generate_inner(extras_schema) + return json_schema + + def field_is_present(self, field: CoreSchemaField) -> bool: + """Whether the field should be included in the generated JSON schema. + + Args: + field: The schema for the field itself. + + Returns: + `True` if the field should be included in the generated JSON schema, `False` otherwise. + """ + if self.mode == 'serialization': + # If you still want to include the field in the generated JSON schema, + # override this method and return True + return not field.get('serialization_exclude') + elif self.mode == 'validation': + return True + else: + assert_never(self.mode) + + def field_is_required( + self, + field: core_schema.ModelField | core_schema.DataclassField | core_schema.TypedDictField, + total: bool, + ) -> bool: + """Whether the field should be marked as required in the generated JSON schema. + (Note that this is irrelevant if the field is not present in the JSON schema.). + + Args: + field: The schema for the field itself. + total: Only applies to `TypedDictField`s. + Indicates if the `TypedDict` this field belongs to is total, in which case any fields that don't + explicitly specify `required=False` are required. + + Returns: + `True` if the field should be marked as required in the generated JSON schema, `False` otherwise. + """ + if field['type'] == 'typed-dict-field': + required = field.get('required', total) + else: + required = field['schema']['type'] != 'default' + + if self.mode == 'serialization': + has_exclude_if = field.get('serialization_exclude_if') is not None + if self._config.json_schema_serialization_defaults_required: + return not has_exclude_if + else: + return required and not has_exclude_if + else: + return required + + def dataclass_args_schema(self, schema: core_schema.DataclassArgsSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a dataclass's constructor arguments. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + named_required_fields: list[tuple[str, bool, CoreSchemaField]] = [ + (field['name'], self.field_is_required(field, total=True), field) + for field in schema['fields'] + if self.field_is_present(field) + ] + if self.mode == 'serialization': + named_required_fields.extend(self._name_required_computed_fields(schema.get('computed_fields', []))) + return self._named_required_fields_schema(named_required_fields) + + def dataclass_schema(self, schema: core_schema.DataclassSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a dataclass. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + + cls = schema['cls'] + config = cast('ConfigDict', getattr(cls, '__pydantic_config__', {})) + + with self._config_wrapper_stack.push(config): + json_schema = self.generate_inner(schema['schema']).copy() + + self._update_class_schema(json_schema, cls, config) + + return json_schema + + def arguments_schema(self, schema: core_schema.ArgumentsSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a function's arguments. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + prefer_positional = schema.get('metadata', {}).get('pydantic_js_prefer_positional_arguments') + + arguments = schema['arguments_schema'] + kw_only_arguments = [a for a in arguments if a.get('mode') == 'keyword_only'] + kw_or_p_arguments = [a for a in arguments if a.get('mode') in {'positional_or_keyword', None}] + p_only_arguments = [a for a in arguments if a.get('mode') == 'positional_only'] + var_args_schema = schema.get('var_args_schema') + var_kwargs_schema = schema.get('var_kwargs_schema') + + if prefer_positional: + positional_possible = not kw_only_arguments and not var_kwargs_schema + if positional_possible: + return self.p_arguments_schema(p_only_arguments + kw_or_p_arguments, var_args_schema) + + keyword_possible = not p_only_arguments and not var_args_schema + if keyword_possible: + return self.kw_arguments_schema(kw_or_p_arguments + kw_only_arguments, var_kwargs_schema) + + if not prefer_positional: + positional_possible = not kw_only_arguments and not var_kwargs_schema + if positional_possible: + return self.p_arguments_schema(p_only_arguments + kw_or_p_arguments, var_args_schema) + + raise PydanticInvalidForJsonSchema( + 'Unable to generate JSON schema for arguments validator with positional-only and keyword-only arguments' + ) + + def kw_arguments_schema( + self, arguments: list[core_schema.ArgumentsParameter], var_kwargs_schema: CoreSchema | None + ) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a function's keyword arguments. + + Args: + arguments: The core schema. + + Returns: + The generated JSON schema. + """ + properties: dict[str, JsonSchemaValue] = {} + required: list[str] = [] + for argument in arguments: + name = self.get_argument_name(argument) + argument_schema = self.generate_inner(argument['schema']).copy() + if 'title' not in argument_schema and self.field_title_should_be_set(argument['schema']): + argument_schema['title'] = self.get_title_from_name(name) + properties[name] = argument_schema + + if argument['schema']['type'] != 'default': + # This assumes that if the argument has a default value, + # the inner schema must be of type WithDefaultSchema. + # I believe this is true, but I am not 100% sure + required.append(name) + + json_schema: JsonSchemaValue = {'type': 'object', 'properties': properties} + if required: + json_schema['required'] = required + + if var_kwargs_schema: + additional_properties_schema = self.generate_inner(var_kwargs_schema) + if additional_properties_schema: + json_schema['additionalProperties'] = additional_properties_schema + else: + json_schema['additionalProperties'] = False + return json_schema + + def p_arguments_schema( + self, arguments: list[core_schema.ArgumentsParameter], var_args_schema: CoreSchema | None + ) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a function's positional arguments. + + Args: + arguments: The core schema. + + Returns: + The generated JSON schema. + """ + prefix_items: list[JsonSchemaValue] = [] + min_items = 0 + + for argument in arguments: + name = self.get_argument_name(argument) + + argument_schema = self.generate_inner(argument['schema']).copy() + if 'title' not in argument_schema and self.field_title_should_be_set(argument['schema']): + argument_schema['title'] = self.get_title_from_name(name) + prefix_items.append(argument_schema) + + if argument['schema']['type'] != 'default': + # This assumes that if the argument has a default value, + # the inner schema must be of type WithDefaultSchema. + # I believe this is true, but I am not 100% sure + min_items += 1 + + json_schema: JsonSchemaValue = {'type': 'array'} + if prefix_items: + json_schema['prefixItems'] = prefix_items + if min_items: + json_schema['minItems'] = min_items + + if var_args_schema: + items_schema = self.generate_inner(var_args_schema) + if items_schema: + json_schema['items'] = items_schema + else: + json_schema['maxItems'] = len(prefix_items) + + return json_schema + + def get_argument_name(self, argument: core_schema.ArgumentsParameter | core_schema.ArgumentsV3Parameter) -> str: + """Retrieves the name of an argument. + + Args: + argument: The core schema. + + Returns: + The name of the argument. + """ + name = argument['name'] + if self.by_alias: + alias = argument.get('alias') + if isinstance(alias, str): + name = alias + else: + pass # might want to do something else? + return name + + def arguments_v3_schema(self, schema: core_schema.ArgumentsV3Schema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a function's arguments. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + arguments = schema['arguments_schema'] + properties: dict[str, JsonSchemaValue] = {} + required: list[str] = [] + for argument in arguments: + mode = argument.get('mode', 'positional_or_keyword') + name = self.get_argument_name(argument) + argument_schema = self.generate_inner(argument['schema']).copy() + if mode == 'var_args': + argument_schema = {'type': 'array', 'items': argument_schema} + elif mode == 'var_kwargs_uniform': + argument_schema = {'type': 'object', 'additionalProperties': argument_schema} + + argument_schema.setdefault('title', self.get_title_from_name(name)) + properties[name] = argument_schema + + if ( + (mode == 'var_kwargs_unpacked_typed_dict' and 'required' in argument_schema) + or mode not in {'var_args', 'var_kwargs_uniform', 'var_kwargs_unpacked_typed_dict'} + and argument['schema']['type'] != 'default' + ): + # This assumes that if the argument has a default value, + # the inner schema must be of type WithDefaultSchema. + # I believe this is true, but I am not 100% sure + required.append(name) + + json_schema: JsonSchemaValue = {'type': 'object', 'properties': properties} + if required: + json_schema['required'] = required + return json_schema + + def call_schema(self, schema: core_schema.CallSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a function call. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['arguments_schema']) + + def custom_error_schema(self, schema: core_schema.CustomErrorSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a custom error. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return self.generate_inner(schema['schema']) + + def json_schema(self, schema: core_schema.JsonSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a JSON object. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + content_core_schema = schema.get('schema') or core_schema.any_schema() + content_json_schema = self.generate_inner(content_core_schema) + if self.mode == 'validation': + return {'type': 'string', 'contentMediaType': 'application/json', 'contentSchema': content_json_schema} + else: + # self.mode == 'serialization' + return content_json_schema + + def url_schema(self, schema: core_schema.UrlSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a URL. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + json_schema = {'type': 'string', 'format': 'uri', 'minLength': 1} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.string) + return json_schema + + def multi_host_url_schema(self, schema: core_schema.MultiHostUrlSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a URL that can be used with multiple hosts. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + # Note: 'multi-host-uri' is a custom/pydantic-specific format, not part of the JSON Schema spec + json_schema = {'type': 'string', 'format': 'multi-host-uri', 'minLength': 1} + self.update_with_validations(json_schema, schema, self.ValidationsMapping.string) + return json_schema + + def uuid_schema(self, schema: core_schema.UuidSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a UUID. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'string', 'format': 'uuid'} + + def definitions_schema(self, schema: core_schema.DefinitionsSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that defines a JSON object with definitions. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + for definition in schema['definitions']: + try: + self.generate_inner(definition) + except PydanticInvalidForJsonSchema as e: # noqa: PERF203 + core_ref: CoreRef = CoreRef(definition['ref']) # type: ignore + self._core_defs_invalid_for_json_schema[self.get_defs_ref((core_ref, self.mode))] = e + continue + return self.generate_inner(schema['schema']) + + def definition_ref_schema(self, schema: core_schema.DefinitionReferenceSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a schema that references a definition. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + core_ref = CoreRef(schema['schema_ref']) + _, ref_json_schema = self.get_cache_defs_ref_schema(core_ref) + return ref_json_schema + + def ser_schema( + self, schema: core_schema.SerSchema | core_schema.IncExSeqSerSchema | core_schema.IncExDictSerSchema + ) -> JsonSchemaValue | None: + """Generates a JSON schema that matches a schema that defines a serialized object. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + schema_type = schema['type'] + if schema_type == 'function-plain' or schema_type == 'function-wrap': + # PlainSerializerFunctionSerSchema or WrapSerializerFunctionSerSchema + return_schema = schema.get('return_schema') + if return_schema is not None: + return self.generate_inner(return_schema) + elif schema_type == 'format' or schema_type == 'to-string': + # FormatSerSchema or ToStringSerSchema + return self.str_schema(core_schema.str_schema()) + elif schema['type'] == 'model': + # ModelSerSchema + return self.generate_inner(schema['schema']) + return None + + def complex_schema(self, schema: core_schema.ComplexSchema) -> JsonSchemaValue: + """Generates a JSON schema that matches a complex number. + + JSON has no standard way to represent complex numbers. Complex number is not a numeric + type. Here we represent complex number as strings following the rule defined by Python. + For instance, '1+2j' is an accepted complex string. Details can be found in + [Python's `complex` documentation][complex]. + + Args: + schema: The core schema. + + Returns: + The generated JSON schema. + """ + return {'type': 'string'} + + # ### Utility methods + + def get_title_from_name(self, name: str) -> str: + """Retrieves a title from a name. + + Args: + name: The name to retrieve a title from. + + Returns: + The title. + """ + return name.title().replace('_', ' ').strip() + + def field_title_should_be_set(self, schema: CoreSchemaOrField) -> bool: + """Returns true if a field with the given schema should have a title set based on the field name. + + Intuitively, we want this to return true for schemas that wouldn't otherwise provide their own title + (e.g., int, float, str), and false for those that would (e.g., BaseModel subclasses). + + Args: + schema: The schema to check. + + Returns: + `True` if the field should have a title set, `False` otherwise. + """ + if _core_utils.is_core_schema_field(schema): + if schema['type'] == 'computed-field': + field_schema = schema['return_schema'] + else: + field_schema = schema['schema'] + return self.field_title_should_be_set(field_schema) + + elif _core_utils.is_core_schema(schema): + if schema.get('ref'): # things with refs, such as models and enums, should not have titles set + return False + if schema['type'] in {'default', 'nullable', 'definitions'}: + return self.field_title_should_be_set(schema['schema']) # type: ignore[typeddict-item] + if _core_utils.is_function_with_inner_schema(schema): + return self.field_title_should_be_set(schema['schema']) + if schema['type'] == 'definition-ref': + # Referenced schemas should not have titles set for the same reason + # schemas with refs should not + return False + return True # anything else should have title set + + else: + raise PydanticInvalidForJsonSchema(f'Unexpected schema type: schema={schema}') # pragma: no cover + + def normalize_name(self, name: str) -> str: + """Normalizes a name to be used as a key in a dictionary. + + Args: + name: The name to normalize. + + Returns: + The normalized name. + """ + return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name).replace('.', '__') + + def get_defs_ref(self, core_mode_ref: CoreModeRef) -> DefsRef: + """Override this method to change the way that definitions keys are generated from a core reference. + + Args: + core_mode_ref: The core reference. + + Returns: + The definitions key. + """ + # Split the core ref into "components"; generic origins and arguments are each separate components + core_ref, mode = core_mode_ref + components = re.split(r'([\][,])', core_ref) + # Remove IDs from each component + components = [x.rsplit(':', 1)[0] for x in components] + core_ref_no_id = ''.join(components) + # Remove everything before the last period from each "component" + components = [re.sub(r'(?:[^.[\]]+\.)+((?:[^.[\]]+))', r'\1', x) for x in components] + short_ref = ''.join(components) + + mode_title = _MODE_TITLE_MAPPING[mode] + + # It is important that the generated defs_ref values be such that at least one choice will not + # be generated for any other core_ref. Currently, this should be the case because we include + # the id of the source type in the core_ref + name = DefsRef(self.normalize_name(short_ref)) + name_mode = DefsRef(self.normalize_name(short_ref) + f'-{mode_title}') + module_qualname = DefsRef(self.normalize_name(core_ref_no_id)) + module_qualname_mode = DefsRef(f'{module_qualname}-{mode_title}') + module_qualname_id = DefsRef(self.normalize_name(core_ref)) + occurrence_index = self._collision_index.get(module_qualname_id) + if occurrence_index is None: + self._collision_counter[module_qualname] += 1 + occurrence_index = self._collision_index[module_qualname_id] = self._collision_counter[module_qualname] + + module_qualname_occurrence = DefsRef(f'{module_qualname}__{occurrence_index}') + module_qualname_occurrence_mode = DefsRef(f'{module_qualname_mode}__{occurrence_index}') + + self._prioritized_defsref_choices[module_qualname_occurrence_mode] = [ + name, + name_mode, + module_qualname, + module_qualname_mode, + module_qualname_occurrence, + module_qualname_occurrence_mode, + ] + + return module_qualname_occurrence_mode + + def get_cache_defs_ref_schema(self, core_ref: CoreRef) -> tuple[DefsRef, JsonSchemaValue]: + """This method wraps the get_defs_ref method with some cache-lookup/population logic, + and returns both the produced defs_ref and the JSON schema that will refer to the right definition. + + Args: + core_ref: The core reference to get the definitions reference for. + + Returns: + A tuple of the definitions reference and the JSON schema that will refer to it. + """ + core_mode_ref = (core_ref, self.mode) + maybe_defs_ref = self.core_to_defs_refs.get(core_mode_ref) + if maybe_defs_ref is not None: + json_ref = self.core_to_json_refs[core_mode_ref] + return maybe_defs_ref, {'$ref': json_ref} + + defs_ref = self.get_defs_ref(core_mode_ref) + + # populate the ref translation mappings + self.core_to_defs_refs[core_mode_ref] = defs_ref + self.defs_to_core_refs[defs_ref] = core_mode_ref + + json_ref = JsonRef(self.ref_template.format(model=defs_ref)) + self.core_to_json_refs[core_mode_ref] = json_ref + self.json_to_defs_refs[json_ref] = defs_ref + ref_json_schema = {'$ref': json_ref} + return defs_ref, ref_json_schema + + def handle_ref_overrides(self, json_schema: JsonSchemaValue) -> JsonSchemaValue: + """Remove any sibling keys that are redundant with the referenced schema. + + Args: + json_schema: The schema to remove redundant sibling keys from. + + Returns: + The schema with redundant sibling keys removed. + """ + if '$ref' in json_schema: + # prevent modifications to the input; this copy may be safe to drop if there is significant overhead + json_schema = json_schema.copy() + + referenced_json_schema = self.get_schema_from_definitions(JsonRef(json_schema['$ref'])) + if referenced_json_schema is None: + # This can happen when building schemas for models with not-yet-defined references. + # It may be a good idea to do a recursive pass at the end of the generation to remove + # any redundant override keys. + return json_schema + for k, v in list(json_schema.items()): + if k == '$ref': + continue + if k in referenced_json_schema and referenced_json_schema[k] == v: + del json_schema[k] # redundant key + + return json_schema + + def get_schema_from_definitions(self, json_ref: JsonRef) -> JsonSchemaValue | None: + try: + def_ref = self.json_to_defs_refs[json_ref] + if def_ref in self._core_defs_invalid_for_json_schema: + raise self._core_defs_invalid_for_json_schema[def_ref] + return self.definitions.get(def_ref, None) + except KeyError: + if json_ref.startswith(('http://', 'https://')): + return None + raise + + def encode_default(self, dft: Any) -> Any: + """Encode a default value to a JSON-serializable value. + + This is used to encode default values for fields in the generated JSON schema. + + Args: + dft: The default value to encode. + + Returns: + The encoded default value. + """ + from .type_adapter import TypeAdapter, _type_has_config + + config = self._config + try: + default = ( + dft + if _type_has_config(type(dft)) + else TypeAdapter(type(dft), config=config.config_dict).dump_python( + dft, by_alias=self.by_alias, mode='json' + ) + ) + except PydanticSchemaGenerationError: + raise pydantic_core.PydanticSerializationError(f'Unable to encode default value {dft}') + + return pydantic_core.to_jsonable_python( + default, timedelta_mode=config.ser_json_timedelta, bytes_mode=config.ser_json_bytes, by_alias=self.by_alias + ) + + def update_with_validations( + self, json_schema: JsonSchemaValue, core_schema: CoreSchema, mapping: dict[str, str] + ) -> None: + """Update the json_schema with the corresponding validations specified in the core_schema, + using the provided mapping to translate keys in core_schema to the appropriate keys for a JSON schema. + + Args: + json_schema: The JSON schema to update. + core_schema: The core schema to get the validations from. + mapping: A mapping from core_schema attribute names to the corresponding JSON schema attribute names. + """ + for core_key, json_schema_key in mapping.items(): + if core_key in core_schema: + json_schema[json_schema_key] = core_schema[core_key] + + class ValidationsMapping: + """This class just contains mappings from core_schema attribute names to the corresponding + JSON schema attribute names. While I suspect it is unlikely to be necessary, you can in + principle override this class in a subclass of GenerateJsonSchema (by inheriting from + GenerateJsonSchema.ValidationsMapping) to change these mappings. + """ + + numeric = { + 'multiple_of': 'multipleOf', + 'le': 'maximum', + 'ge': 'minimum', + 'lt': 'exclusiveMaximum', + 'gt': 'exclusiveMinimum', + } + bytes = { + 'min_length': 'minLength', + 'max_length': 'maxLength', + } + string = { + 'min_length': 'minLength', + 'max_length': 'maxLength', + 'pattern': 'pattern', + } + array = { + 'min_length': 'minItems', + 'max_length': 'maxItems', + } + object = { + 'min_length': 'minProperties', + 'max_length': 'maxProperties', + } + + def get_flattened_anyof(self, schemas: list[JsonSchemaValue]) -> JsonSchemaValue: + members = [] + for schema in schemas: + if len(schema) == 1 and 'anyOf' in schema: + members.extend(schema['anyOf']) + else: + members.append(schema) + members = _deduplicate_schemas(members) + if len(members) == 1: + return members[0] + return {'anyOf': members} + + def get_json_ref_counts(self, json_schema: JsonSchemaValue) -> dict[JsonRef, int]: + """Get all values corresponding to the key '$ref' anywhere in the json_schema.""" + json_refs: dict[JsonRef, int] = Counter() + + def _add_json_refs(schema: Any) -> None: + if isinstance(schema, dict): + if '$ref' in schema: + json_ref = JsonRef(schema['$ref']) + if not isinstance(json_ref, str): + return # in this case, '$ref' might have been the name of a property + already_visited = json_ref in json_refs + json_refs[json_ref] += 1 + if already_visited: + return # prevent recursion on a definition that was already visited + try: + defs_ref = self.json_to_defs_refs[json_ref] + if defs_ref in self._core_defs_invalid_for_json_schema: + raise self._core_defs_invalid_for_json_schema[defs_ref] + _add_json_refs(self.definitions[defs_ref]) + except KeyError: + if not json_ref.startswith(('http://', 'https://')): + raise + + for k, v in schema.items(): + if k == 'examples' and isinstance(v, list): + # Skip examples that may contain arbitrary values and references + # (see the comment in `_get_all_json_refs` for more details). + continue + _add_json_refs(v) + elif isinstance(schema, list): + for v in schema: + _add_json_refs(v) + + _add_json_refs(json_schema) + return json_refs + + def handle_invalid_for_json_schema(self, schema: CoreSchemaOrField, error_info: str) -> JsonSchemaValue: + raise PydanticInvalidForJsonSchema(f'Cannot generate a JsonSchema for {error_info}') + + def emit_warning(self, kind: JsonSchemaWarningKind, detail: str) -> None: + """This method simply emits PydanticJsonSchemaWarnings based on handling in the `warning_message` method.""" + message = self.render_warning_message(kind, detail) + if message is not None: + warnings.warn(message, PydanticJsonSchemaWarning) + + def render_warning_message(self, kind: JsonSchemaWarningKind, detail: str) -> str | None: + """This method is responsible for ignoring warnings as desired, and for formatting the warning messages. + + You can override the value of `ignored_warning_kinds` in a subclass of GenerateJsonSchema + to modify what warnings are generated. If you want more control, you can override this method; + just return None in situations where you don't want warnings to be emitted. + + Args: + kind: The kind of warning to render. It can be one of the following: + + - 'skipped-choice': A choice field was skipped because it had no valid choices. + - 'non-serializable-default': A default value was skipped because it was not JSON-serializable. + detail: A string with additional details about the warning. + + Returns: + The formatted warning message, or `None` if no warning should be emitted. + """ + if kind in self.ignored_warning_kinds: + return None + return f'{detail} [{kind}]' + + def _build_definitions_remapping(self) -> _DefinitionsRemapping: + defs_to_json: dict[DefsRef, JsonRef] = {} + for defs_refs in self._prioritized_defsref_choices.values(): + for defs_ref in defs_refs: + json_ref = JsonRef(self.ref_template.format(model=defs_ref)) + defs_to_json[defs_ref] = json_ref + + return _DefinitionsRemapping.from_prioritized_choices( + self._prioritized_defsref_choices, defs_to_json, self.definitions + ) + + def _garbage_collect_definitions(self, schema: JsonSchemaValue) -> None: + visited_defs_refs: set[DefsRef] = set() + unvisited_json_refs = _get_all_json_refs(schema) + while unvisited_json_refs: + next_json_ref = unvisited_json_refs.pop() + try: + next_defs_ref = self.json_to_defs_refs[next_json_ref] + if next_defs_ref in visited_defs_refs: + continue + visited_defs_refs.add(next_defs_ref) + unvisited_json_refs.update(_get_all_json_refs(self.definitions[next_defs_ref])) + except KeyError: + if not next_json_ref.startswith(('http://', 'https://')): + raise + + self.definitions = {k: v for k, v in self.definitions.items() if k in visited_defs_refs} + + +# ##### Start JSON Schema Generation Functions ##### + + +def model_json_schema( + cls: type[BaseModel] | type[PydanticDataclass], + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + union_format: Literal['any_of', 'primitive_type_array'] = 'any_of', + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + mode: JsonSchemaMode = 'validation', +) -> dict[str, Any]: + """Utility function to generate a JSON Schema for a model. + + Args: + cls: The model class to generate a JSON Schema for. + by_alias: If `True` (the default), fields will be serialized according to their alias. + If `False`, fields will be serialized according to their attribute name. + ref_template: The template to use for generating JSON Schema references. + union_format: The format to use when combining schemas from unions together. Can be one of: + + - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf) + keyword to combine schemas (the default). + - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type) + keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive + type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to + `any_of`. + schema_generator: The class to use for generating the JSON Schema. + mode: The mode to use for generating the JSON Schema. It can be one of the following: + + - 'validation': Generate a JSON Schema for validating data. + - 'serialization': Generate a JSON Schema for serializing data. + + Returns: + The generated JSON Schema. + """ + from .main import BaseModel + + schema_generator_instance = schema_generator( + by_alias=by_alias, ref_template=ref_template, union_format=union_format + ) + + if isinstance(cls.__pydantic_core_schema__, _mock_val_ser.MockCoreSchema): + cls.__pydantic_core_schema__.rebuild() + + if cls is BaseModel: + raise AttributeError('model_json_schema() must be called on a subclass of BaseModel, not BaseModel itself.') + + assert not isinstance(cls.__pydantic_core_schema__, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it' + return schema_generator_instance.generate(cls.__pydantic_core_schema__, mode=mode) + + +def models_json_schema( + models: Sequence[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode]], + *, + by_alias: bool = True, + title: str | None = None, + description: str | None = None, + ref_template: str = DEFAULT_REF_TEMPLATE, + union_format: Literal['any_of', 'primitive_type_array'] = 'any_of', + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, +) -> tuple[dict[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]: + """Utility function to generate a JSON Schema for multiple models. + + Args: + models: A sequence of tuples of the form (model, mode). + by_alias: Whether field aliases should be used as keys in the generated JSON Schema. + title: The title of the generated JSON Schema. + description: The description of the generated JSON Schema. + ref_template: The reference template to use for generating JSON Schema references. + union_format: The format to use when combining schemas from unions together. Can be one of: + + - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf) + keyword to combine schemas (the default). + - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type) + keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive + type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to + `any_of`. + schema_generator: The schema generator to use for generating the JSON Schema. + + Returns: + A tuple where: + - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and + whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have + JsonRef references to definitions that are defined in the second returned element.) + - The second element is a JSON schema containing all definitions referenced in the first returned + element, along with the optional title and description keys. + """ + for cls, _ in models: + if isinstance(cls.__pydantic_core_schema__, _mock_val_ser.MockCoreSchema): + cls.__pydantic_core_schema__.rebuild() + + instance = schema_generator(by_alias=by_alias, ref_template=ref_template, union_format=union_format) + inputs: list[tuple[type[BaseModel] | type[PydanticDataclass], JsonSchemaMode, CoreSchema]] = [ + (m, mode, m.__pydantic_core_schema__) for m, mode in models + ] + json_schemas_map, definitions = instance.generate_definitions(inputs) + + json_schema: dict[str, Any] = {} + if definitions: + json_schema['$defs'] = definitions + if title: + json_schema['title'] = title + if description: + json_schema['description'] = description + + return json_schemas_map, json_schema + + +# ##### End JSON Schema Generation Functions ##### + + +_HashableJsonValue: TypeAlias = Union[ + int, float, str, bool, None, tuple['_HashableJsonValue', ...], tuple[tuple[str, '_HashableJsonValue'], ...] +] + + +def _deduplicate_schemas(schemas: Iterable[JsonDict]) -> list[JsonDict]: + return list({_make_json_hashable(schema): schema for schema in schemas}.values()) + + +def _make_json_hashable(value: JsonValue) -> _HashableJsonValue: + if isinstance(value, dict): + return tuple(sorted((k, _make_json_hashable(v)) for k, v in value.items())) + elif isinstance(value, list): + return tuple(_make_json_hashable(v) for v in value) + else: + return value + + +@dataclasses.dataclass(**_internal_dataclass.slots_true) +class WithJsonSchema: + """!!! abstract "Usage Documentation" + [`WithJsonSchema` Annotation](../concepts/json_schema.md#withjsonschema-annotation) + + An annotation used to override the JSON Schema for a type. + + This is useful when you want to set a JSON Schema for a type that don't produce any JSON Schemas by default + (e.g. [`Callable`][collections.abc.Callable]). + + If `mode` is set this will only apply to that schema generation mode, allowing you to set different JSON Schemas for validation and serialization. + + !!! note + If the `WithJsonSchema` annotation is coupled with the [`Field()`][pydantic.Field] function, the behavior overriding will vary depending on the location: + + * If the [`Annotated`][typing.Annotated] metadata is specified at the "top-level" field, `Field()` metadata arguments + (excluding [constraints](../concepts/fields.md#field-constraints)) such as `title` and `description` will be applied on + top of the `WithJsonSchema`, no matter the order: + + ```python + from typing import Annotated + + from pydantic import BaseModel, Field, WithJsonSchema + + class Model(BaseModel): + field: Annotated[ + int, + Field(title='My Field'), + WithJsonSchema({'type': 'integer', 'extra': 'data'}), + ] + + Model.model_json_schema()['properties']['field'] + #> {'type': 'integer', 'extra': 'data', 'title': 'My Field'} + ``` + + * If the [`Annotated`][typing.Annotated] metadata is specified on a specific inner type, `WithJsonSchema` will unconditionally + override the JSON Schema: + + ```python + from typing import Annotated + + from pydantic import BaseModel, Field, WithJsonSchema + + class Model(BaseModel): + field: list[ + Annotated[ + int, + Field(title='My Field'), + WithJsonSchema({'type': 'integer', 'extra': 'data'}), + ] + ] + + Model.model_json_schema()['properties']['field'] + #> {'items': {'extra': 'data', 'type': 'integer'}, 'title': 'Field', 'type': 'array'} + ``` + + See also the documentation about [the annotated pattern](../concepts/fields.md#the-annotated-pattern). + """ + + json_schema: JsonSchemaValue | None + mode: Literal['validation', 'serialization'] | None = None + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + if self.mode is not None and self.mode != handler.mode: + return handler(core_schema) + if self.json_schema is None: + # This exception is handled in pydantic.json_schema.GenerateJsonSchema._named_required_fields_schema + raise PydanticOmit + else: + return self.json_schema.copy() + + def __hash__(self) -> int: + return hash(type(self.mode)) + + +class Examples: + """Add examples to a JSON schema. + + If the JSON Schema already contains examples, the provided examples + will be appended. + + If `mode` is set this will only apply to that schema generation mode, + allowing you to add different examples for validation and serialization. + """ + + @overload + @deprecated('Using a dict for `examples` is deprecated since v2.9 and will be removed in v3.0. Use a list instead.') + def __init__( + self, examples: dict[str, Any], mode: Literal['validation', 'serialization'] | None = None + ) -> None: ... + + @overload + def __init__(self, examples: list[Any], mode: Literal['validation', 'serialization'] | None = None) -> None: ... + + def __init__( + self, examples: dict[str, Any] | list[Any], mode: Literal['validation', 'serialization'] | None = None + ) -> None: + if isinstance(examples, dict): + warnings.warn( + 'Using a dict for `examples` is deprecated, use a list instead.', + PydanticDeprecatedSince29, + stacklevel=2, + ) + self.examples = examples + self.mode = mode + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + mode = self.mode or handler.mode + json_schema = handler(core_schema) + if mode != handler.mode: + return json_schema + examples = json_schema.get('examples') + if examples is None: + json_schema['examples'] = to_jsonable_python(self.examples) + if isinstance(examples, dict): + if isinstance(self.examples, list): + warnings.warn( + 'Updating existing JSON Schema examples of type dict with examples of type list. ' + 'Only the existing examples values will be retained. Note that dict support for ' + 'examples is deprecated and will be removed in v3.0.', + UserWarning, + ) + json_schema['examples'] = to_jsonable_python( + [ex for value in examples.values() for ex in value] + self.examples + ) + else: + json_schema['examples'] = to_jsonable_python({**examples, **self.examples}) + if isinstance(examples, list): + if isinstance(self.examples, list): + json_schema['examples'] = to_jsonable_python(examples + self.examples) + elif isinstance(self.examples, dict): + warnings.warn( + 'Updating existing JSON Schema examples of type list with examples of type dict. ' + 'Only the examples values will be retained. Note that dict support for ' + 'examples is deprecated and will be removed in v3.0.', + UserWarning, + ) + json_schema['examples'] = to_jsonable_python( + examples + [ex for value in self.examples.values() for ex in value] + ) + + return json_schema + + def __hash__(self) -> int: + return hash(type(self.mode)) + + +def _get_all_json_refs(item: Any) -> set[JsonRef]: + """Get all the definitions references from a JSON schema.""" + refs: set[JsonRef] = set() + stack = [item] + + while stack: + current = stack.pop() + if isinstance(current, dict): + for key, value in current.items(): + if key == 'examples' and isinstance(value, list): + # Skip examples that may contain arbitrary values and references + # (e.g. `{"examples": [{"$ref": "..."}]}`). Note: checking for value + # of type list is necessary to avoid skipping valid portions of the schema, + # for instance when "examples" is used as a property key. A more robust solution + # could be found, but would require more advanced JSON Schema parsing logic. + continue + if key == '$ref' and isinstance(value, str): + refs.add(JsonRef(value)) + elif isinstance(value, dict): + stack.append(value) + elif isinstance(value, list): + stack.extend(value) + elif isinstance(current, list): + stack.extend(current) + + return refs + + +AnyType = TypeVar('AnyType') + +if TYPE_CHECKING: + SkipJsonSchema = Annotated[AnyType, ...] +else: + + @dataclasses.dataclass(**_internal_dataclass.slots_true) + class SkipJsonSchema: + """!!! abstract "Usage Documentation" + [`SkipJsonSchema` Annotation](../concepts/json_schema.md#skipjsonschema-annotation) + + Add this as an annotation on a field to skip generating a JSON schema for that field. + + Example: + ```python + from pprint import pprint + from typing import Union + + from pydantic import BaseModel + from pydantic.json_schema import SkipJsonSchema + + class Model(BaseModel): + a: Union[int, None] = None # (1)! + b: Union[int, SkipJsonSchema[None]] = None # (2)! + c: SkipJsonSchema[Union[int, None]] = None # (3)! + + pprint(Model.model_json_schema()) + ''' + { + 'properties': { + 'a': { + 'anyOf': [ + {'type': 'integer'}, + {'type': 'null'} + ], + 'default': None, + 'title': 'A' + }, + 'b': { + 'default': None, + 'title': 'B', + 'type': 'integer' + } + }, + 'title': 'Model', + 'type': 'object' + } + ''' + ``` + + 1. The integer and null types are both included in the schema for `a`. + 2. The integer type is the only type included in the schema for `b`. + 3. The entirety of the `c` field is omitted from the schema. + """ + + def __class_getitem__(cls, item: AnyType) -> AnyType: + return Annotated[item, cls()] + + def __get_pydantic_json_schema__( + self, core_schema: CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + raise PydanticOmit + + def __hash__(self) -> int: + return hash(type(self)) + + +def _get_typed_dict_config(cls: type[Any] | None) -> ConfigDict: + if cls is not None: + try: + return _decorators.get_attribute_from_bases(cls, '__pydantic_config__') + except AttributeError: + pass + return {} + + +def _get_ser_schema_for_default_value(schema: CoreSchema) -> core_schema.PlainSerializerFunctionSerSchema | None: + """Get a `'function-plain'` serialization schema that can be used to serialize a default value. + + This takes into account having the serialization schema nested under validation schema(s). + """ + if ( + (ser_schema := schema.get('serialization')) + and ser_schema['type'] == 'function-plain' + and not ser_schema.get('info_arg') + ): + return ser_schema + if _core_utils.is_function_with_inner_schema(schema): + return _get_ser_schema_for_default_value(schema['schema']) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/main.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/main.py new file mode 100644 index 0000000000000000000000000000000000000000..2ed62e5f3eded5d1e6b0052130e8d5f8627a60c6 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/main.py @@ -0,0 +1,1838 @@ +"""Logic for creating models.""" + +# Because `dict` is in the local namespace of the `BaseModel` class, we use `Dict` for annotations. +# TODO v3 fallback to `dict` when the deprecated `dict` method gets removed. +# ruff: noqa: UP035 + +from __future__ import annotations as _annotations + +import operator +import sys +import types +import warnings +from collections.abc import Generator, Mapping +from copy import copy, deepcopy +from functools import cached_property +from typing import ( + TYPE_CHECKING, + Any, + Callable, + ClassVar, + Dict, + Generic, + Literal, + TypeVar, + Union, + cast, + overload, +) + +import pydantic_core +import typing_extensions +from pydantic_core import PydanticUndefined, ValidationError +from typing_extensions import Self, TypeAlias, Unpack + +from . import PydanticDeprecatedSince20, PydanticDeprecatedSince211 +from ._internal import ( + _config, + _decorators, + _fields, + _forward_ref, + _generics, + _mock_val_ser, + _model_construction, + _namespace_utils, + _repr, + _typing_extra, + _utils, +) +from ._migration import getattr_migration +from .aliases import AliasChoices, AliasPath +from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler +from .config import ConfigDict, ExtraValues +from .errors import PydanticUndefinedAnnotation, PydanticUserError +from .json_schema import DEFAULT_REF_TEMPLATE, GenerateJsonSchema, JsonSchemaMode, JsonSchemaValue, model_json_schema +from .plugin._schema_validator import PluggableSchemaValidator + +if TYPE_CHECKING: + from inspect import Signature + from pathlib import Path + + from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator + + from ._internal._fields import PydanticExtraInfo + from ._internal._namespace_utils import MappingNamespace + from ._internal._utils import AbstractSetIntStr, MappingIntStrAny + from .deprecated.parse import Protocol as DeprecatedParseProtocol + from .fields import ComputedFieldInfo, FieldInfo, ModelPrivateAttr + + +__all__ = 'BaseModel', 'create_model' + +# Keep these type aliases available at runtime: +TupleGenerator: TypeAlias = Generator[tuple[str, Any], None, None] +# NOTE: In reality, `bool` should be replaced by `Literal[True]` but mypy fails to correctly apply bidirectional +# type inference (e.g. when using `{'a': {'b': True}}`): +# NOTE: Keep this type alias in sync with the stub definition in `pydantic-core`: +IncEx: TypeAlias = Union[set[int], set[str], Mapping[int, Union['IncEx', bool]], Mapping[str, Union['IncEx', bool]]] + +_object_setattr = _model_construction.object_setattr + + +def _check_frozen(model_cls: type[BaseModel], name: str, value: Any) -> None: + if model_cls.model_config.get('frozen'): + error_type = 'frozen_instance' + elif getattr(model_cls.__pydantic_fields__.get(name), 'frozen', False): + error_type = 'frozen_field' + else: + return + + raise ValidationError.from_exception_data( + model_cls.__name__, [{'type': error_type, 'loc': (name,), 'input': value}] + ) + + +def _model_field_setattr_handler(model: BaseModel, name: str, val: Any) -> None: + model.__dict__[name] = val + model.__pydantic_fields_set__.add(name) + + +def _private_setattr_handler(model: BaseModel, name: str, val: Any) -> None: + if getattr(model, '__pydantic_private__', None) is None: + # While the attribute should be present at this point, this may not be the case if + # users do unusual stuff with `model_post_init()` (which is where the `__pydantic_private__` + # is initialized, by wrapping the user-defined `model_post_init()`), e.g. if they mock + # the `model_post_init()` call. Ideally we should find a better way to init private attrs. + object.__setattr__(model, '__pydantic_private__', {}) + model.__pydantic_private__[name] = val # pyright: ignore[reportOptionalSubscript] + + +_SIMPLE_SETATTR_HANDLERS: Mapping[str, Callable[[BaseModel, str, Any], None]] = { + 'model_field': _model_field_setattr_handler, + 'validate_assignment': lambda model, name, val: model.__pydantic_validator__.validate_assignment(model, name, val), # pyright: ignore[reportAssignmentType] + 'private': _private_setattr_handler, + 'cached_property': lambda model, name, val: model.__dict__.__setitem__(name, val), + 'extra_known': lambda model, name, val: _object_setattr(model, name, val), +} + + +class BaseModel(metaclass=_model_construction.ModelMetaclass): + """!!! abstract "Usage Documentation" + [Models](../concepts/models.md) + + A base class for creating Pydantic models. + + Attributes: + __class_vars__: The names of the class variables defined on the model. + __private_attributes__: Metadata about the private attributes of the model. + __signature__: The synthesized `__init__` [`Signature`][inspect.Signature] of the model. + + __pydantic_complete__: Whether model building is completed, or if there are still undefined fields. + __pydantic_core_schema__: The core schema of the model. + __pydantic_custom_init__: Whether the model has a custom `__init__` function. + __pydantic_decorators__: Metadata containing the decorators defined on the model. + This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1. + __pydantic_generic_metadata__: A dictionary containing metadata about generic Pydantic models. + The `origin` and `args` items map to the [`__origin__`][genericalias.__origin__] + and [`__args__`][genericalias.__args__] attributes of [generic aliases][types-genericalias], + and the `parameter` item maps to the `__parameter__` attribute of generic classes. + __pydantic_parent_namespace__: Parent namespace of the model, used for automatic rebuilding of models. + __pydantic_post_init__: The name of the post-init method for the model, if defined. + __pydantic_root_model__: Whether the model is a [`RootModel`][pydantic.root_model.RootModel]. + __pydantic_serializer__: The `pydantic-core` `SchemaSerializer` used to dump instances of the model. + __pydantic_validator__: The `pydantic-core` `SchemaValidator` used to validate instances of the model. + + __pydantic_fields__: A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects. + __pydantic_computed_fields__: A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects. + + __pydantic_extra__: A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] + is set to `'allow'`. + __pydantic_fields_set__: The names of fields explicitly set during instantiation. + __pydantic_private__: Values of private attributes set on the model instance. + """ + + # Note: Many of the below class vars are defined in the metaclass, but we define them here for type checking purposes. + + model_config: ClassVar[ConfigDict] = ConfigDict() + """ + Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict]. + """ + + __class_vars__: ClassVar[set[str]] + """The names of the class variables defined on the model.""" + + __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] # noqa: UP006 + """Metadata about the private attributes of the model.""" + + __signature__: ClassVar[Signature] + """The synthesized `__init__` [`Signature`][inspect.Signature] of the model.""" + + __pydantic_complete__: ClassVar[bool] = False + """Whether model building is completed, or if there are still undefined fields.""" + + __pydantic_core_schema__: ClassVar[CoreSchema] + """The core schema of the model.""" + + __pydantic_custom_init__: ClassVar[bool] + """Whether the model has a custom `__init__` method.""" + + # Must be set for `GenerateSchema.model_schema` to work for a plain `BaseModel` annotation. + __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = _decorators.DecoratorInfos() + """Metadata containing the decorators defined on the model. + This replaces `Model.__validators__` and `Model.__root_validators__` from Pydantic V1.""" + + __pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] + """A dictionary containing metadata about generic Pydantic models. + + The `origin` and `args` items map to the [`__origin__`][genericalias.__origin__] + and [`__args__`][genericalias.__args__] attributes of [generic aliases][types-genericalias], + and the `parameter` item maps to the `__parameter__` attribute of generic classes. + """ + + __pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None # noqa: UP006 + """Parent namespace of the model, used for automatic rebuilding of models.""" + + __pydantic_post_init__: ClassVar[None | Literal['model_post_init']] + """The name of the post-init method for the model, if defined.""" + + __pydantic_root_model__: ClassVar[bool] = False + """Whether the model is a [`RootModel`][pydantic.root_model.RootModel].""" + + __pydantic_serializer__: ClassVar[SchemaSerializer] + """The `pydantic-core` `SchemaSerializer` used to dump instances of the model.""" + + __pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] + """The `pydantic-core` `SchemaValidator` used to validate instances of the model.""" + + __pydantic_fields__: ClassVar[Dict[str, FieldInfo]] # noqa: UP006 + """A dictionary of field names and their corresponding [`FieldInfo`][pydantic.fields.FieldInfo] objects. + This replaces `Model.__fields__` from Pydantic V1. + """ + + __pydantic_setattr_handlers__: ClassVar[Dict[str, Callable[[BaseModel, str, Any], None]]] # noqa: UP006 + """`__setattr__` handlers. Memoizing the handlers leads to a dramatic performance improvement in `__setattr__`""" + + __pydantic_computed_fields__: ClassVar[Dict[str, ComputedFieldInfo]] # noqa: UP006 + """A dictionary of computed field names and their corresponding [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] objects.""" + + __pydantic_extra_info__: ClassVar[PydanticExtraInfo | None] + """A wrapper around the `__pydantic_extra__` annotation, if explicitly annotated on a model. + + This is a private attribute, not meant to be used outside Pydantic. + """ + + __pydantic_extra__: Dict[str, Any] | None = _model_construction.NoInitField(init=False) # noqa: UP006 + """A dictionary containing extra values, if [`extra`][pydantic.config.ConfigDict.extra] is set to `'allow'`.""" + + __pydantic_fields_set__: set[str] = _model_construction.NoInitField(init=False) + """The names of fields explicitly set during instantiation.""" + + __pydantic_private__: Dict[str, Any] | None = _model_construction.NoInitField(init=False) # noqa: UP006 + """Values of private attributes set on the model instance.""" + + if not TYPE_CHECKING: + # Prevent `BaseModel` from being instantiated directly + # (defined in an `if not TYPE_CHECKING` block for clarity and to avoid type checking errors): + __pydantic_core_schema__ = _mock_val_ser.MockCoreSchema( + 'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly', + code='base-model-instantiated', + ) + __pydantic_validator__ = _mock_val_ser.MockValSer( + 'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly', + val_or_ser='validator', + code='base-model-instantiated', + ) + __pydantic_serializer__ = _mock_val_ser.MockValSer( + 'Pydantic models should inherit from BaseModel, BaseModel cannot be instantiated directly', + val_or_ser='serializer', + code='base-model-instantiated', + ) + + __slots__ = '__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__' + + def __init__(self, /, **data: Any) -> None: + """Create a new model by parsing and validating input data from keyword arguments. + + Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be + validated to form a valid model. + + `self` is explicitly positional-only to allow `self` as a field name. + """ + # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks + __tracebackhide__ = True + validated_self = self.__pydantic_validator__.validate_python(data, self_instance=self) + if self is not validated_self: + warnings.warn( + 'A custom validator is returning a value other than `self`.\n' + "Returning anything other than `self` from a top level model validator isn't supported when validating via `__init__`.\n" + 'See the `model_validator` docs (https://docs.pydantic.dev/latest/concepts/validators/#model-validators) for more details.', + stacklevel=2, + ) + + # The following line sets a flag that we use to determine when `__init__` gets overridden by the user + __init__.__pydantic_base_init__ = True # pyright: ignore[reportFunctionMemberAccess] + + @_utils.deprecated_instance_property + @classmethod + def model_fields(cls) -> dict[str, FieldInfo]: + """A mapping of field names to their respective [`FieldInfo`][pydantic.fields.FieldInfo] instances. + + !!! warning + Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. + Instead, you should access this attribute from the model class. + """ + return getattr(cls, '__pydantic_fields__', {}) + + @_utils.deprecated_instance_property + @classmethod + def model_computed_fields(cls) -> dict[str, ComputedFieldInfo]: + """A mapping of computed field names to their respective [`ComputedFieldInfo`][pydantic.fields.ComputedFieldInfo] instances. + + !!! warning + Accessing this attribute from a model instance is deprecated, and will not work in Pydantic V3. + Instead, you should access this attribute from the model class. + """ + return getattr(cls, '__pydantic_computed_fields__', {}) + + @property + def model_extra(self) -> dict[str, Any] | None: + """Get extra fields set during validation. + + Returns: + A dictionary of extra fields, or `None` if `config.extra` is not set to `"allow"`. + """ + return self.__pydantic_extra__ + + @property + def model_fields_set(self) -> set[str]: + """Returns the set of fields that have been explicitly set on this model instance. + + Returns: + A set of strings representing the fields that have been set, + i.e. that were not filled from defaults. + """ + return self.__pydantic_fields_set__ + + @classmethod + def model_construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: C901 + """Creates a new instance of the `Model` class with validated data. + + Creates a new model setting `__dict__` and `__pydantic_fields_set__` from trusted or pre-validated data. + Default values are respected, but no other validation is performed. + + !!! note + `model_construct()` generally respects the `model_config.extra` setting on the provided model. + That is, if `model_config.extra == 'allow'`, then all extra passed values are added to the model instance's `__dict__` + and `__pydantic_extra__` fields. If `model_config.extra == 'ignore'` (the default), then all extra passed values are ignored. + Because no validation is performed with a call to `model_construct()`, having `model_config.extra == 'forbid'` does not result in + an error if extra values are passed, but they will be ignored. + + Args: + _fields_set: A set of field names that were originally explicitly set during instantiation. If provided, + this is directly used for the [`model_fields_set`][pydantic.BaseModel.model_fields_set] attribute. + Otherwise, the field names from the `values` argument will be used. + values: Trusted or pre-validated data dictionary. + + Returns: + A new instance of the `Model` class with validated data. + """ + m = cls.__new__(cls) + fields_values: dict[str, Any] = {} + fields_set = set() + + for name, field in cls.__pydantic_fields__.items(): + if field.alias is not None and field.alias in values: + fields_values[name] = values.pop(field.alias) + fields_set.add(name) + + if (name not in fields_set) and (field.validation_alias is not None): + validation_aliases: list[str | AliasPath] = ( + field.validation_alias.choices + if isinstance(field.validation_alias, AliasChoices) + else [field.validation_alias] + ) + + for alias in validation_aliases: + if isinstance(alias, str) and alias in values: + fields_values[name] = values.pop(alias) + fields_set.add(name) + break + elif isinstance(alias, AliasPath): + value = alias.search_dict_for_path(values) + if value is not PydanticUndefined: + fields_values[name] = value + fields_set.add(name) + break + + if name not in fields_set: + if name in values: + fields_values[name] = values.pop(name) + fields_set.add(name) + elif not field.is_required(): + fields_values[name] = field.get_default(call_default_factory=True, validated_data=fields_values) + if _fields_set is None: + _fields_set = fields_set + + _extra: dict[str, Any] | None = values if cls.model_config.get('extra') == 'allow' else None + _object_setattr(m, '__dict__', fields_values) + _object_setattr(m, '__pydantic_fields_set__', _fields_set) + if not cls.__pydantic_root_model__: + _object_setattr(m, '__pydantic_extra__', _extra) + _object_setattr(m, '__pydantic_private__', None) + + if cls.__pydantic_post_init__: + m.model_post_init(None) + # update private attributes with values set + if hasattr(m, '__pydantic_private__') and m.__pydantic_private__ is not None: + for k, v in values.items(): + if k in m.__private_attributes__: + m.__pydantic_private__[k] = v + + return m + + def model_copy(self, *, update: Mapping[str, Any] | None = None, deep: bool = False) -> Self: + """!!! abstract "Usage Documentation" + [`model_copy`](../concepts/models.md#model-copy) + + Returns a copy of the model. + + !!! note + The underlying instance's [`__dict__`][object.__dict__] attribute is copied. This + might have unexpected side effects if you store anything in it, on top of the model + fields (e.g. the value of [cached properties][functools.cached_property]). + + Args: + update: Values to change/add in the new model. Note: the data is not validated + before creating the new model. You should trust this data. + deep: Set to `True` to make a deep copy of the model. + + Returns: + New model instance. + """ + copied = self.__deepcopy__() if deep else self.__copy__() + if update: + if self.model_config.get('extra') == 'allow': + for k, v in update.items(): + if k in self.__pydantic_fields__: + copied.__dict__[k] = v + else: + if copied.__pydantic_extra__ is None: + copied.__pydantic_extra__ = {} + copied.__pydantic_extra__[k] = v + else: + copied.__dict__.update(update) + copied.__pydantic_fields_set__.update(update.keys()) + return copied + + def model_dump( + self, + *, + mode: Literal['json', 'python'] | str = 'python', + include: IncEx | None = None, + exclude: IncEx | None = None, + context: Any | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + ) -> dict[str, Any]: + """!!! abstract "Usage Documentation" + [`model_dump`](../concepts/serialization.md#python-mode) + + Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + Args: + mode: The mode in which `to_python` should run. + If mode is 'json', the output will only contain JSON serializable types. + If mode is 'python', the output may contain non-JSON-serializable Python objects. + include: A set of fields to include in the output. + exclude: A set of fields to exclude from the output. + context: Additional context to pass to the serializer. + by_alias: Whether to use the field's alias in the dictionary key if defined. + exclude_unset: Whether to exclude fields that have not been explicitly set. + exclude_defaults: Whether to exclude fields that are set to their default value. + exclude_none: Whether to exclude fields that have a value of `None`. + exclude_computed_fields: Whether to exclude computed fields. + While this can be useful for round-tripping, it is usually recommended to use the dedicated + `round_trip` parameter instead. + round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. + warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered. If not provided, + a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + + Returns: + A dictionary representation of the model. + """ + return self.__pydantic_serializer__.to_python( + self, + mode=mode, + by_alias=by_alias, + include=include, + exclude=exclude, + context=context, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + exclude_computed_fields=exclude_computed_fields, + round_trip=round_trip, + warnings=warnings, + fallback=fallback, + serialize_as_any=serialize_as_any, + polymorphic_serialization=polymorphic_serialization, + ) + + def model_dump_json( + self, + *, + indent: int | None = None, + ensure_ascii: bool = False, + include: IncEx | None = None, + exclude: IncEx | None = None, + context: Any | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + ) -> str: + """!!! abstract "Usage Documentation" + [`model_dump_json`](../concepts/serialization.md#json-mode) + + Generates a JSON representation of the model using Pydantic's `to_json` method. + + Args: + indent: Indentation to use in the JSON output. If None is passed, the output will be compact. + ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped. + If `False` (the default), these characters will be output as-is. + include: Field(s) to include in the JSON output. + exclude: Field(s) to exclude from the JSON output. + context: Additional context to pass to the serializer. + by_alias: Whether to serialize using field aliases. + exclude_unset: Whether to exclude fields that have not been explicitly set. + exclude_defaults: Whether to exclude fields that are set to their default value. + exclude_none: Whether to exclude fields that have a value of `None`. + exclude_computed_fields: Whether to exclude computed fields. + While this can be useful for round-tripping, it is usually recommended to use the dedicated + `round_trip` parameter instead. + round_trip: If True, dumped values should be valid as input for non-idempotent types such as Json[T]. + warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered. If not provided, + a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + + Returns: + A JSON string representation of the model. + """ + return self.__pydantic_serializer__.to_json( + self, + indent=indent, + ensure_ascii=ensure_ascii, + include=include, + exclude=exclude, + context=context, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + exclude_computed_fields=exclude_computed_fields, + round_trip=round_trip, + warnings=warnings, + fallback=fallback, + serialize_as_any=serialize_as_any, + polymorphic_serialization=polymorphic_serialization, + ).decode() + + @classmethod + def model_json_schema( + cls, + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + mode: JsonSchemaMode = 'validation', + *, + union_format: Literal['any_of', 'primitive_type_array'] = 'any_of', + ) -> dict[str, Any]: + """Generates a JSON schema for a model class. + + Args: + by_alias: Whether to use attribute aliases or not. + ref_template: The reference template. + union_format: The format to use when combining schemas from unions together. Can be one of: + + - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf) + keyword to combine schemas (the default). + - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type) + keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive + type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to + `any_of`. + schema_generator: To override the logic used to generate the JSON schema, as a subclass of + `GenerateJsonSchema` with your desired modifications + mode: The mode in which to generate the schema. + + Returns: + The JSON schema for the given model class. + """ + return model_json_schema( + cls, + by_alias=by_alias, + ref_template=ref_template, + union_format=union_format, + schema_generator=schema_generator, + mode=mode, + ) + + @classmethod + def model_parametrized_name(cls, params: tuple[type[Any], ...]) -> str: + """Compute the class name for parametrizations of generic classes. + + This method can be overridden to achieve a custom naming scheme for generic BaseModels. + + Args: + params: Tuple of types of the class. Given a generic class + `Model` with 2 type variables and a concrete model `Model[str, int]`, + the value `(str, int)` would be passed to `params`. + + Returns: + String representing the new class where `params` are passed to `cls` as type variables. + + Raises: + TypeError: Raised when trying to generate concrete names for non-generic models. + """ + if not issubclass(cls, Generic): + raise TypeError('Concrete names should only be generated for generic models.') + + # Any strings received should represent forward references, so we handle them specially below. + # If we eventually move toward wrapping them in a ForwardRef in __class_getitem__ in the future, + # we may be able to remove this special case. + param_names = [param if isinstance(param, str) else _repr.display_as_type(param) for param in params] + params_component = ', '.join(param_names) + return f'{cls.__name__}[{params_component}]' + + def model_post_init(self, context: Any, /) -> None: + """Override this method to perform additional initialization after `__init__` and `model_construct`. + This is useful if you want to do some validation that requires the entire model to be initialized. + """ + + @classmethod + def model_rebuild( + cls, + *, + force: bool = False, + raise_errors: bool = True, + _parent_namespace_depth: int = 2, + _types_namespace: MappingNamespace | None = None, + ) -> bool | None: + """Try to rebuild the pydantic-core schema for the model. + + This may be necessary when one of the annotations is a ForwardRef which could not be resolved during + the initial attempt to build the schema, and automatic rebuilding fails. + + Args: + force: Whether to force the rebuilding of the model schema, defaults to `False`. + raise_errors: Whether to raise errors, defaults to `True`. + _parent_namespace_depth: The depth level of the parent namespace, defaults to 2. + _types_namespace: The types namespace, defaults to `None`. + + Returns: + Returns `None` if the schema is already "complete" and rebuilding was not required. + If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`. + """ + already_complete = cls.__pydantic_complete__ + if already_complete and not force: + return None + + cls.__pydantic_complete__ = False + + for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'): + if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer): + # Deleting the validator/serializer is necessary as otherwise they can get reused in + # pydantic-core. We do so only if they aren't mock instances, otherwise — as `model_rebuild()` + # isn't thread-safe — concurrent model instantiations can lead to the parent validator being used. + # Same applies for the core schema that can be reused in schema generation. + delattr(cls, attr) + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} + else: + rebuild_ns = {} + + parent_ns = _model_construction.unpack_lenient_weakvaluedict(cls.__pydantic_parent_namespace__) or {} + + ns_resolver = _namespace_utils.NsResolver( + parent_namespace={**rebuild_ns, **parent_ns}, + ) + + return _model_construction.complete_model_class( + cls, + _config.ConfigWrapper(cls.model_config, check=False), + ns_resolver, + raise_errors=raise_errors, + # If the model was already complete, we don't need to call the hook again. + call_on_complete_hook=not already_complete, + is_force_rebuild=force, + ) + + @classmethod + def model_validate( + cls, + obj: Any, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> Self: + """Validate a pydantic model instance. + + Args: + obj: The object to validate. + strict: Whether to enforce types strictly. + extra: Whether to ignore, allow, or forbid extra data during model validation. + See the [`extra` configuration value][pydantic.ConfigDict.extra] for details. + from_attributes: Whether to extract data from object attributes. + context: Additional context to pass to the validator. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Raises: + ValidationError: If the object could not be validated. + + Returns: + The validated model instance. + """ + # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks + __tracebackhide__ = True + + if by_alias is False and by_name is not True: + raise PydanticUserError( + 'At least one of `by_alias` or `by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return cls.__pydantic_validator__.validate_python( + obj, + strict=strict, + extra=extra, + from_attributes=from_attributes, + context=context, + by_alias=by_alias, + by_name=by_name, + ) + + @classmethod + def model_validate_json( + cls, + json_data: str | bytes | bytearray, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + context: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> Self: + """!!! abstract "Usage Documentation" + [JSON Parsing](../concepts/json.md#json-parsing) + + Validate the given JSON data against the Pydantic model. + + Args: + json_data: The JSON data to validate. + strict: Whether to enforce types strictly. + extra: Whether to ignore, allow, or forbid extra data during model validation. + See the [`extra` configuration value][pydantic.ConfigDict.extra] for details. + context: Extra variables to pass to the validator. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Returns: + The validated Pydantic model. + + Raises: + ValidationError: If `json_data` is not a JSON string or the object could not be validated. + """ + # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks + __tracebackhide__ = True + + if by_alias is False and by_name is not True: + raise PydanticUserError( + 'At least one of `by_alias` or `by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return cls.__pydantic_validator__.validate_json( + json_data, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name + ) + + @classmethod + def model_validate_strings( + cls, + obj: Any, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + context: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> Self: + """Validate the given object with string data against the Pydantic model. + + Args: + obj: The object containing string data to validate. + strict: Whether to enforce types strictly. + extra: Whether to ignore, allow, or forbid extra data during model validation. + See the [`extra` configuration value][pydantic.ConfigDict.extra] for details. + context: Extra variables to pass to the validator. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Returns: + The validated Pydantic model. + """ + # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks + __tracebackhide__ = True + + if by_alias is False and by_name is not True: + raise PydanticUserError( + 'At least one of `by_alias` or `by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return cls.__pydantic_validator__.validate_strings( + obj, strict=strict, extra=extra, context=context, by_alias=by_alias, by_name=by_name + ) + + @classmethod + def __get_pydantic_core_schema__(cls, source: type[BaseModel], handler: GetCoreSchemaHandler, /) -> CoreSchema: + # This warning is only emitted when calling `super().__get_pydantic_core_schema__` from a model subclass. + # In the generate schema logic, this method (`BaseModel.__get_pydantic_core_schema__`) is special cased to + # *not* be called if not overridden. + warnings.warn( + 'The `__get_pydantic_core_schema__` method of the `BaseModel` class is deprecated. If you are calling ' + '`super().__get_pydantic_core_schema__` when overriding the method on a Pydantic model, consider using ' + '`handler(source)` instead. However, note that overriding this method on models can lead to unexpected ' + 'side effects.', + PydanticDeprecatedSince211, + stacklevel=2, + ) + # Logic copied over from `GenerateSchema._model_schema`: + schema = cls.__dict__.get('__pydantic_core_schema__') + if schema is not None and not isinstance(schema, _mock_val_ser.MockCoreSchema): + return cls.__pydantic_core_schema__ + + return handler(source) + + @classmethod + def __get_pydantic_json_schema__( + cls, + core_schema: CoreSchema, + handler: GetJsonSchemaHandler, + /, + ) -> JsonSchemaValue: + """Hook into generating the model's JSON schema. + + Args: + core_schema: A `pydantic-core` CoreSchema. + You can ignore this argument and call the handler with a new CoreSchema, + wrap this CoreSchema (`{'type': 'nullable', 'schema': current_schema}`), + or just call the handler with the original schema. + handler: Call into Pydantic's internal JSON schema generation. + This will raise a `pydantic.errors.PydanticInvalidForJsonSchema` if JSON schema + generation fails. + Since this gets called by `BaseModel.model_json_schema` you can override the + `schema_generator` argument to that function to change JSON schema generation globally + for a type. + + Returns: + A JSON schema, as a Python object. + """ + return handler(core_schema) + + @classmethod + def __pydantic_init_subclass__(cls, **kwargs: Any) -> None: + """This is intended to behave just like `__init_subclass__`, but is called by `ModelMetaclass` + only after basic class initialization is complete. In particular, attributes like `model_fields` will + be present when this is called, but forward annotations are not guaranteed to be resolved yet, + meaning that creating an instance of the class may fail. + + This is necessary because `__init_subclass__` will always be called by `type.__new__`, + and it would require a prohibitively large refactor to the `ModelMetaclass` to ensure that + `type.__new__` was called in such a manner that the class would already be sufficiently initialized. + + This will receive the same `kwargs` that would be passed to the standard `__init_subclass__`, namely, + any kwargs passed to the class definition that aren't used internally by Pydantic. + + Args: + **kwargs: Any keyword arguments passed to the class definition that aren't used internally + by Pydantic. + + Note: + You may want to override [`__pydantic_on_complete__()`][pydantic.main.BaseModel.__pydantic_on_complete__] + instead, which is called once the class and its fields are fully initialized and ready for validation. + """ + + @classmethod + def __pydantic_on_complete__(cls) -> None: + """This is called once the class and its fields are fully initialized and ready to be used. + + This typically happens when the class is created (just before + [`__pydantic_init_subclass__()`][pydantic.main.BaseModel.__pydantic_init_subclass__] is called on the superclass), + except when forward annotations are used that could not immediately be resolved. + In that case, it will be called later, when the model is rebuilt automatically or explicitly using + [`model_rebuild()`][pydantic.main.BaseModel.model_rebuild]. + """ + + def __class_getitem__( + cls, typevar_values: type[Any] | tuple[type[Any], ...] + ) -> type[BaseModel] | _forward_ref.PydanticRecursiveRef: + cached = _generics.get_cached_generic_type_early(cls, typevar_values) + if cached is not None: + return cached + + if cls is BaseModel: + raise TypeError('Type parameters should be placed on typing.Generic, not BaseModel') + if not hasattr(cls, '__parameters__'): + raise TypeError(f'{cls} cannot be parametrized because it does not inherit from typing.Generic') + if not cls.__pydantic_generic_metadata__['parameters'] and Generic not in cls.__bases__: + raise TypeError(f'{cls} is not a generic class') + + if not isinstance(typevar_values, tuple): + typevar_values = (typevar_values,) + + # For a model `class Model[T, U, V = int](BaseModel): ...` parametrized with `(str, bool)`, + # this gives us `{T: str, U: bool, V: int}`: + typevars_map = _generics.map_generic_model_arguments(cls, typevar_values) + # We also update the provided args to use defaults values (`(str, bool)` becomes `(str, bool, int)`): + typevar_values = tuple(v for v in typevars_map.values()) + + if _utils.all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map: + submodel = cls # if arguments are equal to parameters it's the same object + _generics.set_cached_generic_type(cls, typevar_values, submodel) + else: + parent_args = cls.__pydantic_generic_metadata__['args'] + if not parent_args: + args = typevar_values + else: + args = tuple(_generics.replace_types(arg, typevars_map) for arg in parent_args) + + origin = cls.__pydantic_generic_metadata__['origin'] or cls + model_name = origin.model_parametrized_name(args) + params = tuple( + dict.fromkeys(_generics.iter_contained_typevars(typevars_map.values())) + ) # use dict as ordered set + + with _generics.generic_recursion_self_type(origin, args) as maybe_self_type: + cached = _generics.get_cached_generic_type_late(cls, typevar_values, origin, args) + if cached is not None: + return cached + + if maybe_self_type is not None: + return maybe_self_type + + # Attempt to rebuild the origin in case new types have been defined + try: + # depth 2 gets you above this __class_getitem__ call. + # Note that we explicitly provide the parent ns, otherwise + # `model_rebuild` will use the parent ns no matter if it is the ns of a module. + # We don't want this here, as this has unexpected effects when a model + # is being parametrized during a forward annotation evaluation. + parent_ns = _typing_extra.parent_frame_namespace(parent_depth=2) or {} + origin.model_rebuild(_types_namespace=parent_ns) + except PydanticUndefinedAnnotation: + # It's okay if it fails, it just means there are still undefined types + # that could be evaluated later. + pass + + submodel = _generics.create_generic_submodel(model_name, origin, args, params) + + _generics.set_cached_generic_type(cls, typevar_values, submodel, origin, args) + + return submodel + + def __copy__(self) -> Self: + """Returns a shallow copy of the model.""" + cls = type(self) + m = cls.__new__(cls) + _object_setattr(m, '__dict__', copy(self.__dict__)) + _object_setattr(m, '__pydantic_extra__', copy(self.__pydantic_extra__)) + _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__)) + + if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None: + _object_setattr(m, '__pydantic_private__', None) + else: + _object_setattr( + m, + '__pydantic_private__', + {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, + ) + + return m + + def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self: + """Returns a deep copy of the model.""" + cls = type(self) + m = cls.__new__(cls) + _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo)) + _object_setattr(m, '__pydantic_extra__', deepcopy(self.__pydantic_extra__, memo=memo)) + # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str], + # and attempting a deepcopy would be marginally slower. + _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__)) + + if not hasattr(self, '__pydantic_private__') or self.__pydantic_private__ is None: + _object_setattr(m, '__pydantic_private__', None) + else: + _object_setattr( + m, + '__pydantic_private__', + deepcopy({k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined}, memo=memo), + ) + + return m + + if not TYPE_CHECKING: + # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access + # The same goes for __setattr__ and __delattr__, see: https://github.com/pydantic/pydantic/issues/8643 + + def __getattr__(self, item: str) -> Any: + private_attributes = object.__getattribute__(self, '__private_attributes__') + if item in private_attributes: + attribute = private_attributes[item] + if hasattr(attribute, '__get__'): + return attribute.__get__(self, type(self)) # type: ignore + + try: + # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items + return self.__pydantic_private__[item] # type: ignore + except KeyError as exc: + raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') from exc + else: + # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized. + # See `BaseModel.__repr_args__` for more details + try: + pydantic_extra = object.__getattribute__(self, '__pydantic_extra__') + except AttributeError: + pydantic_extra = None + + if pydantic_extra and item in pydantic_extra: + return pydantic_extra[item] + else: + if hasattr(self.__class__, item): + return super().__getattribute__(item) # Raises AttributeError if appropriate + else: + # this is the current error + raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') + + def __setattr__(self, name: str, value: Any) -> None: + if (setattr_handler := self.__pydantic_setattr_handlers__.get(name)) is not None: + setattr_handler(self, name, value) + # if None is returned from _setattr_handler, the attribute was set directly + elif (setattr_handler := self._setattr_handler(name, value)) is not None: + setattr_handler(self, name, value) # call here to not memo on possibly unknown fields + self.__pydantic_setattr_handlers__[name] = setattr_handler # memoize the handler for faster access + + def _setattr_handler(self, name: str, value: Any) -> Callable[[BaseModel, str, Any], None] | None: + """Get a handler for setting an attribute on the model instance. + + Returns: + A handler for setting an attribute on the model instance. Used for memoization of the handler. + Memoizing the handlers leads to a dramatic performance improvement in `__setattr__` + Returns `None` when memoization is not safe, then the attribute is set directly. + """ + cls = self.__class__ + if name in cls.__class_vars__: + raise AttributeError( + f'{name!r} is a ClassVar of `{cls.__name__}` and cannot be set on an instance. ' + f'If you want to set a value on the class, use `{cls.__name__}.{name} = value`.' + ) + elif not _fields.is_valid_field_name(name): + if (attribute := cls.__private_attributes__.get(name)) is not None: + if hasattr(attribute, '__set__'): + return lambda model, _name, val: attribute.__set__(model, val) + else: + return _SIMPLE_SETATTR_HANDLERS['private'] + else: + _object_setattr(self, name, value) + return None # Can not return memoized handler with possibly freeform attr names + + attr = getattr(cls, name, None) + # NOTE: We currently special case properties and `cached_property`, but we might need + # to generalize this to all data/non-data descriptors at some point. For non-data descriptors + # (such as `cached_property`), it isn't obvious though. `cached_property` caches the value + # to the instance's `__dict__`, but other non-data descriptors might do things differently. + if isinstance(attr, cached_property): + return _SIMPLE_SETATTR_HANDLERS['cached_property'] + + _check_frozen(cls, name, value) + + # We allow properties to be set only on non frozen models for now (to match dataclasses). + # This can be changed if it ever gets requested. + if isinstance(attr, property): + return lambda model, _name, val: attr.__set__(model, val) + elif cls.model_config.get('validate_assignment'): + return _SIMPLE_SETATTR_HANDLERS['validate_assignment'] + elif name not in cls.__pydantic_fields__: + if cls.model_config.get('extra') != 'allow': + # TODO - matching error + raise ValueError(f'"{cls.__name__}" object has no field "{name}"') + elif attr is None: + # attribute does not exist, so put it in extra + self.__pydantic_extra__[name] = value + self.__pydantic_fields_set__.add(name) + return None # Can not return memoized handler with possibly freeform attr names + else: + # attribute _does_ exist, and was not in extra, so update it + return _SIMPLE_SETATTR_HANDLERS['extra_known'] + else: + return _SIMPLE_SETATTR_HANDLERS['model_field'] + + def __delattr__(self, item: str) -> Any: + cls = self.__class__ + + if item in self.__private_attributes__: + attribute = self.__private_attributes__[item] + if hasattr(attribute, '__delete__'): + attribute.__delete__(self) # type: ignore + return + + try: + # Note: self.__pydantic_private__ cannot be None if self.__private_attributes__ has items + del self.__pydantic_private__[item] # type: ignore + return + except KeyError as exc: + raise AttributeError(f'{cls.__name__!r} object has no attribute {item!r}') from exc + + # Allow cached properties to be deleted (even if the class is frozen): + attr = getattr(cls, item, None) + if isinstance(attr, cached_property): + return object.__delattr__(self, item) + + _check_frozen(cls, name=item, value=None) + + if item in self.__pydantic_fields__: + object.__delattr__(self, item) + elif self.__pydantic_extra__ is not None and item in self.__pydantic_extra__: + del self.__pydantic_extra__[item] + else: + try: + object.__delattr__(self, item) + except AttributeError: + raise AttributeError(f'{type(self).__name__!r} object has no attribute {item!r}') + + # Because we make use of `@dataclass_transform()`, `__replace__` is already synthesized by + # type checkers, so we define the implementation in this `if not TYPE_CHECKING:` block: + def __replace__(self, **changes: Any) -> Self: + return self.model_copy(update=changes) + + def __getstate__(self) -> dict[Any, Any]: + private = self.__pydantic_private__ + if private: + private = {k: v for k, v in private.items() if v is not PydanticUndefined} + return { + '__dict__': self.__dict__, + '__pydantic_extra__': self.__pydantic_extra__, + '__pydantic_fields_set__': self.__pydantic_fields_set__, + '__pydantic_private__': private, + } + + def __setstate__(self, state: dict[Any, Any]) -> None: + _object_setattr(self, '__pydantic_fields_set__', state.get('__pydantic_fields_set__', {})) + _object_setattr(self, '__pydantic_extra__', state.get('__pydantic_extra__', {})) + _object_setattr(self, '__pydantic_private__', state.get('__pydantic_private__', {})) + _object_setattr(self, '__dict__', state.get('__dict__', {})) + + if not TYPE_CHECKING: + + def __eq__(self, other: Any) -> bool: + if isinstance(other, BaseModel): + # When comparing instances of generic types for equality, as long as all field values are equal, + # only require their generic origin types to be equal, rather than exact type equality. + # This prevents headaches like MyGeneric(x=1) != MyGeneric[Any](x=1). + self_type = self.__pydantic_generic_metadata__['origin'] or self.__class__ + other_type = other.__pydantic_generic_metadata__['origin'] or other.__class__ + + # Perform common checks first + if not ( + self_type is other_type + and getattr(self, '__pydantic_private__', None) == getattr(other, '__pydantic_private__', None) + # We need to assume `None` and `{}` are equivalent, because extra behavior + # can be controlled at validation time: + and (self.__pydantic_extra__ or {}) == (other.__pydantic_extra__ or {}) + ): + return False + + # We only want to compare pydantic fields but ignoring fields is costly. + # We'll perform a fast check first, and fallback only when needed + # See GH-7444 and GH-7825 for rationale and a performance benchmark + + # First, do the fast (and sometimes faulty) __dict__ comparison + if self.__dict__ == other.__dict__: + # If the check above passes, then pydantic fields are equal, we can return early + return True + + # We don't want to trigger unnecessary costly filtering of __dict__ on all unequal objects, so we return + # early if there are no keys to ignore (we would just return False later on anyway) + model_fields = type(self).__pydantic_fields__.keys() + if self.__dict__.keys() <= model_fields and other.__dict__.keys() <= model_fields: + return False + + # If we reach here, there are non-pydantic-fields keys, mapped to unequal values, that we need to ignore + # Resort to costly filtering of the __dict__ objects + # We use operator.itemgetter because it is much faster than dict comprehensions + # NOTE: Contrary to standard python class and instances, when the Model class has a default value for an + # attribute and the model instance doesn't have a corresponding attribute, accessing the missing attribute + # raises an error in BaseModel.__getattr__ instead of returning the class attribute + # So we can use operator.itemgetter() instead of operator.attrgetter() + getter = operator.itemgetter(*model_fields) if model_fields else lambda _: _utils._SENTINEL + try: + return getter(self.__dict__) == getter(other.__dict__) + except KeyError: + # In rare cases (such as when using the deprecated BaseModel.copy() method), + # the __dict__ may not contain all model fields, which is how we can get here. + # getter(self.__dict__) is much faster than any 'safe' method that accounts + # for missing keys, and wrapping it in a `try` doesn't slow things down much + # in the common case. + self_fields_proxy = _utils.SafeGetItemProxy(self.__dict__) + other_fields_proxy = _utils.SafeGetItemProxy(other.__dict__) + return getter(self_fields_proxy) == getter(other_fields_proxy) + + # other instance is not a BaseModel + else: + return NotImplemented # delegate to the other item in the comparison + + if TYPE_CHECKING: + # We put `__init_subclass__` in a TYPE_CHECKING block because, even though we want the type-checking benefits + # described in the signature of `__init_subclass__` below, we don't want to modify the default behavior of + # subclass initialization. + + def __init_subclass__(cls, **kwargs: Unpack[ConfigDict]): + """This signature is included purely to help type-checkers check arguments to class declaration, which + provides a way to conveniently set model_config key/value pairs. + + ```python + from pydantic import BaseModel + + class MyModel(BaseModel, extra='allow'): ... + ``` + + However, this may be deceiving, since the _actual_ calls to `__init_subclass__` will not receive any + of the config arguments, and will only receive any keyword arguments passed during class initialization + that are _not_ expected keys in ConfigDict. (This is due to the way `ModelMetaclass.__new__` works.) + + Args: + **kwargs: Keyword arguments passed to the class definition, which set model_config + + Note: + You may want to override `__pydantic_init_subclass__` instead, which behaves similarly but is called + *after* the class is fully initialized. + """ + + def __iter__(self) -> TupleGenerator: + """So `dict(model)` works.""" + yield from [(k, v) for (k, v) in self.__dict__.items() if not k.startswith('_')] + extra = self.__pydantic_extra__ + if extra: + yield from extra.items() + + def __repr__(self) -> str: + return f'{self.__repr_name__()}({self.__repr_str__(", ")})' + + def __repr_args__(self) -> _repr.ReprArgs: + # Eagerly create the repr of computed fields, as this may trigger access of cached properties and as such + # modify the instance's `__dict__`. If we don't do it now, it could happen when iterating over the `__dict__` + # below if the instance happens to be referenced in a field, and would modify the `__dict__` size *during* iteration. + computed_fields_repr_args = [ + (k, getattr(self, k)) for k, v in self.__pydantic_computed_fields__.items() if v.repr + ] + + for k, v in self.__dict__.items(): + field = self.__pydantic_fields__.get(k) + if field and field.repr: + if v is not self: + yield k, v + else: + yield k, self.__repr_recursion__(v) + # `__pydantic_extra__` can fail to be set if the model is not yet fully initialized. + # This can happen if a `ValidationError` is raised during initialization and the instance's + # repr is generated as part of the exception handling. Therefore, we use `getattr` here + # with a fallback, even though the type hints indicate the attribute will always be present. + try: + pydantic_extra = object.__getattribute__(self, '__pydantic_extra__') + except AttributeError: + pydantic_extra = None + + if pydantic_extra is not None: + yield from ((k, v) for k, v in pydantic_extra.items()) + yield from computed_fields_repr_args + + # take logic from `_repr.Representation` without the side effects of inheritance, see #5740 + __repr_name__ = _repr.Representation.__repr_name__ + __repr_recursion__ = _repr.Representation.__repr_recursion__ + __repr_str__ = _repr.Representation.__repr_str__ + __pretty__ = _repr.Representation.__pretty__ + __rich_repr__ = _repr.Representation.__rich_repr__ + + def __str__(self) -> str: + return self.__repr_str__(' ') + + # ##### Deprecated methods from v1 ##### + @property + @typing_extensions.deprecated( + 'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.', category=None + ) + def __fields__(self) -> dict[str, FieldInfo]: + warnings.warn( + 'The `__fields__` attribute is deprecated, use the `model_fields` class property instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return getattr(type(self), '__pydantic_fields__', {}) + + @property + @typing_extensions.deprecated( + 'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', + category=None, + ) + def __fields_set__(self) -> set[str]: + warnings.warn( + 'The `__fields_set__` attribute is deprecated, use `model_fields_set` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return self.__pydantic_fields_set__ + + @typing_extensions.deprecated('The `dict` method is deprecated; use `model_dump` instead.', category=None) + def dict( # noqa: D102 + self, + *, + include: IncEx | None = None, + exclude: IncEx | None = None, + by_alias: bool = False, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + ) -> Dict[str, Any]: # noqa UP006 + warnings.warn( + 'The `dict` method is deprecated; use `model_dump` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return self.model_dump( + include=include, + exclude=exclude, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + + @typing_extensions.deprecated('The `json` method is deprecated; use `model_dump_json` instead.', category=None) + def json( # noqa: D102 + self, + *, + include: IncEx | None = None, + exclude: IncEx | None = None, + by_alias: bool = False, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + encoder: Callable[[Any], Any] | None = PydanticUndefined, # type: ignore[assignment] + models_as_dict: bool = PydanticUndefined, # type: ignore[assignment] + **dumps_kwargs: Any, + ) -> str: + warnings.warn( + 'The `json` method is deprecated; use `model_dump_json` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + if encoder is not PydanticUndefined: + raise TypeError('The `encoder` argument is no longer supported; use field serializers instead.') + if models_as_dict is not PydanticUndefined: + raise TypeError('The `models_as_dict` argument is no longer supported; use a model serializer instead.') + if dumps_kwargs: + raise TypeError('`dumps_kwargs` keyword arguments are no longer supported.') + return self.model_dump_json( + include=include, + exclude=exclude, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + + @classmethod + @typing_extensions.deprecated('The `parse_obj` method is deprecated; use `model_validate` instead.', category=None) + def parse_obj(cls, obj: Any) -> Self: # noqa: D102 + warnings.warn( + 'The `parse_obj` method is deprecated; use `model_validate` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return cls.model_validate(obj) + + @classmethod + @typing_extensions.deprecated( + 'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, ' + 'otherwise load the data then use `model_validate` instead.', + category=None, + ) + def parse_raw( # noqa: D102 + cls, + b: str | bytes, + *, + content_type: str | None = None, + encoding: str = 'utf8', + proto: DeprecatedParseProtocol | None = None, + allow_pickle: bool = False, + ) -> Self: # pragma: no cover + warnings.warn( + 'The `parse_raw` method is deprecated; if your data is JSON use `model_validate_json`, ' + 'otherwise load the data then use `model_validate` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import parse + + try: + obj = parse.load_str_bytes( + b, + proto=proto, + content_type=content_type, + encoding=encoding, + allow_pickle=allow_pickle, + ) + except (ValueError, TypeError) as exc: + import json + + # try to match V1 + if isinstance(exc, UnicodeDecodeError): + type_str = 'value_error.unicodedecode' + elif isinstance(exc, json.JSONDecodeError): + type_str = 'value_error.jsondecode' + elif isinstance(exc, ValueError): + type_str = 'value_error' + else: + type_str = 'type_error' + + # ctx is missing here, but since we've added `input` to the error, we're not pretending it's the same + error: pydantic_core.InitErrorDetails = { + # The type: ignore on the next line is to ignore the requirement of LiteralString + 'type': pydantic_core.PydanticCustomError(type_str, str(exc)), # type: ignore + 'loc': ('__root__',), + 'input': b, + } + raise pydantic_core.ValidationError.from_exception_data(cls.__name__, [error]) + return cls.model_validate(obj) + + @classmethod + @typing_extensions.deprecated( + 'The `parse_file` method is deprecated; load the data from file, then if your data is JSON ' + 'use `model_validate_json`, otherwise `model_validate` instead.', + category=None, + ) + def parse_file( # noqa: D102 + cls, + path: str | Path, + *, + content_type: str | None = None, + encoding: str = 'utf8', + proto: DeprecatedParseProtocol | None = None, + allow_pickle: bool = False, + ) -> Self: + warnings.warn( + 'The `parse_file` method is deprecated; load the data from file, then if your data is JSON ' + 'use `model_validate_json`, otherwise `model_validate` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import parse + + obj = parse.load_file( + path, + proto=proto, + content_type=content_type, + encoding=encoding, + allow_pickle=allow_pickle, + ) + return cls.parse_obj(obj) + + @classmethod + @typing_extensions.deprecated( + 'The `from_orm` method is deprecated; set ' + "`model_config['from_attributes']=True` and use `model_validate` instead.", + category=None, + ) + def from_orm(cls, obj: Any) -> Self: # noqa: D102 + warnings.warn( + 'The `from_orm` method is deprecated; set ' + "`model_config['from_attributes']=True` and use `model_validate` instead.", + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + if not cls.model_config.get('from_attributes', None): + raise PydanticUserError( + 'You must set the config attribute `from_attributes=True` to use from_orm', code=None + ) + return cls.model_validate(obj) + + @classmethod + @typing_extensions.deprecated('The `construct` method is deprecated; use `model_construct` instead.', category=None) + def construct(cls, _fields_set: set[str] | None = None, **values: Any) -> Self: # noqa: D102 + warnings.warn( + 'The `construct` method is deprecated; use `model_construct` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return cls.model_construct(_fields_set=_fields_set, **values) + + @typing_extensions.deprecated( + 'The `copy` method is deprecated; use `model_copy` instead. ' + 'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.', + category=None, + ) + def copy( + self, + *, + include: AbstractSetIntStr | MappingIntStrAny | None = None, + exclude: AbstractSetIntStr | MappingIntStrAny | None = None, + update: Dict[str, Any] | None = None, # noqa UP006 + deep: bool = False, + ) -> Self: # pragma: no cover + """Returns a copy of the model. + + !!! warning "Deprecated" + This method is now deprecated; use `model_copy` instead. + + If you need `include` or `exclude`, use: + + ```python {test="skip" lint="skip"} + data = self.model_dump(include=include, exclude=exclude, round_trip=True) + data = {**data, **(update or {})} + copied = self.model_validate(data) + ``` + + Args: + include: Optional set or mapping specifying which fields to include in the copied model. + exclude: Optional set or mapping specifying which fields to exclude in the copied model. + update: Optional dictionary of field-value pairs to override field values in the copied model. + deep: If True, the values of fields that are Pydantic models will be deep-copied. + + Returns: + A copy of the model with included, excluded and updated fields as specified. + """ + warnings.warn( + 'The `copy` method is deprecated; use `model_copy` instead. ' + 'See the docstring of `BaseModel.copy` for details about how to handle `include` and `exclude`.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import copy_internals + + values = dict( + copy_internals._iter( + self, to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False + ), + **(update or {}), + ) + if self.__pydantic_private__ is None: + private = None + else: + private = {k: v for k, v in self.__pydantic_private__.items() if v is not PydanticUndefined} + + if self.__pydantic_extra__ is None: + extra: dict[str, Any] | None = None + else: + extra = self.__pydantic_extra__.copy() + for k in list(self.__pydantic_extra__): + if k not in values: # k was in the exclude + extra.pop(k) + for k in list(values): + if k in self.__pydantic_extra__: # k must have come from extra + extra[k] = values.pop(k) + + # new `__pydantic_fields_set__` can have unset optional fields with a set value in `update` kwarg + if update: + fields_set = self.__pydantic_fields_set__ | update.keys() + else: + fields_set = set(self.__pydantic_fields_set__) + + # removing excluded fields from `__pydantic_fields_set__` + if exclude: + fields_set -= set(exclude) + + return copy_internals._copy_and_set_values(self, values, fields_set, extra, private, deep=deep) + + @classmethod + @typing_extensions.deprecated('The `schema` method is deprecated; use `model_json_schema` instead.', category=None) + def schema( # noqa: D102 + cls, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE + ) -> Dict[str, Any]: # noqa UP006 + warnings.warn( + 'The `schema` method is deprecated; use `model_json_schema` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return cls.model_json_schema(by_alias=by_alias, ref_template=ref_template) + + @classmethod + @typing_extensions.deprecated( + 'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.', + category=None, + ) + def schema_json( # noqa: D102 + cls, *, by_alias: bool = True, ref_template: str = DEFAULT_REF_TEMPLATE, **dumps_kwargs: Any + ) -> str: # pragma: no cover + warnings.warn( + 'The `schema_json` method is deprecated; use `model_json_schema` and json.dumps instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + import json + + from .deprecated.json import pydantic_encoder + + return json.dumps( + cls.model_json_schema(by_alias=by_alias, ref_template=ref_template), + default=pydantic_encoder, + **dumps_kwargs, + ) + + @classmethod + @typing_extensions.deprecated('The `validate` method is deprecated; use `model_validate` instead.', category=None) + def validate(cls, value: Any) -> Self: # noqa: D102 + warnings.warn( + 'The `validate` method is deprecated; use `model_validate` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + return cls.model_validate(value) + + @classmethod + @typing_extensions.deprecated( + 'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', + category=None, + ) + def update_forward_refs(cls, **localns: Any) -> None: # noqa: D102 + warnings.warn( + 'The `update_forward_refs` method is deprecated; use `model_rebuild` instead.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + if localns: # pragma: no cover + raise TypeError('`localns` arguments are not longer accepted.') + cls.model_rebuild(force=True) + + @typing_extensions.deprecated( + 'The private method `_iter` will be removed and should no longer be used.', category=None + ) + def _iter(self, *args: Any, **kwargs: Any) -> Any: + warnings.warn( + 'The private method `_iter` will be removed and should no longer be used.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import copy_internals + + return copy_internals._iter(self, *args, **kwargs) + + @typing_extensions.deprecated( + 'The private method `_copy_and_set_values` will be removed and should no longer be used.', + category=None, + ) + def _copy_and_set_values(self, *args: Any, **kwargs: Any) -> Any: + warnings.warn( + 'The private method `_copy_and_set_values` will be removed and should no longer be used.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import copy_internals + + return copy_internals._copy_and_set_values(self, *args, **kwargs) + + @classmethod + @typing_extensions.deprecated( + 'The private method `_get_value` will be removed and should no longer be used.', + category=None, + ) + def _get_value(cls, *args: Any, **kwargs: Any) -> Any: + warnings.warn( + 'The private method `_get_value` will be removed and should no longer be used.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import copy_internals + + return copy_internals._get_value(cls, *args, **kwargs) + + @typing_extensions.deprecated( + 'The private method `_calculate_keys` will be removed and should no longer be used.', + category=None, + ) + def _calculate_keys(self, *args: Any, **kwargs: Any) -> Any: + warnings.warn( + 'The private method `_calculate_keys` will be removed and should no longer be used.', + category=PydanticDeprecatedSince20, + stacklevel=2, + ) + from .deprecated import copy_internals + + return copy_internals._calculate_keys(self, *args, **kwargs) + + +ModelT = TypeVar('ModelT', bound=BaseModel) + + +@overload +def create_model( + model_name: str, + /, + *, + __config__: ConfigDict | None = None, + __doc__: str | None = None, + __base__: None = None, + __module__: str = __name__, + __validators__: dict[str, Callable[..., Any]] | None = None, + __cls_kwargs__: dict[str, Any] | None = None, + __qualname__: str | None = None, + **field_definitions: Any | tuple[Any, Any], +) -> type[BaseModel]: ... + + +@overload +def create_model( + model_name: str, + /, + *, + __config__: ConfigDict | None = None, + __doc__: str | None = None, + __base__: type[ModelT] | tuple[type[ModelT], ...], + __module__: str = __name__, + __validators__: dict[str, Callable[..., Any]] | None = None, + __cls_kwargs__: dict[str, Any] | None = None, + __qualname__: str | None = None, + **field_definitions: Any | tuple[Any, Any], +) -> type[ModelT]: ... + + +def create_model( # noqa: C901 + model_name: str, + /, + *, + __config__: ConfigDict | None = None, + __doc__: str | None = None, + __base__: type[ModelT] | tuple[type[ModelT], ...] | None = None, + __module__: str | None = None, + __validators__: dict[str, Callable[..., Any]] | None = None, + __cls_kwargs__: dict[str, Any] | None = None, + __qualname__: str | None = None, + # TODO PEP 747: replace `Any` by the TypeForm: + **field_definitions: Any | tuple[Any, Any], +) -> type[ModelT]: + """!!! abstract "Usage Documentation" + [Dynamic Model Creation](../concepts/models.md#dynamic-model-creation) + + Dynamically creates and returns a new Pydantic model, in other words, `create_model` dynamically creates a + subclass of [`BaseModel`][pydantic.BaseModel]. + + !!! warning + This function may execute arbitrary code contained in field annotations, if string references need to be evaluated. + + See [Security implications of introspecting annotations](https://docs.python.org/3/library/annotationlib.html#annotationlib-security) for more information. + + Args: + model_name: The name of the newly created model. + __config__: The configuration of the new model. + __doc__: The docstring of the new model. + __base__: The base class or classes for the new model. + __module__: The name of the module that the model belongs to; + if `None`, the value is taken from `sys._getframe(1)` + __validators__: A dictionary of methods that validate fields. The keys are the names of the validation methods to + be added to the model, and the values are the validation methods themselves. You can read more about functional + validators [here](https://docs.pydantic.dev/2.9/concepts/validators/#field-validators). + __cls_kwargs__: A dictionary of keyword arguments for class creation, such as `metaclass`. + __qualname__: The qualified name of the newly created model. + **field_definitions: Field definitions of the new model. Either: + + - a single element, representing the type annotation of the field. + - a two-tuple, the first element being the type and the second element the assigned value + (either a default or the [`Field()`][pydantic.Field] function). + + Returns: + The new [model][pydantic.BaseModel]. + + Raises: + PydanticUserError: If `__base__` and `__config__` are both passed. + """ + if __base__ is None: + __base__ = (cast('type[ModelT]', BaseModel),) + elif not isinstance(__base__, tuple): + __base__ = (__base__,) + + __cls_kwargs__ = __cls_kwargs__ or {} + + fields: dict[str, Any] = {} + annotations: dict[str, Any] = {} + + for f_name, f_def in field_definitions.items(): + if isinstance(f_def, tuple): + if len(f_def) != 2: + raise PydanticUserError( + f'Field definition for {f_name!r} should a single element representing the type or a two-tuple, the first element ' + 'being the type and the second element the assigned value (either a default or the `Field()` function).', + code='create-model-field-definitions', + ) + + annotations[f_name] = f_def[0] + fields[f_name] = f_def[1] + else: + annotations[f_name] = f_def + + if __module__ is None: + f = sys._getframe(1) + __module__ = f.f_globals['__name__'] + + namespace: dict[str, Any] = {'__annotations__': annotations, '__module__': __module__} + if __doc__: + namespace['__doc__'] = __doc__ + if __qualname__ is not None: + namespace['__qualname__'] = __qualname__ + if __validators__: + namespace.update(__validators__) + namespace.update(fields) + if __config__: + namespace['model_config'] = __config__ + resolved_bases = types.resolve_bases(__base__) + meta, ns, kwds = types.prepare_class(model_name, resolved_bases, kwds=__cls_kwargs__) + if resolved_bases is not __base__: + ns['__orig_bases__'] = __base__ + namespace.update(ns) + + return meta( + model_name, + resolved_bases, + namespace, + __pydantic_reset_parent_namespace__=False, + _create_model_module=__module__, + **kwds, + ) + + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/mypy.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/mypy.py new file mode 100644 index 0000000000000000000000000000000000000000..ed4c30869b87b79ed908c7be556a7c04283ec618 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/mypy.py @@ -0,0 +1,1412 @@ +"""This module includes classes and functions designed specifically for use with the mypy plugin.""" + +from __future__ import annotations + +import sys +from collections.abc import Iterator +from configparser import ConfigParser +from typing import Any, Callable + +from mypy.errorcodes import ErrorCode +from mypy.expandtype import expand_type, expand_type_by_instance +from mypy.nodes import ( + ARG_NAMED, + ARG_NAMED_OPT, + ARG_OPT, + ARG_POS, + ARG_STAR2, + INVARIANT, + MDEF, + Argument, + AssignmentStmt, + Block, + CallExpr, + ClassDef, + Context, + Decorator, + DictExpr, + EllipsisExpr, + Expression, + FuncDef, + IfStmt, + JsonDict, + MemberExpr, + NameExpr, + PassStmt, + PlaceholderNode, + RefExpr, + Statement, + StrExpr, + SymbolTableNode, + TempNode, + TypeAlias, + TypeInfo, + Var, +) +from mypy.options import Options +from mypy.plugin import ( + CheckerPluginInterface, + ClassDefContext, + DynamicClassDefContext, + MethodContext, + Plugin, + ReportConfigContext, + SemanticAnalyzerPluginInterface, +) +from mypy.plugins.common import ( + deserialize_and_fixup_type, +) +from mypy.semanal import set_callable_name +from mypy.server.trigger import make_wildcard_trigger +from mypy.state import state +from mypy.type_visitor import TypeTranslator +from mypy.typeops import map_type_from_supertype +from mypy.types import ( + AnyType, + CallableType, + Instance, + NoneType, + Type, + TypeOfAny, + TypeType, + TypeVarType, + UnionType, + get_proper_type, +) +from mypy.typevars import fill_typevars +from mypy.util import get_unique_redefinition_name +from mypy.version import __version__ as mypy_version + +from pydantic._internal import _fields +from pydantic.version import parse_mypy_version + +CONFIGFILE_KEY = 'pydantic-mypy' +METADATA_KEY = 'pydantic-mypy-metadata' +BASEMODEL_FULLNAME = 'pydantic.main.BaseModel' +CREATE_MODEL_FULLNAME = 'pydantic.main.create_model' +BASESETTINGS_FULLNAME = 'pydantic_settings.main.BaseSettings' +ROOT_MODEL_FULLNAME = 'pydantic.root_model.RootModel' +MODEL_METACLASS_FULLNAME = 'pydantic._internal._model_construction.ModelMetaclass' +FIELD_FULLNAME = 'pydantic.fields.Field' +DATACLASS_FULLNAME = 'pydantic.dataclasses.dataclass' +MODEL_VALIDATOR_FULLNAME = 'pydantic.functional_validators.model_validator' +DECORATOR_FULLNAMES = { + 'pydantic.functional_validators.field_validator', + 'pydantic.functional_validators.model_validator', + 'pydantic.functional_serializers.serializer', + 'pydantic.functional_serializers.model_serializer', + 'pydantic.deprecated.class_validators.validator', + 'pydantic.deprecated.class_validators.root_validator', +} +IMPLICIT_CLASSMETHOD_DECORATOR_FULLNAMES = DECORATOR_FULLNAMES - {'pydantic.functional_serializers.model_serializer'} + + +MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version) +BUILTINS_NAME = 'builtins' + +# Increment version if plugin changes and mypy caches should be invalidated +__version__ = 2 + + +def plugin(version: str) -> type[Plugin]: + """`version` is the mypy version string. + + We might want to use this to print a warning if the mypy version being used is + newer, or especially older, than we expect (or need). + + Args: + version: The mypy version string. + + Return: + The Pydantic mypy plugin type. + """ + return PydanticPlugin + + +class PydanticPlugin(Plugin): + """The Pydantic mypy plugin.""" + + def __init__(self, options: Options) -> None: + self.plugin_config = PydanticPluginConfig(options) + self._plugin_data = self.plugin_config.to_data() + super().__init__(options) + + def get_base_class_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None: + """Update Pydantic model class.""" + sym = self.lookup_fully_qualified(fullname) + if sym and isinstance(sym.node, TypeInfo): # pragma: no branch + # No branching may occur if the mypy cache has not been cleared + if sym.node.has_base(BASEMODEL_FULLNAME): + return self._pydantic_model_class_maker_callback + return None + + def get_metaclass_hook(self, fullname: str) -> Callable[[ClassDefContext], None] | None: + """Update Pydantic `ModelMetaclass` definition.""" + if fullname == MODEL_METACLASS_FULLNAME: + return self._pydantic_model_metaclass_marker_callback + return None + + def get_method_hook(self, fullname: str) -> Callable[[MethodContext], Type] | None: + """Adjust return type of `from_orm` method call.""" + if fullname.endswith('.from_orm'): + return from_attributes_callback + return None + + def get_dynamic_class_hook(self, fullname: str) -> Callable[[DynamicClassDefContext], None] | None: + """Recognize `create_model()` calls as dynamic BaseModel subclasses.""" + if fullname == CREATE_MODEL_FULLNAME: + return self._pydantic_create_model_callback + return None + + def report_config_data(self, ctx: ReportConfigContext) -> dict[str, Any]: + """Return all plugin config data. + + Used by mypy to determine if cache needs to be discarded. + """ + return self._plugin_data + + def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> None: + transformer = PydanticModelTransformer(ctx.cls, ctx.reason, ctx.api, self.plugin_config) + transformer.transform() + + def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None: + """Reset dataclass_transform_spec attribute of ModelMetaclass. + + Let the plugin handle it. This behavior can be disabled + if 'debug_dataclass_transform' is set to True', for testing purposes. + """ + if self.plugin_config.debug_dataclass_transform: + return + info_metaclass = ctx.cls.info.declared_metaclass + assert info_metaclass, "callback not passed from 'get_metaclass_hook'" + if getattr(info_metaclass.type, 'dataclass_transform_spec', None): + info_metaclass.type.dataclass_transform_spec = None + + def _pydantic_create_model_callback(self, ctx: DynamicClassDefContext) -> None: + """Make variables assigned from `create_model()` usable as types by mypy.""" + # Determine the base class from __base__ argument if provided + base_fullname = BASEMODEL_FULLNAME + for arg_name, arg_expr in zip(ctx.call.arg_names, ctx.call.args): + if arg_name == '__base__' and isinstance(arg_expr, RefExpr) and arg_expr.node is not None: + if isinstance(arg_expr.node, TypeInfo): + base_fullname = arg_expr.node.fullname + elif isinstance(arg_expr.node, Var) and isinstance(arg_expr.node.type, Instance): + base_fullname = arg_expr.node.type.type.fullname + + base_sym = ctx.api.lookup_fully_qualified_or_none(base_fullname) + if base_sym is None or not isinstance(base_sym.node, TypeInfo): + # Fall back to BaseModel + base_sym = ctx.api.lookup_fully_qualified_or_none(BASEMODEL_FULLNAME) + if base_sym is None or not isinstance(base_sym.node, TypeInfo): + return + + base_info = base_sym.node + base_instance = fill_typevars(base_info) + assert isinstance(base_instance, Instance) + + info = ctx.api.basic_new_typeinfo(ctx.name, base_instance, ctx.call.line) + info.metaclass_type = base_info.metaclass_type + + ctx.api.add_symbol_table_node(ctx.name, SymbolTableNode(MDEF, info)) + + +class PydanticPluginConfig: + """A Pydantic mypy plugin config holder. + + Attributes: + init_forbid_extra: Whether to add a `**kwargs` at the end of the generated `__init__` signature. + init_typed: Whether to annotate fields in the generated `__init__`. + warn_required_dynamic_aliases: Whether to raise required dynamic aliases error. + debug_dataclass_transform: Whether to not reset `dataclass_transform_spec` attribute + of `ModelMetaclass` for testing purposes. + """ + + __slots__ = ( + 'init_forbid_extra', + 'init_typed', + 'warn_required_dynamic_aliases', + 'debug_dataclass_transform', + ) + init_forbid_extra: bool + init_typed: bool + warn_required_dynamic_aliases: bool + debug_dataclass_transform: bool # undocumented + + def __init__(self, options: Options) -> None: + if options.config_file is None: # pragma: no cover + return + + toml_config = parse_toml(options.config_file) + if toml_config is not None: + config = toml_config.get('tool', {}).get('pydantic-mypy', {}) + for key in self.__slots__: + setting = config.get(key, False) + if not isinstance(setting, bool): + raise ValueError(f'Configuration value must be a boolean for key: {key}') + setattr(self, key, setting) + else: + plugin_config = ConfigParser() + plugin_config.read(options.config_file) + for key in self.__slots__: + setting = plugin_config.getboolean(CONFIGFILE_KEY, key, fallback=False) + setattr(self, key, setting) + + def to_data(self) -> dict[str, Any]: + """Returns a dict of config names to their values.""" + return {key: getattr(self, key) for key in self.__slots__} + + +def from_attributes_callback(ctx: MethodContext) -> Type: + """Raise an error if from_attributes is not enabled.""" + model_type: Instance + ctx_type = ctx.type + if isinstance(ctx_type, TypeType): + ctx_type = ctx_type.item + if isinstance(ctx_type, CallableType) and isinstance(ctx_type.ret_type, Instance): + model_type = ctx_type.ret_type # called on the class + elif isinstance(ctx_type, Instance): + model_type = ctx_type # called on an instance (unusual, but still valid) + else: # pragma: no cover + detail = f'ctx.type: {ctx_type} (of type {ctx_type.__class__.__name__})' + error_unexpected_behavior(detail, ctx.api, ctx.context) + return ctx.default_return_type + pydantic_metadata = model_type.type.metadata.get(METADATA_KEY) + if pydantic_metadata is None: + return ctx.default_return_type + if not model_type.type.has_base(BASEMODEL_FULLNAME): + # not a Pydantic v2 model + return ctx.default_return_type + from_attributes = pydantic_metadata.get('config', {}).get('from_attributes') + if from_attributes is not True: + error_from_attributes(model_type.type.name, ctx.api, ctx.context) + return ctx.default_return_type + + +class PydanticModelField: + """Based on mypy.plugins.dataclasses.DataclassAttribute.""" + + def __init__( + self, + name: str, + alias: str | None, + is_frozen: bool, + has_dynamic_alias: bool, + has_default: bool, + strict: bool | None, + line: int, + column: int, + type: Type | None, + info: TypeInfo, + ): + self.name = name + self.alias = alias + self.is_frozen = is_frozen + self.has_dynamic_alias = has_dynamic_alias + self.has_default = has_default + self.strict = strict + self.line = line + self.column = column + self.type = type + self.info = info + + def to_argument( + self, + current_info: TypeInfo, + typed: bool, + model_strict: bool, + force_optional: bool, + use_alias: bool, + api: SemanticAnalyzerPluginInterface, + force_typevars_invariant: bool, + is_root_model_root: bool, + ) -> Argument: + """Based on mypy.plugins.dataclasses.DataclassAttribute.to_argument.""" + variable = self.to_var(current_info, api, use_alias, force_typevars_invariant) + + strict = model_strict if self.strict is None else self.strict + if typed or strict: + type_annotation = self.expand_type(current_info, api, include_root_type=True) + else: + type_annotation = AnyType(TypeOfAny.explicit) + + return Argument( + variable=variable, + type_annotation=type_annotation, + initializer=None, + kind=ARG_OPT + if is_root_model_root + else (ARG_NAMED_OPT if force_optional or self.has_default else ARG_NAMED), + ) + + def expand_type( + self, + current_info: TypeInfo, + api: SemanticAnalyzerPluginInterface, + force_typevars_invariant: bool = False, + include_root_type: bool = False, + ) -> Type | None: + """Based on mypy.plugins.dataclasses.DataclassAttribute.expand_type.""" + if force_typevars_invariant: + # In some cases, mypy will emit an error "Cannot use a covariant type variable as a parameter" + # To prevent that, we add an option to replace typevars with invariant ones while building certain + # method signatures (in particular, `__init__`). There may be a better way to do this, if this causes + # us problems in the future, we should look into why the dataclasses plugin doesn't have this issue. + if isinstance(self.type, TypeVarType): + modified_type = self.type.copy_modified() + modified_type.variance = INVARIANT + self.type = modified_type + + if self.type is not None and self.info.self_type is not None: + # In general, it is not safe to call `expand_type()` during semantic analysis, + # however this plugin is called very late, so all types should be fully ready. + # Also, it is tricky to avoid eager expansion of Self types here (e.g. because + # we serialize attributes). + with state.strict_optional_set(api.options.strict_optional): + filled_with_typevars = fill_typevars(current_info) + # Cannot be TupleType as current_info represents a Pydantic model: + assert isinstance(filled_with_typevars, Instance) + if force_typevars_invariant: + for arg in filled_with_typevars.args: + if isinstance(arg, TypeVarType): + arg.variance = INVARIANT + + expanded_type = expand_type(self.type, {self.info.self_type.id: filled_with_typevars}) + if include_root_type and isinstance(expanded_type, Instance) and is_root_model(expanded_type.type): + # When a root model is used as a field, Pydantic allows both an instance of the root model + # as well as instances of the `root` field type: + root_type = expanded_type.type['root'].type + if root_type is None: + # Happens if the hint for 'root' has unsolved forward references + return expanded_type + expanded_root_type = expand_type_by_instance(root_type, expanded_type) + expanded_type = UnionType([expanded_type, expanded_root_type]) + return expanded_type + return self.type + + def to_var( + self, + current_info: TypeInfo, + api: SemanticAnalyzerPluginInterface, + use_alias: bool, + force_typevars_invariant: bool = False, + ) -> Var: + """Based on mypy.plugins.dataclasses.DataclassAttribute.to_var.""" + if use_alias and self.alias is not None: + name = self.alias + else: + name = self.name + + return Var(name, self.expand_type(current_info, api, force_typevars_invariant)) + + def serialize(self) -> JsonDict: + """Based on mypy.plugins.dataclasses.DataclassAttribute.serialize.""" + assert self.type + return { + 'name': self.name, + 'alias': self.alias, + 'is_frozen': self.is_frozen, + 'has_dynamic_alias': self.has_dynamic_alias, + 'has_default': self.has_default, + 'strict': self.strict, + 'line': self.line, + 'column': self.column, + 'type': self.type.serialize(), + } + + @classmethod + def deserialize(cls, info: TypeInfo, data: JsonDict, api: SemanticAnalyzerPluginInterface) -> PydanticModelField: + """Based on mypy.plugins.dataclasses.DataclassAttribute.deserialize.""" + data = data.copy() + typ = deserialize_and_fixup_type(data.pop('type'), api) + return cls(type=typ, info=info, **data) + + def expand_typevar_from_subtype(self, sub_type: TypeInfo, api: SemanticAnalyzerPluginInterface) -> None: + """Expands type vars in the context of a subtype when an attribute is inherited + from a generic super type. + """ + if self.type is not None: + with state.strict_optional_set(api.options.strict_optional): + self.type = map_type_from_supertype(self.type, sub_type, self.info) + + +class PydanticModelClassVar: + """Based on mypy.plugins.dataclasses.DataclassAttribute. + + ClassVars are ignored by subclasses. + + Attributes: + name: the ClassVar name + """ + + def __init__(self, name): + self.name = name + + @classmethod + def deserialize(cls, data: JsonDict) -> PydanticModelClassVar: + """Based on mypy.plugins.dataclasses.DataclassAttribute.deserialize.""" + data = data.copy() + return cls(**data) + + def serialize(self) -> JsonDict: + """Based on mypy.plugins.dataclasses.DataclassAttribute.serialize.""" + return { + 'name': self.name, + } + + +class PydanticModelTransformer: + """Transform the BaseModel subclass according to the plugin settings. + + Attributes: + tracked_config_fields: A set of field configs that the plugin has to track their value. + """ + + tracked_config_fields: set[str] = { + 'extra', + 'frozen', + 'from_attributes', + 'populate_by_name', + 'validate_by_alias', + 'validate_by_name', + 'alias_generator', + 'strict', + } + + def __init__( + self, + cls: ClassDef, + reason: Expression | Statement, + api: SemanticAnalyzerPluginInterface, + plugin_config: PydanticPluginConfig, + ) -> None: + self._cls = cls + self._reason = reason + self._api = api + + self.plugin_config = plugin_config + + def transform(self) -> bool: + """Configures the BaseModel subclass according to the plugin settings. + + In particular: + + * determines the model config and fields, + * adds a fields-aware signature for the initializer and construct methods + * freezes the class if frozen = True + * stores the fields, config, and if the class is settings in the mypy metadata for access by subclasses + """ + info = self._cls.info + is_a_root_model = is_root_model(info) + config = self.collect_config() + fields, class_vars = self.collect_fields_and_class_vars(config, is_a_root_model) + if fields is None or class_vars is None: + # Some definitions are not ready. We need another pass. + return False + for field in fields: + if field.type is None: + return False + + is_settings = info.has_base(BASESETTINGS_FULLNAME) + self.add_initializer(fields, config, is_settings, is_a_root_model) + self.add_model_construct_method(fields, config, is_settings, is_a_root_model) + self.set_frozen(fields, self._api, frozen=config.frozen is True) + + self.adjust_decorator_signatures() + + info.metadata[METADATA_KEY] = { + 'fields': {field.name: field.serialize() for field in fields}, + 'class_vars': {class_var.name: class_var.serialize() for class_var in class_vars}, + 'config': config.get_values_dict(), + } + + return True + + def adjust_decorator_signatures(self) -> None: + """When we decorate a function `f` with `pydantic.validator(...)`, `pydantic.field_validator` + or `pydantic.serializer(...)`, mypy sees `f` as a regular method taking a `self` instance, + even though pydantic internally wraps `f` with `classmethod` if necessary. + + Teach mypy this by marking any function whose outermost decorator is a `validator()`, + `field_validator()` or `serializer()` call as a `classmethod`. + """ + for sym in self._cls.info.names.values(): + if isinstance(sym.node, Decorator): + first_dec = sym.node.original_decorators[0] + if ( + isinstance(first_dec, CallExpr) + and isinstance(first_dec.callee, NameExpr) + and first_dec.callee.fullname in IMPLICIT_CLASSMETHOD_DECORATOR_FULLNAMES + # @model_validator(mode="after") is an exception, it expects a regular method + and not ( + first_dec.callee.fullname == MODEL_VALIDATOR_FULLNAME + and any( + first_dec.arg_names[i] == 'mode' and isinstance(arg, StrExpr) and arg.value == 'after' + for i, arg in enumerate(first_dec.args) + ) + ) + ): + # TODO: Only do this if the first argument of the decorated function is `cls` + sym.node.func.is_class = True + + def collect_config(self) -> ModelConfigData: # noqa: C901 (ignore complexity) + """Collects the values of the config attributes that are used by the plugin, accounting for parent classes.""" + cls = self._cls + config = ModelConfigData() + + has_config_kwargs = False + has_config_from_namespace = False + + # Handle `class MyModel(BaseModel, =, ...):` + for name, expr in cls.keywords.items(): + config_data = self.get_config_update(name, expr) + if config_data: + has_config_kwargs = True + config.update(config_data) + + # Handle `model_config` + stmt: Statement | None = None + for stmt in cls.defs.body: + if not isinstance(stmt, (AssignmentStmt, ClassDef)): + continue + + if isinstance(stmt, AssignmentStmt): + lhs = stmt.lvalues[0] + if not isinstance(lhs, NameExpr) or lhs.name != 'model_config': + continue + + if isinstance(stmt.rvalue, CallExpr): # calls to `dict` or `ConfigDict` + for arg_name, arg in zip(stmt.rvalue.arg_names, stmt.rvalue.args): + if arg_name is None: + continue + config.update(self.get_config_update(arg_name, arg, lax_extra=True)) + elif isinstance(stmt.rvalue, DictExpr): # dict literals + for key_expr, value_expr in stmt.rvalue.items: + if not isinstance(key_expr, StrExpr): + continue + config.update(self.get_config_update(key_expr.value, value_expr)) + + elif isinstance(stmt, ClassDef): + if stmt.name != 'Config': # 'deprecated' Config-class + continue + for substmt in stmt.defs.body: + if not isinstance(substmt, AssignmentStmt): + continue + lhs = substmt.lvalues[0] + if not isinstance(lhs, NameExpr): + continue + config.update(self.get_config_update(lhs.name, substmt.rvalue)) + + if has_config_kwargs: + self._api.fail( + 'Specifying config in two places is ambiguous, use either Config attribute or class kwargs', + cls, + ) + break + + has_config_from_namespace = True + + if has_config_kwargs or has_config_from_namespace: + if ( + stmt + and config.has_alias_generator + and not (config.validate_by_name or config.populate_by_name) + and self.plugin_config.warn_required_dynamic_aliases + ): + error_required_dynamic_aliases(self._api, stmt) + + for info in cls.info.mro[1:]: # 0 is the current class + if METADATA_KEY not in info.metadata: + continue + + # Each class depends on the set of fields in its ancestors + self._api.add_plugin_dependency(make_wildcard_trigger(info.fullname)) + for name, value in info.metadata[METADATA_KEY]['config'].items(): + config.setdefault(name, value) + return config + + def collect_fields_and_class_vars( + self, model_config: ModelConfigData, is_root_model: bool + ) -> tuple[list[PydanticModelField] | None, list[PydanticModelClassVar] | None]: + """Collects the fields for the model, accounting for parent classes.""" + cls = self._cls + + # First, collect fields and ClassVars belonging to any class in the MRO, ignoring duplicates. + # + # We iterate through the MRO in reverse because attrs defined in the parent must appear + # earlier in the attributes list than attrs defined in the child. See: + # https://docs.python.org/3/library/dataclasses.html#inheritance + # + # However, we also want fields defined in the subtype to override ones defined + # in the parent. We can implement this via a dict without disrupting the attr order + # because dicts preserve insertion order in Python 3.7+. + found_fields: dict[str, PydanticModelField] = {} + found_class_vars: dict[str, PydanticModelClassVar] = {} + for info in reversed(cls.info.mro[1:-1]): # 0 is the current class, -2 is BaseModel, -1 is object + # if BASEMODEL_METADATA_TAG_KEY in info.metadata and BASEMODEL_METADATA_KEY not in info.metadata: + # # We haven't processed the base class yet. Need another pass. + # return None, None + if METADATA_KEY not in info.metadata: + continue + + # Each class depends on the set of attributes in its dataclass ancestors. + self._api.add_plugin_dependency(make_wildcard_trigger(info.fullname)) + + for name, data in info.metadata[METADATA_KEY]['fields'].items(): + field = PydanticModelField.deserialize(info, data, self._api) + # (The following comment comes directly from the dataclasses plugin) + # TODO: We shouldn't be performing type operations during the main + # semantic analysis pass, since some TypeInfo attributes might + # still be in flux. This should be performed in a later phase. + field.expand_typevar_from_subtype(cls.info, self._api) + found_fields[name] = field + + sym_node = cls.info.names.get(name) + if sym_node and sym_node.node and not isinstance(sym_node.node, (Var, PlaceholderNode)): + self._api.fail( + 'BaseModel field may only be overridden by another field', + sym_node.node, + ) + # Collect ClassVars + for name, data in info.metadata[METADATA_KEY]['class_vars'].items(): + found_class_vars[name] = PydanticModelClassVar.deserialize(data) + + # Second, collect fields and ClassVars belonging to the current class. + current_field_names: set[str] = set() + current_class_vars_names: set[str] = set() + for stmt in self._get_assignment_statements_from_block(cls.defs): + maybe_field = self.collect_field_or_class_var_from_stmt(stmt, model_config, found_class_vars) + if maybe_field is None: + continue + + lhs = stmt.lvalues[0] + assert isinstance(lhs, NameExpr) # collect_field_or_class_var_from_stmt guarantees this + if isinstance(maybe_field, PydanticModelField): + if is_root_model and lhs.name != 'root': + error_extra_fields_on_root_model(self._api, stmt) + else: + current_field_names.add(lhs.name) + found_fields[lhs.name] = maybe_field + elif isinstance(maybe_field, PydanticModelClassVar): + current_class_vars_names.add(lhs.name) + found_class_vars[lhs.name] = maybe_field + + return list(found_fields.values()), list(found_class_vars.values()) + + def _get_assignment_statements_from_if_statement(self, stmt: IfStmt) -> Iterator[AssignmentStmt]: + for body in stmt.body: + if not body.is_unreachable: + yield from self._get_assignment_statements_from_block(body) + if stmt.else_body is not None and not stmt.else_body.is_unreachable: + yield from self._get_assignment_statements_from_block(stmt.else_body) + + def _get_assignment_statements_from_block(self, block: Block) -> Iterator[AssignmentStmt]: + for stmt in block.body: + if isinstance(stmt, AssignmentStmt): + yield stmt + elif isinstance(stmt, IfStmt): + yield from self._get_assignment_statements_from_if_statement(stmt) + + def collect_field_or_class_var_from_stmt( # noqa C901 + self, stmt: AssignmentStmt, model_config: ModelConfigData, class_vars: dict[str, PydanticModelClassVar] + ) -> PydanticModelField | PydanticModelClassVar | None: + """Get pydantic model field from statement. + + Args: + stmt: The statement. + model_config: Configuration settings for the model. + class_vars: ClassVars already known to be defined on the model. + + Returns: + A pydantic model field if it could find the field in statement. Otherwise, `None`. + """ + cls = self._cls + + lhs = stmt.lvalues[0] + if not isinstance(lhs, NameExpr) or not _fields.is_valid_field_name(lhs.name) or lhs.name == 'model_config': + return None + + if not stmt.new_syntax: + if ( + isinstance(stmt.rvalue, CallExpr) + and isinstance(stmt.rvalue.callee, CallExpr) + and isinstance(stmt.rvalue.callee.callee, NameExpr) + and stmt.rvalue.callee.callee.fullname in DECORATOR_FULLNAMES + ): + # This is a (possibly-reused) validator or serializer, not a field + # In particular, it looks something like: my_validator = validator('my_field')(f) + # Eventually, we may want to attempt to respect model_config['ignored_types'] + return None + + if lhs.name in class_vars: + # Class vars are not fields and are not required to be annotated + return None + + # The assignment does not have an annotation, and it's not anything else we recognize + error_untyped_fields(self._api, stmt) + return None + + lhs = stmt.lvalues[0] + if not isinstance(lhs, NameExpr): + return None + + if not _fields.is_valid_field_name(lhs.name) or lhs.name == 'model_config': + return None + + sym = cls.info.names.get(lhs.name) + if sym is None: # pragma: no cover + # This is likely due to a star import (see the dataclasses plugin for a more detailed explanation) + # This is the same logic used in the dataclasses plugin + return None + + node = sym.node + if isinstance(node, PlaceholderNode): # pragma: no cover + # See the PlaceholderNode docstring for more detail about how this can occur + # Basically, it is an edge case when dealing with complex import logic + + # The dataclasses plugin now asserts this cannot happen, but I'd rather not error if it does.. + return None + + if isinstance(node, TypeAlias): + self._api.fail( + 'Type aliases inside BaseModel definitions are not supported at runtime', + node, + ) + # Skip processing this node. This doesn't match the runtime behaviour, + # but the only alternative would be to modify the SymbolTable, + # and it's a little hairy to do that in a plugin. + return None + + if not isinstance(node, Var): # pragma: no cover + # Don't know if this edge case still happens with the `is_valid_field` check above + # but better safe than sorry + + # The dataclasses plugin now asserts this cannot happen, but I'd rather not error if it does.. + return None + + # x: ClassVar[int] is not a field + if node.is_classvar: + return PydanticModelClassVar(lhs.name) + + # x: InitVar[int] is not supported in BaseModel + node_type = get_proper_type(node.type) + if isinstance(node_type, Instance) and node_type.type.fullname == 'dataclasses.InitVar': + self._api.fail( + 'InitVar is not supported in BaseModel', + node, + ) + + has_default = self.get_has_default(stmt) + strict = self.get_strict(stmt) + + if sym.type is None and node.is_final and node.is_inferred: + # This follows the logic from the dataclasses plugin. The following comment is taken verbatim: + # + # This is a special case, assignment like x: Final = 42 is classified + # annotated above, but mypy strips the `Final` turning it into x = 42. + # We do not support inferred types in dataclasses, so we can try inferring + # type for simple literals, and otherwise require an explicit type + # argument for Final[...]. + typ = self._api.analyze_simple_literal_type(stmt.rvalue, is_final=True) + if typ: + node.type = typ + else: + self._api.fail( + 'Need type argument for Final[...] with non-literal default in BaseModel', + stmt, + ) + node.type = AnyType(TypeOfAny.from_error) + + if node.is_final and has_default: + # TODO this path should be removed (see https://github.com/pydantic/pydantic/issues/11119) + return PydanticModelClassVar(lhs.name) + + alias, has_dynamic_alias = self.get_alias_info(stmt) + if ( + has_dynamic_alias + and not (model_config.validate_by_name or model_config.populate_by_name) + and self.plugin_config.warn_required_dynamic_aliases + ): + error_required_dynamic_aliases(self._api, stmt) + is_frozen = self.is_field_frozen(stmt) + + init_type = self._infer_dataclass_attr_init_type(sym, lhs.name, stmt) + return PydanticModelField( + name=lhs.name, + has_dynamic_alias=has_dynamic_alias, + has_default=has_default, + strict=strict, + alias=alias, + is_frozen=is_frozen, + line=stmt.line, + column=stmt.column, + type=init_type, + info=cls.info, + ) + + def _infer_dataclass_attr_init_type(self, sym: SymbolTableNode, name: str, context: Context) -> Type | None: + """Infer __init__ argument type for an attribute. + + In particular, possibly use the signature of __set__. + """ + default = sym.type + if sym.implicit: + return default + t = get_proper_type(sym.type) + + # Perform a simple-minded inference from the signature of __set__, if present. + # We can't use mypy.checkmember here, since this plugin runs before type checking. + # We only support some basic scanerios here, which is hopefully sufficient for + # the vast majority of use cases. + if not isinstance(t, Instance): + return default + setter = t.type.get('__set__') + if setter: + if isinstance(setter.node, FuncDef): + super_info = t.type.get_containing_type_info('__set__') + assert super_info + if setter.type: + setter_type = get_proper_type(map_type_from_supertype(setter.type, t.type, super_info)) + else: + return AnyType(TypeOfAny.unannotated) + if isinstance(setter_type, CallableType) and setter_type.arg_kinds == [ + ARG_POS, + ARG_POS, + ARG_POS, + ]: + return expand_type_by_instance(setter_type.arg_types[2], t) + else: + self._api.fail(f'Unsupported signature for "__set__" in "{t.type.name}"', context) + else: + self._api.fail(f'Unsupported "__set__" in "{t.type.name}"', context) + + return default + + def add_initializer( + self, fields: list[PydanticModelField], config: ModelConfigData, is_settings: bool, is_root_model: bool + ) -> None: + """Adds a fields-aware `__init__` method to the class. + + The added `__init__` will be annotated with types vs. all `Any` depending on the plugin settings. + """ + if '__init__' in self._cls.info.names and not self._cls.info.names['__init__'].plugin_generated: + return # Don't generate an __init__ if one already exists + + typed = self.plugin_config.init_typed + model_strict = bool(config.strict) + use_alias = not (config.validate_by_name or config.populate_by_name) and config.validate_by_alias is not False + requires_dynamic_aliases = bool(config.has_alias_generator and not config.validate_by_name) + args = self.get_field_arguments( + fields, + typed=typed, + model_strict=model_strict, + requires_dynamic_aliases=requires_dynamic_aliases, + use_alias=use_alias, + is_settings=is_settings, + is_root_model=is_root_model, + force_typevars_invariant=True, + ) + + if is_settings: + base_settings_node = self._api.lookup_fully_qualified(BASESETTINGS_FULLNAME).node + assert isinstance(base_settings_node, TypeInfo) + if '__init__' in base_settings_node.names: + base_settings_init_node = base_settings_node.names['__init__'].node + assert isinstance(base_settings_init_node, FuncDef) + if base_settings_init_node is not None and base_settings_init_node.type is not None: + func_type = base_settings_init_node.type + assert isinstance(func_type, CallableType) + for arg_idx, arg_name in enumerate(func_type.arg_names): + if arg_name is None or arg_name.startswith('__') or not arg_name.startswith('_'): + continue + analyzed_variable_type = self._api.anal_type(func_type.arg_types[arg_idx]) + if analyzed_variable_type is not None and arg_name in ( + '_cli_settings_source', + '_build_sources', + ): + # These arg names are annotated with types explicitly parameterized with `Any`, and as such + # the Any causes issues with --disallow-any-explicit. As a workaround, change + # the Any type (as if the generic type was left unparameterized): + analyzed_variable_type = analyzed_variable_type.accept( + ChangeExplicitTypeOfAny(TypeOfAny.from_omitted_generics) + ) + variable = Var(arg_name, analyzed_variable_type) + args.append(Argument(variable, analyzed_variable_type, None, ARG_OPT)) + + if not self.should_init_forbid_extra(fields, config): + var = Var('kwargs') + args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2)) + + add_method(self._api, self._cls, '__init__', args=args, return_type=NoneType()) + + def add_model_construct_method( + self, + fields: list[PydanticModelField], + config: ModelConfigData, + is_settings: bool, + is_root_model: bool, + ) -> None: + """Adds a fully typed `model_construct` classmethod to the class. + + Similar to the fields-aware __init__ method, but always uses the field names (not aliases), + and does not treat settings fields as optional. + """ + set_str = self._api.named_type(f'{BUILTINS_NAME}.set', [self._api.named_type(f'{BUILTINS_NAME}.str')]) + optional_set_str = UnionType([set_str, NoneType()]) + fields_set_argument = Argument(Var('_fields_set', optional_set_str), optional_set_str, None, ARG_OPT) + with state.strict_optional_set(self._api.options.strict_optional): + args = self.get_field_arguments( + fields, + typed=True, + model_strict=bool(config.strict), + requires_dynamic_aliases=False, + use_alias=False, + is_settings=is_settings, + is_root_model=is_root_model, + ) + if not self.should_init_forbid_extra(fields, config): + var = Var('kwargs') + args.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2)) + + args = args + [fields_set_argument] if is_root_model else [fields_set_argument] + args + + add_method( + self._api, + self._cls, + 'model_construct', + args=args, + return_type=fill_typevars(self._cls.info), + is_classmethod=True, + ) + + def set_frozen(self, fields: list[PydanticModelField], api: SemanticAnalyzerPluginInterface, frozen: bool) -> None: + """Marks all fields as properties so that attempts to set them trigger mypy errors. + + This is the same approach used by the attrs and dataclasses plugins. + """ + info = self._cls.info + for field in fields: + sym_node = info.names.get(field.name) + if sym_node is not None: + var = sym_node.node + if isinstance(var, Var): + var.is_property = frozen or field.is_frozen + elif isinstance(var, PlaceholderNode) and not self._api.final_iteration: + # See https://github.com/pydantic/pydantic/issues/5191 to hit this branch for test coverage + self._api.defer() + # `var` can also be a FuncDef or Decorator node (e.g. when overriding a field with a function or property). + # In that case, we don't want to do anything. Mypy will already raise an error that a field was not properly + # overridden. + else: + var = field.to_var(info, api, use_alias=False) + var.info = info + var.is_property = frozen + var._fullname = info.fullname + '.' + var.name + info.names[var.name] = SymbolTableNode(MDEF, var) + + def get_config_update(self, name: str, arg: Expression, lax_extra: bool = False) -> ModelConfigData | None: + """Determines the config update due to a single kwarg in the ConfigDict definition. + + Warns if a tracked config attribute is set to a value the plugin doesn't know how to interpret (e.g., an int) + """ + if name not in self.tracked_config_fields: + return None + if name == 'extra': + if isinstance(arg, StrExpr): + forbid_extra = arg.value == 'forbid' + elif isinstance(arg, MemberExpr): + forbid_extra = arg.name == 'forbid' + else: + if not lax_extra: + # Only emit an error for other types of `arg` (e.g., `NameExpr`, `ConditionalExpr`, etc.) when + # reading from a config class, etc. If a ConfigDict is used, then we don't want to emit an error + # because you'll get type checking from the ConfigDict itself. + # + # It would be nice if we could introspect the types better otherwise, but I don't know what the API + # is to evaluate an expr into its type and then check if that type is compatible with the expected + # type. Note that you can still get proper type checking via: `model_config = ConfigDict(...)`, just + # if you don't use an explicit string, the plugin won't be able to infer whether extra is forbidden. + error_invalid_config_value(name, self._api, arg) + return None + return ModelConfigData(forbid_extra=forbid_extra) + if name == 'alias_generator': + has_alias_generator = True + if isinstance(arg, NameExpr) and arg.fullname == 'builtins.None': + has_alias_generator = False + return ModelConfigData(has_alias_generator=has_alias_generator) + if isinstance(arg, NameExpr) and arg.fullname in ('builtins.True', 'builtins.False'): + return ModelConfigData(**{name: arg.fullname == 'builtins.True'}) + error_invalid_config_value(name, self._api, arg) + return None + + @staticmethod + def get_has_default(stmt: AssignmentStmt) -> bool: + """Returns a boolean indicating whether the field defined in `stmt` is a required field.""" + expr = stmt.rvalue + if isinstance(expr, TempNode): + # TempNode means annotation-only, so has no default + return False + if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME: + # The "default value" is a call to `Field`; at this point, the field has a default if and only if: + # * there is a positional argument that is not `...` + # * there is a keyword argument named "default" that is not `...` + # * there is a "default_factory" that is not `None` + for arg, name in zip(expr.args, expr.arg_names): + # If name is None, then this arg is the default because it is the only positional argument. + if name is None or name == 'default': + return arg.__class__ is not EllipsisExpr + if name == 'default_factory': + return not (isinstance(arg, NameExpr) and arg.fullname == 'builtins.None') + return False + # Has no default if the "default value" is Ellipsis (i.e., `field_name: Annotation = ...`) + return not isinstance(expr, EllipsisExpr) + + @staticmethod + def get_strict(stmt: AssignmentStmt) -> bool | None: + """Returns a the `strict` value of a field if defined, otherwise `None`.""" + expr = stmt.rvalue + if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME: + for arg, name in zip(expr.args, expr.arg_names): + if name != 'strict': + continue + if isinstance(arg, NameExpr): + if arg.fullname == 'builtins.True': + return True + elif arg.fullname == 'builtins.False': + return False + return None + return None + + @staticmethod + def get_alias_info(stmt: AssignmentStmt) -> tuple[str | None, bool]: + """Returns a pair (alias, has_dynamic_alias), extracted from the declaration of the field defined in `stmt`. + + `has_dynamic_alias` is True if and only if an alias is provided, but not as a string literal. + If `has_dynamic_alias` is True, `alias` will be None. + """ + expr = stmt.rvalue + if isinstance(expr, TempNode): + # TempNode means annotation-only + return None, False + + if not ( + isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME + ): + # Assigned value is not a call to pydantic.fields.Field + return None, False + + if 'validation_alias' in expr.arg_names: + arg = expr.args[expr.arg_names.index('validation_alias')] + elif 'alias' in expr.arg_names: + arg = expr.args[expr.arg_names.index('alias')] + else: + return None, False + + if isinstance(arg, StrExpr): + return arg.value, False + else: + return None, True + + @staticmethod + def is_field_frozen(stmt: AssignmentStmt) -> bool: + """Returns whether the field is frozen, extracted from the declaration of the field defined in `stmt`. + + Note that this is only whether the field was declared to be frozen in a ` = Field(frozen=True)` + sense; this does not determine whether the field is frozen because the entire model is frozen; that is + handled separately. + """ + expr = stmt.rvalue + if isinstance(expr, TempNode): + # TempNode means annotation-only + return False + + if not ( + isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME + ): + # Assigned value is not a call to pydantic.fields.Field + return False + + for i, arg_name in enumerate(expr.arg_names): + if arg_name == 'frozen': + arg = expr.args[i] + return isinstance(arg, NameExpr) and arg.fullname == 'builtins.True' + return False + + def get_field_arguments( + self, + fields: list[PydanticModelField], + typed: bool, + model_strict: bool, + use_alias: bool, + requires_dynamic_aliases: bool, + is_settings: bool, + is_root_model: bool, + force_typevars_invariant: bool = False, + ) -> list[Argument]: + """Helper function used during the construction of the `__init__` and `model_construct` method signatures. + + Returns a list of mypy Argument instances for use in the generated signatures. + """ + info = self._cls.info + arguments = [ + field.to_argument( + info, + typed=typed, + model_strict=model_strict, + force_optional=requires_dynamic_aliases or is_settings, + use_alias=use_alias, + api=self._api, + force_typevars_invariant=force_typevars_invariant, + is_root_model_root=is_root_model and field.name == 'root', + ) + for field in fields + if not (use_alias and field.has_dynamic_alias) + ] + return arguments + + def should_init_forbid_extra(self, fields: list[PydanticModelField], config: ModelConfigData) -> bool: + """Indicates whether the generated `__init__` should get a `**kwargs` at the end of its signature. + + We disallow arbitrary kwargs if the extra config setting is "forbid", or if the plugin config says to, + *unless* a required dynamic alias is present (since then we can't determine a valid signature). + """ + if not (config.validate_by_name or config.populate_by_name): + if self.is_dynamic_alias_present(fields, bool(config.has_alias_generator)): + return False + if config.forbid_extra: + return True + return self.plugin_config.init_forbid_extra + + @staticmethod + def is_dynamic_alias_present(fields: list[PydanticModelField], has_alias_generator: bool) -> bool: + """Returns whether any fields on the model have a "dynamic alias", i.e., an alias that cannot be + determined during static analysis. + """ + for field in fields: + if field.has_dynamic_alias: + return True + if has_alias_generator: + for field in fields: + if field.alias is None: + return True + return False + + +class ChangeExplicitTypeOfAny(TypeTranslator): + """A type translator used to change type of Any's, if explicit.""" + + def __init__(self, type_of_any: int) -> None: + self._type_of_any = type_of_any + super().__init__() + + def visit_any(self, t: AnyType) -> Type: # noqa: D102 + if t.type_of_any == TypeOfAny.explicit: + return t.copy_modified(type_of_any=self._type_of_any) + else: + return t + + +class ModelConfigData: + """Pydantic mypy plugin model config class.""" + + def __init__( + self, + forbid_extra: bool | None = None, + frozen: bool | None = None, + from_attributes: bool | None = None, + populate_by_name: bool | None = None, + validate_by_alias: bool | None = None, + validate_by_name: bool | None = None, + has_alias_generator: bool | None = None, + strict: bool | None = None, + ): + self.forbid_extra = forbid_extra + self.frozen = frozen + self.from_attributes = from_attributes + self.populate_by_name = populate_by_name + self.validate_by_alias = validate_by_alias + self.validate_by_name = validate_by_name + self.has_alias_generator = has_alias_generator + self.strict = strict + + def get_values_dict(self) -> dict[str, Any]: + """Returns a dict of Pydantic model config names to their values. + + It includes the config if config value is not `None`. + """ + return {k: v for k, v in self.__dict__.items() if v is not None} + + def update(self, config: ModelConfigData | None) -> None: + """Update Pydantic model config values.""" + if config is None: + return + for k, v in config.get_values_dict().items(): + setattr(self, k, v) + + def setdefault(self, key: str, value: Any) -> None: + """Set default value for Pydantic model config if config value is `None`.""" + if getattr(self, key) is None: + setattr(self, key, value) + + +def is_root_model(info: TypeInfo) -> bool: + """Return whether the type info is a root model subclass (or the `RootModel` class itself).""" + return info.has_base(ROOT_MODEL_FULLNAME) + + +ERROR_ORM = ErrorCode('pydantic-orm', 'Invalid from_attributes call', 'Pydantic') +ERROR_CONFIG = ErrorCode('pydantic-config', 'Invalid config value', 'Pydantic') +ERROR_ALIAS = ErrorCode('pydantic-alias', 'Dynamic alias disallowed', 'Pydantic') +ERROR_UNEXPECTED = ErrorCode('pydantic-unexpected', 'Unexpected behavior', 'Pydantic') +ERROR_UNTYPED = ErrorCode('pydantic-field', 'Untyped field disallowed', 'Pydantic') +ERROR_FIELD_DEFAULTS = ErrorCode('pydantic-field', 'Invalid Field defaults', 'Pydantic') +ERROR_EXTRA_FIELD_ROOT_MODEL = ErrorCode('pydantic-field', 'Extra field on RootModel subclass', 'Pydantic') + + +def error_from_attributes(model_name: str, api: CheckerPluginInterface, context: Context) -> None: + """Emits an error when the model does not have `from_attributes=True`.""" + api.fail(f'"{model_name}" does not have from_attributes=True', context, code=ERROR_ORM) + + +def error_invalid_config_value(name: str, api: SemanticAnalyzerPluginInterface, context: Context) -> None: + """Emits an error when the config value is invalid.""" + api.fail(f'Invalid value for "Config.{name}"', context, code=ERROR_CONFIG) + + +def error_required_dynamic_aliases(api: SemanticAnalyzerPluginInterface, context: Context) -> None: + """Emits required dynamic aliases error. + + This will be called when `warn_required_dynamic_aliases=True`. + """ + api.fail('Required dynamic aliases disallowed', context, code=ERROR_ALIAS) + + +def error_unexpected_behavior( + detail: str, api: CheckerPluginInterface | SemanticAnalyzerPluginInterface, context: Context +) -> None: # pragma: no cover + """Emits unexpected behavior error.""" + # Can't think of a good way to test this, but I confirmed it renders as desired by adding to a non-error path + link = 'https://github.com/pydantic/pydantic/issues/new/choose' + full_message = f'The pydantic mypy plugin ran into unexpected behavior: {detail}\n' + full_message += f'Please consider reporting this bug at {link} so we can try to fix it!' + api.fail(full_message, context, code=ERROR_UNEXPECTED) + + +def error_untyped_fields(api: SemanticAnalyzerPluginInterface, context: Context) -> None: + """Emits an error when there is an untyped field in the model.""" + api.fail('Untyped fields disallowed', context, code=ERROR_UNTYPED) + + +def error_extra_fields_on_root_model(api: CheckerPluginInterface, context: Context) -> None: + """Emits an error when there is more than just a root field defined for a subclass of RootModel.""" + api.fail('Only `root` is allowed as a field of a `RootModel`', context, code=ERROR_EXTRA_FIELD_ROOT_MODEL) + + +def add_method( + api: SemanticAnalyzerPluginInterface | CheckerPluginInterface, + cls: ClassDef, + name: str, + args: list[Argument], + return_type: Type, + self_type: Type | None = None, + tvar_def: TypeVarType | None = None, + is_classmethod: bool = False, +) -> None: + """Very closely related to `mypy.plugins.common.add_method_to_class`, with a few pydantic-specific changes.""" + info = cls.info + + # First remove any previously generated methods with the same name + # to avoid clashes and problems in the semantic analyzer. + if name in info.names: + sym = info.names[name] + if sym.plugin_generated and isinstance(sym.node, FuncDef): + cls.defs.body.remove(sym.node) # pragma: no cover + + if isinstance(api, SemanticAnalyzerPluginInterface): + function_type = api.named_type('builtins.function') + else: + function_type = api.named_generic_type('builtins.function', []) + + if is_classmethod: + self_type = self_type or TypeType(fill_typevars(info)) + first = [Argument(Var('_cls'), self_type, None, ARG_POS, True)] + else: + self_type = self_type or fill_typevars(info) + # `self` is positional *ONLY* here, but this can't be expressed + # fully in the mypy internal API. ARG_POS is the closest we can get. + # Using ARG_POS will, however, give mypy errors if a `self` field + # is present on a model: + # + # Name "self" already defined (possibly by an import) [no-redef] + # + # As a workaround, we give this argument a name that will + # never conflict. By its positional nature, this name will not + # be used or exposed to users. + first = [Argument(Var('__pydantic_self__'), self_type, None, ARG_POS)] + args = first + args + + arg_types, arg_names, arg_kinds = [], [], [] + for arg in args: + assert arg.type_annotation, 'All arguments must be fully typed.' + arg_types.append(arg.type_annotation) + arg_names.append(arg.variable.name) + arg_kinds.append(arg.kind) + + signature = CallableType( + arg_types, arg_kinds, arg_names, return_type, function_type, variables=[tvar_def] if tvar_def else None + ) + + func = FuncDef(name, args, Block([PassStmt()])) + func.info = info + func.type = set_callable_name(signature, func) + func.is_class = is_classmethod + func._fullname = info.fullname + '.' + name + func.line = info.line + + # NOTE: we would like the plugin generated node to dominate, but we still + # need to keep any existing definitions so they get semantically analyzed. + if name in info.names: + # Get a nice unique name instead. + r_name = get_unique_redefinition_name(name, info.names) + info.names[r_name] = info.names[name] + + # Add decorator for is_classmethod + # The dataclasses plugin claims this is unnecessary for classmethods, but not including it results in a + # signature incompatible with the superclass, which causes mypy errors to occur for every subclass of BaseModel. + if is_classmethod: + func.is_decorated = True + v = Var(name, func.type) + v.info = info + v._fullname = func._fullname + v.is_classmethod = True + dec = Decorator(func, [NameExpr('classmethod')], v) + dec.line = info.line + sym = SymbolTableNode(MDEF, dec) + else: + sym = SymbolTableNode(MDEF, func) + sym.plugin_generated = True + info.names[name] = sym + + info.defn.defs.body.append(func) + + +def parse_toml(config_file: str) -> dict[str, Any] | None: + """Returns a dict of config keys to values. + + It reads configs from toml file and returns `None` if the file is not a toml file. + """ + if not config_file.endswith('.toml'): + return None + + if sys.version_info >= (3, 11): + import tomllib as toml_ + else: + try: + import tomli as toml_ + except ImportError: # pragma: no cover + import warnings + + warnings.warn('No TOML parser installed, cannot read configuration from `pyproject.toml`.', stacklevel=2) + return None + + with open(config_file, 'rb') as rf: + return toml_.load(rf) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/networks.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/networks.py new file mode 100644 index 0000000000000000000000000000000000000000..4015bad6b0db49006923bc1ba05fd0b8c06e072a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/networks.py @@ -0,0 +1,1332 @@ +"""The networks module contains types for common network-related fields.""" + +from __future__ import annotations as _annotations + +import dataclasses as _dataclasses +import re +from dataclasses import fields +from functools import lru_cache +from importlib.metadata import version +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from typing import TYPE_CHECKING, Annotated, Any, ClassVar + +from pydantic_core import ( + MultiHostHost, + PydanticCustomError, + PydanticSerializationUnexpectedValue, + SchemaSerializer, + core_schema, +) +from pydantic_core import MultiHostUrl as _CoreMultiHostUrl +from pydantic_core import Url as _CoreUrl +from typing_extensions import Self, TypeAlias + +from pydantic.errors import PydanticUserError + +from ._internal import _repr, _schema_generation_shared +from ._migration import getattr_migration +from .annotated_handlers import GetCoreSchemaHandler +from .json_schema import JsonSchemaValue +from .type_adapter import TypeAdapter + +if TYPE_CHECKING: + import email_validator + + NetworkType: TypeAlias = 'str | bytes | int | tuple[str | bytes | int, str | int]' + +else: + email_validator = None + + +__all__ = [ + 'AnyUrl', + 'AnyHttpUrl', + 'FileUrl', + 'FtpUrl', + 'HttpUrl', + 'WebsocketUrl', + 'AnyWebsocketUrl', + 'UrlConstraints', + 'EmailStr', + 'NameEmail', + 'IPvAnyAddress', + 'IPvAnyInterface', + 'IPvAnyNetwork', + 'PostgresDsn', + 'CockroachDsn', + 'AmqpDsn', + 'RedisDsn', + 'MongoDsn', + 'KafkaDsn', + 'NatsDsn', + 'validate_email', + 'MySQLDsn', + 'MariaDBDsn', + 'ClickHouseDsn', + 'SnowflakeDsn', +] + + +@_dataclasses.dataclass +class UrlConstraints: + """Url constraints. + + Attributes: + max_length: The maximum length of the url. Defaults to `None`. + allowed_schemes: The allowed schemes. Defaults to `None`. + host_required: Whether the host is required. Defaults to `None`. + default_host: The default host. Defaults to `None`. + default_port: The default port. Defaults to `None`. + default_path: The default path. Defaults to `None`. + preserve_empty_path: Whether to preserve empty URL paths. Defaults to `None`. + """ + + max_length: int | None = None + allowed_schemes: list[str] | None = None + host_required: bool | None = None + default_host: str | None = None + default_port: int | None = None + default_path: str | None = None + preserve_empty_path: bool | None = None + + def __hash__(self) -> int: + return hash( + ( + self.max_length, + tuple(self.allowed_schemes) if self.allowed_schemes is not None else None, + self.host_required, + self.default_host, + self.default_port, + self.default_path, + self.preserve_empty_path, + ) + ) + + @property + def defined_constraints(self) -> dict[str, Any]: + """Fetch a key / value mapping of constraints to values that are not None. Used for core schema updates.""" + return {field.name: value for field in fields(self) if (value := getattr(self, field.name)) is not None} + + def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source) + + # for function-wrap schemas, url constraints is applied to the inner schema + # because when we generate schemas for urls, we wrap a core_schema.url_schema() with a function-wrap schema + # that helps with validation on initialization, see _BaseUrl and _BaseMultiHostUrl below. + schema_to_mutate = schema['schema'] if schema['type'] == 'function-wrap' else schema + if (annotated_type := schema_to_mutate['type']) not in ('url', 'multi-host-url'): + raise PydanticUserError( + f"'UrlConstraints' cannot annotate '{annotated_type}'.", code='invalid-annotated-type' + ) + for constraint_key, constraint_value in self.defined_constraints.items(): + schema_to_mutate[constraint_key] = constraint_value + return schema + + +class _BaseUrl: + _constraints: ClassVar[UrlConstraints] = UrlConstraints() + _url: _CoreUrl + + def __init__(self, url: str | _CoreUrl | _BaseUrl) -> None: + self._url = _build_type_adapter(self.__class__).validate_python(url)._url + + @property + def scheme(self) -> str: + """The scheme part of the URL. + + e.g. `https` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.scheme + + @property + def username(self) -> str | None: + """The username part of the URL, or `None`. + + e.g. `user` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.username + + @property + def password(self) -> str | None: + """The password part of the URL, or `None`. + + e.g. `pass` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.password + + @property + def host(self) -> str | None: + """The host part of the URL, or `None`. + + If the URL must be punycode encoded, this is the encoded host, e.g if the input URL is `https://£££.com`, + `host` will be `xn--9aaa.com` + """ + return self._url.host + + def unicode_host(self) -> str | None: + """The host part of the URL as a unicode string, or `None`. + + e.g. `host` in `https://user:pass@host:port/path?query#fragment` + + If the URL must be punycode encoded, this is the decoded host, e.g if the input URL is `https://£££.com`, + `unicode_host()` will be `£££.com` + """ + return self._url.unicode_host() + + @property + def port(self) -> int | None: + """The port part of the URL, or `None`. + + e.g. `port` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.port + + @property + def path(self) -> str | None: + """The path part of the URL, or `None`. + + e.g. `/path` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.path + + @property + def query(self) -> str | None: + """The query part of the URL, or `None`. + + e.g. `query` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.query + + def query_params(self) -> list[tuple[str, str]]: + """The query part of the URL as a list of key-value pairs. + + e.g. `[('foo', 'bar')]` in `https://user:pass@host:port/path?foo=bar#fragment` + """ + return self._url.query_params() + + @property + def fragment(self) -> str | None: + """The fragment part of the URL, or `None`. + + e.g. `fragment` in `https://user:pass@host:port/path?query#fragment` + """ + return self._url.fragment + + def unicode_string(self) -> str: + """The URL as a unicode string, unlike `__str__()` this will not punycode encode the host. + + If the URL must be punycode encoded, this is the decoded string, e.g if the input URL is `https://£££.com`, + `unicode_string()` will be `https://£££.com` + """ + return self._url.unicode_string() + + def encoded_string(self) -> str: + """The URL's encoded string representation via __str__(). + + This returns the punycode-encoded host version of the URL as a string. + """ + return str(self) + + def __str__(self) -> str: + """The URL as a string, this will punycode encode the host if required.""" + return str(self._url) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}({str(self._url)!r})' + + def __deepcopy__(self, memo: dict) -> Self: + return self.__class__(self._url) + + def __eq__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url == other._url + + def __lt__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url < other._url + + def __gt__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url > other._url + + def __le__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url <= other._url + + def __ge__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url >= other._url + + def __hash__(self) -> int: + return hash(self._url) + + def __len__(self) -> int: + return len(str(self._url)) + + @classmethod + def build( + cls, + *, + scheme: str, + username: str | None = None, + password: str | None = None, + host: str, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: + """Build a new `Url` instance from its component parts. + + Args: + scheme: The scheme part of the URL. + username: The username part of the URL, or omit for no username. + password: The password part of the URL, or omit for no password. + host: The host part of the URL. + port: The port part of the URL, or omit for no port. + path: The path part of the URL, or omit for no path. + query: The query part of the URL, or omit for no query. + fragment: The fragment part of the URL, or omit for no fragment. + + Returns: + An instance of URL + """ + return cls( + _CoreUrl.build( + scheme=scheme, + username=username, + password=password, + host=host, + port=port, + path=path, + query=query, + fragment=fragment, + ) + ) + + @classmethod + def serialize_url(cls, url: Any, info: core_schema.SerializationInfo) -> str | Self: + if not isinstance(url, cls): + raise PydanticSerializationUnexpectedValue( + f"Expected `{cls}` but got `{type(url)}` with value `'{url}'` - serialized value may not be as expected." + ) + if info.mode == 'json': + return str(url) + return url + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[_BaseUrl], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + def wrap_val(v, h): + if isinstance(v, source): + return v + if isinstance(v, _BaseUrl): + v = str(v) + core_url = h(v) + instance = source.__new__(source) + instance._url = core_url + return instance + + return core_schema.no_info_wrap_validator_function( + wrap_val, + schema=core_schema.url_schema(**cls._constraints.defined_constraints), + serialization=core_schema.plain_serializer_function_ser_schema( + cls.serialize_url, info_arg=True, when_used='always' + ), + ) + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + # we use the url schema for json schema generation, but we might have to extract it from + # the function-wrap schema we use as a tool for validation on initialization + inner_schema = core_schema['schema'] if core_schema['type'] == 'function-wrap' else core_schema + return handler(inner_schema) + + __pydantic_serializer__ = SchemaSerializer(core_schema.any_schema(serialization=core_schema.to_string_ser_schema())) + + +class _BaseMultiHostUrl: + _constraints: ClassVar[UrlConstraints] = UrlConstraints() + _url: _CoreMultiHostUrl + + def __init__(self, url: str | _CoreMultiHostUrl | _BaseMultiHostUrl) -> None: + self._url = _build_type_adapter(self.__class__).validate_python(url)._url + + @property + def scheme(self) -> str: + """The scheme part of the URL. + + e.g. `https` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.scheme + + @property + def path(self) -> str | None: + """The path part of the URL, or `None`. + + e.g. `/path` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.path + + @property + def query(self) -> str | None: + """The query part of the URL, or `None`. + + e.g. `query` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.query + + def query_params(self) -> list[tuple[str, str]]: + """The query part of the URL as a list of key-value pairs. + + e.g. `[('foo', 'bar')]` in `https://foo.com,bar.com/path?foo=bar#fragment` + """ + return self._url.query_params() + + @property + def fragment(self) -> str | None: + """The fragment part of the URL, or `None`. + + e.g. `fragment` in `https://foo.com,bar.com/path?query#fragment` + """ + return self._url.fragment + + def hosts(self) -> list[MultiHostHost]: + '''The hosts of the `MultiHostUrl` as [`MultiHostHost`][pydantic_core.MultiHostHost] typed dicts. + + ```python + from pydantic_core import MultiHostUrl + + mhu = MultiHostUrl('https://foo.com:123,foo:bar@bar.com/path') + print(mhu.hosts()) + """ + [ + {'username': None, 'password': None, 'host': 'foo.com', 'port': 123}, + {'username': 'foo', 'password': 'bar', 'host': 'bar.com', 'port': 443} + ] + ``` + Returns: + A list of dicts, each representing a host. + ''' + return self._url.hosts() + + def encoded_string(self) -> str: + """The URL's encoded string representation via __str__(). + + This returns the punycode-encoded host version of the URL as a string. + """ + return str(self) + + def unicode_string(self) -> str: + """The URL as a unicode string, unlike `__str__()` this will not punycode encode the hosts.""" + return self._url.unicode_string() + + def __str__(self) -> str: + """The URL as a string, this will punycode encode the host if required.""" + return str(self._url) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}({str(self._url)!r})' + + def __deepcopy__(self, memo: dict) -> Self: + return self.__class__(self._url) + + def __eq__(self, other: Any) -> bool: + return self.__class__ is other.__class__ and self._url == other._url + + def __hash__(self) -> int: + return hash(self._url) + + def __len__(self) -> int: + return len(str(self._url)) + + @classmethod + def build( + cls, + *, + scheme: str, + hosts: list[MultiHostHost] | None = None, + username: str | None = None, + password: str | None = None, + host: str | None = None, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: + """Build a new `MultiHostUrl` instance from its component parts. + + This method takes either `hosts` - a list of `MultiHostHost` typed dicts, or the individual components + `username`, `password`, `host` and `port`. + + Args: + scheme: The scheme part of the URL. + hosts: Multiple hosts to build the URL from. + username: The username part of the URL. + password: The password part of the URL. + host: The host part of the URL. + port: The port part of the URL. + path: The path part of the URL. + query: The query part of the URL, or omit for no query. + fragment: The fragment part of the URL, or omit for no fragment. + + Returns: + An instance of `MultiHostUrl` + """ + return cls( + _CoreMultiHostUrl.build( + scheme=scheme, + hosts=hosts, + username=username, + password=password, + host=host, + port=port, + path=path, + query=query, + fragment=fragment, + ) + ) + + @classmethod + def serialize_url(cls, url: Any, info: core_schema.SerializationInfo) -> str | Self: + if not isinstance(url, cls): + raise PydanticSerializationUnexpectedValue( + f"Expected `{cls}` but got `{type(url)}` with value `'{url}'` - serialized value may not be as expected." + ) + if info.mode == 'json': + return str(url) + return url + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[_BaseMultiHostUrl], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + def wrap_val(v, h): + if isinstance(v, source): + return v + if isinstance(v, _BaseMultiHostUrl): + v = str(v) + core_url = h(v) + instance = source.__new__(source) + instance._url = core_url + return instance + + return core_schema.no_info_wrap_validator_function( + wrap_val, + schema=core_schema.multi_host_url_schema(**cls._constraints.defined_constraints), + serialization=core_schema.plain_serializer_function_ser_schema( + cls.serialize_url, info_arg=True, when_used='always' + ), + ) + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + # we use the url schema for json schema generation, but we might have to extract it from + # the function-wrap schema we use as a tool for validation on initialization + inner_schema = core_schema['schema'] if core_schema['type'] == 'function-wrap' else core_schema + return handler(inner_schema) + + __pydantic_serializer__ = SchemaSerializer(core_schema.any_schema(serialization=core_schema.to_string_ser_schema())) + + +@lru_cache +def _build_type_adapter(cls: type[_BaseUrl | _BaseMultiHostUrl]) -> TypeAdapter: + return TypeAdapter(cls) + + +class AnyUrl(_BaseUrl): + """Base type for all URLs. + + * Any scheme allowed + * Top-level domain (TLD) not required + * Host not required + + Assuming an input URL of `http://samuel:pass@example.com:8000/the/path/?query=here#fragment=is;this=bit`, + the types export the following properties: + + - `scheme`: the URL scheme (`http`), always set. + - `host`: the URL host (`example.com`). + - `username`: optional username if included (`samuel`). + - `password`: optional password if included (`pass`). + - `port`: optional port (`8000`). + - `path`: optional path (`/the/path/`). + - `query`: optional URL query (for example, `GET` arguments or "search string", such as `query=here`). + - `fragment`: optional fragment (`fragment=is;this=bit`). + """ + + +# Note: all single host urls inherit from `AnyUrl` to preserve compatibility with pre-v2.10 code +# Where urls were annotated variants of `AnyUrl`, which was an alias to `pydantic_core.Url` + + +class AnyHttpUrl(AnyUrl): + """A type that will accept any http or https URL. + + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['http', 'https']) + + +class HttpUrl(AnyUrl): + """A type that will accept any http or https URL. + + * TLD not required + * Host not required + * Max length 2083 + + ```python + from pydantic import BaseModel, HttpUrl, ValidationError + + class MyModel(BaseModel): + url: HttpUrl + + m = MyModel(url='http://www.example.com') # (1)! + print(m.url) + #> http://www.example.com/ + + try: + MyModel(url='ftp://invalid.url') + except ValidationError as e: + print(e) + ''' + 1 validation error for MyModel + url + URL scheme should be 'http' or 'https' [type=url_scheme, input_value='ftp://invalid.url', input_type=str] + ''' + + try: + MyModel(url='not a url') + except ValidationError as e: + print(e) + ''' + 1 validation error for MyModel + url + Input should be a valid URL, relative URL without a base [type=url_parsing, input_value='not a url', input_type=str] + ''' + ``` + + 1. Note: mypy would prefer `m = MyModel(url=HttpUrl('http://www.example.com'))`, but Pydantic will convert the string to an HttpUrl instance anyway. + + "International domains" (e.g. a URL where the host or TLD includes non-ascii characters) will be encoded via + [punycode](https://en.wikipedia.org/wiki/Punycode) (see + [this article](https://www.xudongz.com/blog/2017/idn-phishing/) for a good description of why this is important): + + ```python + from pydantic import BaseModel, HttpUrl + + class MyModel(BaseModel): + url: HttpUrl + + m1 = MyModel(url='http://puny£code.com') + print(m1.url) + #> http://xn--punycode-eja.com/ + m2 = MyModel(url='https://www.аррӏе.com/') + print(m2.url) + #> https://www.xn--80ak6aa92e.com/ + m3 = MyModel(url='https://www.example.珠宝/') + print(m3.url) + #> https://www.example.xn--pbt977c/ + ``` + + + !!! warning "Underscores in Hostnames" + In Pydantic, underscores are allowed in all parts of a domain except the TLD. + Technically this might be wrong - in theory the hostname cannot have underscores, but subdomains can. + + To explain this; consider the following two cases: + + - `exam_ple.co.uk`: the hostname is `exam_ple`, which should not be allowed since it contains an underscore. + - `foo_bar.example.com` the hostname is `example`, which should be allowed since the underscore is in the subdomain. + + Without having an exhaustive list of TLDs, it would be impossible to differentiate between these two. Therefore + underscores are allowed, but you can always do further validation in a validator if desired. + + Also, Chrome, Firefox, and Safari all currently accept `http://exam_ple.com` as a URL, so we're in good + (or at least big) company. + """ + + _constraints = UrlConstraints(max_length=2083, allowed_schemes=['http', 'https']) + + +class AnyWebsocketUrl(AnyUrl): + """A type that will accept any ws or wss URL. + + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['ws', 'wss']) + + +class WebsocketUrl(AnyUrl): + """A type that will accept any ws or wss URL. + + * TLD not required + * Host not required + * Max length 2083 + """ + + _constraints = UrlConstraints(max_length=2083, allowed_schemes=['ws', 'wss']) + + +class FileUrl(AnyUrl): + """A type that will accept any file URL. + + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['file']) + + +class FtpUrl(AnyUrl): + """A type that will accept ftp URL. + + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['ftp']) + + +class PostgresDsn(_BaseMultiHostUrl): + """A type that will accept any Postgres DSN. + + * User info required + * TLD not required + * Host required + * Supports multiple hosts + + If further validation is required, these properties can be used by validators to enforce specific behaviour: + + ```python + from pydantic import ( + BaseModel, + HttpUrl, + PostgresDsn, + ValidationError, + field_validator, + ) + + class MyModel(BaseModel): + url: HttpUrl + + m = MyModel(url='http://www.example.com') + + # the repr() method for a url will display all properties of the url + print(repr(m.url)) + #> HttpUrl('http://www.example.com/') + print(m.url.scheme) + #> http + print(m.url.host) + #> www.example.com + print(m.url.port) + #> 80 + + class MyDatabaseModel(BaseModel): + db: PostgresDsn + + @field_validator('db') + def check_db_name(cls, v): + assert v.path and len(v.path) > 1, 'database must be provided' + return v + + m = MyDatabaseModel(db='postgres://user:pass@localhost:5432/foobar') + print(m.db) + #> postgres://user:pass@localhost:5432/foobar + + try: + MyDatabaseModel(db='postgres://user:pass@localhost:5432') + except ValidationError as e: + print(e) + ''' + 1 validation error for MyDatabaseModel + db + Assertion failed, database must be provided + assert (None) + + where None = PostgresDsn('postgres://user:pass@localhost:5432').path [type=assertion_error, input_value='postgres://user:pass@localhost:5432', input_type=str] + ''' + ``` + """ + + _constraints = UrlConstraints( + host_required=True, + allowed_schemes=[ + 'postgres', + 'postgresql', + 'postgresql+asyncpg', + 'postgresql+pg8000', + 'postgresql+psycopg', + 'postgresql+psycopg2', + 'postgresql+psycopg2cffi', + 'postgresql+py-postgresql', + 'postgresql+pygresql', + ], + ) + + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # pyright: ignore[reportAttributeAccessIssue] + + +class CockroachDsn(AnyUrl): + """A type that will accept any Cockroach DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( + host_required=True, + allowed_schemes=[ + 'cockroachdb', + 'cockroachdb+psycopg2', + 'cockroachdb+asyncpg', + ], + ) + + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # pyright: ignore[reportReturnType] + + +class AmqpDsn(AnyUrl): + """A type that will accept any AMQP DSN. + + * User info required + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['amqp', 'amqps']) + + +class RedisDsn(AnyUrl): + """A type that will accept any Redis DSN. + + * User info required + * TLD not required + * Host required (e.g., `rediss://:pass@localhost`) + """ + + _constraints = UrlConstraints( + allowed_schemes=['redis', 'rediss'], + default_host='localhost', + default_port=6379, + default_path='/0', + host_required=True, + ) + + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # pyright: ignore[reportReturnType] + + +class MongoDsn(_BaseMultiHostUrl): + """A type that will accept any MongoDB DSN. + + * User info not required + * Database name not required + * Port not required + * User info may be passed without user part (e.g., `mongodb://mongodb0.example.com:27017`). + + !!! warning + If a port isn't specified, the default MongoDB port `27017` will be used. If this behavior is + undesirable, you can use the following: + + ```python + from typing import Annotated + + from pydantic_core import MultiHostUrl + + from pydantic import UrlConstraints + + MongoDsnNoDefaultPort = Annotated[ + MultiHostUrl, + UrlConstraints(allowed_schemes=['mongodb', 'mongodb+srv']), + ] + ``` + """ + + _constraints = UrlConstraints(allowed_schemes=['mongodb', 'mongodb+srv'], default_port=27017) + + +class KafkaDsn(AnyUrl): + """A type that will accept any Kafka DSN. + + * User info required + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints(allowed_schemes=['kafka'], default_host='localhost', default_port=9092) + + +class NatsDsn(_BaseMultiHostUrl): + """A type that will accept any NATS DSN. + + NATS is a connective technology built for the ever increasingly hyper-connected world. + It is a single technology that enables applications to securely communicate across + any combination of cloud vendors, on-premise, edge, web and mobile, and devices. + More: https://nats.io + """ + + _constraints = UrlConstraints( + allowed_schemes=['nats', 'tls', 'ws', 'wss'], default_host='localhost', default_port=4222 + ) + + +class MySQLDsn(AnyUrl): + """A type that will accept any MySQL DSN. + + * User info required + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints( + allowed_schemes=[ + 'mysql', + 'mysql+mysqlconnector', + 'mysql+aiomysql', + 'mysql+asyncmy', + 'mysql+mysqldb', + 'mysql+pymysql', + 'mysql+cymysql', + 'mysql+pyodbc', + ], + default_port=3306, + host_required=True, + ) + + +class MariaDBDsn(AnyUrl): + """A type that will accept any MariaDB DSN. + + * User info required + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints( + allowed_schemes=['mariadb', 'mariadb+mariadbconnector', 'mariadb+pymysql'], + default_port=3306, + ) + + +class ClickHouseDsn(AnyUrl): + """A type that will accept any ClickHouse DSN. + + * User info required + * TLD not required + * Host not required + """ + + _constraints = UrlConstraints( + allowed_schemes=[ + 'clickhouse+native', + 'clickhouse+asynch', + 'clickhouse+http', + 'clickhouse', + 'clickhouses', + 'clickhousedb', + ], + default_host='localhost', + default_port=9000, + ) + + +class SnowflakeDsn(AnyUrl): + """A type that will accept any Snowflake DSN. + + * User info required + * TLD not required + * Host required + """ + + _constraints = UrlConstraints( + allowed_schemes=['snowflake'], + host_required=True, + ) + + @property + def host(self) -> str: + """The required URL host.""" + return self._url.host # pyright: ignore[reportReturnType] + + +def import_email_validator() -> None: + global email_validator + try: + import email_validator + except ImportError as e: + raise ImportError("email-validator is not installed, run `pip install 'pydantic[email]'`") from e + if not version('email-validator').partition('.')[0] == '2': + raise ImportError('email-validator version >= 2.0 required, run pip install -U email-validator') + + +if TYPE_CHECKING: + EmailStr = Annotated[str, ...] +else: + + class EmailStr: + """ + Info: + To use this type, you need to install the optional + [`email-validator`](https://github.com/JoshData/python-email-validator) package: + + ```bash + pip install email-validator + ``` + + Validate email addresses. + + ```python + from pydantic import BaseModel, EmailStr + + class Model(BaseModel): + email: EmailStr + + print(Model(email='contact@mail.com')) + #> email='contact@mail.com' + ``` + """ # noqa: D212 + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source: type[Any], + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + import_email_validator() + return core_schema.no_info_after_validator_function(cls._validate, core_schema.str_schema()) + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + field_schema.update(type='string', format='email') + return field_schema + + @classmethod + def _validate(cls, input_value: str, /) -> str: + return validate_email(input_value)[1] + + +class NameEmail(_repr.Representation): + """ + Info: + To use this type, you need to install the optional + [`email-validator`](https://github.com/JoshData/python-email-validator) package: + + ```bash + pip install email-validator + ``` + + Validate a name and email address combination, as specified by + [RFC 5322](https://datatracker.ietf.org/doc/html/rfc5322#section-3.4). + + The `NameEmail` has two properties: `name` and `email`. + In case the `name` is not provided, it's inferred from the email address. + + ```python + from pydantic import BaseModel, NameEmail + + class User(BaseModel): + email: NameEmail + + user = User(email='Fred Bloggs ') + print(user.email) + #> Fred Bloggs + print(user.email.name) + #> Fred Bloggs + + user = User(email='fred.bloggs@example.com') + print(user.email) + #> fred.bloggs + print(user.email.name) + #> fred.bloggs + ``` + """ # noqa: D212 + + __slots__ = 'name', 'email' + + def __init__(self, name: str, email: str): + self.name = name + self.email = email + + def __eq__(self, other: Any) -> bool: + return isinstance(other, NameEmail) and (self.name, self.email) == (other.name, other.email) + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + field_schema.update(type='string', format='name-email') + return field_schema + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source: type[Any], + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + import_email_validator() + + return core_schema.no_info_after_validator_function( + cls._validate, + core_schema.json_or_python_schema( + json_schema=core_schema.str_schema(), + python_schema=core_schema.union_schema( + [core_schema.is_instance_schema(cls), core_schema.str_schema()], + custom_error_type='name_email_type', + custom_error_message='Input is not a valid NameEmail', + ), + serialization=core_schema.to_string_ser_schema(), + ), + ) + + @classmethod + def _validate(cls, input_value: Self | str, /) -> Self: + if isinstance(input_value, str): + name, email = validate_email(input_value) + return cls(name, email) + else: + return input_value + + def __str__(self) -> str: + if '@' in self.name: + return f'"{self.name}" <{self.email}>' + + return f'{self.name} <{self.email}>' + + +IPvAnyAddressType: TypeAlias = 'IPv4Address | IPv6Address' +IPvAnyInterfaceType: TypeAlias = 'IPv4Interface | IPv6Interface' +IPvAnyNetworkType: TypeAlias = 'IPv4Network | IPv6Network' + +if TYPE_CHECKING: + IPvAnyAddress = IPvAnyAddressType + IPvAnyInterface = IPvAnyInterfaceType + IPvAnyNetwork = IPvAnyNetworkType +else: + + class IPvAnyAddress: + """Validate an IPv4 or IPv6 address. + + ```python + from pydantic import BaseModel + from pydantic.networks import IPvAnyAddress + + class IpModel(BaseModel): + ip: IPvAnyAddress + + print(IpModel(ip='127.0.0.1')) + #> ip=IPv4Address('127.0.0.1') + + try: + IpModel(ip='http://www.example.com') + except ValueError as e: + print(e.errors()) + ''' + [ + { + 'type': 'ip_any_address', + 'loc': ('ip',), + 'msg': 'value is not a valid IPv4 or IPv6 address', + 'input': 'http://www.example.com', + } + ] + ''' + ``` + """ + + __slots__ = () + + def __new__(cls, value: Any) -> IPvAnyAddressType: + """Validate an IPv4 or IPv6 address.""" + try: + return IPv4Address(value) + except ValueError: + pass + + try: + return IPv6Address(value) + except ValueError: + raise PydanticCustomError('ip_any_address', 'value is not a valid IPv4 or IPv6 address') + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = {} + field_schema.update(type='string', format='ipvanyaddress') + return field_schema + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source: type[Any], + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + return core_schema.no_info_plain_validator_function( + cls._validate, serialization=core_schema.to_string_ser_schema() + ) + + @classmethod + def _validate(cls, input_value: Any, /) -> IPvAnyAddressType: + return cls(input_value) # type: ignore[return-value] + + class IPvAnyInterface: + """Validate an IPv4 or IPv6 interface.""" + + __slots__ = () + + def __new__(cls, value: NetworkType) -> IPvAnyInterfaceType: + """Validate an IPv4 or IPv6 interface.""" + try: + return IPv4Interface(value) + except ValueError: + pass + + try: + return IPv6Interface(value) + except ValueError: + raise PydanticCustomError('ip_any_interface', 'value is not a valid IPv4 or IPv6 interface') + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = {} + field_schema.update(type='string', format='ipvanyinterface') + return field_schema + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source: type[Any], + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + return core_schema.no_info_plain_validator_function( + cls._validate, serialization=core_schema.to_string_ser_schema() + ) + + @classmethod + def _validate(cls, input_value: NetworkType, /) -> IPvAnyInterfaceType: + return cls(input_value) # type: ignore[return-value] + + class IPvAnyNetwork: + """Validate an IPv4 or IPv6 network.""" + + __slots__ = () + + def __new__(cls, value: NetworkType) -> IPvAnyNetworkType: + """Validate an IPv4 or IPv6 network.""" + # Assume IP Network is defined with a default value for `strict` argument. + # Define your own class if you want to specify network address check strictness. + try: + return IPv4Network(value) + except ValueError: + pass + + try: + return IPv6Network(value) + except ValueError: + raise PydanticCustomError('ip_any_network', 'value is not a valid IPv4 or IPv6 network') + + @classmethod + def __get_pydantic_json_schema__( + cls, core_schema: core_schema.CoreSchema, handler: _schema_generation_shared.GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = {} + field_schema.update(type='string', format='ipvanynetwork') + return field_schema + + @classmethod + def __get_pydantic_core_schema__( + cls, + _source: type[Any], + _handler: GetCoreSchemaHandler, + ) -> core_schema.CoreSchema: + return core_schema.no_info_plain_validator_function( + cls._validate, serialization=core_schema.to_string_ser_schema() + ) + + @classmethod + def _validate(cls, input_value: NetworkType, /) -> IPvAnyNetworkType: + return cls(input_value) # type: ignore[return-value] + + +def _build_pretty_email_regex() -> re.Pattern[str]: + name_chars = r'[\w!#$%&\'*+\-/=?^_`{|}~]' + unquoted_name_group = rf'((?:{name_chars}+\s+)*{name_chars}+)' + quoted_name_group = r'"((?:[^"]|\")+)"' + email_group = r'<(.+)>' + return re.compile(rf'\s*(?:{unquoted_name_group}|{quoted_name_group})?\s*{email_group}\s*') + + +pretty_email_regex = _build_pretty_email_regex() + +MAX_EMAIL_LENGTH = 2048 +"""Maximum length for an email. +A somewhat arbitrary but very generous number compared to what is allowed by most implementations. +""" + + +def validate_email(value: str) -> tuple[str, str]: + """Email address validation using [email-validator](https://pypi.org/project/email-validator/). + + Returns: + A tuple containing the local part of the email (or the name for "pretty" email addresses) + and the normalized email. + + Raises: + PydanticCustomError: If the email is invalid. + + Note: + Note that: + + * Raw IP address (literal) domain parts are not allowed. + * `"John Doe "` style "pretty" email addresses are processed. + * Spaces are striped from the beginning and end of addresses, but no error is raised. + """ + if email_validator is None: + import_email_validator() + + if len(value) > MAX_EMAIL_LENGTH: + raise PydanticCustomError( + 'value_error', + 'value is not a valid email address: {reason}', + {'reason': f'Length must not exceed {MAX_EMAIL_LENGTH} characters'}, + ) + + m = pretty_email_regex.fullmatch(value) + name: str | None = None + if m: + unquoted_name, quoted_name, value = m.groups() + name = unquoted_name or quoted_name + + email = value.strip() + + try: + parts = email_validator.validate_email(email, check_deliverability=False) + except email_validator.EmailNotValidError as e: + raise PydanticCustomError( + 'value_error', 'value is not a valid email address: {reason}', {'reason': str(e.args[0])} + ) from e + + email = parts.normalized + assert email is not None + name = name or parts.local_part + return name, email + + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/parse.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/parse.py new file mode 100644 index 0000000000000000000000000000000000000000..68b7f0464ece32edfb955eec0cb24655752716d6 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/parse.py @@ -0,0 +1,5 @@ +"""The `parse` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..840d20a09ef27b084440ad16e221403bddd2ac3a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/__init__.py @@ -0,0 +1,193 @@ +"""!!! abstract "Usage Documentation" + [Build a Plugin](../concepts/plugins.md#build-a-plugin) + +Plugin interface for Pydantic plugins, and related types. +""" + +from __future__ import annotations + +from typing import Any, Callable, Literal, NamedTuple + +from pydantic_core import CoreConfig, CoreSchema, ValidationError +from typing_extensions import Protocol, TypeAlias + +from pydantic.config import ExtraValues + +__all__ = ( + 'PydanticPluginProtocol', + 'BaseValidateHandlerProtocol', + 'ValidatePythonHandlerProtocol', + 'ValidateJsonHandlerProtocol', + 'ValidateStringsHandlerProtocol', + 'NewSchemaReturns', + 'SchemaTypePath', + 'SchemaKind', +) + +NewSchemaReturns: TypeAlias = 'tuple[ValidatePythonHandlerProtocol | None, ValidateJsonHandlerProtocol | None, ValidateStringsHandlerProtocol | None]' + + +class SchemaTypePath(NamedTuple): + """Path defining where `schema_type` was defined, or where `TypeAdapter` was called.""" + + module: str + name: str + + +SchemaKind: TypeAlias = Literal['BaseModel', 'TypeAdapter', 'dataclass', 'create_model', 'validate_call'] + + +class PydanticPluginProtocol(Protocol): + """Protocol defining the interface for Pydantic plugins.""" + + def new_schema_validator( + self, + schema: CoreSchema, + schema_type: Any, + schema_type_path: SchemaTypePath, + schema_kind: SchemaKind, + config: CoreConfig | None, + plugin_settings: dict[str, object], + ) -> tuple[ + ValidatePythonHandlerProtocol | None, ValidateJsonHandlerProtocol | None, ValidateStringsHandlerProtocol | None + ]: + """This method is called for each plugin every time a new [`SchemaValidator`][pydantic_core.SchemaValidator] + is created. + + It should return an event handler for each of the three validation methods, or `None` if the plugin does not + implement that method. + + Args: + schema: The schema to validate against. + schema_type: The original type which the schema was created from, e.g. the model class. + schema_type_path: Path defining where `schema_type` was defined, or where `TypeAdapter` was called. + schema_kind: The kind of schema to validate against. + config: The config to use for validation. + plugin_settings: Any plugin settings. + + Returns: + A tuple of optional event handlers for each of the three validation methods - + `validate_python`, `validate_json`, `validate_strings`. + """ + raise NotImplementedError('Pydantic plugins should implement `new_schema_validator`.') + + +class BaseValidateHandlerProtocol(Protocol): + """Base class for plugin callbacks protocols. + + You shouldn't implement this protocol directly, instead use one of the subclasses with adds the correctly + typed `on_error` method. + """ + + on_enter: Callable[..., None] + """`on_enter` is changed to be more specific on all subclasses""" + + def on_success(self, result: Any) -> None: + """Callback to be notified of successful validation. + + Args: + result: The result of the validation. + """ + return + + def on_error(self, error: ValidationError) -> None: + """Callback to be notified of validation errors. + + Args: + error: The validation error. + """ + return + + def on_exception(self, exception: Exception) -> None: + """Callback to be notified of validation exceptions. + + Args: + exception: The exception raised during validation. + """ + return + + +class ValidatePythonHandlerProtocol(BaseValidateHandlerProtocol, Protocol): + """Event handler for `SchemaValidator.validate_python`.""" + + def on_enter( + self, + input: Any, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + self_instance: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> None: + """Callback to be notified of validation start, and create an instance of the event handler. + + Args: + input: The input to be validated. + strict: Whether to validate the object in strict mode. + extra: Whether to ignore, allow, or forbid extra data during model validation. + from_attributes: Whether to validate objects as inputs by extracting attributes. + context: The context to use for validation, this is passed to functional validators. + self_instance: An instance of a model to set attributes on from validation, this is used when running + validation from the `__init__` method of a model. + by_alias: Whether to use the field's alias to match the input data to an attribute. + by_name: Whether to use the field's name to match the input data to an attribute. + """ + + +class ValidateJsonHandlerProtocol(BaseValidateHandlerProtocol, Protocol): + """Event handler for `SchemaValidator.validate_json`.""" + + def on_enter( + self, + input: str | bytes | bytearray, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + context: Any | None = None, + self_instance: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> None: + """Callback to be notified of validation start, and create an instance of the event handler. + + Args: + input: The JSON data to be validated. + strict: Whether to validate the object in strict mode. + extra: Whether to ignore, allow, or forbid extra data during model validation. + context: The context to use for validation, this is passed to functional validators. + self_instance: An instance of a model to set attributes on from validation, this is used when running + validation from the `__init__` method of a model. + by_alias: Whether to use the field's alias to match the input data to an attribute. + by_name: Whether to use the field's name to match the input data to an attribute. + """ + + +StringInput: TypeAlias = 'dict[str, StringInput]' + + +class ValidateStringsHandlerProtocol(BaseValidateHandlerProtocol, Protocol): + """Event handler for `SchemaValidator.validate_strings`.""" + + def on_enter( + self, + input: StringInput, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + context: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> None: + """Callback to be notified of validation start, and create an instance of the event handler. + + Args: + input: The string data to be validated. + strict: Whether to validate the object in strict mode. + extra: Whether to ignore, allow, or forbid extra data during model validation. + context: The context to use for validation, this is passed to functional validators. + by_alias: Whether to use the field's alias to match the input data to an attribute. + by_name: Whether to use the field's name to match the input data to an attribute. + """ diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/_loader.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..e8226fafe98131afa3bee9b70d0324d694c72da5 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/_loader.py @@ -0,0 +1,58 @@ +from __future__ import annotations + +import importlib.metadata as importlib_metadata +import os +import warnings +from collections.abc import Iterable +from typing import TYPE_CHECKING, Final + +if TYPE_CHECKING: + from . import PydanticPluginProtocol + + +PYDANTIC_ENTRY_POINT_GROUP: Final[str] = 'pydantic' + +# cache of plugins +_plugins: dict[str, PydanticPluginProtocol] | None = None +# return no plugins while loading plugins to avoid recursion and errors while importing plugins +# this means that if plugins use pydantic +_loading_plugins: bool = False + + +def get_plugins() -> Iterable[PydanticPluginProtocol]: + """Load plugins for Pydantic. + + Inspired by: https://github.com/pytest-dev/pluggy/blob/1.3.0/src/pluggy/_manager.py#L376-L402 + """ + disabled_plugins = os.getenv('PYDANTIC_DISABLE_PLUGINS') + global _plugins, _loading_plugins + if _loading_plugins: + # this happens when plugins themselves use pydantic, we return no plugins + return () + elif disabled_plugins in ('__all__', '1', 'true'): + return () + elif _plugins is None: + _plugins = {} + # set _loading_plugins so any plugins that use pydantic don't themselves use plugins + _loading_plugins = True + try: + for dist in importlib_metadata.distributions(): + for entry_point in dist.entry_points: + if entry_point.group != PYDANTIC_ENTRY_POINT_GROUP: + continue + if entry_point.value in _plugins: + continue + if disabled_plugins is not None and entry_point.name in disabled_plugins.split(','): + continue + try: + _plugins[entry_point.value] = entry_point.load() + except (ImportError, AttributeError) as e: + warnings.warn( + f'{e.__class__.__name__} while loading the `{entry_point.name}` Pydantic plugin, ' + f'this plugin will not be installed.\n\n{e!r}', + stacklevel=2, + ) + finally: + _loading_plugins = False + + return _plugins.values() diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/_schema_validator.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/_schema_validator.py new file mode 100644 index 0000000000000000000000000000000000000000..492cc39cba7f1244b8c152df244064067270e2d7 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/plugin/_schema_validator.py @@ -0,0 +1,143 @@ +"""Pluggable schema validator for pydantic.""" + +from __future__ import annotations + +import functools +from collections.abc import Iterable +from typing import TYPE_CHECKING, Any, Callable, Literal, TypeVar + +from pydantic_core import CoreConfig, CoreSchema, SchemaValidator, ValidationError +from typing_extensions import ParamSpec + +if TYPE_CHECKING: + from . import BaseValidateHandlerProtocol, PydanticPluginProtocol, SchemaKind, SchemaTypePath + + +P = ParamSpec('P') +R = TypeVar('R') +Event = Literal['on_validate_python', 'on_validate_json', 'on_validate_strings'] +events: list[Event] = list(Event.__args__) # type: ignore + + +def create_schema_validator( + schema: CoreSchema, + schema_type: Any, + schema_type_module: str, + schema_type_name: str, + schema_kind: SchemaKind, + config: CoreConfig | None = None, + plugin_settings: dict[str, Any] | None = None, + _use_prebuilt: bool = True, +) -> SchemaValidator | PluggableSchemaValidator: + """Create a `SchemaValidator` or `PluggableSchemaValidator` if plugins are installed. + + Returns: + If plugins are installed then return `PluggableSchemaValidator`, otherwise return `SchemaValidator`. + """ + from . import SchemaTypePath + from ._loader import get_plugins + + plugins = get_plugins() + if plugins: + return PluggableSchemaValidator( + schema, + schema_type, + SchemaTypePath(schema_type_module, schema_type_name), + schema_kind, + config, + plugins, + plugin_settings or {}, + _use_prebuilt=_use_prebuilt, + ) + else: + return SchemaValidator(schema, config, _use_prebuilt=_use_prebuilt) + + +class PluggableSchemaValidator: + """Pluggable schema validator.""" + + __slots__ = '_schema_validator', 'validate_json', 'validate_python', 'validate_strings' + + def __init__( + self, + schema: CoreSchema, + schema_type: Any, + schema_type_path: SchemaTypePath, + schema_kind: SchemaKind, + config: CoreConfig | None, + plugins: Iterable[PydanticPluginProtocol], + plugin_settings: dict[str, Any], + _use_prebuilt: bool = True, + ) -> None: + self._schema_validator = SchemaValidator(schema, config, _use_prebuilt=_use_prebuilt) + + python_event_handlers: list[BaseValidateHandlerProtocol] = [] + json_event_handlers: list[BaseValidateHandlerProtocol] = [] + strings_event_handlers: list[BaseValidateHandlerProtocol] = [] + for plugin in plugins: + try: + p, j, s = plugin.new_schema_validator( + schema, schema_type, schema_type_path, schema_kind, config, plugin_settings + ) + except TypeError as e: # pragma: no cover + raise TypeError(f'Error using plugin `{plugin.__module__}:{plugin.__class__.__name__}`: {e}') from e + if p is not None: + python_event_handlers.append(p) + if j is not None: + json_event_handlers.append(j) + if s is not None: + strings_event_handlers.append(s) + + self.validate_python = build_wrapper(self._schema_validator.validate_python, python_event_handlers) + self.validate_json = build_wrapper(self._schema_validator.validate_json, json_event_handlers) + self.validate_strings = build_wrapper(self._schema_validator.validate_strings, strings_event_handlers) + + def __getattr__(self, name: str) -> Any: + return getattr(self._schema_validator, name) + + +def build_wrapper(func: Callable[P, R], event_handlers: list[BaseValidateHandlerProtocol]) -> Callable[P, R]: + if not event_handlers: + return func + else: + on_enters = tuple(h.on_enter for h in event_handlers if filter_handlers(h, 'on_enter')) + on_successes = tuple(h.on_success for h in event_handlers if filter_handlers(h, 'on_success')) + on_errors = tuple(h.on_error for h in event_handlers if filter_handlers(h, 'on_error')) + on_exceptions = tuple(h.on_exception for h in event_handlers if filter_handlers(h, 'on_exception')) + + @functools.wraps(func) + def wrapper(*args: P.args, **kwargs: P.kwargs) -> R: + for on_enter_handler in on_enters: + on_enter_handler(*args, **kwargs) + + try: + result = func(*args, **kwargs) + except ValidationError as error: + for on_error_handler in on_errors: + on_error_handler(error) + raise + except Exception as exception: + for on_exception_handler in on_exceptions: + on_exception_handler(exception) + raise + else: + for on_success_handler in on_successes: + on_success_handler(result) + return result + + return wrapper + + +def filter_handlers(handler_cls: BaseValidateHandlerProtocol, method_name: str) -> bool: + """Filter out handler methods which are not implemented by the plugin directly - e.g. those that are missing + or are inherited from the protocol. + """ + handler = getattr(handler_cls, method_name, None) + if handler is None: + return False + elif handler.__module__ == 'pydantic.plugin': + # this is the original handler, from the protocol due to runtime inheritance + # we don't want to call it + return False + else: + return True diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/py.typed b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/root_model.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/root_model.py new file mode 100644 index 0000000000000000000000000000000000000000..d2900433ed5d2d186f3dca0cbdaabfeecaaf5cde --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/root_model.py @@ -0,0 +1,157 @@ +"""RootModel class and type definitions.""" + +from __future__ import annotations as _annotations + +from copy import copy, deepcopy +from typing import TYPE_CHECKING, Any, Generic, Literal, TypeVar + +from pydantic_core import PydanticUndefined +from typing_extensions import Self, dataclass_transform + +from . import PydanticUserError +from ._internal import _model_construction, _repr +from .main import BaseModel, _object_setattr + +if TYPE_CHECKING: + from .fields import Field as PydanticModelField + from .fields import PrivateAttr as PydanticModelPrivateAttr + + # dataclass_transform could be applied to RootModel directly, but `ModelMetaclass`'s dataclass_transform + # takes priority (at least with pyright). We trick type checkers into thinking we apply dataclass_transform + # on a new metaclass. + @dataclass_transform(kw_only_default=False, field_specifiers=(PydanticModelField, PydanticModelPrivateAttr)) + class _RootModelMetaclass(_model_construction.ModelMetaclass): ... +else: + _RootModelMetaclass = _model_construction.ModelMetaclass + +__all__ = ('RootModel',) + +RootModelRootType = TypeVar('RootModelRootType') + + +class RootModel(BaseModel, Generic[RootModelRootType], metaclass=_RootModelMetaclass): + """!!! abstract "Usage Documentation" + [`RootModel` and Custom Root Types](../concepts/models.md#rootmodel-and-custom-root-types) + + A Pydantic `BaseModel` for the root object of the model. + + Attributes: + root: The root object of the model. + __pydantic_root_model__: Whether the model is a RootModel. + __pydantic_private__: Private fields in the model. + __pydantic_extra__: Extra fields in the model. + + """ + + __pydantic_root_model__ = True + __pydantic_private__ = None + __pydantic_extra__ = None + + root: RootModelRootType + + def __init_subclass__(cls, **kwargs): + extra = cls.model_config.get('extra') + if extra is not None: + raise PydanticUserError( + "`RootModel` does not support setting `model_config['extra']`", code='root-model-extra' + ) + super().__init_subclass__(**kwargs) + + def __init__(self, /, root: RootModelRootType = PydanticUndefined, **data) -> None: # type: ignore + __tracebackhide__ = True + if data: + if root is not PydanticUndefined: + raise ValueError( + '"RootModel.__init__" accepts either a single positional argument or arbitrary keyword arguments' + ) + root = data # type: ignore + self.__pydantic_validator__.validate_python(root, self_instance=self) + + __init__.__pydantic_base_init__ = True # pyright: ignore[reportFunctionMemberAccess] + + @classmethod + def model_construct(cls, root: RootModelRootType, _fields_set: set[str] | None = None) -> Self: # type: ignore + """Create a new model using the provided root object and update fields set. + + Args: + root: The root object of the model. + _fields_set: The set of fields to be updated. + + Returns: + The new model. + + Raises: + NotImplemented: If the model is not a subclass of `RootModel`. + """ + return super().model_construct(root=root, _fields_set=_fields_set) + + def __getstate__(self) -> dict[Any, Any]: + return { + '__dict__': self.__dict__, + '__pydantic_fields_set__': self.__pydantic_fields_set__, + } + + def __setstate__(self, state: dict[Any, Any]) -> None: + _object_setattr(self, '__pydantic_fields_set__', state['__pydantic_fields_set__']) + _object_setattr(self, '__dict__', state['__dict__']) + + def __copy__(self) -> Self: + """Returns a shallow copy of the model.""" + cls = type(self) + m = cls.__new__(cls) + new_dict = copy(self.__dict__) + new_dict['root'] = copy(self.__dict__['root']) + _object_setattr(m, '__dict__', new_dict) + _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__)) + return m + + def __deepcopy__(self, memo: dict[int, Any] | None = None) -> Self: + """Returns a deep copy of the model.""" + cls = type(self) + m = cls.__new__(cls) + _object_setattr(m, '__dict__', deepcopy(self.__dict__, memo=memo)) + # This next line doesn't need a deepcopy because __pydantic_fields_set__ is a set[str], + # and attempting a deepcopy would be marginally slower. + _object_setattr(m, '__pydantic_fields_set__', copy(self.__pydantic_fields_set__)) + return m + + if TYPE_CHECKING: + + def model_dump( # type: ignore + self, + *, + mode: Literal['json', 'python'] | str = 'python', + include: Any = None, + exclude: Any = None, + context: dict[str, Any] | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + serialize_as_any: bool = False, + ) -> Any: + """This method is included just to get a more accurate return type for type checkers. + It is included in this `if TYPE_CHECKING:` block since no override is actually necessary. + + See the documentation of `BaseModel.model_dump` for more details about the arguments. + + Generally, this method will have a return type of `RootModelRootType`, assuming that `RootModelRootType` is + not a `BaseModel` subclass. If `RootModelRootType` is a `BaseModel` subclass, then the return + type will likely be `dict[str, Any]`, as `model_dump` calls are recursive. The return type could + even be something different, in the case of a custom serializer. + Thus, `Any` is used here to catch all of these cases. + """ + ... + + def __eq__(self, other: Any) -> bool: + if not isinstance(other, RootModel): + return NotImplemented + return self.__pydantic_fields__['root'].annotation == other.__pydantic_fields__[ + 'root' + ].annotation and super().__eq__(other) + + def __repr_args__(self) -> _repr.ReprArgs: + yield 'root', self.root diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/schema.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..a3245a61afd0f59143f38ee809ee2d784d9198eb --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/schema.py @@ -0,0 +1,5 @@ +"""The `schema` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/tools.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..fdc68c4f4a35e8d8200b3aafcda63d43839da375 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/tools.py @@ -0,0 +1,5 @@ +"""The `tools` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/type_adapter.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/type_adapter.py new file mode 100644 index 0000000000000000000000000000000000000000..d962305f2c923a6046b8234a334bdca0e644b159 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/type_adapter.py @@ -0,0 +1,801 @@ +"""Type adapter specification.""" + +from __future__ import annotations as _annotations + +import sys +import types +from collections.abc import Callable, Iterable +from dataclasses import is_dataclass +from types import FrameType +from typing import ( + Any, + Generic, + Literal, + TypeVar, + cast, + final, + overload, +) + +from pydantic_core import CoreSchema, SchemaSerializer, SchemaValidator, Some +from typing_extensions import ParamSpec, is_typeddict + +from pydantic.errors import PydanticUserError +from pydantic.main import BaseModel, IncEx + +from ._internal import _config, _generate_schema, _mock_val_ser, _namespace_utils, _repr, _typing_extra, _utils +from .config import ConfigDict, ExtraValues +from .errors import PydanticUndefinedAnnotation +from .json_schema import ( + DEFAULT_REF_TEMPLATE, + GenerateJsonSchema, + JsonSchemaKeyT, + JsonSchemaMode, + JsonSchemaValue, +) +from .plugin._schema_validator import PluggableSchemaValidator, create_schema_validator + +T = TypeVar('T') +R = TypeVar('R') +P = ParamSpec('P') +TypeAdapterT = TypeVar('TypeAdapterT', bound='TypeAdapter') + + +def _getattr_no_parents(obj: Any, attribute: str) -> Any: + """Returns the attribute value without attempting to look up attributes from parent types.""" + if hasattr(obj, '__dict__'): + try: + return obj.__dict__[attribute] + except KeyError: + pass + + slots = getattr(obj, '__slots__', None) + if slots is not None and attribute in slots: + return getattr(obj, attribute) + else: + raise AttributeError(attribute) + + +def _type_has_config(type_: Any) -> bool: + """Returns whether the type has config.""" + type_ = _typing_extra.annotated_type(type_) or type_ + try: + return issubclass(type_, BaseModel) or is_dataclass(type_) or is_typeddict(type_) + except TypeError: + # type is not a class + return False + + +@final +class TypeAdapter(Generic[T]): + """!!! abstract "Usage Documentation" + [`TypeAdapter`](../concepts/type_adapter.md) + + Type adapters provide a flexible way to perform validation and serialization based on a Python type. + + A `TypeAdapter` instance exposes some of the functionality from `BaseModel` instance methods + for types that do not have such methods (such as dataclasses, primitive types, and more). + + **Note:** `TypeAdapter` instances are not types, and cannot be used as type annotations for fields. + + Args: + type: The type associated with the `TypeAdapter`. + config: Configuration for the `TypeAdapter`, should be a dictionary conforming to + [`ConfigDict`][pydantic.config.ConfigDict]. + + !!! note + You cannot provide a configuration when instantiating a `TypeAdapter` if the type you're using + has its own config that cannot be overridden (ex: `BaseModel`, `TypedDict`, and `dataclass`). A + [`type-adapter-config-unused`](../errors/usage_errors.md#type-adapter-config-unused) error will + be raised in this case. + _parent_depth: Depth at which to search for the [parent frame][frame-objects]. This frame is used when + resolving forward annotations during schema building, by looking for the globals and locals of this + frame. Defaults to 2, which will result in the frame where the `TypeAdapter` was instantiated. + + !!! note + This parameter is named with an underscore to suggest its private nature and discourage use. + It may be deprecated in a minor version, so we only recommend using it if you're comfortable + with potential change in behavior/support. It's default value is 2 because internally, + the `TypeAdapter` class makes another call to fetch the frame. + module: The module that passes to plugin if provided. + + Attributes: + core_schema: The core schema for the type. + validator: The schema validator for the type. + serializer: The schema serializer for the type. + pydantic_complete: Whether the core schema for the type is successfully built. + + ??? tip "Compatibility with `mypy`" + Depending on the type used, `mypy` might raise an error when instantiating a `TypeAdapter`. As a workaround, you can explicitly + annotate your variable: + + ```py + from typing import Union + + from pydantic import TypeAdapter + + ta: TypeAdapter[Union[str, int]] = TypeAdapter(Union[str, int]) # type: ignore[arg-type] + ``` + + ??? info "Namespace management nuances and implementation details" + + Here, we collect some notes on namespace management, and subtle differences from `BaseModel`: + + `BaseModel` uses its own `__module__` to find out where it was defined + and then looks for symbols to resolve forward references in those globals. + On the other hand, `TypeAdapter` can be initialized with arbitrary objects, + which may not be types and thus do not have a `__module__` available. + So instead we look at the globals in our parent stack frame. + + It is expected that the `ns_resolver` passed to this function will have the correct + namespace for the type we're adapting. See the source code for `TypeAdapter.__init__` + and `TypeAdapter.rebuild` for various ways to construct this namespace. + + This works for the case where this function is called in a module that + has the target of forward references in its scope, but + does not always work for more complex cases. + + For example, take the following: + + ```python {title="a.py"} + IntList = list[int] + OuterDict = dict[str, 'IntList'] + ``` + + ```python {test="skip" title="b.py"} + from a import OuterDict + + from pydantic import TypeAdapter + + IntList = int # replaces the symbol the forward reference is looking for + v = TypeAdapter(OuterDict) + v({'x': 1}) # should fail but doesn't + ``` + + If `OuterDict` were a `BaseModel`, this would work because it would resolve + the forward reference within the `a.py` namespace. + But `TypeAdapter(OuterDict)` can't determine what module `OuterDict` came from. + + In other words, the assumption that _all_ forward references exist in the + module we are being called from is not technically always true. + Although most of the time it is and it works fine for recursive models and such, + `BaseModel`'s behavior isn't perfect either and _can_ break in similar ways, + so there is no right or wrong between the two. + + But at the very least this behavior is _subtly_ different from `BaseModel`'s. + """ + + core_schema: CoreSchema + validator: SchemaValidator | PluggableSchemaValidator + serializer: SchemaSerializer + pydantic_complete: bool + + @overload + def __init__( + self, + type: type[T], + *, + config: ConfigDict | None = ..., + _parent_depth: int = ..., + module: str | None = ..., + ) -> None: ... + + # This second overload is for unsupported special forms (such as Annotated, Union, etc.) + # Currently there is no way to type this correctly + # See https://github.com/python/typing/pull/1618 + @overload + def __init__( + self, + type: Any, + *, + config: ConfigDict | None = ..., + _parent_depth: int = ..., + module: str | None = ..., + ) -> None: ... + + def __init__( + self, + type: Any, + *, + config: ConfigDict | None = None, + _parent_depth: int = 2, + module: str | None = None, + ) -> None: + if _type_has_config(type) and config is not None: + raise PydanticUserError( + 'Cannot use `config` when the type is a BaseModel, dataclass or TypedDict.' + ' These types can have their own config and setting the config via the `config`' + ' parameter to TypeAdapter will not override it, thus the `config` you passed to' + ' TypeAdapter becomes meaningless, which is probably not what you want.', + code='type-adapter-config-unused', + ) + + self._type = type + self._config = config + self._parent_depth = _parent_depth + self.pydantic_complete = False + + parent_frame = self._fetch_parent_frame() + if isinstance(type, types.FunctionType): + # Special case functions, which are *not* pushed to the `NsResolver` stack and without this special case + # would only have access to the parent namespace where the `TypeAdapter` was instantiated (if the function is defined + # in another module, we need to look at that module's globals). + if parent_frame is not None: + # `f_locals` is the namespace where the type adapter was instantiated (~ to `f_globals` if at the module level): + parent_ns = parent_frame.f_locals + else: # pragma: no cover + parent_ns = None + globalns, localns = _namespace_utils.ns_for_function( + type, + parent_namespace=parent_ns, + ) + parent_namespace = None + else: + if parent_frame is not None: + globalns = parent_frame.f_globals + # Do not provide a local ns if the type adapter happens to be instantiated at the module level: + localns = parent_frame.f_locals if parent_frame.f_locals is not globalns else {} + else: # pragma: no cover + globalns = {} + localns = {} + parent_namespace = localns + + self._module_name = module or cast(str, globalns.get('__name__', '')) + self._init_core_attrs( + ns_resolver=_namespace_utils.NsResolver( + namespaces_tuple=_namespace_utils.NamespacesTuple(locals=localns, globals=globalns), + parent_namespace=parent_namespace, + ), + force=False, + ) + + def _fetch_parent_frame(self) -> FrameType | None: + frame = sys._getframe(self._parent_depth) + if frame.f_globals.get('__name__') == 'typing': + # Because `TypeAdapter` is generic, explicitly parametrizing the class results + # in a `typing._GenericAlias` instance, which proxies instantiation calls to the + # "real" `TypeAdapter` class and thus adding an extra frame to the call. To avoid + # pulling anything from the `typing` module, use the correct frame (the one before): + return frame.f_back + + return frame + + def _init_core_attrs( + self, ns_resolver: _namespace_utils.NsResolver, force: bool, raise_errors: bool = False + ) -> bool: + """Initialize the core schema, validator, and serializer for the type. + + Args: + ns_resolver: The namespace resolver to use when building the core schema for the adapted type. + force: Whether to force the construction of the core schema, validator, and serializer. + If `force` is set to `False` and `_defer_build` is `True`, the core schema, validator, and serializer will be set to mocks. + raise_errors: Whether to raise errors if initializing any of the core attrs fails. + + Returns: + `True` if the core schema, validator, and serializer were successfully initialized, otherwise `False`. + + Raises: + PydanticUndefinedAnnotation: If `PydanticUndefinedAnnotation` occurs in`__get_pydantic_core_schema__` + and `raise_errors=True`. + """ + if not force and self._defer_build: + _mock_val_ser.set_type_adapter_mocks(self) + self.pydantic_complete = False + return False + + try: + self.core_schema = _getattr_no_parents(self._type, '__pydantic_core_schema__') + self.validator = _getattr_no_parents(self._type, '__pydantic_validator__') + self.serializer = _getattr_no_parents(self._type, '__pydantic_serializer__') + + # TODO: we don't go through the rebuild logic here directly because we don't want + # to repeat all of the namespace fetching logic that we've already done + # so we simply skip to the block below that does the actual schema generation + if ( + isinstance(self.core_schema, _mock_val_ser.MockCoreSchema) + or isinstance(self.validator, _mock_val_ser.MockValSer) + or isinstance(self.serializer, _mock_val_ser.MockValSer) + ): + raise AttributeError() + except AttributeError: + config_wrapper = _config.ConfigWrapper(self._config) + + schema_generator = _generate_schema.GenerateSchema(config_wrapper, ns_resolver=ns_resolver) + + try: + core_schema = schema_generator.generate_schema(self._type) + except PydanticUndefinedAnnotation: + if raise_errors: + raise + _mock_val_ser.set_type_adapter_mocks(self) + return False + + try: + self.core_schema = schema_generator.clean_schema(core_schema) + except _generate_schema.InvalidSchemaError: + _mock_val_ser.set_type_adapter_mocks(self) + return False + + core_config = config_wrapper.core_config(None) + + self.validator = create_schema_validator( + schema=self.core_schema, + schema_type=self._type, + schema_type_module=self._module_name, + schema_type_name=str(self._type), + schema_kind='TypeAdapter', + config=core_config, + plugin_settings=config_wrapper.plugin_settings, + ) + self.serializer = SchemaSerializer(self.core_schema, core_config) + + self.pydantic_complete = True + return True + + @property + def _defer_build(self) -> bool: + config = self._config if self._config is not None else self._model_config + if config: + return config.get('defer_build') is True + return False + + @property + def _model_config(self) -> ConfigDict | None: + type_: Any = _typing_extra.annotated_type(self._type) or self._type # Eg FastAPI heavily uses Annotated + if _utils.lenient_issubclass(type_, BaseModel): + return type_.model_config + return getattr(type_, '__pydantic_config__', None) + + def __repr__(self) -> str: + return f'TypeAdapter({_repr.display_as_type(self._type)})' + + def rebuild( + self, + *, + force: bool = False, + raise_errors: bool = True, + _parent_namespace_depth: int = 2, + _types_namespace: _namespace_utils.MappingNamespace | None = None, + ) -> bool | None: + """Try to rebuild the pydantic-core schema for the adapter's type. + + This may be necessary when one of the annotations is a ForwardRef which could not be resolved during + the initial attempt to build the schema, and automatic rebuilding fails. + + Args: + force: Whether to force the rebuilding of the type adapter's schema, defaults to `False`. + raise_errors: Whether to raise errors, defaults to `True`. + _parent_namespace_depth: Depth at which to search for the [parent frame][frame-objects]. This + frame is used when resolving forward annotations during schema rebuilding, by looking for + the locals of this frame. Defaults to 2, which will result in the frame where the method + was called. + _types_namespace: An explicit types namespace to use, instead of using the local namespace + from the parent frame. Defaults to `None`. + + Returns: + Returns `None` if the schema is already "complete" and rebuilding was not required. + If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`. + """ + if not force and self.pydantic_complete: + return None + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} + else: + rebuild_ns = {} + + # we have to manually fetch globals here because there's no type on the stack of the NsResolver + # and so we skip the globalns = get_module_ns_of(typ) call that would normally happen + globalns = sys._getframe(max(_parent_namespace_depth - 1, 1)).f_globals + ns_resolver = _namespace_utils.NsResolver( + namespaces_tuple=_namespace_utils.NamespacesTuple(locals=rebuild_ns, globals=globalns), + parent_namespace=rebuild_ns, + ) + return self._init_core_attrs(ns_resolver=ns_resolver, force=True, raise_errors=raise_errors) + + def validate_python( + self, + object: Any, + /, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> T: + """Validate a Python object against the model. + + Args: + object: The Python object to validate against the model. + strict: Whether to strictly check types. + extra: Whether to ignore, allow, or forbid extra data during model validation. + See the [`extra` configuration value][pydantic.ConfigDict.extra] for details. + from_attributes: Whether to extract data from object attributes. + context: Additional context to pass to the validator. + experimental_allow_partial: **Experimental** whether to enable + [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams. + * False / 'off': Default behavior, no partial validation. + * True / 'on': Enable partial validation. + * 'trailing-strings': Enable partial validation and allow trailing strings in the input. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + !!! note + When using `TypeAdapter` with a Pydantic `dataclass`, the use of the `from_attributes` + argument is not supported. + + Returns: + The validated object. + """ + if by_alias is False and by_name is not True: + raise PydanticUserError( + 'At least one of `by_alias` or `by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return self.validator.validate_python( + object, + strict=strict, + extra=extra, + from_attributes=from_attributes, + context=context, + allow_partial=experimental_allow_partial, + by_alias=by_alias, + by_name=by_name, + ) + + def validate_json( + self, + data: str | bytes | bytearray, + /, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + context: Any | None = None, + experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> T: + """!!! abstract "Usage Documentation" + [JSON Parsing](../concepts/json.md#json-parsing) + + Validate a JSON string or bytes against the model. + + Args: + data: The JSON data to validate against the model. + strict: Whether to strictly check types. + extra: Whether to ignore, allow, or forbid extra data during model validation. + See the [`extra` configuration value][pydantic.ConfigDict.extra] for details. + context: Additional context to use during validation. + experimental_allow_partial: **Experimental** whether to enable + [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams. + * False / 'off': Default behavior, no partial validation. + * True / 'on': Enable partial validation. + * 'trailing-strings': Enable partial validation and allow trailing strings in the input. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Returns: + The validated object. + """ + if by_alias is False and by_name is not True: + raise PydanticUserError( + 'At least one of `by_alias` or `by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return self.validator.validate_json( + data, + strict=strict, + extra=extra, + context=context, + allow_partial=experimental_allow_partial, + by_alias=by_alias, + by_name=by_name, + ) + + def validate_strings( + self, + obj: Any, + /, + *, + strict: bool | None = None, + extra: ExtraValues | None = None, + context: Any | None = None, + experimental_allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> T: + """Validate object contains string data against the model. + + Args: + obj: The object contains string data to validate. + strict: Whether to strictly check types. + extra: Whether to ignore, allow, or forbid extra data during model validation. + See the [`extra` configuration value][pydantic.ConfigDict.extra] for details. + context: Additional context to use during validation. + experimental_allow_partial: **Experimental** whether to enable + [partial validation](../concepts/experimental.md#partial-validation), e.g. to process streams. + * False / 'off': Default behavior, no partial validation. + * True / 'on': Enable partial validation. + * 'trailing-strings': Enable partial validation and allow trailing strings in the input. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Returns: + The validated object. + """ + if by_alias is False and by_name is not True: + raise PydanticUserError( + 'At least one of `by_alias` or `by_name` must be set to True.', + code='validate-by-alias-and-name-false', + ) + + return self.validator.validate_strings( + obj, + strict=strict, + extra=extra, + context=context, + allow_partial=experimental_allow_partial, + by_alias=by_alias, + by_name=by_name, + ) + + def get_default_value(self, *, strict: bool | None = None, context: Any | None = None) -> Some[T] | None: + """Get the default value for the wrapped type. + + Args: + strict: Whether to strictly check types. + context: Additional context to pass to the validator. + + Returns: + The default value wrapped in a `Some` if there is one or None if not. + """ + return self.validator.get_default_value(strict=strict, context=context) + + def dump_python( + self, + instance: T, + /, + *, + mode: Literal['json', 'python'] = 'python', + include: IncEx | None = None, + exclude: IncEx | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + context: Any | None = None, + ) -> Any: + """Dump an instance of the adapted type to a Python object. + + Args: + instance: The Python object to serialize. + mode: The output format. + include: Fields to include in the output. + exclude: Fields to exclude from the output. + by_alias: Whether to use alias names for field names. + exclude_unset: Whether to exclude unset fields. + exclude_defaults: Whether to exclude fields with default values. + exclude_none: Whether to exclude fields with None values. + exclude_computed_fields: Whether to exclude computed fields. + While this can be useful for round-tripping, it is usually recommended to use the dedicated + `round_trip` parameter instead. + round_trip: Whether to output the serialized data in a way that is compatible with deserialization. + warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered. If not provided, + a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + context: Additional context to pass to the serializer. + + Returns: + The serialized object. + """ + return self.serializer.to_python( + instance, + mode=mode, + by_alias=by_alias, + include=include, + exclude=exclude, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + exclude_computed_fields=exclude_computed_fields, + round_trip=round_trip, + warnings=warnings, + fallback=fallback, + serialize_as_any=serialize_as_any, + polymorphic_serialization=polymorphic_serialization, + context=context, + ) + + def dump_json( + self, + instance: T, + /, + *, + indent: int | None = None, + ensure_ascii: bool = False, + include: IncEx | None = None, + exclude: IncEx | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + context: Any | None = None, + ) -> bytes: + """!!! abstract "Usage Documentation" + [JSON Serialization](../concepts/json.md#json-serialization) + + Serialize an instance of the adapted type to JSON. + + Args: + instance: The instance to be serialized. + indent: Number of spaces for JSON indentation. + ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped. + If `False` (the default), these characters will be output as-is. + include: Fields to include. + exclude: Fields to exclude. + by_alias: Whether to use alias names for field names. + exclude_unset: Whether to exclude unset fields. + exclude_defaults: Whether to exclude fields with default values. + exclude_none: Whether to exclude fields with a value of `None`. + exclude_computed_fields: Whether to exclude computed fields. + While this can be useful for round-tripping, it is usually recommended to use the dedicated + `round_trip` parameter instead. + round_trip: Whether to serialize and deserialize the instance to ensure round-tripping. + warnings: How to handle serialization errors. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered. If not provided, + a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + context: Additional context to pass to the serializer. + + Returns: + The JSON representation of the given instance as bytes. + """ + return self.serializer.to_json( + instance, + indent=indent, + ensure_ascii=ensure_ascii, + include=include, + exclude=exclude, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + exclude_computed_fields=exclude_computed_fields, + round_trip=round_trip, + warnings=warnings, + fallback=fallback, + serialize_as_any=serialize_as_any, + polymorphic_serialization=polymorphic_serialization, + context=context, + ) + + def json_schema( + self, + *, + by_alias: bool = True, + ref_template: str = DEFAULT_REF_TEMPLATE, + union_format: Literal['any_of', 'primitive_type_array'] = 'any_of', + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + mode: JsonSchemaMode = 'validation', + ) -> dict[str, Any]: + """Generate a JSON schema for the adapted type. + + Args: + by_alias: Whether to use alias names for field names. + ref_template: The format string used for generating $ref strings. + union_format: The format to use when combining schemas from unions together. Can be one of: + + - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf) + keyword to combine schemas (the default). + - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type) + keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive + type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to + `any_of`. + schema_generator: To override the logic used to generate the JSON schema, as a subclass of + `GenerateJsonSchema` with your desired modifications + mode: The mode in which to generate the schema. + schema_generator: The generator class used for creating the schema. + mode: The mode to use for schema generation. + + Returns: + The JSON schema for the model as a dictionary. + """ + schema_generator_instance = schema_generator( + by_alias=by_alias, ref_template=ref_template, union_format=union_format + ) + if isinstance(self.core_schema, _mock_val_ser.MockCoreSchema): + self.core_schema.rebuild() + assert not isinstance(self.core_schema, _mock_val_ser.MockCoreSchema), 'this is a bug! please report it' + return schema_generator_instance.generate(self.core_schema, mode=mode) + + @staticmethod + def json_schemas( + inputs: Iterable[tuple[JsonSchemaKeyT, JsonSchemaMode, TypeAdapter[Any]]], + /, + *, + by_alias: bool = True, + title: str | None = None, + description: str | None = None, + ref_template: str = DEFAULT_REF_TEMPLATE, + union_format: Literal['any_of', 'primitive_type_array'] = 'any_of', + schema_generator: type[GenerateJsonSchema] = GenerateJsonSchema, + ) -> tuple[dict[tuple[JsonSchemaKeyT, JsonSchemaMode], JsonSchemaValue], JsonSchemaValue]: + """Generate a JSON schema including definitions from multiple type adapters. + + Args: + inputs: Inputs to schema generation. The first two items will form the keys of the (first) + output mapping; the type adapters will provide the core schemas that get converted into + definitions in the output JSON schema. + by_alias: Whether to use alias names. + title: The title for the schema. + description: The description for the schema. + ref_template: The format string used for generating $ref strings. + union_format: The format to use when combining schemas from unions together. Can be one of: + + - `'any_of'`: Use the [`anyOf`](https://json-schema.org/understanding-json-schema/reference/combining#anyOf) + keyword to combine schemas (the default). + - `'primitive_type_array'`: Use the [`type`](https://json-schema.org/understanding-json-schema/reference/type) + keyword as an array of strings, containing each type of the combination. If any of the schemas is not a primitive + type (`string`, `boolean`, `null`, `integer` or `number`) or contains constraints/metadata, falls back to + `any_of`. + schema_generator: The generator class used for creating the schema. + + Returns: + A tuple where: + + - The first element is a dictionary whose keys are tuples of JSON schema key type and JSON mode, and + whose values are the JSON schema corresponding to that pair of inputs. (These schemas may have + JsonRef references to definitions that are defined in the second returned element.) + - The second element is a JSON schema containing all definitions referenced in the first returned + element, along with the optional title and description keys. + + """ + schema_generator_instance = schema_generator( + by_alias=by_alias, ref_template=ref_template, union_format=union_format + ) + + inputs_ = [] + for key, mode, adapter in inputs: + # This is the same pattern we follow for model json schemas - we attempt a core schema rebuild if we detect a mock + if isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema): + adapter.core_schema.rebuild() + assert not isinstance(adapter.core_schema, _mock_val_ser.MockCoreSchema), ( + 'this is a bug! please report it' + ) + inputs_.append((key, mode, adapter.core_schema)) + + json_schemas_map, definitions = schema_generator_instance.generate_definitions(inputs_) + + json_schema: dict[str, Any] = {} + if definitions: + json_schema['$defs'] = definitions + if title: + json_schema['title'] = title + if description: + json_schema['description'] = description + + return json_schemas_map, json_schema diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/types.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/types.py new file mode 100644 index 0000000000000000000000000000000000000000..467bc8285b4d6a1fa271d0aaa7d1f6fb7735c7a0 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/types.py @@ -0,0 +1,3310 @@ +"""The types module contains custom types used by pydantic.""" + +from __future__ import annotations as _annotations + +import base64 +import dataclasses as _dataclasses +import re +from collections.abc import Hashable, Iterator +from datetime import date, datetime +from decimal import Decimal +from enum import Enum +from pathlib import Path +from re import Pattern +from types import ModuleType +from typing import ( + TYPE_CHECKING, + Annotated, + Any, + Callable, + ClassVar, + Generic, + Literal, + TypeVar, + Union, + cast, +) +from uuid import UUID + +import annotated_types +from annotated_types import BaseMetadata, MaxLen, MinLen +from pydantic_core import CoreSchema, PydanticCustomError, SchemaSerializer, core_schema +from typing_extensions import Protocol, TypeAlias, TypeAliasType, deprecated, get_args, get_origin + +from ._internal import _fields, _internal_dataclass, _utils, _validators +from ._migration import getattr_migration +from .annotated_handlers import GetCoreSchemaHandler, GetJsonSchemaHandler +from .errors import PydanticUserError +from .json_schema import JsonSchemaValue +from .warnings import PydanticDeprecatedSince20 + +if TYPE_CHECKING: + from ._internal._core_metadata import CoreMetadata + +__all__ = ( + 'Strict', + 'StrictStr', + 'SocketPath', + 'conbytes', + 'conlist', + 'conset', + 'confrozenset', + 'constr', + 'ImportString', + 'conint', + 'PositiveInt', + 'NegativeInt', + 'NonNegativeInt', + 'NonPositiveInt', + 'confloat', + 'PositiveFloat', + 'NegativeFloat', + 'NonNegativeFloat', + 'NonPositiveFloat', + 'FiniteFloat', + 'condecimal', + 'UUID1', + 'UUID3', + 'UUID4', + 'UUID5', + 'UUID6', + 'UUID7', + 'UUID8', + 'FilePath', + 'DirectoryPath', + 'NewPath', + 'Json', + 'Secret', + 'SecretStr', + 'SecretBytes', + 'StrictBool', + 'StrictBytes', + 'StrictInt', + 'StrictFloat', + 'PaymentCardNumber', + 'ByteSize', + 'PastDate', + 'FutureDate', + 'PastDatetime', + 'FutureDatetime', + 'condate', + 'AwareDatetime', + 'NaiveDatetime', + 'AllowInfNan', + 'EncoderProtocol', + 'EncodedBytes', + 'EncodedStr', + 'Base64Encoder', + 'Base64Bytes', + 'Base64Str', + 'Base64UrlBytes', + 'Base64UrlStr', + 'GetPydanticSchema', + 'StringConstraints', + 'Tag', + 'Discriminator', + 'JsonValue', + 'OnErrorOmit', + 'FailFast', +) + + +T = TypeVar('T') + + +@_dataclasses.dataclass +class Strict(_fields.PydanticMetadata, BaseMetadata): + """!!! abstract "Usage Documentation" + [Strict Mode with `Annotated` `Strict`](../concepts/strict_mode.md#strict-mode-with-annotated-strict) + + A field metadata class to indicate that a field should be validated in strict mode. + Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below. + + Attributes: + strict: Whether to validate the field in strict mode. + + Example: + ```python + from typing import Annotated + + from pydantic.types import Strict + + StrictBool = Annotated[bool, Strict()] + ``` + """ + + strict: bool = True + + def __hash__(self) -> int: + return hash(self.strict) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BOOLEAN TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +StrictBool = Annotated[bool, Strict()] +"""A boolean that must be either ``True`` or ``False``.""" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INTEGER TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +def conint( + *, + strict: bool | None = None, + gt: int | None = None, + ge: int | None = None, + lt: int | None = None, + le: int | None = None, + multiple_of: int | None = None, +) -> type[int]: + """ + !!! warning "Discouraged" + This function is **discouraged** in favor of using + [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with + [`Field`][pydantic.fields.Field] instead. + + This function will be **deprecated** in Pydantic 3.0. + + The reason is that `conint` returns a type, which doesn't play well with static analysis tools. + + === ":x: Don't do this" + ```python + from pydantic import BaseModel, conint + + class Foo(BaseModel): + bar: conint(strict=True, gt=0) + ``` + + === ":white_check_mark: Do this" + ```python + from typing import Annotated + + from pydantic import BaseModel, Field + + class Foo(BaseModel): + bar: Annotated[int, Field(strict=True, gt=0)] + ``` + + A wrapper around `int` that allows for additional constraints. + + Args: + strict: Whether to validate the integer in strict mode. Defaults to `None`. + gt: The value must be greater than this. + ge: The value must be greater than or equal to this. + lt: The value must be less than this. + le: The value must be less than or equal to this. + multiple_of: The value must be a multiple of this. + + Returns: + The wrapped integer type. + + ```python + from pydantic import BaseModel, ValidationError, conint + + class ConstrainedExample(BaseModel): + constrained_int: conint(gt=1) + + m = ConstrainedExample(constrained_int=2) + print(repr(m)) + #> ConstrainedExample(constrained_int=2) + + try: + ConstrainedExample(constrained_int=0) + except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('constrained_int',), + 'msg': 'Input should be greater than 1', + 'input': 0, + 'ctx': {'gt': 1}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than', + } + ] + ''' + ``` + + """ # noqa: D212 + return Annotated[ # pyright: ignore[reportReturnType] + int, + Strict(strict) if strict is not None else None, + annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le), + annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None, + ] + + +PositiveInt = Annotated[int, annotated_types.Gt(0)] +"""An integer that must be greater than zero. + +```python +from pydantic import BaseModel, PositiveInt, ValidationError + +class Model(BaseModel): + positive_int: PositiveInt + +m = Model(positive_int=1) +print(repr(m)) +#> Model(positive_int=1) + +try: + Model(positive_int=-1) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('positive_int',), + 'msg': 'Input should be greater than 0', + 'input': -1, + 'ctx': {'gt': 0}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than', + } + ] + ''' +``` +""" +NegativeInt = Annotated[int, annotated_types.Lt(0)] +"""An integer that must be less than zero. + +```python +from pydantic import BaseModel, NegativeInt, ValidationError + +class Model(BaseModel): + negative_int: NegativeInt + +m = Model(negative_int=-1) +print(repr(m)) +#> Model(negative_int=-1) + +try: + Model(negative_int=1) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'less_than', + 'loc': ('negative_int',), + 'msg': 'Input should be less than 0', + 'input': 1, + 'ctx': {'lt': 0}, + 'url': 'https://errors.pydantic.dev/2/v/less_than', + } + ] + ''' +``` +""" +NonPositiveInt = Annotated[int, annotated_types.Le(0)] +"""An integer that must be less than or equal to zero. + +```python +from pydantic import BaseModel, NonPositiveInt, ValidationError + +class Model(BaseModel): + non_positive_int: NonPositiveInt + +m = Model(non_positive_int=0) +print(repr(m)) +#> Model(non_positive_int=0) + +try: + Model(non_positive_int=1) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'less_than_equal', + 'loc': ('non_positive_int',), + 'msg': 'Input should be less than or equal to 0', + 'input': 1, + 'ctx': {'le': 0}, + 'url': 'https://errors.pydantic.dev/2/v/less_than_equal', + } + ] + ''' +``` +""" +NonNegativeInt = Annotated[int, annotated_types.Ge(0)] +"""An integer that must be greater than or equal to zero. + +```python +from pydantic import BaseModel, NonNegativeInt, ValidationError + +class Model(BaseModel): + non_negative_int: NonNegativeInt + +m = Model(non_negative_int=0) +print(repr(m)) +#> Model(non_negative_int=0) + +try: + Model(non_negative_int=-1) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than_equal', + 'loc': ('non_negative_int',), + 'msg': 'Input should be greater than or equal to 0', + 'input': -1, + 'ctx': {'ge': 0}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than_equal', + } + ] + ''' +``` +""" +StrictInt = Annotated[int, Strict()] +"""An integer that must be validated in strict mode. + +```python +from pydantic import BaseModel, StrictInt, ValidationError + +class StrictIntModel(BaseModel): + strict_int: StrictInt + +try: + StrictIntModel(strict_int=3.14159) +except ValidationError as e: + print(e) + ''' + 1 validation error for StrictIntModel + strict_int + Input should be a valid integer [type=int_type, input_value=3.14159, input_type=float] + ''' +``` +""" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FLOAT TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +@_dataclasses.dataclass +class AllowInfNan(_fields.PydanticMetadata): + """A field metadata class to indicate that a field should allow `-inf`, `inf`, and `nan`. + + Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below. + + Attributes: + allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`. Defaults to `True`. + + Example: + ```python + from typing import Annotated + + from pydantic.types import AllowInfNan + + LaxFloat = Annotated[float, AllowInfNan()] + ``` + """ + + allow_inf_nan: bool = True + + def __hash__(self) -> int: + return hash(self.allow_inf_nan) + + +def confloat( + *, + strict: bool | None = None, + gt: float | None = None, + ge: float | None = None, + lt: float | None = None, + le: float | None = None, + multiple_of: float | None = None, + allow_inf_nan: bool | None = None, +) -> type[float]: + """ + !!! warning "Discouraged" + This function is **discouraged** in favor of using + [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with + [`Field`][pydantic.fields.Field] instead. + + This function will be **deprecated** in Pydantic 3.0. + + The reason is that `confloat` returns a type, which doesn't play well with static analysis tools. + + === ":x: Don't do this" + ```python + from pydantic import BaseModel, confloat + + class Foo(BaseModel): + bar: confloat(strict=True, gt=0) + ``` + + === ":white_check_mark: Do this" + ```python + from typing import Annotated + + from pydantic import BaseModel, Field + + class Foo(BaseModel): + bar: Annotated[float, Field(strict=True, gt=0)] + ``` + + A wrapper around `float` that allows for additional constraints. + + Args: + strict: Whether to validate the float in strict mode. + gt: The value must be greater than this. + ge: The value must be greater than or equal to this. + lt: The value must be less than this. + le: The value must be less than or equal to this. + multiple_of: The value must be a multiple of this. + allow_inf_nan: Whether to allow `-inf`, `inf`, and `nan`. + + Returns: + The wrapped float type. + + ```python + from pydantic import BaseModel, ValidationError, confloat + + class ConstrainedExample(BaseModel): + constrained_float: confloat(gt=1.0) + + m = ConstrainedExample(constrained_float=1.1) + print(repr(m)) + #> ConstrainedExample(constrained_float=1.1) + + try: + ConstrainedExample(constrained_float=0.9) + except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('constrained_float',), + 'msg': 'Input should be greater than 1', + 'input': 0.9, + 'ctx': {'gt': 1.0}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than', + } + ] + ''' + ``` + """ # noqa: D212 + return Annotated[ # pyright: ignore[reportReturnType] + float, + Strict(strict) if strict is not None else None, + annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le), + annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None, + AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None, + ] + + +PositiveFloat = Annotated[float, annotated_types.Gt(0)] +"""A float that must be greater than zero. + +```python +from pydantic import BaseModel, PositiveFloat, ValidationError + +class Model(BaseModel): + positive_float: PositiveFloat + +m = Model(positive_float=1.0) +print(repr(m)) +#> Model(positive_float=1.0) + +try: + Model(positive_float=-1.0) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('positive_float',), + 'msg': 'Input should be greater than 0', + 'input': -1.0, + 'ctx': {'gt': 0.0}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than', + } + ] + ''' +``` +""" +NegativeFloat = Annotated[float, annotated_types.Lt(0)] +"""A float that must be less than zero. + +```python +from pydantic import BaseModel, NegativeFloat, ValidationError + +class Model(BaseModel): + negative_float: NegativeFloat + +m = Model(negative_float=-1.0) +print(repr(m)) +#> Model(negative_float=-1.0) + +try: + Model(negative_float=1.0) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'less_than', + 'loc': ('negative_float',), + 'msg': 'Input should be less than 0', + 'input': 1.0, + 'ctx': {'lt': 0.0}, + 'url': 'https://errors.pydantic.dev/2/v/less_than', + } + ] + ''' +``` +""" +NonPositiveFloat = Annotated[float, annotated_types.Le(0)] +"""A float that must be less than or equal to zero. + +```python +from pydantic import BaseModel, NonPositiveFloat, ValidationError + +class Model(BaseModel): + non_positive_float: NonPositiveFloat + +m = Model(non_positive_float=0.0) +print(repr(m)) +#> Model(non_positive_float=0.0) + +try: + Model(non_positive_float=1.0) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'less_than_equal', + 'loc': ('non_positive_float',), + 'msg': 'Input should be less than or equal to 0', + 'input': 1.0, + 'ctx': {'le': 0.0}, + 'url': 'https://errors.pydantic.dev/2/v/less_than_equal', + } + ] + ''' +``` +""" +NonNegativeFloat = Annotated[float, annotated_types.Ge(0)] +"""A float that must be greater than or equal to zero. + +```python +from pydantic import BaseModel, NonNegativeFloat, ValidationError + +class Model(BaseModel): + non_negative_float: NonNegativeFloat + +m = Model(non_negative_float=0.0) +print(repr(m)) +#> Model(non_negative_float=0.0) + +try: + Model(non_negative_float=-1.0) +except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than_equal', + 'loc': ('non_negative_float',), + 'msg': 'Input should be greater than or equal to 0', + 'input': -1.0, + 'ctx': {'ge': 0.0}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than_equal', + } + ] + ''' +``` +""" +StrictFloat = Annotated[float, Strict(True)] +"""A float that must be validated in strict mode. + +```python +from pydantic import BaseModel, StrictFloat, ValidationError + +class StrictFloatModel(BaseModel): + strict_float: StrictFloat + +try: + StrictFloatModel(strict_float='1.0') +except ValidationError as e: + print(e) + ''' + 1 validation error for StrictFloatModel + strict_float + Input should be a valid number [type=float_type, input_value='1.0', input_type=str] + ''' +``` +""" +FiniteFloat = Annotated[float, AllowInfNan(False)] +"""A float that must be finite (not ``-inf``, ``inf``, or ``nan``). + +```python +from pydantic import BaseModel, FiniteFloat + +class Model(BaseModel): + finite: FiniteFloat + +m = Model(finite=1.0) +print(m) +#> finite=1.0 +``` +""" + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTES TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +def conbytes( + *, + min_length: int | None = None, + max_length: int | None = None, + strict: bool | None = None, +) -> type[bytes]: + """A wrapper around `bytes` that allows for additional constraints. + + Args: + min_length: The minimum length of the bytes. + max_length: The maximum length of the bytes. + strict: Whether to validate the bytes in strict mode. + + Returns: + The wrapped bytes type. + """ + return Annotated[ # pyright: ignore[reportReturnType] + bytes, + Strict(strict) if strict is not None else None, + annotated_types.Len(min_length or 0, max_length), + ] + + +StrictBytes = Annotated[bytes, Strict()] +"""A bytes that must be validated in strict mode.""" + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ STRING TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +@_dataclasses.dataclass(frozen=True) +class StringConstraints(annotated_types.GroupedMetadata): + """!!! abstract "Usage Documentation" + [String types](./standard_library_types.md#strings) + + A field metadata class to apply constraints to `str` types. + Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below. + + Attributes: + strip_whitespace: Whether to remove leading and trailing whitespace. + to_upper: Whether to convert the string to uppercase. + to_lower: Whether to convert the string to lowercase. + strict: Whether to validate the string in strict mode. + min_length: The minimum length of the string. + max_length: The maximum length of the string. + pattern: A regex pattern that the string must match. + ascii_only: Whether the string should contain only ASCII characters. + + Example: + ```python + from typing import Annotated + + from pydantic.types import StringConstraints + + ConstrainedStr = Annotated[str, StringConstraints(min_length=1, max_length=10)] + ``` + """ + + strip_whitespace: bool | None = None + to_upper: bool | None = None + to_lower: bool | None = None + strict: bool | None = None + min_length: int | None = None + max_length: int | None = None + pattern: str | Pattern[str] | None = None + ascii_only: bool | None = None + + def __iter__(self) -> Iterator[BaseMetadata]: + if self.min_length is not None: + yield MinLen(self.min_length) + if self.max_length is not None: + yield MaxLen(self.max_length) + if self.strict is not None: + yield Strict(self.strict) + if ( + self.strip_whitespace is not None + or self.pattern is not None + or self.to_lower is not None + or self.to_upper is not None + or self.ascii_only is not None + ): + yield _fields.pydantic_general_metadata( + strip_whitespace=self.strip_whitespace, + to_upper=self.to_upper, + to_lower=self.to_lower, + pattern=self.pattern, + ascii_only=self.ascii_only, + ) + + +def constr( + *, + strip_whitespace: bool | None = None, + to_upper: bool | None = None, + to_lower: bool | None = None, + strict: bool | None = None, + min_length: int | None = None, + max_length: int | None = None, + pattern: str | Pattern[str] | None = None, + ascii_only: bool | None = None, +) -> type[str]: + """ + !!! warning "Discouraged" + This function is **discouraged** in favor of using + [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with + [`StringConstraints`][pydantic.types.StringConstraints] instead. + + This function will be **deprecated** in Pydantic 3.0. + + The reason is that `constr` returns a type, which doesn't play well with static analysis tools. + + === ":x: Don't do this" + ```python + from pydantic import BaseModel, constr + + class Foo(BaseModel): + bar: constr(strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$') + ``` + + === ":white_check_mark: Do this" + ```python + from typing import Annotated + + from pydantic import BaseModel, StringConstraints + + class Foo(BaseModel): + bar: Annotated[ + str, + StringConstraints( + strip_whitespace=True, to_upper=True, pattern=r'^[A-Z]+$' + ), + ] + ``` + + A wrapper around `str` that allows for additional constraints. + + ```python + from pydantic import BaseModel, constr + + class Foo(BaseModel): + bar: constr(strip_whitespace=True, to_upper=True) + + foo = Foo(bar=' hello ') + print(foo) + #> bar='HELLO' + ``` + + Args: + strip_whitespace: Whether to remove leading and trailing whitespace. + to_upper: Whether to turn all characters to uppercase. + to_lower: Whether to turn all characters to lowercase. + strict: Whether to validate the string in strict mode. + min_length: The minimum length of the string. + max_length: The maximum length of the string. + pattern: A regex pattern to validate the string against. + ascii_only: Whether the string should contain only ASCII characters. + + Returns: + The wrapped string type. + """ # noqa: D212 + return Annotated[ # pyright: ignore[reportReturnType] + str, + StringConstraints( + strip_whitespace=strip_whitespace, + to_upper=to_upper, + to_lower=to_lower, + strict=strict, + min_length=min_length, + max_length=max_length, + pattern=pattern, + ascii_only=ascii_only, + ), + ] + + +StrictStr = Annotated[str, Strict()] +"""A string that must be validated in strict mode.""" + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~ COLLECTION TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +HashableItemType = TypeVar('HashableItemType', bound=Hashable) + + +def conset( + item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None +) -> type[set[HashableItemType]]: + """A wrapper around `typing.Set` that allows for additional constraints. + + Args: + item_type: The type of the items in the set. + min_length: The minimum length of the set. + max_length: The maximum length of the set. + + Returns: + The wrapped set type. + """ + return Annotated[set[item_type], annotated_types.Len(min_length or 0, max_length)] # pyright: ignore[reportReturnType] + + +def confrozenset( + item_type: type[HashableItemType], *, min_length: int | None = None, max_length: int | None = None +) -> type[frozenset[HashableItemType]]: + """A wrapper around `typing.FrozenSet` that allows for additional constraints. + + Args: + item_type: The type of the items in the frozenset. + min_length: The minimum length of the frozenset. + max_length: The maximum length of the frozenset. + + Returns: + The wrapped frozenset type. + """ + return Annotated[frozenset[item_type], annotated_types.Len(min_length or 0, max_length)] # pyright: ignore[reportReturnType] + + +AnyItemType = TypeVar('AnyItemType') + + +def conlist( + item_type: type[AnyItemType], + *, + min_length: int | None = None, + max_length: int | None = None, + unique_items: bool | None = None, +) -> type[list[AnyItemType]]: + """A wrapper around [`list`][] that adds validation. + + Args: + item_type: The type of the items in the list. + min_length: The minimum length of the list. Defaults to None. + max_length: The maximum length of the list. Defaults to None. + unique_items: Whether the items in the list must be unique. Defaults to None. + !!! warning Deprecated + The `unique_items` parameter is deprecated, use `Set` instead. + See [this issue](https://github.com/pydantic/pydantic-core/issues/296) for more details. + + Returns: + The wrapped list type. + """ + if unique_items is not None: + raise PydanticUserError( + ( + '`unique_items` is removed, use `Set` instead' + '(this feature is discussed in https://github.com/pydantic/pydantic-core/issues/296)' + ), + code='removed-kwargs', + ) + return Annotated[list[item_type], annotated_types.Len(min_length or 0, max_length)] # pyright: ignore[reportReturnType] + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~ IMPORT STRING TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +AnyType = TypeVar('AnyType') +if TYPE_CHECKING: + ImportString = Annotated[AnyType, ...] +else: + + class ImportString: + """A type that can be used to import a Python object from a string. + + `ImportString` expects a string and loads the Python object importable at that dotted path. + Attributes of modules may be separated from the module by `:` or `.`, e.g. if `'math:cos'` is provided, + the resulting field value would be the function `cos`. If a `.` is used and both an attribute and submodule + are present at the same path, the module will be preferred. + + On model instantiation, pointers will be evaluated and imported. There is + some nuance to this behavior, demonstrated in the examples below. + + ```python + import math + + from pydantic import BaseModel, Field, ImportString, ValidationError + + class ImportThings(BaseModel): + obj: ImportString + + # A string value will cause an automatic import + my_cos = ImportThings(obj='math.cos') + + # You can use the imported function as you would expect + cos_of_0 = my_cos.obj(0) + assert cos_of_0 == 1 + + # A string whose value cannot be imported will raise an error + try: + ImportThings(obj='foo.bar') + except ValidationError as e: + print(e) + ''' + 1 validation error for ImportThings + obj + Invalid python path: No module named 'foo' [type=import_error, input_value='foo.bar', input_type=str] + ''' + + # Actual python objects can be assigned as well + my_cos = ImportThings(obj=math.cos) + my_cos_2 = ImportThings(obj='math.cos') + my_cos_3 = ImportThings(obj='math:cos') + assert my_cos == my_cos_2 == my_cos_3 + + # You can set default field value either as Python object: + class ImportThingsDefaultPyObj(BaseModel): + obj: ImportString = math.cos + + # or as a string value (but only if used with `validate_default=True`) + class ImportThingsDefaultString(BaseModel): + obj: ImportString = Field(default='math.cos', validate_default=True) + + my_cos_default1 = ImportThingsDefaultPyObj() + my_cos_default2 = ImportThingsDefaultString() + assert my_cos_default1.obj == my_cos_default2.obj == math.cos + + # note: this will not work! + class ImportThingsMissingValidateDefault(BaseModel): + obj: ImportString = 'math.cos' + + my_cos_default3 = ImportThingsMissingValidateDefault() + assert my_cos_default3.obj == 'math.cos' # just string, not evaluated + ``` + + Serializing an `ImportString` type to json is also possible. + + ```python + from pydantic import BaseModel, ImportString + + class ImportThings(BaseModel): + obj: ImportString + + # Create an instance + m = ImportThings(obj='math.cos') + print(m) + #> obj= + print(m.model_dump_json()) + #> {"obj":"math.cos"} + ``` + """ + + @classmethod + def __class_getitem__(cls, item: AnyType) -> AnyType: + return Annotated[item, cls()] + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + serializer = core_schema.plain_serializer_function_ser_schema(cls._serialize, when_used='json') + if cls is source: + # Treat bare usage of ImportString (`schema is None`) as the same as ImportString[Any] + return core_schema.no_info_plain_validator_function( + function=_validators.import_string, serialization=serializer + ) + else: + return core_schema.no_info_before_validator_function( + function=_validators.import_string, schema=handler(source), serialization=serializer + ) + + @classmethod + def __get_pydantic_json_schema__(cls, cs: CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue: + return handler(core_schema.str_schema()) + + @staticmethod + def _serialize(v: Any) -> str: + if isinstance(v, ModuleType): + return v.__name__ + elif hasattr(v, '__module__') and hasattr(v, '__name__'): + return f'{v.__module__}.{v.__name__}' + # Handle special cases for sys.XXX streams + # if we see more of these, we should consider a more general solution + elif hasattr(v, 'name'): + if v.name == '': + return 'sys.stdout' + elif v.name == '': + return 'sys.stdin' + elif v.name == '': + return 'sys.stderr' + return v + + def __repr__(self) -> str: + return 'ImportString' + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DECIMAL TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +def condecimal( + *, + strict: bool | None = None, + gt: int | Decimal | None = None, + ge: int | Decimal | None = None, + lt: int | Decimal | None = None, + le: int | Decimal | None = None, + multiple_of: int | Decimal | None = None, + max_digits: int | None = None, + decimal_places: int | None = None, + allow_inf_nan: bool | None = None, +) -> type[Decimal]: + """ + !!! warning "Discouraged" + This function is **discouraged** in favor of using + [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated) with + [`Field`][pydantic.fields.Field] instead. + + This function will be **deprecated** in Pydantic 3.0. + + The reason is that `condecimal` returns a type, which doesn't play well with static analysis tools. + + === ":x: Don't do this" + ```python + from pydantic import BaseModel, condecimal + + class Foo(BaseModel): + bar: condecimal(strict=True, allow_inf_nan=True) + ``` + + === ":white_check_mark: Do this" + ```python + from decimal import Decimal + from typing import Annotated + + from pydantic import BaseModel, Field + + class Foo(BaseModel): + bar: Annotated[Decimal, Field(strict=True, allow_inf_nan=True)] + ``` + + A wrapper around Decimal that adds validation. + + Args: + strict: Whether to validate the value in strict mode. Defaults to `None`. + gt: The value must be greater than this. Defaults to `None`. + ge: The value must be greater than or equal to this. Defaults to `None`. + lt: The value must be less than this. Defaults to `None`. + le: The value must be less than or equal to this. Defaults to `None`. + multiple_of: The value must be a multiple of this. Defaults to `None`. + max_digits: The maximum number of digits. Defaults to `None`. + decimal_places: The number of decimal places. Defaults to `None`. + allow_inf_nan: Whether to allow infinity and NaN. Defaults to `None`. + + ```python + from decimal import Decimal + + from pydantic import BaseModel, ValidationError, condecimal + + class ConstrainedExample(BaseModel): + constrained_decimal: condecimal(gt=Decimal('1.0')) + + m = ConstrainedExample(constrained_decimal=Decimal('1.1')) + print(repr(m)) + #> ConstrainedExample(constrained_decimal=Decimal('1.1')) + + try: + ConstrainedExample(constrained_decimal=Decimal('0.9')) + except ValidationError as e: + print(e.errors()) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('constrained_decimal',), + 'msg': 'Input should be greater than 1.0', + 'input': Decimal('0.9'), + 'ctx': {'gt': Decimal('1.0')}, + 'url': 'https://errors.pydantic.dev/2/v/greater_than', + } + ] + ''' + ``` + """ # noqa: D212 + return Annotated[ # pyright: ignore[reportReturnType] + Decimal, + Strict(strict) if strict is not None else None, + annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le), + annotated_types.MultipleOf(multiple_of) if multiple_of is not None else None, + _fields.pydantic_general_metadata(max_digits=max_digits, decimal_places=decimal_places), + AllowInfNan(allow_inf_nan) if allow_inf_nan is not None else None, + ] + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ UUID TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +@_dataclasses.dataclass(**_internal_dataclass.slots_true) +class UuidVersion: + """A field metadata class to indicate a [UUID](https://docs.python.org/3/library/uuid.html) version. + + Use this class as an annotation via [`Annotated`](https://docs.python.org/3/library/typing.html#typing.Annotated), as seen below. + + Attributes: + uuid_version: The version of the UUID. Must be one of 1, 3, 4, 5, 6, 7 or 8. + + Example: + ```python + from typing import Annotated + from uuid import UUID + + from pydantic.types import UuidVersion + + UUID1 = Annotated[UUID, UuidVersion(1)] + ``` + """ + + uuid_version: Literal[1, 3, 4, 5, 6, 7, 8] + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + field_schema.pop('anyOf', None) # remove the bytes/str union + field_schema.update(type='string', format=f'uuid{self.uuid_version}') + return field_schema + + def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source) + _check_annotated_type(schema['type'], 'uuid', self.__class__.__name__) + schema['version'] = self.uuid_version # type: ignore + return schema + + def __hash__(self) -> int: + return hash(self.uuid_version) + + +UUID1 = Annotated[UUID, UuidVersion(1)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 1. + +```python +import uuid + +from pydantic import UUID1, BaseModel + +class Model(BaseModel): + uuid1: UUID1 + +Model(uuid1=uuid.uuid1()) +``` +""" +UUID3 = Annotated[UUID, UuidVersion(3)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 3. + +```python +import uuid + +from pydantic import UUID3, BaseModel + +class Model(BaseModel): + uuid3: UUID3 + +Model(uuid3=uuid.uuid3(uuid.NAMESPACE_DNS, 'pydantic.org')) +``` +""" +UUID4 = Annotated[UUID, UuidVersion(4)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 4. + +```python +import uuid + +from pydantic import UUID4, BaseModel + +class Model(BaseModel): + uuid4: UUID4 + +Model(uuid4=uuid.uuid4()) +``` +""" +UUID5 = Annotated[UUID, UuidVersion(5)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 5. + +```python +import uuid + +from pydantic import UUID5, BaseModel + +class Model(BaseModel): + uuid5: UUID5 + +Model(uuid5=uuid.uuid5(uuid.NAMESPACE_DNS, 'pydantic.org')) +``` +""" +UUID6 = Annotated[UUID, UuidVersion(6)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 6. + +```python +import uuid + +from pydantic import UUID6, BaseModel + +class Model(BaseModel): + uuid6: UUID6 + +Model(uuid6=uuid.UUID('1efea953-c2d6-6790-aa0a-69db8c87df97')) +``` +""" +UUID7 = Annotated[UUID, UuidVersion(7)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 7. + +```python +import uuid + +from pydantic import UUID7, BaseModel + +class Model(BaseModel): + uuid7: UUID7 + +Model(uuid7=uuid.UUID('0194fdcb-1c47-7a09-b52c-561154de0b4a')) +``` +""" +UUID8 = Annotated[UUID, UuidVersion(8)] +"""A [UUID](https://docs.python.org/3/library/uuid.html) that must be version 8. + +```python +import uuid + +from pydantic import UUID8, BaseModel + +class Model(BaseModel): + uuid8: UUID8 + +Model(uuid8=uuid.UUID('81a0b92e-6078-8551-9c81-8ccb666bdab8')) +``` +""" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PATH TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +@_dataclasses.dataclass +class PathType: + path_type: Literal['file', 'dir', 'new', 'socket'] + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + format_conversion = {'file': 'file-path', 'dir': 'directory-path'} + field_schema.update(format=format_conversion.get(self.path_type, 'path'), type='string') + return field_schema + + def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + function_lookup = { + 'file': cast(core_schema.WithInfoValidatorFunction, self.validate_file), + 'dir': cast(core_schema.WithInfoValidatorFunction, self.validate_directory), + 'new': cast(core_schema.WithInfoValidatorFunction, self.validate_new), + 'socket': cast(core_schema.WithInfoValidatorFunction, self.validate_socket), + } + + return core_schema.with_info_after_validator_function( + function_lookup[self.path_type], + handler(source), + ) + + @staticmethod + def validate_file(path: Path, _: core_schema.ValidationInfo) -> Path: + if path.is_file(): + return path + else: + raise PydanticCustomError('path_not_file', 'Path does not point to a file') + + @staticmethod + def validate_socket(path: Path, _: core_schema.ValidationInfo) -> Path: + if path.is_socket(): + return path + else: + raise PydanticCustomError('path_not_socket', 'Path does not point to a socket') + + @staticmethod + def validate_directory(path: Path, _: core_schema.ValidationInfo) -> Path: + if path.is_dir(): + return path + else: + raise PydanticCustomError('path_not_directory', 'Path does not point to a directory') + + @staticmethod + def validate_new(path: Path, _: core_schema.ValidationInfo) -> Path: + if path.exists(): + raise PydanticCustomError('path_exists', 'Path already exists') + elif not path.parent.exists(): + raise PydanticCustomError('parent_does_not_exist', 'Parent directory does not exist') + else: + return path + + def __hash__(self) -> int: + return hash(self.path_type) + + +FilePath = Annotated[Path, PathType('file')] +"""A path that must point to a file. + +```python +from pathlib import Path + +from pydantic import BaseModel, FilePath, ValidationError + +class Model(BaseModel): + f: FilePath + +path = Path('text.txt') +path.touch() +m = Model(f='text.txt') +print(m.model_dump()) +#> {'f': PosixPath('text.txt')} +path.unlink() + +path = Path('directory') +path.mkdir(exist_ok=True) +try: + Model(f='directory') # directory +except ValidationError as e: + print(e) + ''' + 1 validation error for Model + f + Path does not point to a file [type=path_not_file, input_value='directory', input_type=str] + ''' +path.rmdir() + +try: + Model(f='not-exists-file') +except ValidationError as e: + print(e) + ''' + 1 validation error for Model + f + Path does not point to a file [type=path_not_file, input_value='not-exists-file', input_type=str] + ''' +``` +""" +DirectoryPath = Annotated[Path, PathType('dir')] +"""A path that must point to a directory. + +```python +from pathlib import Path + +from pydantic import BaseModel, DirectoryPath, ValidationError + +class Model(BaseModel): + f: DirectoryPath + +path = Path('directory/') +path.mkdir() +m = Model(f='directory/') +print(m.model_dump()) +#> {'f': PosixPath('directory')} +path.rmdir() + +path = Path('file.txt') +path.touch() +try: + Model(f='file.txt') # file +except ValidationError as e: + print(e) + ''' + 1 validation error for Model + f + Path does not point to a directory [type=path_not_directory, input_value='file.txt', input_type=str] + ''' +path.unlink() + +try: + Model(f='not-exists-directory') +except ValidationError as e: + print(e) + ''' + 1 validation error for Model + f + Path does not point to a directory [type=path_not_directory, input_value='not-exists-directory', input_type=str] + ''' +``` +""" +NewPath = Annotated[Path, PathType('new')] +"""A path for a new file or directory that must not already exist. The parent directory must already exist.""" + +SocketPath = Annotated[Path, PathType('socket')] +"""A path to an existing socket file""" + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ JSON TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +if TYPE_CHECKING: + # Json[list[str]] will be recognized by type checkers as list[str] + Json = Annotated[AnyType, ...] + +else: + + class Json: + """A special type wrapper which loads JSON before parsing. + + You can use the `Json` data type to make Pydantic first load a raw JSON string before + validating the loaded data into the parametrized type: + + ```python + from typing import Any + + from pydantic import BaseModel, Json, ValidationError + + class AnyJsonModel(BaseModel): + json_obj: Json[Any] + + class ConstrainedJsonModel(BaseModel): + json_obj: Json[list[int]] + + print(AnyJsonModel(json_obj='{"b": 1}')) + #> json_obj={'b': 1} + print(ConstrainedJsonModel(json_obj='[1, 2, 3]')) + #> json_obj=[1, 2, 3] + + try: + ConstrainedJsonModel(json_obj=12) + except ValidationError as e: + print(e) + ''' + 1 validation error for ConstrainedJsonModel + json_obj + JSON input should be string, bytes or bytearray [type=json_type, input_value=12, input_type=int] + ''' + + try: + ConstrainedJsonModel(json_obj='[a, b]') + except ValidationError as e: + print(e) + ''' + 1 validation error for ConstrainedJsonModel + json_obj + Invalid JSON: expected value at line 1 column 2 [type=json_invalid, input_value='[a, b]', input_type=str] + ''' + + try: + ConstrainedJsonModel(json_obj='["a", "b"]') + except ValidationError as e: + print(e) + ''' + 2 validation errors for ConstrainedJsonModel + json_obj.0 + Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str] + json_obj.1 + Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='b', input_type=str] + ''' + ``` + + When you dump the model using `model_dump` or `model_dump_json`, the dumped value will be the result of validation, + not the original JSON string. However, you can use the argument `round_trip=True` to get the original JSON string back: + + ```python + from pydantic import BaseModel, Json + + class ConstrainedJsonModel(BaseModel): + json_obj: Json[list[int]] + + print(ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json()) + #> {"json_obj":[1,2,3]} + print( + ConstrainedJsonModel(json_obj='[1, 2, 3]').model_dump_json(round_trip=True) + ) + #> {"json_obj":"[1,2,3]"} + ``` + """ + + @classmethod + def __class_getitem__(cls, item: AnyType) -> AnyType: + return Annotated[item, cls()] + + @classmethod + def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + if cls is source: + return core_schema.json_schema(None) + else: + return core_schema.json_schema(handler(source)) + + def __repr__(self) -> str: + return 'Json' + + def __hash__(self) -> int: + return hash(type(self)) + + def __eq__(self, other: Any) -> bool: + return type(other) is type(self) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SECRET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +# The `Secret` class being conceptually immutable, make the type variable covariant: +SecretType = TypeVar('SecretType', covariant=True) + + +class _SecretBase(Generic[SecretType]): + def __init__(self, secret_value: SecretType) -> None: + self._secret_value: SecretType = secret_value + + def get_secret_value(self) -> SecretType: + """Get the secret value. + + Returns: + The secret value. + """ + return self._secret_value + + def __eq__(self, other: Any) -> bool: + return isinstance(other, self.__class__) and self.get_secret_value() == other.get_secret_value() + + def __hash__(self) -> int: + return hash(self.get_secret_value()) + + def __str__(self) -> str: + return str(self._display()) + + def __repr__(self) -> str: + return f'{self.__class__.__name__}({self._display()!r})' + + def _display(self) -> str | bytes: + raise NotImplementedError + + +def _serialize_secret(value: Secret[SecretType], info: core_schema.SerializationInfo) -> str | Secret[SecretType]: + if info.mode == 'json': + return str(value) + else: + return value + + +class Secret(_SecretBase[SecretType]): + """A generic base class used for defining a field with sensitive information that you do not want to be visible in logging or tracebacks. + + You may either directly parametrize `Secret` with a type, or subclass from `Secret` with a parametrized type. The benefit of subclassing + is that you can define a custom `_display` method, which will be used for `repr()` and `str()` methods. The examples below demonstrate both + ways of using `Secret` to create a new secret type. + + 1. Directly parametrizing `Secret` with a type: + + ```python + from pydantic import BaseModel, Secret + + SecretBool = Secret[bool] + + class Model(BaseModel): + secret_bool: SecretBool + + m = Model(secret_bool=True) + print(m.model_dump()) + #> {'secret_bool': Secret('**********')} + + print(m.model_dump_json()) + #> {"secret_bool":"**********"} + + print(m.secret_bool.get_secret_value()) + #> True + ``` + + 2. Subclassing from parametrized `Secret`: + + ```python + from datetime import date + + from pydantic import BaseModel, Secret + + class SecretDate(Secret[date]): + def _display(self) -> str: + return '****/**/**' + + class Model(BaseModel): + secret_date: SecretDate + + m = Model(secret_date=date(2022, 1, 1)) + print(m.model_dump()) + #> {'secret_date': SecretDate('****/**/**')} + + print(m.model_dump_json()) + #> {"secret_date":"****/**/**"} + + print(m.secret_date.get_secret_value()) + #> 2022-01-01 + ``` + + The value returned by the `_display` method will be used for `repr()` and `str()`. + + You can enforce constraints on the underlying type through annotations: + For example: + + ```python + from typing import Annotated + + from pydantic import BaseModel, Field, Secret, ValidationError + + SecretPosInt = Secret[Annotated[int, Field(gt=0, strict=True)]] + + class Model(BaseModel): + sensitive_int: SecretPosInt + + m = Model(sensitive_int=42) + print(m.model_dump()) + #> {'sensitive_int': Secret('**********')} + + try: + m = Model(sensitive_int=-42) # (1)! + except ValidationError as exc_info: + print(exc_info.errors(include_url=False, include_input=False)) + ''' + [ + { + 'type': 'greater_than', + 'loc': ('sensitive_int',), + 'msg': 'Input should be greater than 0', + 'ctx': {'gt': 0}, + } + ] + ''' + + try: + m = Model(sensitive_int='42') # (2)! + except ValidationError as exc_info: + print(exc_info.errors(include_url=False, include_input=False)) + ''' + [ + { + 'type': 'int_type', + 'loc': ('sensitive_int',), + 'msg': 'Input should be a valid integer', + } + ] + ''' + ``` + + 1. The input value is not greater than 0, so it raises a validation error. + 2. The input value is not an integer, so it raises a validation error because the `SecretPosInt` type has strict mode enabled. + """ + + def _display(self) -> str | bytes: + return '**********' if self.get_secret_value() else '' + + @classmethod + def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + inner_type = None + # if origin_type is Secret, then cls is a GenericAlias, and we can extract the inner type directly + origin_type = get_origin(source) + if origin_type is not None: + inner_type = get_args(source)[0] + # otherwise, we need to get the inner type from the base class + else: + bases = getattr(cls, '__orig_bases__', getattr(cls, '__bases__', [])) + for base in bases: + if get_origin(base) is Secret: + inner_type = get_args(base)[0] + if bases == [] or inner_type is None: + raise TypeError( + f"Can't get secret type from {cls.__name__}. " + 'Please use Secret[], or subclass from Secret[] instead.' + ) + + inner_schema = handler.generate_schema(inner_type) # type: ignore + + def validate_secret_value(value, handler) -> Secret[SecretType]: + if isinstance(value, Secret): + value = value.get_secret_value() + validated_inner = handler(value) + return cls(validated_inner) + + return core_schema.json_or_python_schema( + python_schema=core_schema.no_info_wrap_validator_function( + validate_secret_value, + inner_schema, + ), + json_schema=core_schema.no_info_after_validator_function(lambda x: cls(x), inner_schema), + serialization=core_schema.plain_serializer_function_ser_schema( + _serialize_secret, + info_arg=True, + when_used='always', + ), + ) + + __pydantic_serializer__ = SchemaSerializer( + core_schema.any_schema( + serialization=core_schema.plain_serializer_function_ser_schema( + _serialize_secret, + info_arg=True, + when_used='always', + ) + ) + ) + + +def _secret_display(value: SecretType) -> str: # type: ignore + return '**********' if value else '' + + +def _serialize_secret_field( + value: _SecretField[SecretType], info: core_schema.SerializationInfo +) -> str | _SecretField[SecretType]: + if info.mode == 'json': + # we want the output to always be string without the `b'` prefix for bytes, + # hence we just use `secret_display` + return _secret_display(value.get_secret_value()) + else: + return value + + +class _SecretField(_SecretBase[SecretType]): + _inner_schema: ClassVar[CoreSchema] + _error_kind: ClassVar[str] + + @classmethod + def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + def get_json_schema(_core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler) -> JsonSchemaValue: + json_schema = handler(cls._inner_schema) + _utils.update_not_none( + json_schema, + type='string', + writeOnly=True, + format='password', + ) + return json_schema + + def get_secret_schema(strict: bool) -> CoreSchema: + inner_schema = {**cls._inner_schema, 'strict': strict} + json_schema = core_schema.no_info_after_validator_function( + source, # construct the type + inner_schema, # pyright: ignore[reportArgumentType] + ) + return core_schema.json_or_python_schema( + python_schema=core_schema.union_schema( + [ + core_schema.is_instance_schema(source), + json_schema, + ], + custom_error_type=cls._error_kind, + ), + json_schema=json_schema, + serialization=core_schema.plain_serializer_function_ser_schema( + _serialize_secret_field, + info_arg=True, + when_used='always', + ), + ) + + return core_schema.lax_or_strict_schema( + lax_schema=get_secret_schema(strict=False), + strict_schema=get_secret_schema(strict=True), + metadata={'pydantic_js_functions': [get_json_schema]}, + ) + + __pydantic_serializer__ = SchemaSerializer( + core_schema.any_schema( + serialization=core_schema.plain_serializer_function_ser_schema( + _serialize_secret_field, + info_arg=True, + when_used='always', + ) + ) + ) + + +class SecretStr(_SecretField[str]): + """A string used for storing sensitive information that you do not want to be visible in logging or tracebacks. + + When the secret value is nonempty, it is displayed as `'**********'` instead of the underlying value in + calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `''`. + + ```python + from pydantic import BaseModel, SecretStr + + class User(BaseModel): + username: str + password: SecretStr + + user = User(username='scolvin', password='password1') + + print(user) + #> username='scolvin' password=SecretStr('**********') + print(user.password.get_secret_value()) + #> password1 + print((SecretStr('password'), SecretStr(''))) + #> (SecretStr('**********'), SecretStr('')) + ``` + + As seen above, by default, [`SecretStr`][pydantic.types.SecretStr] (and [`SecretBytes`][pydantic.types.SecretBytes]) + will be serialized as `**********` when serializing to json. + + You can use the [`field_serializer`][pydantic.functional_serializers.field_serializer] to dump the + secret as plain-text when serializing to json. + + ```python + from pydantic import BaseModel, SecretBytes, SecretStr, field_serializer + + class Model(BaseModel): + password: SecretStr + password_bytes: SecretBytes + + @field_serializer('password', 'password_bytes', when_used='json') + def dump_secret(self, v): + return v.get_secret_value() + + model = Model(password='IAmSensitive', password_bytes=b'IAmSensitiveBytes') + print(model) + #> password=SecretStr('**********') password_bytes=SecretBytes(b'**********') + print(model.password) + #> ********** + print(model.model_dump()) + ''' + { + 'password': SecretStr('**********'), + 'password_bytes': SecretBytes(b'**********'), + } + ''' + print(model.model_dump_json()) + #> {"password":"IAmSensitive","password_bytes":"IAmSensitiveBytes"} + ``` + """ + + _inner_schema: ClassVar[CoreSchema] = core_schema.str_schema() + _error_kind: ClassVar[str] = 'string_type' + + def __len__(self) -> int: + return len(self._secret_value) + + def _display(self) -> str: + return _secret_display(self._secret_value) + + +class SecretBytes(_SecretField[bytes]): + """A bytes used for storing sensitive information that you do not want to be visible in logging or tracebacks. + + It displays `b'**********'` instead of the string value on `repr()` and `str()` calls. + When the secret value is nonempty, it is displayed as `b'**********'` instead of the underlying value in + calls to `repr()` and `str()`. If the value _is_ empty, it is displayed as `b''`. + + ```python + from pydantic import BaseModel, SecretBytes + + class User(BaseModel): + username: str + password: SecretBytes + + user = User(username='scolvin', password=b'password1') + #> username='scolvin' password=SecretBytes(b'**********') + print(user.password.get_secret_value()) + #> b'password1' + print((SecretBytes(b'password'), SecretBytes(b''))) + #> (SecretBytes(b'**********'), SecretBytes(b'')) + ``` + """ + + _inner_schema: ClassVar[CoreSchema] = core_schema.bytes_schema() + _error_kind: ClassVar[str] = 'bytes_type' + + def __len__(self) -> int: + return len(self._secret_value) + + def _display(self) -> bytes: + return _secret_display(self._secret_value).encode() + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PAYMENT CARD TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class PaymentCardBrand(str, Enum): + amex = 'American Express' + mastercard = 'Mastercard' + visa = 'Visa' + other = 'other' + + def __str__(self) -> str: + return self.value + + +@deprecated( + 'The `PaymentCardNumber` class is deprecated, use `pydantic_extra_types` instead. ' + 'See https://docs.pydantic.dev/latest/api/pydantic_extra_types_payment/#pydantic_extra_types.payment.PaymentCardNumber.', + category=PydanticDeprecatedSince20, +) +class PaymentCardNumber(str): + """Based on: https://en.wikipedia.org/wiki/Payment_card_number.""" + + strip_whitespace: ClassVar[bool] = True + min_length: ClassVar[int] = 12 + max_length: ClassVar[int] = 19 + bin: str + last4: str + brand: PaymentCardBrand + + def __init__(self, card_number: str): + self.validate_digits(card_number) + + card_number = self.validate_luhn_check_digit(card_number) + + self.bin = card_number[:6] + self.last4 = card_number[-4:] + self.brand = self.validate_brand(card_number) + + @classmethod + def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + return core_schema.with_info_after_validator_function( + cls.validate, + core_schema.str_schema( + min_length=cls.min_length, max_length=cls.max_length, strip_whitespace=cls.strip_whitespace + ), + ) + + @classmethod + def validate(cls, input_value: str, /, _: core_schema.ValidationInfo) -> PaymentCardNumber: + """Validate the card number and return a `PaymentCardNumber` instance.""" + return cls(input_value) + + @property + def masked(self) -> str: + """Mask all but the last 4 digits of the card number. + + Returns: + A masked card number string. + """ + num_masked = len(self) - 10 # len(bin) + len(last4) == 10 + return f'{self.bin}{"*" * num_masked}{self.last4}' + + @classmethod + def validate_digits(cls, card_number: str) -> None: + """Validate that the card number is all digits.""" + if not card_number.isdigit(): + raise PydanticCustomError('payment_card_number_digits', 'Card number is not all digits') + + @classmethod + def validate_luhn_check_digit(cls, card_number: str) -> str: + """Based on: https://en.wikipedia.org/wiki/Luhn_algorithm.""" + sum_ = int(card_number[-1]) + length = len(card_number) + parity = length % 2 + for i in range(length - 1): + digit = int(card_number[i]) + if i % 2 == parity: + digit *= 2 + if digit > 9: + digit -= 9 + sum_ += digit + valid = sum_ % 10 == 0 + if not valid: + raise PydanticCustomError('payment_card_number_luhn', 'Card number is not luhn valid') + return card_number + + @staticmethod + def validate_brand(card_number: str) -> PaymentCardBrand: + """Validate length based on BIN for major brands: + https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN). + """ + if card_number[0] == '4': + brand = PaymentCardBrand.visa + elif 51 <= int(card_number[:2]) <= 55: + brand = PaymentCardBrand.mastercard + elif card_number[:2] in {'34', '37'}: + brand = PaymentCardBrand.amex + else: + brand = PaymentCardBrand.other + + required_length: None | int | str = None + if brand in PaymentCardBrand.mastercard: + required_length = 16 + valid = len(card_number) == required_length + elif brand == PaymentCardBrand.visa: + required_length = '13, 16 or 19' + valid = len(card_number) in {13, 16, 19} + elif brand == PaymentCardBrand.amex: + required_length = 15 + valid = len(card_number) == required_length + else: + valid = True + + if not valid: + raise PydanticCustomError( + 'payment_card_number_brand', + 'Length for a {brand} card must be {required_length}', + {'brand': brand, 'required_length': required_length}, + ) + return brand + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTE SIZE TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class ByteSize(int): + """Converts a string representing a number of bytes with units (such as `'1KB'` or `'11.5MiB'`) into an integer. + + You can use the `ByteSize` data type to (case-insensitively) convert a string representation of a number of bytes into + an integer, and also to print out human-readable strings representing a number of bytes. + + In conformance with [IEC 80000-13 Standard](https://en.wikipedia.org/wiki/ISO/IEC_80000) we interpret `'1KB'` to mean 1000 bytes, + and `'1KiB'` to mean 1024 bytes. In general, including a middle `'i'` will cause the unit to be interpreted as a power of 2, + rather than a power of 10 (so, for example, `'1 MB'` is treated as `1_000_000` bytes, whereas `'1 MiB'` is treated as `1_048_576` bytes). + + !!! info + Note that `1b` will be parsed as "1 byte" and not "1 bit". + + ```python + from pydantic import BaseModel, ByteSize + + class MyModel(BaseModel): + size: ByteSize + + print(MyModel(size=52000).size) + #> 52000 + print(MyModel(size='3000 KiB').size) + #> 3072000 + + m = MyModel(size='50 PB') + print(m.size.human_readable()) + #> 44.4PiB + print(m.size.human_readable(decimal=True)) + #> 50.0PB + print(m.size.human_readable(separator=' ')) + #> 44.4 PiB + + print(m.size.to('TiB')) + #> 45474.73508864641 + ``` + """ + + byte_sizes = { + 'b': 1, + 'kb': 10**3, + 'mb': 10**6, + 'gb': 10**9, + 'tb': 10**12, + 'pb': 10**15, + 'eb': 10**18, + 'kib': 2**10, + 'mib': 2**20, + 'gib': 2**30, + 'tib': 2**40, + 'pib': 2**50, + 'eib': 2**60, + 'bit': 1 / 8, + 'kbit': 10**3 / 8, + 'mbit': 10**6 / 8, + 'gbit': 10**9 / 8, + 'tbit': 10**12 / 8, + 'pbit': 10**15 / 8, + 'ebit': 10**18 / 8, + 'kibit': 2**10 / 8, + 'mibit': 2**20 / 8, + 'gibit': 2**30 / 8, + 'tibit': 2**40 / 8, + 'pibit': 2**50 / 8, + 'eibit': 2**60 / 8, + } + byte_sizes.update({k.lower()[0]: v for k, v in byte_sizes.items() if 'i' not in k}) + + byte_string_pattern = r'^\s*(\d*\.?\d+)\s*(\w+)?' + byte_string_re = re.compile(byte_string_pattern, re.IGNORECASE) + + @classmethod + def __get_pydantic_core_schema__(cls, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + return core_schema.with_info_after_validator_function( + function=cls._validate, + schema=core_schema.union_schema( + [ + core_schema.str_schema(pattern=cls.byte_string_pattern), + core_schema.int_schema(ge=0), + ], + custom_error_type='byte_size', + custom_error_message='could not parse value and unit from byte string', + ), + serialization=core_schema.plain_serializer_function_ser_schema( + int, return_schema=core_schema.int_schema(ge=0) + ), + ) + + @classmethod + def _validate(cls, input_value: Any, /, _: core_schema.ValidationInfo) -> ByteSize: + try: + return cls(int(input_value)) + except ValueError: + pass + + str_match = cls.byte_string_re.match(str(input_value)) + if str_match is None: + raise PydanticCustomError('byte_size', 'could not parse value and unit from byte string') + + scalar, unit = str_match.groups() + if unit is None: + unit = 'b' + + try: + unit_mult = cls.byte_sizes[unit.lower()] + except KeyError: + raise PydanticCustomError('byte_size_unit', 'could not interpret byte unit: {unit}', {'unit': unit}) + + return cls(int(float(scalar) * unit_mult)) + + def human_readable(self, decimal: bool = False, separator: str = '') -> str: + """Converts a byte size to a human readable string. + + Args: + decimal: If True, use decimal units (e.g. 1000 bytes per KB). If False, use binary units + (e.g. 1024 bytes per KiB). + separator: A string used to split the value and unit. Defaults to an empty string (''). + + Returns: + A human readable string representation of the byte size. + """ + if decimal: + divisor = 1000 + units = 'B', 'KB', 'MB', 'GB', 'TB', 'PB' + final_unit = 'EB' + else: + divisor = 1024 + units = 'B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB' + final_unit = 'EiB' + + num = float(self) + for unit in units: + if abs(num) < divisor: + if unit == 'B': + return f'{num:0.0f}{separator}{unit}' + else: + return f'{num:0.1f}{separator}{unit}' + num /= divisor + + return f'{num:0.1f}{separator}{final_unit}' + + def to(self, unit: str) -> float: + """Converts a byte size to another unit, including both byte and bit units. + + Args: + unit: The unit to convert to. Must be one of the following: B, KB, MB, GB, TB, PB, EB, + KiB, MiB, GiB, TiB, PiB, EiB (byte units) and + bit, kbit, mbit, gbit, tbit, pbit, ebit, + kibit, mibit, gibit, tibit, pibit, eibit (bit units). + + Returns: + The byte size in the new unit. + """ + try: + unit_div = self.byte_sizes[unit.lower()] + except KeyError: + raise PydanticCustomError('byte_size_unit', 'Could not interpret byte unit: {unit}', {'unit': unit}) + + return self / unit_div + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATE TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +def _check_annotated_type(annotated_type: str, expected_type: str, annotation: str) -> None: + if annotated_type != expected_type: + raise PydanticUserError(f"'{annotation}' cannot annotate '{annotated_type}'.", code='invalid-annotated-type') + + +if TYPE_CHECKING: + PastDate = Annotated[date, ...] + FutureDate = Annotated[date, ...] +else: + + class PastDate: + """A date in the past.""" + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if cls is source: + # used directly as a type + return core_schema.date_schema(now_op='past') + else: + schema = handler(source) + _check_annotated_type(schema['type'], 'date', cls.__name__) + schema['now_op'] = 'past' + return schema + + def __repr__(self) -> str: + return 'PastDate' + + class FutureDate: + """A date in the future.""" + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if cls is source: + # used directly as a type + return core_schema.date_schema(now_op='future') + else: + schema = handler(source) + _check_annotated_type(schema['type'], 'date', cls.__name__) + schema['now_op'] = 'future' + return schema + + def __repr__(self) -> str: + return 'FutureDate' + + +def condate( + *, + strict: bool | None = None, + gt: date | None = None, + ge: date | None = None, + lt: date | None = None, + le: date | None = None, +) -> type[date]: + """A wrapper for date that adds constraints. + + Args: + strict: Whether to validate the date value in strict mode. Defaults to `None`. + gt: The value must be greater than this. Defaults to `None`. + ge: The value must be greater than or equal to this. Defaults to `None`. + lt: The value must be less than this. Defaults to `None`. + le: The value must be less than or equal to this. Defaults to `None`. + + Returns: + A date type with the specified constraints. + """ + return Annotated[ # pyright: ignore[reportReturnType] + date, + Strict(strict) if strict is not None else None, + annotated_types.Interval(gt=gt, ge=ge, lt=lt, le=le), + ] + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATETIME TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +if TYPE_CHECKING: + AwareDatetime = Annotated[datetime, ...] + NaiveDatetime = Annotated[datetime, ...] + PastDatetime = Annotated[datetime, ...] + FutureDatetime = Annotated[datetime, ...] + +else: + + class AwareDatetime: + """A datetime that requires timezone info.""" + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if cls is source: + # used directly as a type + return core_schema.datetime_schema(tz_constraint='aware') + else: + schema = handler(source) + _check_annotated_type(schema['type'], 'datetime', cls.__name__) + schema['tz_constraint'] = 'aware' + return schema + + def __repr__(self) -> str: + return 'AwareDatetime' + + class NaiveDatetime: + """A datetime that doesn't require timezone info.""" + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if cls is source: + # used directly as a type + return core_schema.datetime_schema(tz_constraint='naive') + else: + schema = handler(source) + _check_annotated_type(schema['type'], 'datetime', cls.__name__) + schema['tz_constraint'] = 'naive' + return schema + + def __repr__(self) -> str: + return 'NaiveDatetime' + + class PastDatetime: + """A datetime that must be in the past.""" + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if cls is source: + # used directly as a type + return core_schema.datetime_schema(now_op='past') + else: + schema = handler(source) + _check_annotated_type(schema['type'], 'datetime', cls.__name__) + schema['now_op'] = 'past' + return schema + + def __repr__(self) -> str: + return 'PastDatetime' + + class FutureDatetime: + """A datetime that must be in the future.""" + + @classmethod + def __get_pydantic_core_schema__( + cls, source: type[Any], handler: GetCoreSchemaHandler + ) -> core_schema.CoreSchema: + if cls is source: + # used directly as a type + return core_schema.datetime_schema(now_op='future') + else: + schema = handler(source) + _check_annotated_type(schema['type'], 'datetime', cls.__name__) + schema['now_op'] = 'future' + return schema + + def __repr__(self) -> str: + return 'FutureDatetime' + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Encoded TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class EncoderProtocol(Protocol): + """Protocol for encoding and decoding data to and from bytes.""" + + @classmethod + def decode(cls, data: bytes) -> bytes: + """Decode the data using the encoder. + + Args: + data: The data to decode. + + Returns: + The decoded data. + """ + ... + + @classmethod + def encode(cls, value: bytes) -> bytes: + """Encode the data using the encoder. + + Args: + value: The data to encode. + + Returns: + The encoded data. + """ + ... + + @classmethod + def get_json_format(cls) -> str: + """Get the JSON format for the encoded data. + + Returns: + The JSON format for the encoded data. + """ + ... + + +class Base64Encoder(EncoderProtocol): + """Standard (non-URL-safe) Base64 encoder.""" + + @classmethod + def decode(cls, data: bytes) -> bytes: + """Decode the data from base64 encoded bytes to original bytes data. + + Args: + data: The data to decode. + + Returns: + The decoded data. + """ + try: + return base64.b64decode(data) + except ValueError as e: + raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)}) + + @classmethod + def encode(cls, value: bytes) -> bytes: + """Encode the data from bytes to a base64 encoded bytes. + + Args: + value: The data to encode. + + Returns: + The encoded data. + """ + return base64.b64encode(value) + + @classmethod + def get_json_format(cls) -> Literal['base64']: + """Get the JSON format for the encoded data. + + Returns: + The JSON format for the encoded data. + """ + return 'base64' + + +class Base64UrlEncoder(EncoderProtocol): + """URL-safe Base64 encoder.""" + + @classmethod + def decode(cls, data: bytes) -> bytes: + """Decode the data from base64 encoded bytes to original bytes data. + + Args: + data: The data to decode. + + Returns: + The decoded data. + """ + try: + return base64.urlsafe_b64decode(data) + except ValueError as e: + raise PydanticCustomError('base64_decode', "Base64 decoding error: '{error}'", {'error': str(e)}) + + @classmethod + def encode(cls, value: bytes) -> bytes: + """Encode the data from bytes to a base64 encoded bytes. + + Args: + value: The data to encode. + + Returns: + The encoded data. + """ + return base64.urlsafe_b64encode(value) + + @classmethod + def get_json_format(cls) -> Literal['base64url']: + """Get the JSON format for the encoded data. + + Returns: + The JSON format for the encoded data. + """ + return 'base64url' + + +@_dataclasses.dataclass(**_internal_dataclass.slots_true) +class EncodedBytes: + """A bytes type that is encoded and decoded using the specified encoder. + + `EncodedBytes` needs an encoder that implements `EncoderProtocol` to operate. + + ```python + from typing import Annotated + + from pydantic import BaseModel, EncodedBytes, EncoderProtocol, ValidationError + + class MyEncoder(EncoderProtocol): + @classmethod + def decode(cls, data: bytes) -> bytes: + if data == b'**undecodable**': + raise ValueError('Cannot decode data') + return data[13:] + + @classmethod + def encode(cls, value: bytes) -> bytes: + return b'**encoded**: ' + value + + @classmethod + def get_json_format(cls) -> str: + return 'my-encoder' + + MyEncodedBytes = Annotated[bytes, EncodedBytes(encoder=MyEncoder)] + + class Model(BaseModel): + my_encoded_bytes: MyEncodedBytes + + # Initialize the model with encoded data + m = Model(my_encoded_bytes=b'**encoded**: some bytes') + + # Access decoded value + print(m.my_encoded_bytes) + #> b'some bytes' + + # Serialize into the encoded form + print(m.model_dump()) + #> {'my_encoded_bytes': b'**encoded**: some bytes'} + + # Validate encoded data + try: + Model(my_encoded_bytes=b'**undecodable**') + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + my_encoded_bytes + Value error, Cannot decode data [type=value_error, input_value=b'**undecodable**', input_type=bytes] + ''' + ``` + """ + + encoder: type[EncoderProtocol] + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + field_schema.update(type='string', format=self.encoder.get_json_format()) + return field_schema + + def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source) + _check_annotated_type(schema['type'], 'bytes', self.__class__.__name__) + return core_schema.with_info_after_validator_function( + function=self.decode, + schema=schema, + serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode), + ) + + def decode(self, data: bytes, _: core_schema.ValidationInfo) -> bytes: + """Decode the data using the specified encoder. + + Args: + data: The data to decode. + + Returns: + The decoded data. + """ + return self.encoder.decode(data) + + def encode(self, value: bytes) -> bytes: + """Encode the data using the specified encoder. + + Args: + value: The data to encode. + + Returns: + The encoded data. + """ + return self.encoder.encode(value) + + def __hash__(self) -> int: + return hash(self.encoder) + + +@_dataclasses.dataclass(**_internal_dataclass.slots_true) +class EncodedStr: + """A str type that is encoded and decoded using the specified encoder. + + `EncodedStr` needs an encoder that implements `EncoderProtocol` to operate. + + ```python + from typing import Annotated + + from pydantic import BaseModel, EncodedStr, EncoderProtocol, ValidationError + + class MyEncoder(EncoderProtocol): + @classmethod + def decode(cls, data: bytes) -> bytes: + if data == b'**undecodable**': + raise ValueError('Cannot decode data') + return data[13:] + + @classmethod + def encode(cls, value: bytes) -> bytes: + return b'**encoded**: ' + value + + @classmethod + def get_json_format(cls) -> str: + return 'my-encoder' + + MyEncodedStr = Annotated[str, EncodedStr(encoder=MyEncoder)] + + class Model(BaseModel): + my_encoded_str: MyEncodedStr + + # Initialize the model with encoded data + m = Model(my_encoded_str='**encoded**: some str') + + # Access decoded value + print(m.my_encoded_str) + #> some str + + # Serialize into the encoded form + print(m.model_dump()) + #> {'my_encoded_str': '**encoded**: some str'} + + # Validate encoded data + try: + Model(my_encoded_str='**undecodable**') + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + my_encoded_str + Value error, Cannot decode data [type=value_error, input_value='**undecodable**', input_type=str] + ''' + ``` + """ + + encoder: type[EncoderProtocol] + + def __get_pydantic_json_schema__( + self, core_schema: core_schema.CoreSchema, handler: GetJsonSchemaHandler + ) -> JsonSchemaValue: + field_schema = handler(core_schema) + field_schema.update(type='string', format=self.encoder.get_json_format()) + return field_schema + + def __get_pydantic_core_schema__(self, source: type[Any], handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source) + _check_annotated_type(schema['type'], 'str', self.__class__.__name__) + return core_schema.with_info_after_validator_function( + function=self.decode_str, + schema=schema, + serialization=core_schema.plain_serializer_function_ser_schema(function=self.encode_str), + ) + + def decode_str(self, data: str, _: core_schema.ValidationInfo) -> str: + """Decode the data using the specified encoder. + + Args: + data: The data to decode. + + Returns: + The decoded data. + """ + return self.encoder.decode(data.encode()).decode() + + def encode_str(self, value: str) -> str: + """Encode the data using the specified encoder. + + Args: + value: The data to encode. + + Returns: + The encoded data. + """ + return self.encoder.encode(value.encode()).decode() # noqa: UP008 + + def __hash__(self) -> int: + return hash(self.encoder) + + +Base64Bytes = Annotated[bytes, EncodedBytes(encoder=Base64Encoder)] +"""A bytes type that is encoded and decoded using the standard (non-URL-safe) base64 encoder. + +Note: + Under the hood, `Base64Bytes` uses the standard library [`base64.b64encode()`][base64.b64encode] and [`base64.b64decode()`][base64.b64decode] functions. + + As a result, attempting to decode url-safe base64 data using the `Base64Bytes` type may fail or produce an incorrect + decoding. + +/// version-changed | v2.10 +`Base64Bytes` now uses [`base64.b64encode()`][base64.b64encode] and [`base64.b64decode()`][base64.b64decode] +instead of [`base64.encodebytes()`][base64.encodebytes] and [`base64.decodebytes()`][base64.decodebytes]. + +These methods are considered legacy implementation. If you'd still like to use these legacy encoders/decoders, +you can achieve this by creating a custom annotated type, like follows: +```python +import base64 +from typing import Annotated, Literal + +from pydantic_core import PydanticCustomError + +from pydantic import EncodedBytes, EncoderProtocol + +class LegacyBase64Encoder(EncoderProtocol): + @classmethod + def decode(cls, data: bytes) -> bytes: + try: + return base64.decodebytes(data) + except ValueError as e: + raise PydanticCustomError( + 'base64_decode', + "Base64 decoding error: '{error}'", + {'error': str(e)}, + ) + + @classmethod + def encode(cls, value: bytes) -> bytes: + return base64.encodebytes(value) + + @classmethod + def get_json_format(cls) -> Literal['base64']: + return 'base64' + +LegacyBase64Bytes = Annotated[bytes, EncodedBytes(encoder=LegacyBase64Encoder)] +``` +/// + +```python +from pydantic import Base64Bytes, BaseModel, ValidationError + +class Model(BaseModel): + base64_bytes: Base64Bytes + +# Initialize the model with base64 data +m = Model(base64_bytes=b'VGhpcyBpcyB0aGUgd2F5') + +# Access decoded value +print(m.base64_bytes) +#> b'This is the way' + +# Serialize into the base64 form +print(m.model_dump()) +#> {'base64_bytes': b'VGhpcyBpcyB0aGUgd2F5'} + +# Validate base64 data +try: + print(Model(base64_bytes=b'undecodable').base64_bytes) +except ValidationError as e: + print(e) + ''' + 1 validation error for Model + base64_bytes + Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value=b'undecodable', input_type=bytes] + ''' +``` +""" +Base64Str = Annotated[str, EncodedStr(encoder=Base64Encoder)] +"""A string type that is encoded and decoded using the standard (non-URL-safe) base64 encoder. + +Note: + Under the hood, `Base64Str` uses the standard library [`base64.b64encode()`][base64.b64encode] and [`base64.b64decode()`][base64.b64decode] functions. + + As a result, attempting to decode url-safe base64 data using the `Base64Str` type may fail or produce an incorrect + decoding. + +/// version-changed | v2.10 +`Base64Str` now uses [`base64.b64encode()`][base64.b64encode] and [`base64.b64decode()`][base64.b64decode] +instead of [`base64.encodebytes()`][base64.encodebytes] and [`base64.decodebytes()`][base64.decodebytes]. + +These methods are considered legacy implementation. See the documentation about the [`Base64Bytes`][pydantic.types.Base64Bytes] type +for more information on how to replicate the old behavior with the legacy encoders/decoders. +/// + + +```python +from pydantic import Base64Str, BaseModel, ValidationError + +class Model(BaseModel): + base64_str: Base64Str + +# Initialize the model with base64 data +m = Model(base64_str='VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y') + +# Access decoded value +print(m.base64_str) +#> These aren't the droids you're looking for + +# Serialize into the base64 form +print(m.model_dump()) +#> {'base64_str': 'VGhlc2UgYXJlbid0IHRoZSBkcm9pZHMgeW91J3JlIGxvb2tpbmcgZm9y'} + +# Validate base64 data +try: + print(Model(base64_str='undecodable').base64_str) +except ValidationError as e: + print(e) + ''' + 1 validation error for Model + base64_str + Base64 decoding error: 'Incorrect padding' [type=base64_decode, input_value='undecodable', input_type=str] + ''' +``` +""" +Base64UrlBytes = Annotated[bytes, EncodedBytes(encoder=Base64UrlEncoder)] +"""A bytes type that is encoded and decoded using the URL-safe base64 encoder. + +Note: + Under the hood, `Base64UrlBytes` use standard library `base64.urlsafe_b64encode` and `base64.urlsafe_b64decode` + functions. + + As a result, the `Base64UrlBytes` type can be used to faithfully decode "vanilla" base64 data + (using `'+'` and `'/'`). + +```python +from pydantic import Base64UrlBytes, BaseModel + +class Model(BaseModel): + base64url_bytes: Base64UrlBytes + +# Initialize the model with base64 data +m = Model(base64url_bytes=b'SHc_dHc-TXc==') +print(m) +#> base64url_bytes=b'Hw?tw>Mw' +``` +""" +Base64UrlStr = Annotated[str, EncodedStr(encoder=Base64UrlEncoder)] +"""A str type that is encoded and decoded using the URL-safe base64 encoder. + +Note: + Under the hood, `Base64UrlStr` use standard library `base64.urlsafe_b64encode` and `base64.urlsafe_b64decode` + functions. + + As a result, the `Base64UrlStr` type can be used to faithfully decode "vanilla" base64 data (using `'+'` and `'/'`). + +```python +from pydantic import Base64UrlStr, BaseModel + +class Model(BaseModel): + base64url_str: Base64UrlStr + +# Initialize the model with base64 data +m = Model(base64url_str='SHc_dHc-TXc==') +print(m) +#> base64url_str='Hw?tw>Mw' +``` +""" + + +__getattr__ = getattr_migration(__name__) + + +@_dataclasses.dataclass(**_internal_dataclass.slots_true) +class GetPydanticSchema: + """!!! abstract "Usage Documentation" + [Using `GetPydanticSchema` to Reduce Boilerplate](../concepts/types.md#using-getpydanticschema-to-reduce-boilerplate) + + A convenience class for creating an annotation that provides pydantic custom type hooks. + + This class is intended to eliminate the need to create a custom "marker" which defines the + `__get_pydantic_core_schema__` and `__get_pydantic_json_schema__` custom hook methods. + + For example, to have a field treated by type checkers as `int`, but by pydantic as `Any`, you can do: + ```python + from typing import Annotated, Any + + from pydantic import BaseModel, GetPydanticSchema + + HandleAsAny = GetPydanticSchema(lambda _s, h: h(Any)) + + class Model(BaseModel): + x: Annotated[int, HandleAsAny] # pydantic sees `x: Any` + + print(repr(Model(x='abc').x)) + #> 'abc' + ``` + """ + + get_pydantic_core_schema: Callable[[Any, GetCoreSchemaHandler], CoreSchema] | None = None + get_pydantic_json_schema: Callable[[Any, GetJsonSchemaHandler], JsonSchemaValue] | None = None + + # Note: we may want to consider adding a convenience staticmethod `def for_type(type_: Any) -> GetPydanticSchema:` + # which returns `GetPydanticSchema(lambda _s, h: h(type_))` + + if not TYPE_CHECKING: + # We put `__getattr__` in a non-TYPE_CHECKING block because otherwise, mypy allows arbitrary attribute access + + def __getattr__(self, item: str) -> Any: + """Use this rather than defining `__get_pydantic_core_schema__` etc. to reduce the number of nested calls.""" + if item == '__get_pydantic_core_schema__' and self.get_pydantic_core_schema: + return self.get_pydantic_core_schema + elif item == '__get_pydantic_json_schema__' and self.get_pydantic_json_schema: + return self.get_pydantic_json_schema + else: + return object.__getattribute__(self, item) + + __hash__ = object.__hash__ + + +@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True) +class Tag: + """Provides a way to specify the expected tag to use for a case of a (callable) discriminated union. + + Also provides a way to label a union case in error messages. + + When using a callable `Discriminator`, attach a `Tag` to each case in the `Union` to specify the tag that + should be used to identify that case. For example, in the below example, the `Tag` is used to specify that + if `get_discriminator_value` returns `'apple'`, the input should be validated as an `ApplePie`, and if it + returns `'pumpkin'`, the input should be validated as a `PumpkinPie`. + + The primary role of the `Tag` here is to map the return value from the callable `Discriminator` function to + the appropriate member of the `Union` in question. + + ```python + from typing import Annotated, Any, Literal, Union + + from pydantic import BaseModel, Discriminator, Tag + + class Pie(BaseModel): + time_to_cook: int + num_ingredients: int + + class ApplePie(Pie): + fruit: Literal['apple'] = 'apple' + + class PumpkinPie(Pie): + filling: Literal['pumpkin'] = 'pumpkin' + + def get_discriminator_value(v: Any) -> str: + if isinstance(v, dict): + return v.get('fruit', v.get('filling')) + return getattr(v, 'fruit', getattr(v, 'filling', None)) + + class ThanksgivingDinner(BaseModel): + dessert: Annotated[ + Union[ + Annotated[ApplePie, Tag('apple')], + Annotated[PumpkinPie, Tag('pumpkin')], + ], + Discriminator(get_discriminator_value), + ] + + apple_variation = ThanksgivingDinner.model_validate( + {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}} + ) + print(repr(apple_variation)) + ''' + ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple')) + ''' + + pumpkin_variation = ThanksgivingDinner.model_validate( + { + 'dessert': { + 'filling': 'pumpkin', + 'time_to_cook': 40, + 'num_ingredients': 6, + } + } + ) + print(repr(pumpkin_variation)) + ''' + ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin')) + ''' + ``` + + !!! note + You must specify a `Tag` for every case in a `Tag` that is associated with a + callable `Discriminator`. Failing to do so will result in a `PydanticUserError` with code + [`callable-discriminator-no-tag`](../errors/usage_errors.md#callable-discriminator-no-tag). + + See the [Discriminated Unions] concepts docs for more details on how to use `Tag`s. + + [Discriminated Unions]: ../concepts/unions.md#discriminated-unions + """ + + tag: str + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: + schema = handler(source_type) + metadata = cast('CoreMetadata', schema.setdefault('metadata', {})) + metadata['pydantic_internal_union_tag_key'] = self.tag + return schema + + +@_dataclasses.dataclass(**_internal_dataclass.slots_true, frozen=True) +class Discriminator: + """!!! abstract "Usage Documentation" + [Discriminated Unions with `Callable` `Discriminator`](../concepts/unions.md#discriminated-unions-with-callable-discriminator) + + Provides a way to use a custom callable as the way to extract the value of a union discriminator. + + This allows you to get validation behavior like you'd get from `Field(discriminator=)`, + but without needing to have a single shared field across all the union choices. This also makes it + possible to handle unions of models and primitive types with discriminated-union-style validation errors. + Finally, this allows you to use a custom callable as the way to identify which member of a union a value + belongs to, while still seeing all the performance benefits of a discriminated union. + + Consider this example, which is much more performant with the use of `Discriminator` and thus a `TaggedUnion` + than it would be as a normal `Union`. + + ```python + from typing import Annotated, Any, Literal, Union + + from pydantic import BaseModel, Discriminator, Tag + + class Pie(BaseModel): + time_to_cook: int + num_ingredients: int + + class ApplePie(Pie): + fruit: Literal['apple'] = 'apple' + + class PumpkinPie(Pie): + filling: Literal['pumpkin'] = 'pumpkin' + + def get_discriminator_value(v: Any) -> str: + if isinstance(v, dict): + return v.get('fruit', v.get('filling')) + return getattr(v, 'fruit', getattr(v, 'filling', None)) + + class ThanksgivingDinner(BaseModel): + dessert: Annotated[ + Union[ + Annotated[ApplePie, Tag('apple')], + Annotated[PumpkinPie, Tag('pumpkin')], + ], + Discriminator(get_discriminator_value), + ] + + apple_variation = ThanksgivingDinner.model_validate( + {'dessert': {'fruit': 'apple', 'time_to_cook': 60, 'num_ingredients': 8}} + ) + print(repr(apple_variation)) + ''' + ThanksgivingDinner(dessert=ApplePie(time_to_cook=60, num_ingredients=8, fruit='apple')) + ''' + + pumpkin_variation = ThanksgivingDinner.model_validate( + { + 'dessert': { + 'filling': 'pumpkin', + 'time_to_cook': 40, + 'num_ingredients': 6, + } + } + ) + print(repr(pumpkin_variation)) + ''' + ThanksgivingDinner(dessert=PumpkinPie(time_to_cook=40, num_ingredients=6, filling='pumpkin')) + ''' + ``` + + See the [Discriminated Unions] concepts docs for more details on how to use `Discriminator`s. + + [Discriminated Unions]: ../concepts/unions.md#discriminated-unions + """ + + discriminator: str | Callable[[Any], Hashable] + """The callable or field name for discriminating the type in a tagged union. + + A `Callable` discriminator must extract the value of the discriminator from the input. + A `str` discriminator must be the name of a field to discriminate against. + """ + custom_error_type: str | None = None + """Type to use in [custom errors](../errors/errors.md) replacing the standard discriminated union + validation errors. + """ + custom_error_message: str | None = None + """Message to use in custom errors.""" + custom_error_context: dict[str, int | str | float] | None = None + """Context to use in custom errors.""" + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: + if isinstance(self.discriminator, str): + from pydantic import Field + + return handler(Annotated[source_type, Field(discriminator=self.discriminator)]) + else: + original_schema = handler(source_type) + return self._convert_schema(original_schema, handler) + + def _convert_schema( + self, original_schema: core_schema.CoreSchema, handler: GetCoreSchemaHandler | None = None + ) -> core_schema.TaggedUnionSchema: + if handler is not None and original_schema['type'] == 'definition-ref': + # Same logic as `_ApplyInferredDiscriminator._apply_to_root()` + try: + def_schema = handler.resolve_ref_schema(original_schema) + except LookupError: # pragma: no cover + from pydantic._internal._discriminated_union import MissingDefinitionForUnionRef + + raise MissingDefinitionForUnionRef(original_schema['schema_ref']) + + # If using a referenceable union as discriminated (e.g. `type Pet = Cat | Dog; field: Pet = Field(discriminator=...)`): + if def_schema['type'] == 'union': + original_schema = def_schema.copy() + original_schema.pop('ref') + + if original_schema['type'] != 'union': + # This likely indicates that the schema was a single-item union that was simplified. + # In this case, we do the same thing we do in + # `pydantic._internal._discriminated_union._ApplyInferredDiscriminator._apply_to_root`, namely, + # package the generated schema back into a single-item union. + original_schema = core_schema.union_schema([original_schema]) + + tagged_union_choices = {} + for choice in original_schema['choices']: + tag = None + if isinstance(choice, tuple): + choice, tag = choice + metadata = cast('CoreMetadata | None', choice.get('metadata')) + if metadata is not None: + tag = metadata.get('pydantic_internal_union_tag_key') or tag + if tag is None: + # `handler` is None when this method is called from `apply_discriminator()` (deferred discriminators) + if handler is not None and choice['type'] == 'definition-ref': + # If choice was built from a PEP 695 type alias, try to resolve the def: + try: + choice = handler.resolve_ref_schema(choice) + except LookupError: + pass + else: + metadata = cast('CoreMetadata | None', choice.get('metadata')) + if metadata is not None: + tag = metadata.get('pydantic_internal_union_tag_key') + + if tag is None: + raise PydanticUserError( + f'`Tag` not provided for choice {choice} used with `Discriminator`', + code='callable-discriminator-no-tag', + ) + tagged_union_choices[tag] = choice + + # Have to do these verbose checks to ensure falsy values ('' and {}) don't get ignored + custom_error_type = self.custom_error_type + if custom_error_type is None: + custom_error_type = original_schema.get('custom_error_type') + + custom_error_message = self.custom_error_message + if custom_error_message is None: + custom_error_message = original_schema.get('custom_error_message') + + custom_error_context = self.custom_error_context + if custom_error_context is None: + custom_error_context = original_schema.get('custom_error_context') + + custom_error_type = original_schema.get('custom_error_type') if custom_error_type is None else custom_error_type + return core_schema.tagged_union_schema( + tagged_union_choices, + self.discriminator, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + strict=original_schema.get('strict'), + ref=original_schema.get('ref'), + metadata=original_schema.get('metadata'), + serialization=original_schema.get('serialization'), + ) + + +_JSON_TYPES = {int, float, str, bool, list, dict, type(None)} + + +def _get_type_name(x: Any) -> str: + type_ = type(x) + if type_ in _JSON_TYPES: + return type_.__name__ + + # Handle proper subclasses; note we don't need to handle None or bool here + if isinstance(x, int): + return 'int' + if isinstance(x, float): + return 'float' + if isinstance(x, str): + return 'str' + if isinstance(x, list): + return 'list' + if isinstance(x, dict): + return 'dict' + + # Fail by returning the type's actual name + return getattr(type_, '__name__', '') + + +class _AllowAnyJson: + @classmethod + def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: + python_schema = handler(source_type) + return core_schema.json_or_python_schema(json_schema=core_schema.any_schema(), python_schema=python_schema) + + +if TYPE_CHECKING: + # This seems to only be necessary for mypy + JsonValue: TypeAlias = Union[ + list['JsonValue'], + dict[str, 'JsonValue'], + str, + bool, + int, + float, + None, + ] + """A `JsonValue` is used to represent a value that can be serialized to JSON. + + It may be one of: + + * `list['JsonValue']` + * `dict[str, 'JsonValue']` + * `str` + * `bool` + * `int` + * `float` + * `None` + + The following example demonstrates how to use `JsonValue` to validate JSON data, + and what kind of errors to expect when input data is not json serializable. + + ```python + import json + + from pydantic import BaseModel, JsonValue, ValidationError + + class Model(BaseModel): + j: JsonValue + + valid_json_data = {'j': {'a': {'b': {'c': 1, 'd': [2, None]}}}} + invalid_json_data = {'j': {'a': {'b': ...}}} + + print(repr(Model.model_validate(valid_json_data))) + #> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}}) + print(repr(Model.model_validate_json(json.dumps(valid_json_data)))) + #> Model(j={'a': {'b': {'c': 1, 'd': [2, None]}}}) + + try: + Model.model_validate(invalid_json_data) + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + j.dict.a.dict.b + input was not a valid JSON value [type=invalid-json-value, input_value=Ellipsis, input_type=ellipsis] + ''' + ``` + """ + +else: + JsonValue = TypeAliasType( + 'JsonValue', + Annotated[ + Union[ + Annotated[list['JsonValue'], Tag('list')], + Annotated[dict[str, 'JsonValue'], Tag('dict')], + Annotated[str, Tag('str')], + Annotated[bool, Tag('bool')], + Annotated[int, Tag('int')], + Annotated[float, Tag('float')], + Annotated[None, Tag('NoneType')], + ], + Discriminator( + _get_type_name, + custom_error_type='invalid-json-value', + custom_error_message='input was not a valid JSON value', + ), + _AllowAnyJson, + ], + ) + + +class _OnErrorOmit: + @classmethod + def __get_pydantic_core_schema__(cls, source_type: Any, handler: GetCoreSchemaHandler) -> CoreSchema: + # there is no actual default value here but we use with_default_schema since it already has the on_error + # behavior implemented and it would be no more efficient to implement it on every other validator + # or as a standalone validator + return core_schema.with_default_schema(schema=handler(source_type), on_error='omit') + + +OnErrorOmit = Annotated[T, _OnErrorOmit] +""" +When used as an item in a list, the key type in a dict, optional values of a TypedDict, etc. +this annotation omits the item from the iteration if there is any error validating it. +That is, instead of a [`ValidationError`][pydantic_core.ValidationError] being propagated up and the entire iterable being discarded +any invalid items are discarded and the valid ones are returned. +""" + + +@_dataclasses.dataclass +class FailFast(_fields.PydanticMetadata, BaseMetadata): + """A `FailFast` annotation can be used to specify that validation should stop at the first error. + + This can be useful when you want to validate a large amount of data and you only need to know if it's valid or not. + + You might want to enable this setting if you want to validate your data faster (basically, if you use this, + validation will be more performant with the caveat that you get less information). + + ```python + from typing import Annotated + + from pydantic import BaseModel, FailFast, ValidationError + + class Model(BaseModel): + x: Annotated[list[int], FailFast()] + + # This will raise a single error for the first invalid value and stop validation + try: + obj = Model(x=[1, 2, 'a', 4, 5, 'b', 7, 8, 9, 'c']) + except ValidationError as e: + print(e) + ''' + 1 validation error for Model + x.2 + Input should be a valid integer, unable to parse string as an integer [type=int_parsing, input_value='a', input_type=str] + ''' + ``` + """ + + fail_fast: bool = True diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/typing.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/typing.py new file mode 100644 index 0000000000000000000000000000000000000000..0bda22d02ffc59f306ded475805343aef2855f49 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/typing.py @@ -0,0 +1,5 @@ +"""`typing` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/utils.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..8d1e2a81c5f0d1bd858bf12ccf15b1461008cf99 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/utils.py @@ -0,0 +1,5 @@ +"""The `utils` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..6ad3f466684df5c377480a8e181971a1b2f0016a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/__init__.py @@ -0,0 +1,131 @@ +# flake8: noqa +from pydantic.v1 import dataclasses +from pydantic.v1.annotated_types import create_model_from_namedtuple, create_model_from_typeddict +from pydantic.v1.class_validators import root_validator, validator +from pydantic.v1.config import BaseConfig, ConfigDict, Extra +from pydantic.v1.decorator import validate_arguments +from pydantic.v1.env_settings import BaseSettings +from pydantic.v1.error_wrappers import ValidationError +from pydantic.v1.errors import * +from pydantic.v1.fields import Field, PrivateAttr, Required +from pydantic.v1.main import * +from pydantic.v1.networks import * +from pydantic.v1.parse import Protocol +from pydantic.v1.tools import * +from pydantic.v1.types import * +from pydantic.v1.version import VERSION, compiled + +__version__ = VERSION + +# WARNING __all__ from pydantic.errors is not included here, it will be removed as an export here in v2 +# please use "from pydantic.v1.errors import ..." instead +__all__ = [ + # annotated types utils + 'create_model_from_namedtuple', + 'create_model_from_typeddict', + # dataclasses + 'dataclasses', + # class_validators + 'root_validator', + 'validator', + # config + 'BaseConfig', + 'ConfigDict', + 'Extra', + # decorator + 'validate_arguments', + # env_settings + 'BaseSettings', + # error_wrappers + 'ValidationError', + # fields + 'Field', + 'Required', + # main + 'BaseModel', + 'create_model', + 'validate_model', + # network + 'AnyUrl', + 'AnyHttpUrl', + 'FileUrl', + 'HttpUrl', + 'stricturl', + 'EmailStr', + 'NameEmail', + 'IPvAnyAddress', + 'IPvAnyInterface', + 'IPvAnyNetwork', + 'PostgresDsn', + 'CockroachDsn', + 'AmqpDsn', + 'RedisDsn', + 'MongoDsn', + 'KafkaDsn', + 'validate_email', + # parse + 'Protocol', + # tools + 'parse_file_as', + 'parse_obj_as', + 'parse_raw_as', + 'schema_of', + 'schema_json_of', + # types + 'NoneStr', + 'NoneBytes', + 'StrBytes', + 'NoneStrBytes', + 'StrictStr', + 'ConstrainedBytes', + 'conbytes', + 'ConstrainedList', + 'conlist', + 'ConstrainedSet', + 'conset', + 'ConstrainedFrozenSet', + 'confrozenset', + 'ConstrainedStr', + 'constr', + 'PyObject', + 'ConstrainedInt', + 'conint', + 'PositiveInt', + 'NegativeInt', + 'NonNegativeInt', + 'NonPositiveInt', + 'ConstrainedFloat', + 'confloat', + 'PositiveFloat', + 'NegativeFloat', + 'NonNegativeFloat', + 'NonPositiveFloat', + 'FiniteFloat', + 'ConstrainedDecimal', + 'condecimal', + 'ConstrainedDate', + 'condate', + 'UUID1', + 'UUID3', + 'UUID4', + 'UUID5', + 'FilePath', + 'DirectoryPath', + 'Json', + 'JsonWrapper', + 'SecretField', + 'SecretStr', + 'SecretBytes', + 'StrictBool', + 'StrictBytes', + 'StrictInt', + 'StrictFloat', + 'PaymentCardNumber', + 'PrivateAttr', + 'ByteSize', + 'PastDate', + 'FutureDate', + # version + 'compiled', + 'VERSION', +] diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/_hypothesis_plugin.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/_hypothesis_plugin.py new file mode 100644 index 0000000000000000000000000000000000000000..b62234d50750cbcf6d3ebba02d2ee54afb8c7347 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/_hypothesis_plugin.py @@ -0,0 +1,391 @@ +""" +Register Hypothesis strategies for Pydantic custom types. + +This enables fully-automatic generation of test data for most Pydantic classes. + +Note that this module has *no* runtime impact on Pydantic itself; instead it +is registered as a setuptools entry point and Hypothesis will import it if +Pydantic is installed. See also: + +https://hypothesis.readthedocs.io/en/latest/strategies.html#registering-strategies-via-setuptools-entry-points +https://hypothesis.readthedocs.io/en/latest/data.html#hypothesis.strategies.register_type_strategy +https://hypothesis.readthedocs.io/en/latest/strategies.html#interaction-with-pytest-cov +https://docs.pydantic.dev/usage/types/#pydantic-types + +Note that because our motivation is to *improve user experience*, the strategies +are always sound (never generate invalid data) but sacrifice completeness for +maintainability (ie may be unable to generate some tricky but valid data). + +Finally, this module makes liberal use of `# type: ignore[]` pragmas. +This is because Hypothesis annotates `register_type_strategy()` with +`(T, SearchStrategy[T])`, but in most cases we register e.g. `ConstrainedInt` +to generate instances of the builtin `int` type which match the constraints. +""" + +import contextlib +import datetime +import ipaddress +import json +import math +from fractions import Fraction +from typing import Callable, Dict, Type, Union, cast, overload + +import hypothesis.strategies as st + +import pydantic +import pydantic.color +import pydantic.types +from pydantic.v1.utils import lenient_issubclass + +# FilePath and DirectoryPath are explicitly unsupported, as we'd have to create +# them on-disk, and that's unsafe in general without being told *where* to do so. +# +# URLs are unsupported because it's easy for users to define their own strategy for +# "normal" URLs, and hard for us to define a general strategy which includes "weird" +# URLs but doesn't also have unpredictable performance problems. +# +# conlist() and conset() are unsupported for now, because the workarounds for +# Cython and Hypothesis to handle parametrized generic types are incompatible. +# We are rethinking Hypothesis compatibility in Pydantic v2. + +# Emails +try: + import email_validator +except ImportError: # pragma: no cover + pass +else: + + def is_valid_email(s: str) -> bool: + # Hypothesis' st.emails() occasionally generates emails like 0@A0--0.ac + # that are invalid according to email-validator, so we filter those out. + try: + email_validator.validate_email(s, check_deliverability=False) + return True + except email_validator.EmailNotValidError: # pragma: no cover + return False + + # Note that these strategies deliberately stay away from any tricky Unicode + # or other encoding issues; we're just trying to generate *something* valid. + st.register_type_strategy(pydantic.EmailStr, st.emails().filter(is_valid_email)) # type: ignore[arg-type] + st.register_type_strategy( + pydantic.NameEmail, + st.builds( + '{} <{}>'.format, # type: ignore[arg-type] + st.from_regex('[A-Za-z0-9_]+( [A-Za-z0-9_]+){0,5}', fullmatch=True), + st.emails().filter(is_valid_email), + ), + ) + +# PyObject - dotted names, in this case taken from the math module. +st.register_type_strategy( + pydantic.PyObject, # type: ignore[arg-type] + st.sampled_from( + [cast(pydantic.PyObject, f'math.{name}') for name in sorted(vars(math)) if not name.startswith('_')] + ), +) + +# CSS3 Colors; as name, hex, rgb(a) tuples or strings, or hsl strings +_color_regexes = ( + '|'.join( + ( + pydantic.color.r_hex_short, + pydantic.color.r_hex_long, + pydantic.color.r_rgb, + pydantic.color.r_rgba, + pydantic.color.r_hsl, + pydantic.color.r_hsla, + ) + ) + # Use more precise regex patterns to avoid value-out-of-range errors + .replace(pydantic.color._r_sl, r'(?:(\d\d?(?:\.\d+)?|100(?:\.0+)?)%)') + .replace(pydantic.color._r_alpha, r'(?:(0(?:\.\d+)?|1(?:\.0+)?|\.\d+|\d{1,2}%))') + .replace(pydantic.color._r_255, r'(?:((?:\d|\d\d|[01]\d\d|2[0-4]\d|25[0-4])(?:\.\d+)?|255(?:\.0+)?))') +) +st.register_type_strategy( + pydantic.color.Color, + st.one_of( + st.sampled_from(sorted(pydantic.color.COLORS_BY_NAME)), + st.tuples( + st.integers(0, 255), + st.integers(0, 255), + st.integers(0, 255), + st.none() | st.floats(0, 1) | st.floats(0, 100).map('{}%'.format), + ), + st.from_regex(_color_regexes, fullmatch=True), + ), +) + + +# Card numbers, valid according to the Luhn algorithm + + +def add_luhn_digit(card_number: str) -> str: + # See https://en.wikipedia.org/wiki/Luhn_algorithm + for digit in '0123456789': + with contextlib.suppress(Exception): + pydantic.PaymentCardNumber.validate_luhn_check_digit(card_number + digit) + return card_number + digit + raise AssertionError('Unreachable') # pragma: no cover + + +card_patterns = ( + # Note that these patterns omit the Luhn check digit; that's added by the function above + '4[0-9]{14}', # Visa + '5[12345][0-9]{13}', # Mastercard + '3[47][0-9]{12}', # American Express + '[0-26-9][0-9]{10,17}', # other (incomplete to avoid overlap) +) +st.register_type_strategy( + pydantic.PaymentCardNumber, + st.from_regex('|'.join(card_patterns), fullmatch=True).map(add_luhn_digit), # type: ignore[arg-type] +) + +# UUIDs +st.register_type_strategy(pydantic.UUID1, st.uuids(version=1)) +st.register_type_strategy(pydantic.UUID3, st.uuids(version=3)) +st.register_type_strategy(pydantic.UUID4, st.uuids(version=4)) +st.register_type_strategy(pydantic.UUID5, st.uuids(version=5)) + +# Secrets +st.register_type_strategy(pydantic.SecretBytes, st.binary().map(pydantic.SecretBytes)) +st.register_type_strategy(pydantic.SecretStr, st.text().map(pydantic.SecretStr)) + +# IP addresses, networks, and interfaces +st.register_type_strategy(pydantic.IPvAnyAddress, st.ip_addresses()) # type: ignore[arg-type] +st.register_type_strategy( + pydantic.IPvAnyInterface, + st.from_type(ipaddress.IPv4Interface) | st.from_type(ipaddress.IPv6Interface), # type: ignore[arg-type] +) +st.register_type_strategy( + pydantic.IPvAnyNetwork, + st.from_type(ipaddress.IPv4Network) | st.from_type(ipaddress.IPv6Network), # type: ignore[arg-type] +) + +# We hook into the con***() functions and the ConstrainedNumberMeta metaclass, +# so here we only have to register subclasses for other constrained types which +# don't go via those mechanisms. Then there are the registration hooks below. +st.register_type_strategy(pydantic.StrictBool, st.booleans()) +st.register_type_strategy(pydantic.StrictStr, st.text()) + + +# FutureDate, PastDate +st.register_type_strategy(pydantic.FutureDate, st.dates(min_value=datetime.date.today() + datetime.timedelta(days=1))) +st.register_type_strategy(pydantic.PastDate, st.dates(max_value=datetime.date.today() - datetime.timedelta(days=1))) + + +# Constrained-type resolver functions +# +# For these ones, we actually want to inspect the type in order to work out a +# satisfying strategy. First up, the machinery for tracking resolver functions: + +RESOLVERS: Dict[type, Callable[[type], st.SearchStrategy]] = {} # type: ignore[type-arg] + + +@overload +def _registered(typ: Type[pydantic.types.T]) -> Type[pydantic.types.T]: + pass + + +@overload +def _registered(typ: pydantic.types.ConstrainedNumberMeta) -> pydantic.types.ConstrainedNumberMeta: + pass + + +def _registered( + typ: Union[Type[pydantic.types.T], pydantic.types.ConstrainedNumberMeta] +) -> Union[Type[pydantic.types.T], pydantic.types.ConstrainedNumberMeta]: + # This function replaces the version in `pydantic.types`, in order to + # effect the registration of new constrained types so that Hypothesis + # can generate valid examples. + pydantic.types._DEFINED_TYPES.add(typ) + for supertype, resolver in RESOLVERS.items(): + if issubclass(typ, supertype): + st.register_type_strategy(typ, resolver(typ)) # type: ignore + return typ + raise NotImplementedError(f'Unknown type {typ!r} has no resolver to register') # pragma: no cover + + +def resolves( + typ: Union[type, pydantic.types.ConstrainedNumberMeta] +) -> Callable[[Callable[..., st.SearchStrategy]], Callable[..., st.SearchStrategy]]: # type: ignore[type-arg] + def inner(f): # type: ignore + assert f not in RESOLVERS + RESOLVERS[typ] = f + return f + + return inner + + +# Type-to-strategy resolver functions + + +@resolves(pydantic.JsonWrapper) +def resolve_json(cls): # type: ignore[no-untyped-def] + try: + inner = st.none() if cls.inner_type is None else st.from_type(cls.inner_type) + except Exception: # pragma: no cover + finite = st.floats(allow_infinity=False, allow_nan=False) + inner = st.recursive( + base=st.one_of(st.none(), st.booleans(), st.integers(), finite, st.text()), + extend=lambda x: st.lists(x) | st.dictionaries(st.text(), x), # type: ignore + ) + inner_type = getattr(cls, 'inner_type', None) + return st.builds( + cls.inner_type.json if lenient_issubclass(inner_type, pydantic.BaseModel) else json.dumps, + inner, + ensure_ascii=st.booleans(), + indent=st.none() | st.integers(0, 16), + sort_keys=st.booleans(), + ) + + +@resolves(pydantic.ConstrainedBytes) +def resolve_conbytes(cls): # type: ignore[no-untyped-def] # pragma: no cover + min_size = cls.min_length or 0 + max_size = cls.max_length + if not cls.strip_whitespace: + return st.binary(min_size=min_size, max_size=max_size) + # Fun with regex to ensure we neither start nor end with whitespace + repeats = '{{{},{}}}'.format( + min_size - 2 if min_size > 2 else 0, + max_size - 2 if (max_size or 0) > 2 else '', + ) + if min_size >= 2: + pattern = rf'\W.{repeats}\W' + elif min_size == 1: + pattern = rf'\W(.{repeats}\W)?' + else: + assert min_size == 0 + pattern = rf'(\W(.{repeats}\W)?)?' + return st.from_regex(pattern.encode(), fullmatch=True) + + +@resolves(pydantic.ConstrainedDecimal) +def resolve_condecimal(cls): # type: ignore[no-untyped-def] + min_value = cls.ge + max_value = cls.le + if cls.gt is not None: + assert min_value is None, 'Set `gt` or `ge`, but not both' + min_value = cls.gt + if cls.lt is not None: + assert max_value is None, 'Set `lt` or `le`, but not both' + max_value = cls.lt + s = st.decimals(min_value, max_value, allow_nan=False, places=cls.decimal_places) + if cls.lt is not None: + s = s.filter(lambda d: d < cls.lt) + if cls.gt is not None: + s = s.filter(lambda d: cls.gt < d) + return s + + +@resolves(pydantic.ConstrainedFloat) +def resolve_confloat(cls): # type: ignore[no-untyped-def] + min_value = cls.ge + max_value = cls.le + exclude_min = False + exclude_max = False + + if cls.gt is not None: + assert min_value is None, 'Set `gt` or `ge`, but not both' + min_value = cls.gt + exclude_min = True + if cls.lt is not None: + assert max_value is None, 'Set `lt` or `le`, but not both' + max_value = cls.lt + exclude_max = True + + if cls.multiple_of is None: + return st.floats(min_value, max_value, exclude_min=exclude_min, exclude_max=exclude_max, allow_nan=False) + + if min_value is not None: + min_value = math.ceil(min_value / cls.multiple_of) + if exclude_min: + min_value = min_value + 1 + if max_value is not None: + assert max_value >= cls.multiple_of, 'Cannot build model with max value smaller than multiple of' + max_value = math.floor(max_value / cls.multiple_of) + if exclude_max: + max_value = max_value - 1 + + return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of) + + +@resolves(pydantic.ConstrainedInt) +def resolve_conint(cls): # type: ignore[no-untyped-def] + min_value = cls.ge + max_value = cls.le + if cls.gt is not None: + assert min_value is None, 'Set `gt` or `ge`, but not both' + min_value = cls.gt + 1 + if cls.lt is not None: + assert max_value is None, 'Set `lt` or `le`, but not both' + max_value = cls.lt - 1 + + if cls.multiple_of is None or cls.multiple_of == 1: + return st.integers(min_value, max_value) + + # These adjustments and the .map handle integer-valued multiples, while the + # .filter handles trickier cases as for confloat. + if min_value is not None: + min_value = math.ceil(Fraction(min_value) / Fraction(cls.multiple_of)) + if max_value is not None: + max_value = math.floor(Fraction(max_value) / Fraction(cls.multiple_of)) + return st.integers(min_value, max_value).map(lambda x: x * cls.multiple_of) + + +@resolves(pydantic.ConstrainedDate) +def resolve_condate(cls): # type: ignore[no-untyped-def] + if cls.ge is not None: + assert cls.gt is None, 'Set `gt` or `ge`, but not both' + min_value = cls.ge + elif cls.gt is not None: + min_value = cls.gt + datetime.timedelta(days=1) + else: + min_value = datetime.date.min + if cls.le is not None: + assert cls.lt is None, 'Set `lt` or `le`, but not both' + max_value = cls.le + elif cls.lt is not None: + max_value = cls.lt - datetime.timedelta(days=1) + else: + max_value = datetime.date.max + return st.dates(min_value, max_value) + + +@resolves(pydantic.ConstrainedStr) +def resolve_constr(cls): # type: ignore[no-untyped-def] # pragma: no cover + min_size = cls.min_length or 0 + max_size = cls.max_length + + if cls.regex is None and not cls.strip_whitespace: + return st.text(min_size=min_size, max_size=max_size) + + if cls.regex is not None: + strategy = st.from_regex(cls.regex) + if cls.strip_whitespace: + strategy = strategy.filter(lambda s: s == s.strip()) + elif cls.strip_whitespace: + repeats = '{{{},{}}}'.format( + min_size - 2 if min_size > 2 else 0, + max_size - 2 if (max_size or 0) > 2 else '', + ) + if min_size >= 2: + strategy = st.from_regex(rf'\W.{repeats}\W') + elif min_size == 1: + strategy = st.from_regex(rf'\W(.{repeats}\W)?') + else: + assert min_size == 0 + strategy = st.from_regex(rf'(\W(.{repeats}\W)?)?') + + if min_size == 0 and max_size is None: + return strategy + elif max_size is None: + return strategy.filter(lambda s: min_size <= len(s)) + return strategy.filter(lambda s: min_size <= len(s) <= max_size) + + +# Finally, register all previously-defined types, and patch in our new function +for typ in list(pydantic.types._DEFINED_TYPES): + _registered(typ) +pydantic.types._registered = _registered +st.register_type_strategy(pydantic.Json, resolve_json) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/annotated_types.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/annotated_types.py new file mode 100644 index 0000000000000000000000000000000000000000..d9eaaafd5b6b2617baf3b3c889659c5bac807ff2 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/annotated_types.py @@ -0,0 +1,72 @@ +import sys +from typing import TYPE_CHECKING, Any, Dict, FrozenSet, NamedTuple, Type + +from pydantic.v1.fields import Required +from pydantic.v1.main import BaseModel, create_model +from pydantic.v1.typing import is_typeddict, is_typeddict_special + +if TYPE_CHECKING: + from typing_extensions import TypedDict + +if sys.version_info < (3, 11): + + def is_legacy_typeddict(typeddict_cls: Type['TypedDict']) -> bool: # type: ignore[valid-type] + return is_typeddict(typeddict_cls) and type(typeddict_cls).__module__ == 'typing' + +else: + + def is_legacy_typeddict(_: Any) -> Any: + return False + + +def create_model_from_typeddict( + # Mypy bug: `Type[TypedDict]` is resolved as `Any` https://github.com/python/mypy/issues/11030 + typeddict_cls: Type['TypedDict'], # type: ignore[valid-type] + **kwargs: Any, +) -> Type['BaseModel']: + """ + Create a `BaseModel` based on the fields of a `TypedDict`. + Since `typing.TypedDict` in Python 3.8 does not store runtime information about optional keys, + we raise an error if this happens (see https://bugs.python.org/issue38834). + """ + field_definitions: Dict[str, Any] + + # Best case scenario: with python 3.9+ or when `TypedDict` is imported from `typing_extensions` + if not hasattr(typeddict_cls, '__required_keys__'): + raise TypeError( + 'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.9.2. ' + 'Without it, there is no way to differentiate required and optional fields when subclassed.' + ) + + if is_legacy_typeddict(typeddict_cls) and any( + is_typeddict_special(t) for t in typeddict_cls.__annotations__.values() + ): + raise TypeError( + 'You should use `typing_extensions.TypedDict` instead of `typing.TypedDict` with Python < 3.11. ' + 'Without it, there is no way to reflect Required/NotRequired keys.' + ) + + required_keys: FrozenSet[str] = typeddict_cls.__required_keys__ # type: ignore[attr-defined] + field_definitions = { + field_name: (field_type, Required if field_name in required_keys else None) + for field_name, field_type in typeddict_cls.__annotations__.items() + } + + return create_model(typeddict_cls.__name__, **kwargs, **field_definitions) + + +def create_model_from_namedtuple(namedtuple_cls: Type['NamedTuple'], **kwargs: Any) -> Type['BaseModel']: + """ + Create a `BaseModel` based on the fields of a named tuple. + A named tuple can be created with `typing.NamedTuple` and declared annotations + but also with `collections.namedtuple`, in this case we consider all fields + to have type `Any`. + """ + # With python 3.10+, `__annotations__` always exists but can be empty hence the `getattr... or...` logic + namedtuple_annotations: Dict[str, Type[Any]] = getattr(namedtuple_cls, '__annotations__', None) or { + k: Any for k in namedtuple_cls._fields + } + field_definitions: Dict[str, Any] = { + field_name: (field_type, Required) for field_name, field_type in namedtuple_annotations.items() + } + return create_model(namedtuple_cls.__name__, **kwargs, **field_definitions) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/class_validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/class_validators.py new file mode 100644 index 0000000000000000000000000000000000000000..2f68fc8600038d8de10017b0d02a3fde77a06ba6 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/class_validators.py @@ -0,0 +1,361 @@ +import warnings +from collections import ChainMap +from functools import partial, partialmethod, wraps +from itertools import chain +from types import FunctionType +from typing import TYPE_CHECKING, Any, Callable, Dict, Iterable, List, Optional, Set, Tuple, Type, Union, overload + +from pydantic.v1.errors import ConfigError +from pydantic.v1.typing import AnyCallable +from pydantic.v1.utils import ROOT_KEY, in_ipython + +if TYPE_CHECKING: + from pydantic.v1.typing import AnyClassMethod + + +class Validator: + __slots__ = 'func', 'pre', 'each_item', 'always', 'check_fields', 'skip_on_failure' + + def __init__( + self, + func: AnyCallable, + pre: bool = False, + each_item: bool = False, + always: bool = False, + check_fields: bool = False, + skip_on_failure: bool = False, + ): + self.func = func + self.pre = pre + self.each_item = each_item + self.always = always + self.check_fields = check_fields + self.skip_on_failure = skip_on_failure + + +if TYPE_CHECKING: + from inspect import Signature + + from pydantic.v1.config import BaseConfig + from pydantic.v1.fields import ModelField + from pydantic.v1.types import ModelOrDc + + ValidatorCallable = Callable[[Optional[ModelOrDc], Any, Dict[str, Any], ModelField, Type[BaseConfig]], Any] + ValidatorsList = List[ValidatorCallable] + ValidatorListDict = Dict[str, List[Validator]] + +_FUNCS: Set[str] = set() +VALIDATOR_CONFIG_KEY = '__validator_config__' +ROOT_VALIDATOR_CONFIG_KEY = '__root_validator_config__' + + +def validator( + *fields: str, + pre: bool = False, + each_item: bool = False, + always: bool = False, + check_fields: bool = True, + whole: Optional[bool] = None, + allow_reuse: bool = False, +) -> Callable[[AnyCallable], 'AnyClassMethod']: + """ + Decorate methods on the class indicating that they should be used to validate fields + :param fields: which field(s) the method should be called on + :param pre: whether or not this validator should be called before the standard validators (else after) + :param each_item: for complex objects (sets, lists etc.) whether to validate individual elements rather than the + whole object + :param always: whether this method and other validators should be called even if the value is missing + :param check_fields: whether to check that the fields actually exist on the model + :param allow_reuse: whether to track and raise an error if another validator refers to the decorated function + """ + if not fields: + raise ConfigError('validator with no fields specified') + elif isinstance(fields[0], FunctionType): + raise ConfigError( + "validators should be used with fields and keyword arguments, not bare. " # noqa: Q000 + "E.g. usage should be `@validator('', ...)`" + ) + elif not all(isinstance(field, str) for field in fields): + raise ConfigError( + "validator fields should be passed as separate string args. " # noqa: Q000 + "E.g. usage should be `@validator('', '', ...)`" + ) + + if whole is not None: + warnings.warn( + 'The "whole" keyword argument is deprecated, use "each_item" (inverse meaning, default False) instead', + DeprecationWarning, + ) + assert each_item is False, '"each_item" and "whole" conflict, remove "whole"' + each_item = not whole + + def dec(f: AnyCallable) -> 'AnyClassMethod': + f_cls = _prepare_validator(f, allow_reuse) + setattr( + f_cls, + VALIDATOR_CONFIG_KEY, + ( + fields, + Validator(func=f_cls.__func__, pre=pre, each_item=each_item, always=always, check_fields=check_fields), + ), + ) + return f_cls + + return dec + + +@overload +def root_validator(_func: AnyCallable) -> 'AnyClassMethod': + ... + + +@overload +def root_validator( + *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False +) -> Callable[[AnyCallable], 'AnyClassMethod']: + ... + + +def root_validator( + _func: Optional[AnyCallable] = None, *, pre: bool = False, allow_reuse: bool = False, skip_on_failure: bool = False +) -> Union['AnyClassMethod', Callable[[AnyCallable], 'AnyClassMethod']]: + """ + Decorate methods on a model indicating that they should be used to validate (and perhaps modify) data either + before or after standard model parsing/validation is performed. + """ + if _func: + f_cls = _prepare_validator(_func, allow_reuse) + setattr( + f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure) + ) + return f_cls + + def dec(f: AnyCallable) -> 'AnyClassMethod': + f_cls = _prepare_validator(f, allow_reuse) + setattr( + f_cls, ROOT_VALIDATOR_CONFIG_KEY, Validator(func=f_cls.__func__, pre=pre, skip_on_failure=skip_on_failure) + ) + return f_cls + + return dec + + +def _prepare_validator(function: AnyCallable, allow_reuse: bool) -> 'AnyClassMethod': + """ + Avoid validators with duplicated names since without this, validators can be overwritten silently + which generally isn't the intended behaviour, don't run in ipython (see #312) or if allow_reuse is False. + """ + f_cls = function if isinstance(function, classmethod) else classmethod(function) + if not in_ipython() and not allow_reuse: + ref = ( + getattr(f_cls.__func__, '__module__', '') + + '.' + + getattr(f_cls.__func__, '__qualname__', f'') + ) + if ref in _FUNCS: + raise ConfigError(f'duplicate validator function "{ref}"; if this is intended, set `allow_reuse=True`') + _FUNCS.add(ref) + return f_cls + + +class ValidatorGroup: + def __init__(self, validators: 'ValidatorListDict') -> None: + self.validators = validators + self.used_validators = {'*'} + + def get_validators(self, name: str) -> Optional[Dict[str, Validator]]: + self.used_validators.add(name) + validators = self.validators.get(name, []) + if name != ROOT_KEY: + validators += self.validators.get('*', []) + if validators: + return {getattr(v.func, '__name__', f''): v for v in validators} + else: + return None + + def check_for_unused(self) -> None: + unused_validators = set( + chain.from_iterable( + ( + getattr(v.func, '__name__', f'') + for v in self.validators[f] + if v.check_fields + ) + for f in (self.validators.keys() - self.used_validators) + ) + ) + if unused_validators: + fn = ', '.join(unused_validators) + raise ConfigError( + f"Validators defined with incorrect fields: {fn} " # noqa: Q000 + f"(use check_fields=False if you're inheriting from the model and intended this)" + ) + + +def extract_validators(namespace: Dict[str, Any]) -> Dict[str, List[Validator]]: + validators: Dict[str, List[Validator]] = {} + for var_name, value in namespace.items(): + validator_config = getattr(value, VALIDATOR_CONFIG_KEY, None) + if validator_config: + fields, v = validator_config + for field in fields: + if field in validators: + validators[field].append(v) + else: + validators[field] = [v] + return validators + + +def extract_root_validators(namespace: Dict[str, Any]) -> Tuple[List[AnyCallable], List[Tuple[bool, AnyCallable]]]: + from inspect import signature + + pre_validators: List[AnyCallable] = [] + post_validators: List[Tuple[bool, AnyCallable]] = [] + for name, value in namespace.items(): + validator_config: Optional[Validator] = getattr(value, ROOT_VALIDATOR_CONFIG_KEY, None) + if validator_config: + sig = signature(validator_config.func) + args = list(sig.parameters.keys()) + if args[0] == 'self': + raise ConfigError( + f'Invalid signature for root validator {name}: {sig}, "self" not permitted as first argument, ' + f'should be: (cls, values).' + ) + if len(args) != 2: + raise ConfigError(f'Invalid signature for root validator {name}: {sig}, should be: (cls, values).') + # check function signature + if validator_config.pre: + pre_validators.append(validator_config.func) + else: + post_validators.append((validator_config.skip_on_failure, validator_config.func)) + return pre_validators, post_validators + + +def inherit_validators(base_validators: 'ValidatorListDict', validators: 'ValidatorListDict') -> 'ValidatorListDict': + for field, field_validators in base_validators.items(): + if field not in validators: + validators[field] = [] + validators[field] += field_validators + return validators + + +def make_generic_validator(validator: AnyCallable) -> 'ValidatorCallable': + """ + Make a generic function which calls a validator with the right arguments. + + Unfortunately other approaches (eg. return a partial of a function that builds the arguments) is slow, + hence this laborious way of doing things. + + It's done like this so validators don't all need **kwargs in their signature, eg. any combination of + the arguments "values", "fields" and/or "config" are permitted. + """ + from inspect import signature + + if not isinstance(validator, (partial, partialmethod)): + # This should be the default case, so overhead is reduced + sig = signature(validator) + args = list(sig.parameters.keys()) + else: + # Fix the generated argument lists of partial methods + sig = signature(validator.func) + args = [ + k + for k in signature(validator.func).parameters.keys() + if k not in validator.args | validator.keywords.keys() + ] + + first_arg = args.pop(0) + if first_arg == 'self': + raise ConfigError( + f'Invalid signature for validator {validator}: {sig}, "self" not permitted as first argument, ' + f'should be: (cls, value, values, config, field), "values", "config" and "field" are all optional.' + ) + elif first_arg == 'cls': + # assume the second argument is value + return wraps(validator)(_generic_validator_cls(validator, sig, set(args[1:]))) + else: + # assume the first argument was value which has already been removed + return wraps(validator)(_generic_validator_basic(validator, sig, set(args))) + + +def prep_validators(v_funcs: Iterable[AnyCallable]) -> 'ValidatorsList': + return [make_generic_validator(f) for f in v_funcs if f] + + +all_kwargs = {'values', 'field', 'config'} + + +def _generic_validator_cls(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable': + # assume the first argument is value + has_kwargs = False + if 'kwargs' in args: + has_kwargs = True + args -= {'kwargs'} + + if not args.issubset(all_kwargs): + raise ConfigError( + f'Invalid signature for validator {validator}: {sig}, should be: ' + f'(cls, value, values, config, field), "values", "config" and "field" are all optional.' + ) + + if has_kwargs: + return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config) + elif args == set(): + return lambda cls, v, values, field, config: validator(cls, v) + elif args == {'values'}: + return lambda cls, v, values, field, config: validator(cls, v, values=values) + elif args == {'field'}: + return lambda cls, v, values, field, config: validator(cls, v, field=field) + elif args == {'config'}: + return lambda cls, v, values, field, config: validator(cls, v, config=config) + elif args == {'values', 'field'}: + return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field) + elif args == {'values', 'config'}: + return lambda cls, v, values, field, config: validator(cls, v, values=values, config=config) + elif args == {'field', 'config'}: + return lambda cls, v, values, field, config: validator(cls, v, field=field, config=config) + else: + # args == {'values', 'field', 'config'} + return lambda cls, v, values, field, config: validator(cls, v, values=values, field=field, config=config) + + +def _generic_validator_basic(validator: AnyCallable, sig: 'Signature', args: Set[str]) -> 'ValidatorCallable': + has_kwargs = False + if 'kwargs' in args: + has_kwargs = True + args -= {'kwargs'} + + if not args.issubset(all_kwargs): + raise ConfigError( + f'Invalid signature for validator {validator}: {sig}, should be: ' + f'(value, values, config, field), "values", "config" and "field" are all optional.' + ) + + if has_kwargs: + return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config) + elif args == set(): + return lambda cls, v, values, field, config: validator(v) + elif args == {'values'}: + return lambda cls, v, values, field, config: validator(v, values=values) + elif args == {'field'}: + return lambda cls, v, values, field, config: validator(v, field=field) + elif args == {'config'}: + return lambda cls, v, values, field, config: validator(v, config=config) + elif args == {'values', 'field'}: + return lambda cls, v, values, field, config: validator(v, values=values, field=field) + elif args == {'values', 'config'}: + return lambda cls, v, values, field, config: validator(v, values=values, config=config) + elif args == {'field', 'config'}: + return lambda cls, v, values, field, config: validator(v, field=field, config=config) + else: + # args == {'values', 'field', 'config'} + return lambda cls, v, values, field, config: validator(v, values=values, field=field, config=config) + + +def gather_all_validators(type_: 'ModelOrDc') -> Dict[str, 'AnyClassMethod']: + all_attributes = ChainMap(*[cls.__dict__ for cls in type_.__mro__]) # type: ignore[arg-type,var-annotated] + return { + k: v + for k, v in all_attributes.items() + if hasattr(v, VALIDATOR_CONFIG_KEY) or hasattr(v, ROOT_VALIDATOR_CONFIG_KEY) + } diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/color.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/color.py new file mode 100644 index 0000000000000000000000000000000000000000..b0bbf78f4367c3e13a5aac0df09b5824d8f7e6ea --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/color.py @@ -0,0 +1,494 @@ +""" +Color definitions are used as per CSS3 specification: +http://www.w3.org/TR/css3-color/#svg-color + +A few colors have multiple names referring to the sames colors, eg. `grey` and `gray` or `aqua` and `cyan`. + +In these cases the LAST color when sorted alphabetically takes preferences, +eg. Color((0, 255, 255)).as_named() == 'cyan' because "cyan" comes after "aqua". +""" +import math +import re +from colorsys import hls_to_rgb, rgb_to_hls +from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union, cast + +from pydantic.v1.errors import ColorError +from pydantic.v1.utils import Representation, almost_equal_floats + +if TYPE_CHECKING: + from pydantic.v1.typing import CallableGenerator, ReprArgs + +ColorTuple = Union[Tuple[int, int, int], Tuple[int, int, int, float]] +ColorType = Union[ColorTuple, str] +HslColorTuple = Union[Tuple[float, float, float], Tuple[float, float, float, float]] + + +class RGBA: + """ + Internal use only as a representation of a color. + """ + + __slots__ = 'r', 'g', 'b', 'alpha', '_tuple' + + def __init__(self, r: float, g: float, b: float, alpha: Optional[float]): + self.r = r + self.g = g + self.b = b + self.alpha = alpha + + self._tuple: Tuple[float, float, float, Optional[float]] = (r, g, b, alpha) + + def __getitem__(self, item: Any) -> Any: + return self._tuple[item] + + +# these are not compiled here to avoid import slowdown, they'll be compiled the first time they're used, then cached +r_hex_short = r'\s*(?:#|0x)?([0-9a-f])([0-9a-f])([0-9a-f])([0-9a-f])?\s*' +r_hex_long = r'\s*(?:#|0x)?([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})([0-9a-f]{2})?\s*' +_r_255 = r'(\d{1,3}(?:\.\d+)?)' +_r_comma = r'\s*,\s*' +r_rgb = fr'\s*rgb\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}\)\s*' +_r_alpha = r'(\d(?:\.\d+)?|\.\d+|\d{1,2}%)' +r_rgba = fr'\s*rgba\(\s*{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_255}{_r_comma}{_r_alpha}\s*\)\s*' +_r_h = r'(-?\d+(?:\.\d+)?|-?\.\d+)(deg|rad|turn)?' +_r_sl = r'(\d{1,3}(?:\.\d+)?)%' +r_hsl = fr'\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}\s*\)\s*' +r_hsla = fr'\s*hsl\(\s*{_r_h}{_r_comma}{_r_sl}{_r_comma}{_r_sl}{_r_comma}{_r_alpha}\s*\)\s*' + +# colors where the two hex characters are the same, if all colors match this the short version of hex colors can be used +repeat_colors = {int(c * 2, 16) for c in '0123456789abcdef'} +rads = 2 * math.pi + + +class Color(Representation): + __slots__ = '_original', '_rgba' + + def __init__(self, value: ColorType) -> None: + self._rgba: RGBA + self._original: ColorType + if isinstance(value, (tuple, list)): + self._rgba = parse_tuple(value) + elif isinstance(value, str): + self._rgba = parse_str(value) + elif isinstance(value, Color): + self._rgba = value._rgba + value = value._original + else: + raise ColorError(reason='value must be a tuple, list or string') + + # if we've got here value must be a valid color + self._original = value + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='color') + + def original(self) -> ColorType: + """ + Original value passed to Color + """ + return self._original + + def as_named(self, *, fallback: bool = False) -> str: + if self._rgba.alpha is None: + rgb = cast(Tuple[int, int, int], self.as_rgb_tuple()) + try: + return COLORS_BY_VALUE[rgb] + except KeyError as e: + if fallback: + return self.as_hex() + else: + raise ValueError('no named color found, use fallback=True, as_hex() or as_rgb()') from e + else: + return self.as_hex() + + def as_hex(self) -> str: + """ + Hex string representing the color can be 3, 4, 6 or 8 characters depending on whether the string + a "short" representation of the color is possible and whether there's an alpha channel. + """ + values = [float_to_255(c) for c in self._rgba[:3]] + if self._rgba.alpha is not None: + values.append(float_to_255(self._rgba.alpha)) + + as_hex = ''.join(f'{v:02x}' for v in values) + if all(c in repeat_colors for c in values): + as_hex = ''.join(as_hex[c] for c in range(0, len(as_hex), 2)) + return '#' + as_hex + + def as_rgb(self) -> str: + """ + Color as an rgb(, , ) or rgba(, , , ) string. + """ + if self._rgba.alpha is None: + return f'rgb({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)})' + else: + return ( + f'rgba({float_to_255(self._rgba.r)}, {float_to_255(self._rgba.g)}, {float_to_255(self._rgba.b)}, ' + f'{round(self._alpha_float(), 2)})' + ) + + def as_rgb_tuple(self, *, alpha: Optional[bool] = None) -> ColorTuple: + """ + Color as an RGB or RGBA tuple; red, green and blue are in the range 0 to 255, alpha if included is + in the range 0 to 1. + + :param alpha: whether to include the alpha channel, options are + None - (default) include alpha only if it's set (e.g. not None) + True - always include alpha, + False - always omit alpha, + """ + r, g, b = (float_to_255(c) for c in self._rgba[:3]) + if alpha is None: + if self._rgba.alpha is None: + return r, g, b + else: + return r, g, b, self._alpha_float() + elif alpha: + return r, g, b, self._alpha_float() + else: + # alpha is False + return r, g, b + + def as_hsl(self) -> str: + """ + Color as an hsl(, , ) or hsl(, , , ) string. + """ + if self._rgba.alpha is None: + h, s, li = self.as_hsl_tuple(alpha=False) # type: ignore + return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%})' + else: + h, s, li, a = self.as_hsl_tuple(alpha=True) # type: ignore + return f'hsl({h * 360:0.0f}, {s:0.0%}, {li:0.0%}, {round(a, 2)})' + + def as_hsl_tuple(self, *, alpha: Optional[bool] = None) -> HslColorTuple: + """ + Color as an HSL or HSLA tuple, e.g. hue, saturation, lightness and optionally alpha; all elements are in + the range 0 to 1. + + NOTE: this is HSL as used in HTML and most other places, not HLS as used in python's colorsys. + + :param alpha: whether to include the alpha channel, options are + None - (default) include alpha only if it's set (e.g. not None) + True - always include alpha, + False - always omit alpha, + """ + h, l, s = rgb_to_hls(self._rgba.r, self._rgba.g, self._rgba.b) + if alpha is None: + if self._rgba.alpha is None: + return h, s, l + else: + return h, s, l, self._alpha_float() + if alpha: + return h, s, l, self._alpha_float() + else: + # alpha is False + return h, s, l + + def _alpha_float(self) -> float: + return 1 if self._rgba.alpha is None else self._rgba.alpha + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls + + def __str__(self) -> str: + return self.as_named(fallback=True) + + def __repr_args__(self) -> 'ReprArgs': + return [(None, self.as_named(fallback=True))] + [('rgb', self.as_rgb_tuple())] # type: ignore + + def __eq__(self, other: Any) -> bool: + return isinstance(other, Color) and self.as_rgb_tuple() == other.as_rgb_tuple() + + def __hash__(self) -> int: + return hash(self.as_rgb_tuple()) + + +def parse_tuple(value: Tuple[Any, ...]) -> RGBA: + """ + Parse a tuple or list as a color. + """ + if len(value) == 3: + r, g, b = (parse_color_value(v) for v in value) + return RGBA(r, g, b, None) + elif len(value) == 4: + r, g, b = (parse_color_value(v) for v in value[:3]) + return RGBA(r, g, b, parse_float_alpha(value[3])) + else: + raise ColorError(reason='tuples must have length 3 or 4') + + +def parse_str(value: str) -> RGBA: + """ + Parse a string to an RGBA tuple, trying the following formats (in this order): + * named color, see COLORS_BY_NAME below + * hex short eg. `fff` (prefix can be `#`, `0x` or nothing) + * hex long eg. `ffffff` (prefix can be `#`, `0x` or nothing) + * `rgb(, , ) ` + * `rgba(, , , )` + """ + value_lower = value.lower() + try: + r, g, b = COLORS_BY_NAME[value_lower] + except KeyError: + pass + else: + return ints_to_rgba(r, g, b, None) + + m = re.fullmatch(r_hex_short, value_lower) + if m: + *rgb, a = m.groups() + r, g, b = (int(v * 2, 16) for v in rgb) + if a: + alpha: Optional[float] = int(a * 2, 16) / 255 + else: + alpha = None + return ints_to_rgba(r, g, b, alpha) + + m = re.fullmatch(r_hex_long, value_lower) + if m: + *rgb, a = m.groups() + r, g, b = (int(v, 16) for v in rgb) + if a: + alpha = int(a, 16) / 255 + else: + alpha = None + return ints_to_rgba(r, g, b, alpha) + + m = re.fullmatch(r_rgb, value_lower) + if m: + return ints_to_rgba(*m.groups(), None) # type: ignore + + m = re.fullmatch(r_rgba, value_lower) + if m: + return ints_to_rgba(*m.groups()) # type: ignore + + m = re.fullmatch(r_hsl, value_lower) + if m: + h, h_units, s, l_ = m.groups() + return parse_hsl(h, h_units, s, l_) + + m = re.fullmatch(r_hsla, value_lower) + if m: + h, h_units, s, l_, a = m.groups() + return parse_hsl(h, h_units, s, l_, parse_float_alpha(a)) + + raise ColorError(reason='string not recognised as a valid color') + + +def ints_to_rgba(r: Union[int, str], g: Union[int, str], b: Union[int, str], alpha: Optional[float]) -> RGBA: + return RGBA(parse_color_value(r), parse_color_value(g), parse_color_value(b), parse_float_alpha(alpha)) + + +def parse_color_value(value: Union[int, str], max_val: int = 255) -> float: + """ + Parse a value checking it's a valid int in the range 0 to max_val and divide by max_val to give a number + in the range 0 to 1 + """ + try: + color = float(value) + except ValueError: + raise ColorError(reason='color values must be a valid number') + if 0 <= color <= max_val: + return color / max_val + else: + raise ColorError(reason=f'color values must be in the range 0 to {max_val}') + + +def parse_float_alpha(value: Union[None, str, float, int]) -> Optional[float]: + """ + Parse a value checking it's a valid float in the range 0 to 1 + """ + if value is None: + return None + try: + if isinstance(value, str) and value.endswith('%'): + alpha = float(value[:-1]) / 100 + else: + alpha = float(value) + except ValueError: + raise ColorError(reason='alpha values must be a valid float') + + if almost_equal_floats(alpha, 1): + return None + elif 0 <= alpha <= 1: + return alpha + else: + raise ColorError(reason='alpha values must be in the range 0 to 1') + + +def parse_hsl(h: str, h_units: str, sat: str, light: str, alpha: Optional[float] = None) -> RGBA: + """ + Parse raw hue, saturation, lightness and alpha values and convert to RGBA. + """ + s_value, l_value = parse_color_value(sat, 100), parse_color_value(light, 100) + + h_value = float(h) + if h_units in {None, 'deg'}: + h_value = h_value % 360 / 360 + elif h_units == 'rad': + h_value = h_value % rads / rads + else: + # turns + h_value = h_value % 1 + + r, g, b = hls_to_rgb(h_value, l_value, s_value) + return RGBA(r, g, b, alpha) + + +def float_to_255(c: float) -> int: + return int(round(c * 255)) + + +COLORS_BY_NAME = { + 'aliceblue': (240, 248, 255), + 'antiquewhite': (250, 235, 215), + 'aqua': (0, 255, 255), + 'aquamarine': (127, 255, 212), + 'azure': (240, 255, 255), + 'beige': (245, 245, 220), + 'bisque': (255, 228, 196), + 'black': (0, 0, 0), + 'blanchedalmond': (255, 235, 205), + 'blue': (0, 0, 255), + 'blueviolet': (138, 43, 226), + 'brown': (165, 42, 42), + 'burlywood': (222, 184, 135), + 'cadetblue': (95, 158, 160), + 'chartreuse': (127, 255, 0), + 'chocolate': (210, 105, 30), + 'coral': (255, 127, 80), + 'cornflowerblue': (100, 149, 237), + 'cornsilk': (255, 248, 220), + 'crimson': (220, 20, 60), + 'cyan': (0, 255, 255), + 'darkblue': (0, 0, 139), + 'darkcyan': (0, 139, 139), + 'darkgoldenrod': (184, 134, 11), + 'darkgray': (169, 169, 169), + 'darkgreen': (0, 100, 0), + 'darkgrey': (169, 169, 169), + 'darkkhaki': (189, 183, 107), + 'darkmagenta': (139, 0, 139), + 'darkolivegreen': (85, 107, 47), + 'darkorange': (255, 140, 0), + 'darkorchid': (153, 50, 204), + 'darkred': (139, 0, 0), + 'darksalmon': (233, 150, 122), + 'darkseagreen': (143, 188, 143), + 'darkslateblue': (72, 61, 139), + 'darkslategray': (47, 79, 79), + 'darkslategrey': (47, 79, 79), + 'darkturquoise': (0, 206, 209), + 'darkviolet': (148, 0, 211), + 'deeppink': (255, 20, 147), + 'deepskyblue': (0, 191, 255), + 'dimgray': (105, 105, 105), + 'dimgrey': (105, 105, 105), + 'dodgerblue': (30, 144, 255), + 'firebrick': (178, 34, 34), + 'floralwhite': (255, 250, 240), + 'forestgreen': (34, 139, 34), + 'fuchsia': (255, 0, 255), + 'gainsboro': (220, 220, 220), + 'ghostwhite': (248, 248, 255), + 'gold': (255, 215, 0), + 'goldenrod': (218, 165, 32), + 'gray': (128, 128, 128), + 'green': (0, 128, 0), + 'greenyellow': (173, 255, 47), + 'grey': (128, 128, 128), + 'honeydew': (240, 255, 240), + 'hotpink': (255, 105, 180), + 'indianred': (205, 92, 92), + 'indigo': (75, 0, 130), + 'ivory': (255, 255, 240), + 'khaki': (240, 230, 140), + 'lavender': (230, 230, 250), + 'lavenderblush': (255, 240, 245), + 'lawngreen': (124, 252, 0), + 'lemonchiffon': (255, 250, 205), + 'lightblue': (173, 216, 230), + 'lightcoral': (240, 128, 128), + 'lightcyan': (224, 255, 255), + 'lightgoldenrodyellow': (250, 250, 210), + 'lightgray': (211, 211, 211), + 'lightgreen': (144, 238, 144), + 'lightgrey': (211, 211, 211), + 'lightpink': (255, 182, 193), + 'lightsalmon': (255, 160, 122), + 'lightseagreen': (32, 178, 170), + 'lightskyblue': (135, 206, 250), + 'lightslategray': (119, 136, 153), + 'lightslategrey': (119, 136, 153), + 'lightsteelblue': (176, 196, 222), + 'lightyellow': (255, 255, 224), + 'lime': (0, 255, 0), + 'limegreen': (50, 205, 50), + 'linen': (250, 240, 230), + 'magenta': (255, 0, 255), + 'maroon': (128, 0, 0), + 'mediumaquamarine': (102, 205, 170), + 'mediumblue': (0, 0, 205), + 'mediumorchid': (186, 85, 211), + 'mediumpurple': (147, 112, 219), + 'mediumseagreen': (60, 179, 113), + 'mediumslateblue': (123, 104, 238), + 'mediumspringgreen': (0, 250, 154), + 'mediumturquoise': (72, 209, 204), + 'mediumvioletred': (199, 21, 133), + 'midnightblue': (25, 25, 112), + 'mintcream': (245, 255, 250), + 'mistyrose': (255, 228, 225), + 'moccasin': (255, 228, 181), + 'navajowhite': (255, 222, 173), + 'navy': (0, 0, 128), + 'oldlace': (253, 245, 230), + 'olive': (128, 128, 0), + 'olivedrab': (107, 142, 35), + 'orange': (255, 165, 0), + 'orangered': (255, 69, 0), + 'orchid': (218, 112, 214), + 'palegoldenrod': (238, 232, 170), + 'palegreen': (152, 251, 152), + 'paleturquoise': (175, 238, 238), + 'palevioletred': (219, 112, 147), + 'papayawhip': (255, 239, 213), + 'peachpuff': (255, 218, 185), + 'peru': (205, 133, 63), + 'pink': (255, 192, 203), + 'plum': (221, 160, 221), + 'powderblue': (176, 224, 230), + 'purple': (128, 0, 128), + 'red': (255, 0, 0), + 'rosybrown': (188, 143, 143), + 'royalblue': (65, 105, 225), + 'saddlebrown': (139, 69, 19), + 'salmon': (250, 128, 114), + 'sandybrown': (244, 164, 96), + 'seagreen': (46, 139, 87), + 'seashell': (255, 245, 238), + 'sienna': (160, 82, 45), + 'silver': (192, 192, 192), + 'skyblue': (135, 206, 235), + 'slateblue': (106, 90, 205), + 'slategray': (112, 128, 144), + 'slategrey': (112, 128, 144), + 'snow': (255, 250, 250), + 'springgreen': (0, 255, 127), + 'steelblue': (70, 130, 180), + 'tan': (210, 180, 140), + 'teal': (0, 128, 128), + 'thistle': (216, 191, 216), + 'tomato': (255, 99, 71), + 'turquoise': (64, 224, 208), + 'violet': (238, 130, 238), + 'wheat': (245, 222, 179), + 'white': (255, 255, 255), + 'whitesmoke': (245, 245, 245), + 'yellow': (255, 255, 0), + 'yellowgreen': (154, 205, 50), +} + +COLORS_BY_VALUE = {v: k for k, v in COLORS_BY_NAME.items()} diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/config.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/config.py new file mode 100644 index 0000000000000000000000000000000000000000..18f7c999bee0fab95293b2434047fd20532a6446 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/config.py @@ -0,0 +1,191 @@ +import json +from enum import Enum +from typing import TYPE_CHECKING, Any, Callable, Dict, ForwardRef, Optional, Tuple, Type, Union + +from typing_extensions import Literal, Protocol + +from pydantic.v1.typing import AnyArgTCallable, AnyCallable +from pydantic.v1.utils import GetterDict +from pydantic.v1.version import compiled + +if TYPE_CHECKING: + from typing import overload + + from pydantic.v1.fields import ModelField + from pydantic.v1.main import BaseModel + + ConfigType = Type['BaseConfig'] + + class SchemaExtraCallable(Protocol): + @overload + def __call__(self, schema: Dict[str, Any]) -> None: + pass + + @overload + def __call__(self, schema: Dict[str, Any], model_class: Type[BaseModel]) -> None: + pass + +else: + SchemaExtraCallable = Callable[..., None] + +__all__ = 'BaseConfig', 'ConfigDict', 'get_config', 'Extra', 'inherit_config', 'prepare_config' + + +class Extra(str, Enum): + allow = 'allow' + ignore = 'ignore' + forbid = 'forbid' + + +# https://github.com/cython/cython/issues/4003 +# Fixed in Cython 3 and Pydantic v1 won't support Cython 3. +# Pydantic v2 doesn't depend on Cython at all. +if not compiled: + from typing_extensions import TypedDict + + class ConfigDict(TypedDict, total=False): + title: Optional[str] + anystr_lower: bool + anystr_strip_whitespace: bool + min_anystr_length: int + max_anystr_length: Optional[int] + validate_all: bool + extra: Extra + allow_mutation: bool + frozen: bool + allow_population_by_field_name: bool + use_enum_values: bool + fields: Dict[str, Union[str, Dict[str, str]]] + validate_assignment: bool + error_msg_templates: Dict[str, str] + arbitrary_types_allowed: bool + orm_mode: bool + getter_dict: Type[GetterDict] + alias_generator: Optional[Callable[[str], str]] + keep_untouched: Tuple[type, ...] + schema_extra: Union[Dict[str, object], 'SchemaExtraCallable'] + json_loads: Callable[[str], object] + json_dumps: AnyArgTCallable[str] + json_encoders: Dict[Type[object], AnyCallable] + underscore_attrs_are_private: bool + allow_inf_nan: bool + copy_on_model_validation: Literal['none', 'deep', 'shallow'] + # whether dataclass `__post_init__` should be run after validation + post_init_call: Literal['before_validation', 'after_validation'] + +else: + ConfigDict = dict # type: ignore + + +class BaseConfig: + title: Optional[str] = None + anystr_lower: bool = False + anystr_upper: bool = False + anystr_strip_whitespace: bool = False + min_anystr_length: int = 0 + max_anystr_length: Optional[int] = None + validate_all: bool = False + extra: Extra = Extra.ignore + allow_mutation: bool = True + frozen: bool = False + allow_population_by_field_name: bool = False + use_enum_values: bool = False + fields: Dict[str, Union[str, Dict[str, str]]] = {} + validate_assignment: bool = False + error_msg_templates: Dict[str, str] = {} + arbitrary_types_allowed: bool = False + orm_mode: bool = False + getter_dict: Type[GetterDict] = GetterDict + alias_generator: Optional[Callable[[str], str]] = None + keep_untouched: Tuple[type, ...] = () + schema_extra: Union[Dict[str, Any], 'SchemaExtraCallable'] = {} + json_loads: Callable[[str], Any] = json.loads + json_dumps: Callable[..., str] = json.dumps + json_encoders: Dict[Union[Type[Any], str, ForwardRef], AnyCallable] = {} + underscore_attrs_are_private: bool = False + allow_inf_nan: bool = True + + # whether inherited models as fields should be reconstructed as base model, + # and whether such a copy should be shallow or deep + copy_on_model_validation: Literal['none', 'deep', 'shallow'] = 'shallow' + + # whether `Union` should check all allowed types before even trying to coerce + smart_union: bool = False + # whether dataclass `__post_init__` should be run before or after validation + post_init_call: Literal['before_validation', 'after_validation'] = 'before_validation' + + @classmethod + def get_field_info(cls, name: str) -> Dict[str, Any]: + """ + Get properties of FieldInfo from the `fields` property of the config class. + """ + + fields_value = cls.fields.get(name) + + if isinstance(fields_value, str): + field_info: Dict[str, Any] = {'alias': fields_value} + elif isinstance(fields_value, dict): + field_info = fields_value + else: + field_info = {} + + if 'alias' in field_info: + field_info.setdefault('alias_priority', 2) + + if field_info.get('alias_priority', 0) <= 1 and cls.alias_generator: + alias = cls.alias_generator(name) + if not isinstance(alias, str): + raise TypeError(f'Config.alias_generator must return str, not {alias.__class__}') + field_info.update(alias=alias, alias_priority=1) + return field_info + + @classmethod + def prepare_field(cls, field: 'ModelField') -> None: + """ + Optional hook to check or modify fields during model creation. + """ + pass + + +def get_config(config: Union[ConfigDict, Type[object], None]) -> Type[BaseConfig]: + if config is None: + return BaseConfig + + else: + config_dict = ( + config + if isinstance(config, dict) + else {k: getattr(config, k) for k in dir(config) if not k.startswith('__')} + ) + + class Config(BaseConfig): + ... + + for k, v in config_dict.items(): + setattr(Config, k, v) + return Config + + +def inherit_config(self_config: 'ConfigType', parent_config: 'ConfigType', **namespace: Any) -> 'ConfigType': + if not self_config: + base_classes: Tuple['ConfigType', ...] = (parent_config,) + elif self_config == parent_config: + base_classes = (self_config,) + else: + base_classes = self_config, parent_config + + namespace['json_encoders'] = { + **getattr(parent_config, 'json_encoders', {}), + **getattr(self_config, 'json_encoders', {}), + **namespace.get('json_encoders', {}), + } + + return type('Config', base_classes, namespace) + + +def prepare_config(config: Type[BaseConfig], cls_name: str) -> None: + if not isinstance(config.extra, Extra): + try: + config.extra = Extra(config.extra) + except ValueError: + raise ValueError(f'"{cls_name}": {config.extra} is not a valid value for "extra"') diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/dataclasses.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/dataclasses.py new file mode 100644 index 0000000000000000000000000000000000000000..bd1670291d6622a28ecb5b8e9ad68f4bac9305d7 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/dataclasses.py @@ -0,0 +1,500 @@ +""" +The main purpose is to enhance stdlib dataclasses by adding validation +A pydantic dataclass can be generated from scratch or from a stdlib one. + +Behind the scene, a pydantic dataclass is just like a regular one on which we attach +a `BaseModel` and magic methods to trigger the validation of the data. +`__init__` and `__post_init__` are hence overridden and have extra logic to be +able to validate input data. + +When a pydantic dataclass is generated from scratch, it's just a plain dataclass +with validation triggered at initialization + +The tricky part if for stdlib dataclasses that are converted after into pydantic ones e.g. + +```py +@dataclasses.dataclass +class M: + x: int + +ValidatedM = pydantic.dataclasses.dataclass(M) +``` + +We indeed still want to support equality, hashing, repr, ... as if it was the stdlib one! + +```py +assert isinstance(ValidatedM(x=1), M) +assert ValidatedM(x=1) == M(x=1) +``` + +This means we **don't want to create a new dataclass that inherits from it** +The trick is to create a wrapper around `M` that will act as a proxy to trigger +validation without altering default `M` behaviour. +""" +import copy +import dataclasses +import sys +from contextlib import contextmanager +from functools import wraps + +try: + from functools import cached_property +except ImportError: + # cached_property available only for python3.8+ + pass + +from typing import TYPE_CHECKING, Any, Callable, ClassVar, Dict, Generator, Optional, Type, TypeVar, Union, overload + +from typing_extensions import dataclass_transform + +from pydantic.v1.class_validators import gather_all_validators +from pydantic.v1.config import BaseConfig, ConfigDict, Extra, get_config +from pydantic.v1.error_wrappers import ValidationError +from pydantic.v1.errors import DataclassTypeError +from pydantic.v1.fields import Field, FieldInfo, Required, Undefined +from pydantic.v1.main import create_model, validate_model +from pydantic.v1.utils import ClassAttribute + +if TYPE_CHECKING: + from pydantic.v1.main import BaseModel + from pydantic.v1.typing import CallableGenerator, NoArgAnyCallable + + DataclassT = TypeVar('DataclassT', bound='Dataclass') + + DataclassClassOrWrapper = Union[Type['Dataclass'], 'DataclassProxy'] + + class Dataclass: + # stdlib attributes + __dataclass_fields__: ClassVar[Dict[str, Any]] + __dataclass_params__: ClassVar[Any] # in reality `dataclasses._DataclassParams` + __post_init__: ClassVar[Callable[..., None]] + + # Added by pydantic + __pydantic_run_validation__: ClassVar[bool] + __post_init_post_parse__: ClassVar[Callable[..., None]] + __pydantic_initialised__: ClassVar[bool] + __pydantic_model__: ClassVar[Type[BaseModel]] + __pydantic_validate_values__: ClassVar[Callable[['Dataclass'], None]] + __pydantic_has_field_info_default__: ClassVar[bool] # whether a `pydantic.Field` is used as default value + + def __init__(self, *args: object, **kwargs: object) -> None: + pass + + @classmethod + def __get_validators__(cls: Type['Dataclass']) -> 'CallableGenerator': + pass + + @classmethod + def __validate__(cls: Type['DataclassT'], v: Any) -> 'DataclassT': + pass + + +__all__ = [ + 'dataclass', + 'set_validation', + 'create_pydantic_model_from_dataclass', + 'is_builtin_dataclass', + 'make_dataclass_validator', +] + +_T = TypeVar('_T') + +if sys.version_info >= (3, 10): + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + kw_only: bool = ..., + ) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']: + ... + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + _cls: Type[_T], + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + kw_only: bool = ..., + ) -> 'DataclassClassOrWrapper': + ... + +else: + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + ) -> Callable[[Type[_T]], 'DataclassClassOrWrapper']: + ... + + @dataclass_transform(field_specifiers=(dataclasses.field, Field)) + @overload + def dataclass( + _cls: Type[_T], + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + ) -> 'DataclassClassOrWrapper': + ... + + +@dataclass_transform(field_specifiers=(dataclasses.field, Field)) +def dataclass( + _cls: Optional[Type[_T]] = None, + *, + init: bool = True, + repr: bool = True, + eq: bool = True, + order: bool = False, + unsafe_hash: bool = False, + frozen: bool = False, + config: Union[ConfigDict, Type[object], None] = None, + validate_on_init: Optional[bool] = None, + use_proxy: Optional[bool] = None, + kw_only: bool = False, +) -> Union[Callable[[Type[_T]], 'DataclassClassOrWrapper'], 'DataclassClassOrWrapper']: + """ + Like the python standard lib dataclasses but with type validation. + The result is either a pydantic dataclass that will validate input data + or a wrapper that will trigger validation around a stdlib dataclass + to avoid modifying it directly + """ + the_config = get_config(config) + + def wrap(cls: Type[Any]) -> 'DataclassClassOrWrapper': + should_use_proxy = ( + use_proxy + if use_proxy is not None + else ( + is_builtin_dataclass(cls) + and (cls.__bases__[0] is object or set(dir(cls)) == set(dir(cls.__bases__[0]))) + ) + ) + if should_use_proxy: + dc_cls_doc = '' + dc_cls = DataclassProxy(cls) + default_validate_on_init = False + else: + dc_cls_doc = cls.__doc__ or '' # needs to be done before generating dataclass + if sys.version_info >= (3, 10): + dc_cls = dataclasses.dataclass( + cls, + init=init, + repr=repr, + eq=eq, + order=order, + unsafe_hash=unsafe_hash, + frozen=frozen, + kw_only=kw_only, + ) + else: + dc_cls = dataclasses.dataclass( # type: ignore + cls, init=init, repr=repr, eq=eq, order=order, unsafe_hash=unsafe_hash, frozen=frozen + ) + default_validate_on_init = True + + should_validate_on_init = default_validate_on_init if validate_on_init is None else validate_on_init + _add_pydantic_validation_attributes(cls, the_config, should_validate_on_init, dc_cls_doc) + dc_cls.__pydantic_model__.__try_update_forward_refs__(**{cls.__name__: cls}) + return dc_cls + + if _cls is None: + return wrap + + return wrap(_cls) + + +@contextmanager +def set_validation(cls: Type['DataclassT'], value: bool) -> Generator[Type['DataclassT'], None, None]: + original_run_validation = cls.__pydantic_run_validation__ + try: + cls.__pydantic_run_validation__ = value + yield cls + finally: + cls.__pydantic_run_validation__ = original_run_validation + + +class DataclassProxy: + __slots__ = '__dataclass__' + + def __init__(self, dc_cls: Type['Dataclass']) -> None: + object.__setattr__(self, '__dataclass__', dc_cls) + + def __call__(self, *args: Any, **kwargs: Any) -> Any: + with set_validation(self.__dataclass__, True): + return self.__dataclass__(*args, **kwargs) + + def __getattr__(self, name: str) -> Any: + return getattr(self.__dataclass__, name) + + def __setattr__(self, __name: str, __value: Any) -> None: + return setattr(self.__dataclass__, __name, __value) + + def __instancecheck__(self, instance: Any) -> bool: + return isinstance(instance, self.__dataclass__) + + def __copy__(self) -> 'DataclassProxy': + return DataclassProxy(copy.copy(self.__dataclass__)) + + def __deepcopy__(self, memo: Any) -> 'DataclassProxy': + return DataclassProxy(copy.deepcopy(self.__dataclass__, memo)) + + +def _add_pydantic_validation_attributes( # noqa: C901 (ignore complexity) + dc_cls: Type['Dataclass'], + config: Type[BaseConfig], + validate_on_init: bool, + dc_cls_doc: str, +) -> None: + """ + We need to replace the right method. If no `__post_init__` has been set in the stdlib dataclass + it won't even exist (code is generated on the fly by `dataclasses`) + By default, we run validation after `__init__` or `__post_init__` if defined + """ + init = dc_cls.__init__ + + @wraps(init) + def handle_extra_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None: + if config.extra == Extra.ignore: + init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__}) + + elif config.extra == Extra.allow: + for k, v in kwargs.items(): + self.__dict__.setdefault(k, v) + init(self, *args, **{k: v for k, v in kwargs.items() if k in self.__dataclass_fields__}) + + else: + init(self, *args, **kwargs) + + if hasattr(dc_cls, '__post_init__'): + try: + post_init = dc_cls.__post_init__.__wrapped__ # type: ignore[attr-defined] + except AttributeError: + post_init = dc_cls.__post_init__ + + @wraps(post_init) + def new_post_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None: + if config.post_init_call == 'before_validation': + post_init(self, *args, **kwargs) + + if self.__class__.__pydantic_run_validation__: + self.__pydantic_validate_values__() + if hasattr(self, '__post_init_post_parse__'): + self.__post_init_post_parse__(*args, **kwargs) + + if config.post_init_call == 'after_validation': + post_init(self, *args, **kwargs) + + setattr(dc_cls, '__init__', handle_extra_init) + setattr(dc_cls, '__post_init__', new_post_init) + + else: + + @wraps(init) + def new_init(self: 'Dataclass', *args: Any, **kwargs: Any) -> None: + handle_extra_init(self, *args, **kwargs) + + if self.__class__.__pydantic_run_validation__: + self.__pydantic_validate_values__() + + if hasattr(self, '__post_init_post_parse__'): + # We need to find again the initvars. To do that we use `__dataclass_fields__` instead of + # public method `dataclasses.fields` + + # get all initvars and their default values + initvars_and_values: Dict[str, Any] = {} + for i, f in enumerate(self.__class__.__dataclass_fields__.values()): + if f._field_type is dataclasses._FIELD_INITVAR: # type: ignore[attr-defined] + try: + # set arg value by default + initvars_and_values[f.name] = args[i] + except IndexError: + initvars_and_values[f.name] = kwargs.get(f.name, f.default) + + self.__post_init_post_parse__(**initvars_and_values) + + setattr(dc_cls, '__init__', new_init) + + setattr(dc_cls, '__pydantic_run_validation__', ClassAttribute('__pydantic_run_validation__', validate_on_init)) + setattr(dc_cls, '__pydantic_initialised__', False) + setattr(dc_cls, '__pydantic_model__', create_pydantic_model_from_dataclass(dc_cls, config, dc_cls_doc)) + setattr(dc_cls, '__pydantic_validate_values__', _dataclass_validate_values) + setattr(dc_cls, '__validate__', classmethod(_validate_dataclass)) + setattr(dc_cls, '__get_validators__', classmethod(_get_validators)) + + if dc_cls.__pydantic_model__.__config__.validate_assignment and not dc_cls.__dataclass_params__.frozen: + setattr(dc_cls, '__setattr__', _dataclass_validate_assignment_setattr) + + +def _get_validators(cls: 'DataclassClassOrWrapper') -> 'CallableGenerator': + yield cls.__validate__ + + +def _validate_dataclass(cls: Type['DataclassT'], v: Any) -> 'DataclassT': + with set_validation(cls, True): + if isinstance(v, cls): + v.__pydantic_validate_values__() + return v + elif isinstance(v, (list, tuple)): + return cls(*v) + elif isinstance(v, dict): + return cls(**v) + else: + raise DataclassTypeError(class_name=cls.__name__) + + +def create_pydantic_model_from_dataclass( + dc_cls: Type['Dataclass'], + config: Type[Any] = BaseConfig, + dc_cls_doc: Optional[str] = None, +) -> Type['BaseModel']: + field_definitions: Dict[str, Any] = {} + for field in dataclasses.fields(dc_cls): + default: Any = Undefined + default_factory: Optional['NoArgAnyCallable'] = None + field_info: FieldInfo + + if field.default is not dataclasses.MISSING: + default = field.default + elif field.default_factory is not dataclasses.MISSING: + default_factory = field.default_factory + else: + default = Required + + if isinstance(default, FieldInfo): + field_info = default + dc_cls.__pydantic_has_field_info_default__ = True + else: + field_info = Field(default=default, default_factory=default_factory, **field.metadata) + + field_definitions[field.name] = (field.type, field_info) + + validators = gather_all_validators(dc_cls) + model: Type['BaseModel'] = create_model( + dc_cls.__name__, + __config__=config, + __module__=dc_cls.__module__, + __validators__=validators, + __cls_kwargs__={'__resolve_forward_refs__': False}, + **field_definitions, + ) + model.__doc__ = dc_cls_doc if dc_cls_doc is not None else dc_cls.__doc__ or '' + return model + + +if sys.version_info >= (3, 8): + + def _is_field_cached_property(obj: 'Dataclass', k: str) -> bool: + return isinstance(getattr(type(obj), k, None), cached_property) + +else: + + def _is_field_cached_property(obj: 'Dataclass', k: str) -> bool: + return False + + +def _dataclass_validate_values(self: 'Dataclass') -> None: + # validation errors can occur if this function is called twice on an already initialised dataclass. + # for example if Extra.forbid is enabled, it would consider __pydantic_initialised__ an invalid extra property + if getattr(self, '__pydantic_initialised__'): + return + if getattr(self, '__pydantic_has_field_info_default__', False): + # We need to remove `FieldInfo` values since they are not valid as input + # It's ok to do that because they are obviously the default values! + input_data = { + k: v + for k, v in self.__dict__.items() + if not (isinstance(v, FieldInfo) or _is_field_cached_property(self, k)) + } + else: + input_data = {k: v for k, v in self.__dict__.items() if not _is_field_cached_property(self, k)} + d, _, validation_error = validate_model(self.__pydantic_model__, input_data, cls=self.__class__) + if validation_error: + raise validation_error + self.__dict__.update(d) + object.__setattr__(self, '__pydantic_initialised__', True) + + +def _dataclass_validate_assignment_setattr(self: 'Dataclass', name: str, value: Any) -> None: + if self.__pydantic_initialised__: + d = dict(self.__dict__) + d.pop(name, None) + known_field = self.__pydantic_model__.__fields__.get(name, None) + if known_field: + value, error_ = known_field.validate(value, d, loc=name, cls=self.__class__) + if error_: + raise ValidationError([error_], self.__class__) + + object.__setattr__(self, name, value) + + +def is_builtin_dataclass(_cls: Type[Any]) -> bool: + """ + Whether a class is a stdlib dataclass + (useful to discriminated a pydantic dataclass that is actually a wrapper around a stdlib dataclass) + + we check that + - `_cls` is a dataclass + - `_cls` is not a processed pydantic dataclass (with a basemodel attached) + - `_cls` is not a pydantic dataclass inheriting directly from a stdlib dataclass + e.g. + ``` + @dataclasses.dataclass + class A: + x: int + + @pydantic.dataclasses.dataclass + class B(A): + y: int + ``` + In this case, when we first check `B`, we make an extra check and look at the annotations ('y'), + which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x') + """ + return ( + dataclasses.is_dataclass(_cls) + and not hasattr(_cls, '__pydantic_model__') + and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {}))) + ) + + +def make_dataclass_validator(dc_cls: Type['Dataclass'], config: Type[BaseConfig]) -> 'CallableGenerator': + """ + Create a pydantic.dataclass from a builtin dataclass to add type validation + and yield the validators + It retrieves the parameters of the dataclass and forwards them to the newly created dataclass + """ + yield from _get_validators(dataclass(dc_cls, config=config, use_proxy=True)) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/datetime_parse.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/datetime_parse.py new file mode 100644 index 0000000000000000000000000000000000000000..a7598fc6c56688c4dd6526fc6e8b8e03682f62e8 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/datetime_parse.py @@ -0,0 +1,248 @@ +""" +Functions to parse datetime objects. + +We're using regular expressions rather than time.strptime because: +- They provide both validation and parsing. +- They're more flexible for datetimes. +- The date/datetime/time constructors produce friendlier error messages. + +Stolen from https://raw.githubusercontent.com/django/django/main/django/utils/dateparse.py at +9718fa2e8abe430c3526a9278dd976443d4ae3c6 + +Changed to: +* use standard python datetime types not django.utils.timezone +* raise ValueError when regex doesn't match rather than returning None +* support parsing unix timestamps for dates and datetimes +""" +import re +from datetime import date, datetime, time, timedelta, timezone +from typing import Dict, Optional, Type, Union + +from pydantic.v1 import errors + +date_expr = r'(?P\d{4})-(?P\d{1,2})-(?P\d{1,2})' +time_expr = ( + r'(?P\d{1,2}):(?P\d{1,2})' + r'(?::(?P\d{1,2})(?:\.(?P\d{1,6})\d{0,6})?)?' + r'(?PZ|[+-]\d{2}(?::?\d{2})?)?$' +) + +date_re = re.compile(f'{date_expr}$') +time_re = re.compile(time_expr) +datetime_re = re.compile(f'{date_expr}[T ]{time_expr}') + +standard_duration_re = re.compile( + r'^' + r'(?:(?P-?\d+) (days?, )?)?' + r'((?:(?P-?\d+):)(?=\d+:\d+))?' + r'(?:(?P-?\d+):)?' + r'(?P-?\d+)' + r'(?:\.(?P\d{1,6})\d{0,6})?' + r'$' +) + +# Support the sections of ISO 8601 date representation that are accepted by timedelta +iso8601_duration_re = re.compile( + r'^(?P[-+]?)' + r'P' + r'(?:(?P\d+(.\d+)?)D)?' + r'(?:T' + r'(?:(?P\d+(.\d+)?)H)?' + r'(?:(?P\d+(.\d+)?)M)?' + r'(?:(?P\d+(.\d+)?)S)?' + r')?' + r'$' +) + +EPOCH = datetime(1970, 1, 1) +# if greater than this, the number is in ms, if less than or equal it's in seconds +# (in seconds this is 11th October 2603, in ms it's 20th August 1970) +MS_WATERSHED = int(2e10) +# slightly more than datetime.max in ns - (datetime.max - EPOCH).total_seconds() * 1e9 +MAX_NUMBER = int(3e20) +StrBytesIntFloat = Union[str, bytes, int, float] + + +def get_numeric(value: StrBytesIntFloat, native_expected_type: str) -> Union[None, int, float]: + if isinstance(value, (int, float)): + return value + try: + return float(value) + except ValueError: + return None + except TypeError: + raise TypeError(f'invalid type; expected {native_expected_type}, string, bytes, int or float') + + +def from_unix_seconds(seconds: Union[int, float]) -> datetime: + if seconds > MAX_NUMBER: + return datetime.max + elif seconds < -MAX_NUMBER: + return datetime.min + + while abs(seconds) > MS_WATERSHED: + seconds /= 1000 + dt = EPOCH + timedelta(seconds=seconds) + return dt.replace(tzinfo=timezone.utc) + + +def _parse_timezone(value: Optional[str], error: Type[Exception]) -> Union[None, int, timezone]: + if value == 'Z': + return timezone.utc + elif value is not None: + offset_mins = int(value[-2:]) if len(value) > 3 else 0 + offset = 60 * int(value[1:3]) + offset_mins + if value[0] == '-': + offset = -offset + try: + return timezone(timedelta(minutes=offset)) + except ValueError: + raise error() + else: + return None + + +def parse_date(value: Union[date, StrBytesIntFloat]) -> date: + """ + Parse a date/int/float/string and return a datetime.date. + + Raise ValueError if the input is well formatted but not a valid date. + Raise ValueError if the input isn't well formatted. + """ + if isinstance(value, date): + if isinstance(value, datetime): + return value.date() + else: + return value + + number = get_numeric(value, 'date') + if number is not None: + return from_unix_seconds(number).date() + + if isinstance(value, bytes): + value = value.decode() + + match = date_re.match(value) # type: ignore + if match is None: + raise errors.DateError() + + kw = {k: int(v) for k, v in match.groupdict().items()} + + try: + return date(**kw) + except ValueError: + raise errors.DateError() + + +def parse_time(value: Union[time, StrBytesIntFloat]) -> time: + """ + Parse a time/string and return a datetime.time. + + Raise ValueError if the input is well formatted but not a valid time. + Raise ValueError if the input isn't well formatted, in particular if it contains an offset. + """ + if isinstance(value, time): + return value + + number = get_numeric(value, 'time') + if number is not None: + if number >= 86400: + # doesn't make sense since the time time loop back around to 0 + raise errors.TimeError() + return (datetime.min + timedelta(seconds=number)).time() + + if isinstance(value, bytes): + value = value.decode() + + match = time_re.match(value) # type: ignore + if match is None: + raise errors.TimeError() + + kw = match.groupdict() + if kw['microsecond']: + kw['microsecond'] = kw['microsecond'].ljust(6, '0') + + tzinfo = _parse_timezone(kw.pop('tzinfo'), errors.TimeError) + kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None} + kw_['tzinfo'] = tzinfo + + try: + return time(**kw_) # type: ignore + except ValueError: + raise errors.TimeError() + + +def parse_datetime(value: Union[datetime, StrBytesIntFloat]) -> datetime: + """ + Parse a datetime/int/float/string and return a datetime.datetime. + + This function supports time zone offsets. When the input contains one, + the output uses a timezone with a fixed offset from UTC. + + Raise ValueError if the input is well formatted but not a valid datetime. + Raise ValueError if the input isn't well formatted. + """ + if isinstance(value, datetime): + return value + + number = get_numeric(value, 'datetime') + if number is not None: + return from_unix_seconds(number) + + if isinstance(value, bytes): + value = value.decode() + + match = datetime_re.match(value) # type: ignore + if match is None: + raise errors.DateTimeError() + + kw = match.groupdict() + if kw['microsecond']: + kw['microsecond'] = kw['microsecond'].ljust(6, '0') + + tzinfo = _parse_timezone(kw.pop('tzinfo'), errors.DateTimeError) + kw_: Dict[str, Union[None, int, timezone]] = {k: int(v) for k, v in kw.items() if v is not None} + kw_['tzinfo'] = tzinfo + + try: + return datetime(**kw_) # type: ignore + except ValueError: + raise errors.DateTimeError() + + +def parse_duration(value: StrBytesIntFloat) -> timedelta: + """ + Parse a duration int/float/string and return a datetime.timedelta. + + The preferred format for durations in Django is '%d %H:%M:%S.%f'. + + Also supports ISO 8601 representation. + """ + if isinstance(value, timedelta): + return value + + if isinstance(value, (int, float)): + # below code requires a string + value = f'{value:f}' + elif isinstance(value, bytes): + value = value.decode() + + try: + match = standard_duration_re.match(value) or iso8601_duration_re.match(value) + except TypeError: + raise TypeError('invalid type; expected timedelta, string, bytes, int or float') + + if not match: + raise errors.DurationError() + + kw = match.groupdict() + sign = -1 if kw.pop('sign', '+') == '-' else 1 + if kw.get('microseconds'): + kw['microseconds'] = kw['microseconds'].ljust(6, '0') + + if kw.get('seconds') and kw.get('microseconds') and kw['seconds'].startswith('-'): + kw['microseconds'] = '-' + kw['microseconds'] + + kw_ = {k: float(v) for k, v in kw.items() if v is not None} + + return sign * timedelta(**kw_) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/decorator.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/decorator.py new file mode 100644 index 0000000000000000000000000000000000000000..2c7c2c2ffdb45ca50cd2b7e57bde1fc711adb851 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/decorator.py @@ -0,0 +1,264 @@ +from functools import wraps +from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional, Tuple, Type, TypeVar, Union, overload + +from pydantic.v1 import validator +from pydantic.v1.config import Extra +from pydantic.v1.errors import ConfigError +from pydantic.v1.main import BaseModel, create_model +from pydantic.v1.typing import get_all_type_hints +from pydantic.v1.utils import to_camel + +__all__ = ('validate_arguments',) + +if TYPE_CHECKING: + from pydantic.v1.typing import AnyCallable + + AnyCallableT = TypeVar('AnyCallableT', bound=AnyCallable) + ConfigType = Union[None, Type[Any], Dict[str, Any]] + + +@overload +def validate_arguments(func: None = None, *, config: 'ConfigType' = None) -> Callable[['AnyCallableT'], 'AnyCallableT']: + ... + + +@overload +def validate_arguments(func: 'AnyCallableT') -> 'AnyCallableT': + ... + + +def validate_arguments(func: Optional['AnyCallableT'] = None, *, config: 'ConfigType' = None) -> Any: + """ + Decorator to validate the arguments passed to a function. + """ + + def validate(_func: 'AnyCallable') -> 'AnyCallable': + vd = ValidatedFunction(_func, config) + + @wraps(_func) + def wrapper_function(*args: Any, **kwargs: Any) -> Any: + return vd.call(*args, **kwargs) + + wrapper_function.vd = vd # type: ignore + wrapper_function.validate = vd.init_model_instance # type: ignore + wrapper_function.raw_function = vd.raw_function # type: ignore + wrapper_function.model = vd.model # type: ignore + return wrapper_function + + if func: + return validate(func) + else: + return validate + + +ALT_V_ARGS = 'v__args' +ALT_V_KWARGS = 'v__kwargs' +V_POSITIONAL_ONLY_NAME = 'v__positional_only' +V_DUPLICATE_KWARGS = 'v__duplicate_kwargs' + + +class ValidatedFunction: + def __init__(self, function: 'AnyCallableT', config: 'ConfigType'): # noqa C901 + from inspect import Parameter, signature + + parameters: Mapping[str, Parameter] = signature(function).parameters + + if parameters.keys() & {ALT_V_ARGS, ALT_V_KWARGS, V_POSITIONAL_ONLY_NAME, V_DUPLICATE_KWARGS}: + raise ConfigError( + f'"{ALT_V_ARGS}", "{ALT_V_KWARGS}", "{V_POSITIONAL_ONLY_NAME}" and "{V_DUPLICATE_KWARGS}" ' + f'are not permitted as argument names when using the "{validate_arguments.__name__}" decorator' + ) + + self.raw_function = function + self.arg_mapping: Dict[int, str] = {} + self.positional_only_args = set() + self.v_args_name = 'args' + self.v_kwargs_name = 'kwargs' + + type_hints = get_all_type_hints(function) + takes_args = False + takes_kwargs = False + fields: Dict[str, Tuple[Any, Any]] = {} + for i, (name, p) in enumerate(parameters.items()): + if p.annotation is p.empty: + annotation = Any + else: + annotation = type_hints[name] + + default = ... if p.default is p.empty else p.default + if p.kind == Parameter.POSITIONAL_ONLY: + self.arg_mapping[i] = name + fields[name] = annotation, default + fields[V_POSITIONAL_ONLY_NAME] = List[str], None + self.positional_only_args.add(name) + elif p.kind == Parameter.POSITIONAL_OR_KEYWORD: + self.arg_mapping[i] = name + fields[name] = annotation, default + fields[V_DUPLICATE_KWARGS] = List[str], None + elif p.kind == Parameter.KEYWORD_ONLY: + fields[name] = annotation, default + elif p.kind == Parameter.VAR_POSITIONAL: + self.v_args_name = name + fields[name] = Tuple[annotation, ...], None + takes_args = True + else: + assert p.kind == Parameter.VAR_KEYWORD, p.kind + self.v_kwargs_name = name + fields[name] = Dict[str, annotation], None # type: ignore + takes_kwargs = True + + # these checks avoid a clash between "args" and a field with that name + if not takes_args and self.v_args_name in fields: + self.v_args_name = ALT_V_ARGS + + # same with "kwargs" + if not takes_kwargs and self.v_kwargs_name in fields: + self.v_kwargs_name = ALT_V_KWARGS + + if not takes_args: + # we add the field so validation below can raise the correct exception + fields[self.v_args_name] = List[Any], None + + if not takes_kwargs: + # same with kwargs + fields[self.v_kwargs_name] = Dict[Any, Any], None + + self.create_model(fields, takes_args, takes_kwargs, config) + + def init_model_instance(self, *args: Any, **kwargs: Any) -> BaseModel: + values = self.build_values(args, kwargs) + return self.model(**values) + + def call(self, *args: Any, **kwargs: Any) -> Any: + m = self.init_model_instance(*args, **kwargs) + return self.execute(m) + + def build_values(self, args: Tuple[Any, ...], kwargs: Dict[str, Any]) -> Dict[str, Any]: + values: Dict[str, Any] = {} + if args: + arg_iter = enumerate(args) + while True: + try: + i, a = next(arg_iter) + except StopIteration: + break + arg_name = self.arg_mapping.get(i) + if arg_name is not None: + values[arg_name] = a + else: + values[self.v_args_name] = [a] + [a for _, a in arg_iter] + break + + var_kwargs: Dict[str, Any] = {} + wrong_positional_args = [] + duplicate_kwargs = [] + fields_alias = [ + field.alias + for name, field in self.model.__fields__.items() + if name not in (self.v_args_name, self.v_kwargs_name) + ] + non_var_fields = set(self.model.__fields__) - {self.v_args_name, self.v_kwargs_name} + for k, v in kwargs.items(): + if k in non_var_fields or k in fields_alias: + if k in self.positional_only_args: + wrong_positional_args.append(k) + if k in values: + duplicate_kwargs.append(k) + values[k] = v + else: + var_kwargs[k] = v + + if var_kwargs: + values[self.v_kwargs_name] = var_kwargs + if wrong_positional_args: + values[V_POSITIONAL_ONLY_NAME] = wrong_positional_args + if duplicate_kwargs: + values[V_DUPLICATE_KWARGS] = duplicate_kwargs + return values + + def execute(self, m: BaseModel) -> Any: + d = {k: v for k, v in m._iter() if k in m.__fields_set__ or m.__fields__[k].default_factory} + var_kwargs = d.pop(self.v_kwargs_name, {}) + + if self.v_args_name in d: + args_: List[Any] = [] + in_kwargs = False + kwargs = {} + for name, value in d.items(): + if in_kwargs: + kwargs[name] = value + elif name == self.v_args_name: + args_ += value + in_kwargs = True + else: + args_.append(value) + return self.raw_function(*args_, **kwargs, **var_kwargs) + elif self.positional_only_args: + args_ = [] + kwargs = {} + for name, value in d.items(): + if name in self.positional_only_args: + args_.append(value) + else: + kwargs[name] = value + return self.raw_function(*args_, **kwargs, **var_kwargs) + else: + return self.raw_function(**d, **var_kwargs) + + def create_model(self, fields: Dict[str, Any], takes_args: bool, takes_kwargs: bool, config: 'ConfigType') -> None: + pos_args = len(self.arg_mapping) + + class CustomConfig: + pass + + if not TYPE_CHECKING: # pragma: no branch + if isinstance(config, dict): + CustomConfig = type('Config', (), config) # noqa: F811 + elif config is not None: + CustomConfig = config # noqa: F811 + + if hasattr(CustomConfig, 'fields') or hasattr(CustomConfig, 'alias_generator'): + raise ConfigError( + 'Setting the "fields" and "alias_generator" property on custom Config for ' + '@validate_arguments is not yet supported, please remove.' + ) + + class DecoratorBaseModel(BaseModel): + @validator(self.v_args_name, check_fields=False, allow_reuse=True) + def check_args(cls, v: Optional[List[Any]]) -> Optional[List[Any]]: + if takes_args or v is None: + return v + + raise TypeError(f'{pos_args} positional arguments expected but {pos_args + len(v)} given') + + @validator(self.v_kwargs_name, check_fields=False, allow_reuse=True) + def check_kwargs(cls, v: Optional[Dict[str, Any]]) -> Optional[Dict[str, Any]]: + if takes_kwargs or v is None: + return v + + plural = '' if len(v) == 1 else 's' + keys = ', '.join(map(repr, v.keys())) + raise TypeError(f'unexpected keyword argument{plural}: {keys}') + + @validator(V_POSITIONAL_ONLY_NAME, check_fields=False, allow_reuse=True) + def check_positional_only(cls, v: Optional[List[str]]) -> None: + if v is None: + return + + plural = '' if len(v) == 1 else 's' + keys = ', '.join(map(repr, v)) + raise TypeError(f'positional-only argument{plural} passed as keyword argument{plural}: {keys}') + + @validator(V_DUPLICATE_KWARGS, check_fields=False, allow_reuse=True) + def check_duplicate_kwargs(cls, v: Optional[List[str]]) -> None: + if v is None: + return + + plural = '' if len(v) == 1 else 's' + keys = ', '.join(map(repr, v)) + raise TypeError(f'multiple values for argument{plural}: {keys}') + + class Config(CustomConfig): + extra = getattr(CustomConfig, 'extra', Extra.forbid) + + self.model = create_model(to_camel(self.raw_function.__name__), __base__=DecoratorBaseModel, **fields) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/env_settings.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/env_settings.py new file mode 100644 index 0000000000000000000000000000000000000000..5f6f217500d8ca1e7717f59ae372ee9a27660fe3 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/env_settings.py @@ -0,0 +1,350 @@ +import os +import warnings +from pathlib import Path +from typing import AbstractSet, Any, Callable, ClassVar, Dict, List, Mapping, Optional, Tuple, Type, Union + +from pydantic.v1.config import BaseConfig, Extra +from pydantic.v1.fields import ModelField +from pydantic.v1.main import BaseModel +from pydantic.v1.types import JsonWrapper +from pydantic.v1.typing import StrPath, display_as_type, get_origin, is_union +from pydantic.v1.utils import deep_update, lenient_issubclass, path_type, sequence_like + +env_file_sentinel = str(object()) + +SettingsSourceCallable = Callable[['BaseSettings'], Dict[str, Any]] +DotenvType = Union[StrPath, List[StrPath], Tuple[StrPath, ...]] + + +class SettingsError(ValueError): + pass + + +class BaseSettings(BaseModel): + """ + Base class for settings, allowing values to be overridden by environment variables. + + This is useful in production for secrets you do not wish to save in code, it plays nicely with docker(-compose), + Heroku and any 12 factor app design. + """ + + def __init__( + __pydantic_self__, + _env_file: Optional[DotenvType] = env_file_sentinel, + _env_file_encoding: Optional[str] = None, + _env_nested_delimiter: Optional[str] = None, + _secrets_dir: Optional[StrPath] = None, + **values: Any, + ) -> None: + # Uses something other than `self` the first arg to allow "self" as a settable attribute + super().__init__( + **__pydantic_self__._build_values( + values, + _env_file=_env_file, + _env_file_encoding=_env_file_encoding, + _env_nested_delimiter=_env_nested_delimiter, + _secrets_dir=_secrets_dir, + ) + ) + + def _build_values( + self, + init_kwargs: Dict[str, Any], + _env_file: Optional[DotenvType] = None, + _env_file_encoding: Optional[str] = None, + _env_nested_delimiter: Optional[str] = None, + _secrets_dir: Optional[StrPath] = None, + ) -> Dict[str, Any]: + # Configure built-in sources + init_settings = InitSettingsSource(init_kwargs=init_kwargs) + env_settings = EnvSettingsSource( + env_file=(_env_file if _env_file != env_file_sentinel else self.__config__.env_file), + env_file_encoding=( + _env_file_encoding if _env_file_encoding is not None else self.__config__.env_file_encoding + ), + env_nested_delimiter=( + _env_nested_delimiter if _env_nested_delimiter is not None else self.__config__.env_nested_delimiter + ), + env_prefix_len=len(self.__config__.env_prefix), + ) + file_secret_settings = SecretsSettingsSource(secrets_dir=_secrets_dir or self.__config__.secrets_dir) + # Provide a hook to set built-in sources priority and add / remove sources + sources = self.__config__.customise_sources( + init_settings=init_settings, env_settings=env_settings, file_secret_settings=file_secret_settings + ) + if sources: + return deep_update(*reversed([source(self) for source in sources])) + else: + # no one should mean to do this, but I think returning an empty dict is marginally preferable + # to an informative error and much better than a confusing error + return {} + + class Config(BaseConfig): + env_prefix: str = '' + env_file: Optional[DotenvType] = None + env_file_encoding: Optional[str] = None + env_nested_delimiter: Optional[str] = None + secrets_dir: Optional[StrPath] = None + validate_all: bool = True + extra: Extra = Extra.forbid + arbitrary_types_allowed: bool = True + case_sensitive: bool = False + + @classmethod + def prepare_field(cls, field: ModelField) -> None: + env_names: Union[List[str], AbstractSet[str]] + field_info_from_config = cls.get_field_info(field.name) + + env = field_info_from_config.get('env') or field.field_info.extra.get('env') + if env is None: + if field.has_alias: + warnings.warn( + 'aliases are no longer used by BaseSettings to define which environment variables to read. ' + 'Instead use the "env" field setting. ' + 'See https://pydantic-docs.helpmanual.io/usage/settings/#environment-variable-names', + FutureWarning, + ) + env_names = {cls.env_prefix + field.name} + elif isinstance(env, str): + env_names = {env} + elif isinstance(env, (set, frozenset)): + env_names = env + elif sequence_like(env): + env_names = list(env) + else: + raise TypeError(f'invalid field env: {env!r} ({display_as_type(env)}); should be string, list or set') + + if not cls.case_sensitive: + env_names = env_names.__class__(n.lower() for n in env_names) + field.field_info.extra['env_names'] = env_names + + @classmethod + def customise_sources( + cls, + init_settings: SettingsSourceCallable, + env_settings: SettingsSourceCallable, + file_secret_settings: SettingsSourceCallable, + ) -> Tuple[SettingsSourceCallable, ...]: + return init_settings, env_settings, file_secret_settings + + @classmethod + def parse_env_var(cls, field_name: str, raw_val: str) -> Any: + return cls.json_loads(raw_val) + + # populated by the metaclass using the Config class defined above, annotated here to help IDEs only + __config__: ClassVar[Type[Config]] + + +class InitSettingsSource: + __slots__ = ('init_kwargs',) + + def __init__(self, init_kwargs: Dict[str, Any]): + self.init_kwargs = init_kwargs + + def __call__(self, settings: BaseSettings) -> Dict[str, Any]: + return self.init_kwargs + + def __repr__(self) -> str: + return f'InitSettingsSource(init_kwargs={self.init_kwargs!r})' + + +class EnvSettingsSource: + __slots__ = ('env_file', 'env_file_encoding', 'env_nested_delimiter', 'env_prefix_len') + + def __init__( + self, + env_file: Optional[DotenvType], + env_file_encoding: Optional[str], + env_nested_delimiter: Optional[str] = None, + env_prefix_len: int = 0, + ): + self.env_file: Optional[DotenvType] = env_file + self.env_file_encoding: Optional[str] = env_file_encoding + self.env_nested_delimiter: Optional[str] = env_nested_delimiter + self.env_prefix_len: int = env_prefix_len + + def __call__(self, settings: BaseSettings) -> Dict[str, Any]: # noqa C901 + """ + Build environment variables suitable for passing to the Model. + """ + d: Dict[str, Any] = {} + + if settings.__config__.case_sensitive: + env_vars: Mapping[str, Optional[str]] = os.environ + else: + env_vars = {k.lower(): v for k, v in os.environ.items()} + + dotenv_vars = self._read_env_files(settings.__config__.case_sensitive) + if dotenv_vars: + env_vars = {**dotenv_vars, **env_vars} + + for field in settings.__fields__.values(): + env_val: Optional[str] = None + for env_name in field.field_info.extra['env_names']: + env_val = env_vars.get(env_name) + if env_val is not None: + break + + is_complex, allow_parse_failure = self.field_is_complex(field) + if is_complex: + if env_val is None: + # field is complex but no value found so far, try explode_env_vars + env_val_built = self.explode_env_vars(field, env_vars) + if env_val_built: + d[field.alias] = env_val_built + else: + # field is complex and there's a value, decode that as JSON, then add explode_env_vars + try: + env_val = settings.__config__.parse_env_var(field.name, env_val) + except ValueError as e: + if not allow_parse_failure: + raise SettingsError(f'error parsing env var "{env_name}"') from e + + if isinstance(env_val, dict): + d[field.alias] = deep_update(env_val, self.explode_env_vars(field, env_vars)) + else: + d[field.alias] = env_val + elif env_val is not None: + # simplest case, field is not complex, we only need to add the value if it was found + d[field.alias] = env_val + + return d + + def _read_env_files(self, case_sensitive: bool) -> Dict[str, Optional[str]]: + env_files = self.env_file + if env_files is None: + return {} + + if isinstance(env_files, (str, os.PathLike)): + env_files = [env_files] + + dotenv_vars = {} + for env_file in env_files: + env_path = Path(env_file).expanduser() + if env_path.is_file(): + dotenv_vars.update( + read_env_file(env_path, encoding=self.env_file_encoding, case_sensitive=case_sensitive) + ) + + return dotenv_vars + + def field_is_complex(self, field: ModelField) -> Tuple[bool, bool]: + """ + Find out if a field is complex, and if so whether JSON errors should be ignored + """ + if lenient_issubclass(field.annotation, JsonWrapper): + return False, False + + if field.is_complex(): + allow_parse_failure = False + elif is_union(get_origin(field.type_)) and field.sub_fields and any(f.is_complex() for f in field.sub_fields): + allow_parse_failure = True + else: + return False, False + + return True, allow_parse_failure + + def explode_env_vars(self, field: ModelField, env_vars: Mapping[str, Optional[str]]) -> Dict[str, Any]: + """ + Process env_vars and extract the values of keys containing env_nested_delimiter into nested dictionaries. + + This is applied to a single field, hence filtering by env_var prefix. + """ + prefixes = [f'{env_name}{self.env_nested_delimiter}' for env_name in field.field_info.extra['env_names']] + result: Dict[str, Any] = {} + for env_name, env_val in env_vars.items(): + if not any(env_name.startswith(prefix) for prefix in prefixes): + continue + # we remove the prefix before splitting in case the prefix has characters in common with the delimiter + env_name_without_prefix = env_name[self.env_prefix_len :] + _, *keys, last_key = env_name_without_prefix.split(self.env_nested_delimiter) + env_var = result + for key in keys: + env_var = env_var.setdefault(key, {}) + env_var[last_key] = env_val + + return result + + def __repr__(self) -> str: + return ( + f'EnvSettingsSource(env_file={self.env_file!r}, env_file_encoding={self.env_file_encoding!r}, ' + f'env_nested_delimiter={self.env_nested_delimiter!r})' + ) + + +class SecretsSettingsSource: + __slots__ = ('secrets_dir',) + + def __init__(self, secrets_dir: Optional[StrPath]): + self.secrets_dir: Optional[StrPath] = secrets_dir + + def __call__(self, settings: BaseSettings) -> Dict[str, Any]: + """ + Build fields from "secrets" files. + """ + secrets: Dict[str, Optional[str]] = {} + + if self.secrets_dir is None: + return secrets + + secrets_path = Path(self.secrets_dir).expanduser() + + if not secrets_path.exists(): + warnings.warn(f'directory "{secrets_path}" does not exist') + return secrets + + if not secrets_path.is_dir(): + raise SettingsError(f'secrets_dir must reference a directory, not a {path_type(secrets_path)}') + + for field in settings.__fields__.values(): + for env_name in field.field_info.extra['env_names']: + path = find_case_path(secrets_path, env_name, settings.__config__.case_sensitive) + if not path: + # path does not exist, we currently don't return a warning for this + continue + + if path.is_file(): + secret_value = path.read_text().strip() + if field.is_complex(): + try: + secret_value = settings.__config__.parse_env_var(field.name, secret_value) + except ValueError as e: + raise SettingsError(f'error parsing env var "{env_name}"') from e + + secrets[field.alias] = secret_value + else: + warnings.warn( + f'attempted to load secret file "{path}" but found a {path_type(path)} instead.', + stacklevel=4, + ) + return secrets + + def __repr__(self) -> str: + return f'SecretsSettingsSource(secrets_dir={self.secrets_dir!r})' + + +def read_env_file( + file_path: StrPath, *, encoding: str = None, case_sensitive: bool = False +) -> Dict[str, Optional[str]]: + try: + from dotenv import dotenv_values + except ImportError as e: + raise ImportError('python-dotenv is not installed, run `pip install pydantic[dotenv]`') from e + + file_vars: Dict[str, Optional[str]] = dotenv_values(file_path, encoding=encoding or 'utf8') + if not case_sensitive: + return {k.lower(): v for k, v in file_vars.items()} + else: + return file_vars + + +def find_case_path(dir_path: Path, file_name: str, case_sensitive: bool) -> Optional[Path]: + """ + Find a file within path's directory matching filename, optionally ignoring case. + """ + for f in dir_path.iterdir(): + if f.name == file_name: + return f + elif not case_sensitive and f.name.lower() == file_name.lower(): + return f + return None diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/error_wrappers.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/error_wrappers.py new file mode 100644 index 0000000000000000000000000000000000000000..bc7f263146ee4f1701391a2436ab38e5e16f0e1a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/error_wrappers.py @@ -0,0 +1,161 @@ +import json +from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Sequence, Tuple, Type, Union + +from pydantic.v1.json import pydantic_encoder +from pydantic.v1.utils import Representation + +if TYPE_CHECKING: + from typing_extensions import TypedDict + + from pydantic.v1.config import BaseConfig + from pydantic.v1.types import ModelOrDc + from pydantic.v1.typing import ReprArgs + + Loc = Tuple[Union[int, str], ...] + + class _ErrorDictRequired(TypedDict): + loc: Loc + msg: str + type: str + + class ErrorDict(_ErrorDictRequired, total=False): + ctx: Dict[str, Any] + + +__all__ = 'ErrorWrapper', 'ValidationError' + + +class ErrorWrapper(Representation): + __slots__ = 'exc', '_loc' + + def __init__(self, exc: Exception, loc: Union[str, 'Loc']) -> None: + self.exc = exc + self._loc = loc + + def loc_tuple(self) -> 'Loc': + if isinstance(self._loc, tuple): + return self._loc + else: + return (self._loc,) + + def __repr_args__(self) -> 'ReprArgs': + return [('exc', self.exc), ('loc', self.loc_tuple())] + + +# ErrorList is something like Union[List[Union[List[ErrorWrapper], ErrorWrapper]], ErrorWrapper] +# but recursive, therefore just use: +ErrorList = Union[Sequence[Any], ErrorWrapper] + + +class ValidationError(Representation, ValueError): + __slots__ = 'raw_errors', 'model', '_error_cache' + + def __init__(self, errors: Sequence[ErrorList], model: 'ModelOrDc') -> None: + self.raw_errors = errors + self.model = model + self._error_cache: Optional[List['ErrorDict']] = None + + def errors(self) -> List['ErrorDict']: + if self._error_cache is None: + try: + config = self.model.__config__ # type: ignore + except AttributeError: + config = self.model.__pydantic_model__.__config__ # type: ignore + self._error_cache = list(flatten_errors(self.raw_errors, config)) + return self._error_cache + + def json(self, *, indent: Union[None, int, str] = 2) -> str: + return json.dumps(self.errors(), indent=indent, default=pydantic_encoder) + + def __str__(self) -> str: + errors = self.errors() + no_errors = len(errors) + return ( + f'{no_errors} validation error{"" if no_errors == 1 else "s"} for {self.model.__name__}\n' + f'{display_errors(errors)}' + ) + + def __repr_args__(self) -> 'ReprArgs': + return [('model', self.model.__name__), ('errors', self.errors())] + + +def display_errors(errors: List['ErrorDict']) -> str: + return '\n'.join(f'{_display_error_loc(e)}\n {e["msg"]} ({_display_error_type_and_ctx(e)})' for e in errors) + + +def _display_error_loc(error: 'ErrorDict') -> str: + return ' -> '.join(str(e) for e in error['loc']) + + +def _display_error_type_and_ctx(error: 'ErrorDict') -> str: + t = 'type=' + error['type'] + ctx = error.get('ctx') + if ctx: + return t + ''.join(f'; {k}={v}' for k, v in ctx.items()) + else: + return t + + +def flatten_errors( + errors: Sequence[Any], config: Type['BaseConfig'], loc: Optional['Loc'] = None +) -> Generator['ErrorDict', None, None]: + for error in errors: + if isinstance(error, ErrorWrapper): + if loc: + error_loc = loc + error.loc_tuple() + else: + error_loc = error.loc_tuple() + + if isinstance(error.exc, ValidationError): + yield from flatten_errors(error.exc.raw_errors, config, error_loc) + else: + yield error_dict(error.exc, config, error_loc) + elif isinstance(error, list): + yield from flatten_errors(error, config, loc=loc) + else: + raise RuntimeError(f'Unknown error object: {error}') + + +def error_dict(exc: Exception, config: Type['BaseConfig'], loc: 'Loc') -> 'ErrorDict': + type_ = get_exc_type(exc.__class__) + msg_template = config.error_msg_templates.get(type_) or getattr(exc, 'msg_template', None) + ctx = exc.__dict__ + if msg_template: + msg = msg_template.format(**ctx) + else: + msg = str(exc) + + d: 'ErrorDict' = {'loc': loc, 'msg': msg, 'type': type_} + + if ctx: + d['ctx'] = ctx + + return d + + +_EXC_TYPE_CACHE: Dict[Type[Exception], str] = {} + + +def get_exc_type(cls: Type[Exception]) -> str: + # slightly more efficient than using lru_cache since we don't need to worry about the cache filling up + try: + return _EXC_TYPE_CACHE[cls] + except KeyError: + r = _get_exc_type(cls) + _EXC_TYPE_CACHE[cls] = r + return r + + +def _get_exc_type(cls: Type[Exception]) -> str: + if issubclass(cls, AssertionError): + return 'assertion_error' + + base_name = 'type_error' if issubclass(cls, TypeError) else 'value_error' + if cls in (TypeError, ValueError): + # just TypeError or ValueError, no extra code + return base_name + + # if it's not a TypeError or ValueError, we just take the lowercase of the exception name + # no chaining or snake case logic, use "code" for more complex error types. + code = getattr(cls, 'code', None) or cls.__name__.replace('Error', '').lower() + return base_name + '.' + code diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/errors.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/errors.py new file mode 100644 index 0000000000000000000000000000000000000000..6e8644258a6ccf999a19f69c359452a109e54ffe --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/errors.py @@ -0,0 +1,646 @@ +from decimal import Decimal +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable, Sequence, Set, Tuple, Type, Union + +from pydantic.v1.typing import display_as_type + +if TYPE_CHECKING: + from pydantic.v1.typing import DictStrAny + +# explicitly state exports to avoid "from pydantic.v1.errors import *" also importing Decimal, Path etc. +__all__ = ( + 'PydanticTypeError', + 'PydanticValueError', + 'ConfigError', + 'MissingError', + 'ExtraError', + 'NoneIsNotAllowedError', + 'NoneIsAllowedError', + 'WrongConstantError', + 'NotNoneError', + 'BoolError', + 'BytesError', + 'DictError', + 'EmailError', + 'UrlError', + 'UrlSchemeError', + 'UrlSchemePermittedError', + 'UrlUserInfoError', + 'UrlHostError', + 'UrlHostTldError', + 'UrlPortError', + 'UrlExtraError', + 'EnumError', + 'IntEnumError', + 'EnumMemberError', + 'IntegerError', + 'FloatError', + 'PathError', + 'PathNotExistsError', + 'PathNotAFileError', + 'PathNotADirectoryError', + 'PyObjectError', + 'SequenceError', + 'ListError', + 'SetError', + 'FrozenSetError', + 'TupleError', + 'TupleLengthError', + 'ListMinLengthError', + 'ListMaxLengthError', + 'ListUniqueItemsError', + 'SetMinLengthError', + 'SetMaxLengthError', + 'FrozenSetMinLengthError', + 'FrozenSetMaxLengthError', + 'AnyStrMinLengthError', + 'AnyStrMaxLengthError', + 'StrError', + 'StrRegexError', + 'NumberNotGtError', + 'NumberNotGeError', + 'NumberNotLtError', + 'NumberNotLeError', + 'NumberNotMultipleError', + 'DecimalError', + 'DecimalIsNotFiniteError', + 'DecimalMaxDigitsError', + 'DecimalMaxPlacesError', + 'DecimalWholeDigitsError', + 'DateTimeError', + 'DateError', + 'DateNotInThePastError', + 'DateNotInTheFutureError', + 'TimeError', + 'DurationError', + 'HashableError', + 'UUIDError', + 'UUIDVersionError', + 'ArbitraryTypeError', + 'ClassError', + 'SubclassError', + 'JsonError', + 'JsonTypeError', + 'PatternError', + 'DataclassTypeError', + 'CallableError', + 'IPvAnyAddressError', + 'IPvAnyInterfaceError', + 'IPvAnyNetworkError', + 'IPv4AddressError', + 'IPv6AddressError', + 'IPv4NetworkError', + 'IPv6NetworkError', + 'IPv4InterfaceError', + 'IPv6InterfaceError', + 'ColorError', + 'StrictBoolError', + 'NotDigitError', + 'LuhnValidationError', + 'InvalidLengthForBrand', + 'InvalidByteSize', + 'InvalidByteSizeUnit', + 'MissingDiscriminator', + 'InvalidDiscriminator', +) + + +def cls_kwargs(cls: Type['PydanticErrorMixin'], ctx: 'DictStrAny') -> 'PydanticErrorMixin': + """ + For built-in exceptions like ValueError or TypeError, we need to implement + __reduce__ to override the default behaviour (instead of __getstate__/__setstate__) + By default pickle protocol 2 calls `cls.__new__(cls, *args)`. + Since we only use kwargs, we need a little constructor to change that. + Note: the callable can't be a lambda as pickle looks in the namespace to find it + """ + return cls(**ctx) + + +class PydanticErrorMixin: + code: str + msg_template: str + + def __init__(self, **ctx: Any) -> None: + self.__dict__ = ctx + + def __str__(self) -> str: + return self.msg_template.format(**self.__dict__) + + def __reduce__(self) -> Tuple[Callable[..., 'PydanticErrorMixin'], Tuple[Type['PydanticErrorMixin'], 'DictStrAny']]: + return cls_kwargs, (self.__class__, self.__dict__) + + +class PydanticTypeError(PydanticErrorMixin, TypeError): + pass + + +class PydanticValueError(PydanticErrorMixin, ValueError): + pass + + +class ConfigError(RuntimeError): + pass + + +class MissingError(PydanticValueError): + msg_template = 'field required' + + +class ExtraError(PydanticValueError): + msg_template = 'extra fields not permitted' + + +class NoneIsNotAllowedError(PydanticTypeError): + code = 'none.not_allowed' + msg_template = 'none is not an allowed value' + + +class NoneIsAllowedError(PydanticTypeError): + code = 'none.allowed' + msg_template = 'value is not none' + + +class WrongConstantError(PydanticValueError): + code = 'const' + + def __str__(self) -> str: + permitted = ', '.join(repr(v) for v in self.permitted) # type: ignore + return f'unexpected value; permitted: {permitted}' + + +class NotNoneError(PydanticTypeError): + code = 'not_none' + msg_template = 'value is not None' + + +class BoolError(PydanticTypeError): + msg_template = 'value could not be parsed to a boolean' + + +class BytesError(PydanticTypeError): + msg_template = 'byte type expected' + + +class DictError(PydanticTypeError): + msg_template = 'value is not a valid dict' + + +class EmailError(PydanticValueError): + msg_template = 'value is not a valid email address' + + +class UrlError(PydanticValueError): + code = 'url' + + +class UrlSchemeError(UrlError): + code = 'url.scheme' + msg_template = 'invalid or missing URL scheme' + + +class UrlSchemePermittedError(UrlError): + code = 'url.scheme' + msg_template = 'URL scheme not permitted' + + def __init__(self, allowed_schemes: Set[str]): + super().__init__(allowed_schemes=allowed_schemes) + + +class UrlUserInfoError(UrlError): + code = 'url.userinfo' + msg_template = 'userinfo required in URL but missing' + + +class UrlHostError(UrlError): + code = 'url.host' + msg_template = 'URL host invalid' + + +class UrlHostTldError(UrlError): + code = 'url.host' + msg_template = 'URL host invalid, top level domain required' + + +class UrlPortError(UrlError): + code = 'url.port' + msg_template = 'URL port invalid, port cannot exceed 65535' + + +class UrlExtraError(UrlError): + code = 'url.extra' + msg_template = 'URL invalid, extra characters found after valid URL: {extra!r}' + + +class EnumMemberError(PydanticTypeError): + code = 'enum' + + def __str__(self) -> str: + permitted = ', '.join(repr(v.value) for v in self.enum_values) # type: ignore + return f'value is not a valid enumeration member; permitted: {permitted}' + + +class IntegerError(PydanticTypeError): + msg_template = 'value is not a valid integer' + + +class FloatError(PydanticTypeError): + msg_template = 'value is not a valid float' + + +class PathError(PydanticTypeError): + msg_template = 'value is not a valid path' + + +class _PathValueError(PydanticValueError): + def __init__(self, *, path: Path) -> None: + super().__init__(path=str(path)) + + +class PathNotExistsError(_PathValueError): + code = 'path.not_exists' + msg_template = 'file or directory at path "{path}" does not exist' + + +class PathNotAFileError(_PathValueError): + code = 'path.not_a_file' + msg_template = 'path "{path}" does not point to a file' + + +class PathNotADirectoryError(_PathValueError): + code = 'path.not_a_directory' + msg_template = 'path "{path}" does not point to a directory' + + +class PyObjectError(PydanticTypeError): + msg_template = 'ensure this value contains valid import path or valid callable: {error_message}' + + +class SequenceError(PydanticTypeError): + msg_template = 'value is not a valid sequence' + + +class IterableError(PydanticTypeError): + msg_template = 'value is not a valid iterable' + + +class ListError(PydanticTypeError): + msg_template = 'value is not a valid list' + + +class SetError(PydanticTypeError): + msg_template = 'value is not a valid set' + + +class FrozenSetError(PydanticTypeError): + msg_template = 'value is not a valid frozenset' + + +class DequeError(PydanticTypeError): + msg_template = 'value is not a valid deque' + + +class TupleError(PydanticTypeError): + msg_template = 'value is not a valid tuple' + + +class TupleLengthError(PydanticValueError): + code = 'tuple.length' + msg_template = 'wrong tuple length {actual_length}, expected {expected_length}' + + def __init__(self, *, actual_length: int, expected_length: int) -> None: + super().__init__(actual_length=actual_length, expected_length=expected_length) + + +class ListMinLengthError(PydanticValueError): + code = 'list.min_items' + msg_template = 'ensure this value has at least {limit_value} items' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class ListMaxLengthError(PydanticValueError): + code = 'list.max_items' + msg_template = 'ensure this value has at most {limit_value} items' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class ListUniqueItemsError(PydanticValueError): + code = 'list.unique_items' + msg_template = 'the list has duplicated items' + + +class SetMinLengthError(PydanticValueError): + code = 'set.min_items' + msg_template = 'ensure this value has at least {limit_value} items' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class SetMaxLengthError(PydanticValueError): + code = 'set.max_items' + msg_template = 'ensure this value has at most {limit_value} items' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class FrozenSetMinLengthError(PydanticValueError): + code = 'frozenset.min_items' + msg_template = 'ensure this value has at least {limit_value} items' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class FrozenSetMaxLengthError(PydanticValueError): + code = 'frozenset.max_items' + msg_template = 'ensure this value has at most {limit_value} items' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class AnyStrMinLengthError(PydanticValueError): + code = 'any_str.min_length' + msg_template = 'ensure this value has at least {limit_value} characters' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class AnyStrMaxLengthError(PydanticValueError): + code = 'any_str.max_length' + msg_template = 'ensure this value has at most {limit_value} characters' + + def __init__(self, *, limit_value: int) -> None: + super().__init__(limit_value=limit_value) + + +class StrError(PydanticTypeError): + msg_template = 'str type expected' + + +class StrRegexError(PydanticValueError): + code = 'str.regex' + msg_template = 'string does not match regex "{pattern}"' + + def __init__(self, *, pattern: str) -> None: + super().__init__(pattern=pattern) + + +class _NumberBoundError(PydanticValueError): + def __init__(self, *, limit_value: Union[int, float, Decimal]) -> None: + super().__init__(limit_value=limit_value) + + +class NumberNotGtError(_NumberBoundError): + code = 'number.not_gt' + msg_template = 'ensure this value is greater than {limit_value}' + + +class NumberNotGeError(_NumberBoundError): + code = 'number.not_ge' + msg_template = 'ensure this value is greater than or equal to {limit_value}' + + +class NumberNotLtError(_NumberBoundError): + code = 'number.not_lt' + msg_template = 'ensure this value is less than {limit_value}' + + +class NumberNotLeError(_NumberBoundError): + code = 'number.not_le' + msg_template = 'ensure this value is less than or equal to {limit_value}' + + +class NumberNotFiniteError(PydanticValueError): + code = 'number.not_finite_number' + msg_template = 'ensure this value is a finite number' + + +class NumberNotMultipleError(PydanticValueError): + code = 'number.not_multiple' + msg_template = 'ensure this value is a multiple of {multiple_of}' + + def __init__(self, *, multiple_of: Union[int, float, Decimal]) -> None: + super().__init__(multiple_of=multiple_of) + + +class DecimalError(PydanticTypeError): + msg_template = 'value is not a valid decimal' + + +class DecimalIsNotFiniteError(PydanticValueError): + code = 'decimal.not_finite' + msg_template = 'value is not a valid decimal' + + +class DecimalMaxDigitsError(PydanticValueError): + code = 'decimal.max_digits' + msg_template = 'ensure that there are no more than {max_digits} digits in total' + + def __init__(self, *, max_digits: int) -> None: + super().__init__(max_digits=max_digits) + + +class DecimalMaxPlacesError(PydanticValueError): + code = 'decimal.max_places' + msg_template = 'ensure that there are no more than {decimal_places} decimal places' + + def __init__(self, *, decimal_places: int) -> None: + super().__init__(decimal_places=decimal_places) + + +class DecimalWholeDigitsError(PydanticValueError): + code = 'decimal.whole_digits' + msg_template = 'ensure that there are no more than {whole_digits} digits before the decimal point' + + def __init__(self, *, whole_digits: int) -> None: + super().__init__(whole_digits=whole_digits) + + +class DateTimeError(PydanticValueError): + msg_template = 'invalid datetime format' + + +class DateError(PydanticValueError): + msg_template = 'invalid date format' + + +class DateNotInThePastError(PydanticValueError): + code = 'date.not_in_the_past' + msg_template = 'date is not in the past' + + +class DateNotInTheFutureError(PydanticValueError): + code = 'date.not_in_the_future' + msg_template = 'date is not in the future' + + +class TimeError(PydanticValueError): + msg_template = 'invalid time format' + + +class DurationError(PydanticValueError): + msg_template = 'invalid duration format' + + +class HashableError(PydanticTypeError): + msg_template = 'value is not a valid hashable' + + +class UUIDError(PydanticTypeError): + msg_template = 'value is not a valid uuid' + + +class UUIDVersionError(PydanticValueError): + code = 'uuid.version' + msg_template = 'uuid version {required_version} expected' + + def __init__(self, *, required_version: int) -> None: + super().__init__(required_version=required_version) + + +class ArbitraryTypeError(PydanticTypeError): + code = 'arbitrary_type' + msg_template = 'instance of {expected_arbitrary_type} expected' + + def __init__(self, *, expected_arbitrary_type: Type[Any]) -> None: + super().__init__(expected_arbitrary_type=display_as_type(expected_arbitrary_type)) + + +class ClassError(PydanticTypeError): + code = 'class' + msg_template = 'a class is expected' + + +class SubclassError(PydanticTypeError): + code = 'subclass' + msg_template = 'subclass of {expected_class} expected' + + def __init__(self, *, expected_class: Type[Any]) -> None: + super().__init__(expected_class=display_as_type(expected_class)) + + +class JsonError(PydanticValueError): + msg_template = 'Invalid JSON' + + +class JsonTypeError(PydanticTypeError): + code = 'json' + msg_template = 'JSON object must be str, bytes or bytearray' + + +class PatternError(PydanticValueError): + code = 'regex_pattern' + msg_template = 'Invalid regular expression' + + +class DataclassTypeError(PydanticTypeError): + code = 'dataclass' + msg_template = 'instance of {class_name}, tuple or dict expected' + + +class CallableError(PydanticTypeError): + msg_template = '{value} is not callable' + + +class EnumError(PydanticTypeError): + code = 'enum_instance' + msg_template = '{value} is not a valid Enum instance' + + +class IntEnumError(PydanticTypeError): + code = 'int_enum_instance' + msg_template = '{value} is not a valid IntEnum instance' + + +class IPvAnyAddressError(PydanticValueError): + msg_template = 'value is not a valid IPv4 or IPv6 address' + + +class IPvAnyInterfaceError(PydanticValueError): + msg_template = 'value is not a valid IPv4 or IPv6 interface' + + +class IPvAnyNetworkError(PydanticValueError): + msg_template = 'value is not a valid IPv4 or IPv6 network' + + +class IPv4AddressError(PydanticValueError): + msg_template = 'value is not a valid IPv4 address' + + +class IPv6AddressError(PydanticValueError): + msg_template = 'value is not a valid IPv6 address' + + +class IPv4NetworkError(PydanticValueError): + msg_template = 'value is not a valid IPv4 network' + + +class IPv6NetworkError(PydanticValueError): + msg_template = 'value is not a valid IPv6 network' + + +class IPv4InterfaceError(PydanticValueError): + msg_template = 'value is not a valid IPv4 interface' + + +class IPv6InterfaceError(PydanticValueError): + msg_template = 'value is not a valid IPv6 interface' + + +class ColorError(PydanticValueError): + msg_template = 'value is not a valid color: {reason}' + + +class StrictBoolError(PydanticValueError): + msg_template = 'value is not a valid boolean' + + +class NotDigitError(PydanticValueError): + code = 'payment_card_number.digits' + msg_template = 'card number is not all digits' + + +class LuhnValidationError(PydanticValueError): + code = 'payment_card_number.luhn_check' + msg_template = 'card number is not luhn valid' + + +class InvalidLengthForBrand(PydanticValueError): + code = 'payment_card_number.invalid_length_for_brand' + msg_template = 'Length for a {brand} card must be {required_length}' + + +class InvalidByteSize(PydanticValueError): + msg_template = 'could not parse value and unit from byte string' + + +class InvalidByteSizeUnit(PydanticValueError): + msg_template = 'could not interpret byte unit: {unit}' + + +class MissingDiscriminator(PydanticValueError): + code = 'discriminated_union.missing_discriminator' + msg_template = 'Discriminator {discriminator_key!r} is missing in value' + + +class InvalidDiscriminator(PydanticValueError): + code = 'discriminated_union.invalid_discriminator' + msg_template = ( + 'No match for discriminator {discriminator_key!r} and value {discriminator_value!r} ' + '(allowed values: {allowed_values})' + ) + + def __init__(self, *, discriminator_key: str, discriminator_value: Any, allowed_values: Sequence[Any]) -> None: + super().__init__( + discriminator_key=discriminator_key, + discriminator_value=discriminator_value, + allowed_values=', '.join(map(repr, allowed_values)), + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/fields.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/fields.py new file mode 100644 index 0000000000000000000000000000000000000000..002b60cde1bf28bbbf20af824638503d598fbc60 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/fields.py @@ -0,0 +1,1253 @@ +import copy +import re +from collections import Counter as CollectionCounter, defaultdict, deque +from collections.abc import Callable, Hashable as CollectionsHashable, Iterable as CollectionsIterable +from typing import ( + TYPE_CHECKING, + Any, + Counter, + DefaultDict, + Deque, + Dict, + ForwardRef, + FrozenSet, + Generator, + Iterable, + Iterator, + List, + Mapping, + Optional, + Pattern, + Sequence, + Set, + Tuple, + Type, + TypeVar, + Union, +) + +from typing_extensions import Annotated, Final + +from pydantic.v1 import errors as errors_ +from pydantic.v1.class_validators import Validator, make_generic_validator, prep_validators +from pydantic.v1.error_wrappers import ErrorWrapper +from pydantic.v1.errors import ConfigError, InvalidDiscriminator, MissingDiscriminator, NoneIsNotAllowedError +from pydantic.v1.types import Json, JsonWrapper +from pydantic.v1.typing import ( + NoArgAnyCallable, + convert_generics, + display_as_type, + get_args, + get_origin, + is_finalvar, + is_literal_type, + is_new_type, + is_none_type, + is_typeddict, + is_typeddict_special, + is_union, + new_type_supertype, +) +from pydantic.v1.utils import ( + PyObjectStr, + Representation, + ValueItems, + get_discriminator_alias_and_values, + get_unique_discriminator_alias, + lenient_isinstance, + lenient_issubclass, + sequence_like, + smart_deepcopy, +) +from pydantic.v1.validators import constant_validator, dict_validator, find_validators, validate_json + +Required: Any = Ellipsis + +T = TypeVar('T') + + +class UndefinedType: + def __repr__(self) -> str: + return 'PydanticUndefined' + + def __copy__(self: T) -> T: + return self + + def __reduce__(self) -> str: + return 'Undefined' + + def __deepcopy__(self: T, _: Any) -> T: + return self + + +Undefined = UndefinedType() + +if TYPE_CHECKING: + from pydantic.v1.class_validators import ValidatorsList + from pydantic.v1.config import BaseConfig + from pydantic.v1.error_wrappers import ErrorList + from pydantic.v1.types import ModelOrDc + from pydantic.v1.typing import AbstractSetIntStr, MappingIntStrAny, ReprArgs + + ValidateReturn = Tuple[Optional[Any], Optional[ErrorList]] + LocStr = Union[Tuple[Union[int, str], ...], str] + BoolUndefined = Union[bool, UndefinedType] + + +class FieldInfo(Representation): + """ + Captures extra information about a field. + """ + + __slots__ = ( + 'default', + 'default_factory', + 'alias', + 'alias_priority', + 'title', + 'description', + 'exclude', + 'include', + 'const', + 'gt', + 'ge', + 'lt', + 'le', + 'multiple_of', + 'allow_inf_nan', + 'max_digits', + 'decimal_places', + 'min_items', + 'max_items', + 'unique_items', + 'min_length', + 'max_length', + 'allow_mutation', + 'repr', + 'regex', + 'discriminator', + 'extra', + ) + + # field constraints with the default value, it's also used in update_from_config below + __field_constraints__ = { + 'min_length': None, + 'max_length': None, + 'regex': None, + 'gt': None, + 'lt': None, + 'ge': None, + 'le': None, + 'multiple_of': None, + 'allow_inf_nan': None, + 'max_digits': None, + 'decimal_places': None, + 'min_items': None, + 'max_items': None, + 'unique_items': None, + 'allow_mutation': True, + } + + def __init__(self, default: Any = Undefined, **kwargs: Any) -> None: + self.default = default + self.default_factory = kwargs.pop('default_factory', None) + self.alias = kwargs.pop('alias', None) + self.alias_priority = kwargs.pop('alias_priority', 2 if self.alias is not None else None) + self.title = kwargs.pop('title', None) + self.description = kwargs.pop('description', None) + self.exclude = kwargs.pop('exclude', None) + self.include = kwargs.pop('include', None) + self.const = kwargs.pop('const', None) + self.gt = kwargs.pop('gt', None) + self.ge = kwargs.pop('ge', None) + self.lt = kwargs.pop('lt', None) + self.le = kwargs.pop('le', None) + self.multiple_of = kwargs.pop('multiple_of', None) + self.allow_inf_nan = kwargs.pop('allow_inf_nan', None) + self.max_digits = kwargs.pop('max_digits', None) + self.decimal_places = kwargs.pop('decimal_places', None) + self.min_items = kwargs.pop('min_items', None) + self.max_items = kwargs.pop('max_items', None) + self.unique_items = kwargs.pop('unique_items', None) + self.min_length = kwargs.pop('min_length', None) + self.max_length = kwargs.pop('max_length', None) + self.allow_mutation = kwargs.pop('allow_mutation', True) + self.regex = kwargs.pop('regex', None) + self.discriminator = kwargs.pop('discriminator', None) + self.repr = kwargs.pop('repr', True) + self.extra = kwargs + + def __repr_args__(self) -> 'ReprArgs': + field_defaults_to_hide: Dict[str, Any] = { + 'repr': True, + **self.__field_constraints__, + } + + attrs = ((s, getattr(self, s)) for s in self.__slots__) + return [(a, v) for a, v in attrs if v != field_defaults_to_hide.get(a, None)] + + def get_constraints(self) -> Set[str]: + """ + Gets the constraints set on the field by comparing the constraint value with its default value + + :return: the constraints set on field_info + """ + return {attr for attr, default in self.__field_constraints__.items() if getattr(self, attr) != default} + + def update_from_config(self, from_config: Dict[str, Any]) -> None: + """ + Update this FieldInfo based on a dict from get_field_info, only fields which have not been set are dated. + """ + for attr_name, value in from_config.items(): + try: + current_value = getattr(self, attr_name) + except AttributeError: + # attr_name is not an attribute of FieldInfo, it should therefore be added to extra + # (except if extra already has this value!) + self.extra.setdefault(attr_name, value) + else: + if current_value is self.__field_constraints__.get(attr_name, None): + setattr(self, attr_name, value) + elif attr_name == 'exclude': + self.exclude = ValueItems.merge(value, current_value) + elif attr_name == 'include': + self.include = ValueItems.merge(value, current_value, intersect=True) + + def _validate(self) -> None: + if self.default is not Undefined and self.default_factory is not None: + raise ValueError('cannot specify both default and default_factory') + + +def Field( + default: Any = Undefined, + *, + default_factory: Optional[NoArgAnyCallable] = None, + alias: Optional[str] = None, + title: Optional[str] = None, + description: Optional[str] = None, + exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None, + include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny', Any]] = None, + const: Optional[bool] = None, + gt: Optional[float] = None, + ge: Optional[float] = None, + lt: Optional[float] = None, + le: Optional[float] = None, + multiple_of: Optional[float] = None, + allow_inf_nan: Optional[bool] = None, + max_digits: Optional[int] = None, + decimal_places: Optional[int] = None, + min_items: Optional[int] = None, + max_items: Optional[int] = None, + unique_items: Optional[bool] = None, + min_length: Optional[int] = None, + max_length: Optional[int] = None, + allow_mutation: bool = True, + regex: Optional[str] = None, + discriminator: Optional[str] = None, + repr: bool = True, + **extra: Any, +) -> Any: + """ + Used to provide extra information about a field, either for the model schema or complex validation. Some arguments + apply only to number fields (``int``, ``float``, ``Decimal``) and some apply only to ``str``. + + :param default: since this is replacing the field’s default, its first argument is used + to set the default, use ellipsis (``...``) to indicate the field is required + :param default_factory: callable that will be called when a default value is needed for this field + If both `default` and `default_factory` are set, an error is raised. + :param alias: the public name of the field + :param title: can be any string, used in the schema + :param description: can be any string, used in the schema + :param exclude: exclude this field while dumping. + Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method. + :param include: include this field while dumping. + Takes same values as the ``include`` and ``exclude`` arguments on the ``.dict`` method. + :param const: this field is required and *must* take it's default value + :param gt: only applies to numbers, requires the field to be "greater than". The schema + will have an ``exclusiveMinimum`` validation keyword + :param ge: only applies to numbers, requires the field to be "greater than or equal to". The + schema will have a ``minimum`` validation keyword + :param lt: only applies to numbers, requires the field to be "less than". The schema + will have an ``exclusiveMaximum`` validation keyword + :param le: only applies to numbers, requires the field to be "less than or equal to". The + schema will have a ``maximum`` validation keyword + :param multiple_of: only applies to numbers, requires the field to be "a multiple of". The + schema will have a ``multipleOf`` validation keyword + :param allow_inf_nan: only applies to numbers, allows the field to be NaN or infinity (+inf or -inf), + which is a valid Python float. Default True, set to False for compatibility with JSON. + :param max_digits: only applies to Decimals, requires the field to have a maximum number + of digits within the decimal. It does not include a zero before the decimal point or trailing decimal zeroes. + :param decimal_places: only applies to Decimals, requires the field to have at most a number of decimal places + allowed. It does not include trailing decimal zeroes. + :param min_items: only applies to lists, requires the field to have a minimum number of + elements. The schema will have a ``minItems`` validation keyword + :param max_items: only applies to lists, requires the field to have a maximum number of + elements. The schema will have a ``maxItems`` validation keyword + :param unique_items: only applies to lists, requires the field not to have duplicated + elements. The schema will have a ``uniqueItems`` validation keyword + :param min_length: only applies to strings, requires the field to have a minimum length. The + schema will have a ``minLength`` validation keyword + :param max_length: only applies to strings, requires the field to have a maximum length. The + schema will have a ``maxLength`` validation keyword + :param allow_mutation: a boolean which defaults to True. When False, the field raises a TypeError if the field is + assigned on an instance. The BaseModel Config must set validate_assignment to True + :param regex: only applies to strings, requires the field match against a regular expression + pattern string. The schema will have a ``pattern`` validation keyword + :param discriminator: only useful with a (discriminated a.k.a. tagged) `Union` of sub models with a common field. + The `discriminator` is the name of this common field to shorten validation and improve generated schema + :param repr: show this field in the representation + :param **extra: any additional keyword arguments will be added as is to the schema + """ + field_info = FieldInfo( + default, + default_factory=default_factory, + alias=alias, + title=title, + description=description, + exclude=exclude, + include=include, + const=const, + gt=gt, + ge=ge, + lt=lt, + le=le, + multiple_of=multiple_of, + allow_inf_nan=allow_inf_nan, + max_digits=max_digits, + decimal_places=decimal_places, + min_items=min_items, + max_items=max_items, + unique_items=unique_items, + min_length=min_length, + max_length=max_length, + allow_mutation=allow_mutation, + regex=regex, + discriminator=discriminator, + repr=repr, + **extra, + ) + field_info._validate() + return field_info + + +# used to be an enum but changed to int's for small performance improvement as less access overhead +SHAPE_SINGLETON = 1 +SHAPE_LIST = 2 +SHAPE_SET = 3 +SHAPE_MAPPING = 4 +SHAPE_TUPLE = 5 +SHAPE_TUPLE_ELLIPSIS = 6 +SHAPE_SEQUENCE = 7 +SHAPE_FROZENSET = 8 +SHAPE_ITERABLE = 9 +SHAPE_GENERIC = 10 +SHAPE_DEQUE = 11 +SHAPE_DICT = 12 +SHAPE_DEFAULTDICT = 13 +SHAPE_COUNTER = 14 +SHAPE_NAME_LOOKUP = { + SHAPE_LIST: 'List[{}]', + SHAPE_SET: 'Set[{}]', + SHAPE_TUPLE_ELLIPSIS: 'Tuple[{}, ...]', + SHAPE_SEQUENCE: 'Sequence[{}]', + SHAPE_FROZENSET: 'FrozenSet[{}]', + SHAPE_ITERABLE: 'Iterable[{}]', + SHAPE_DEQUE: 'Deque[{}]', + SHAPE_DICT: 'Dict[{}]', + SHAPE_DEFAULTDICT: 'DefaultDict[{}]', + SHAPE_COUNTER: 'Counter[{}]', +} + +MAPPING_LIKE_SHAPES: Set[int] = {SHAPE_DEFAULTDICT, SHAPE_DICT, SHAPE_MAPPING, SHAPE_COUNTER} + + +class ModelField(Representation): + __slots__ = ( + 'type_', + 'outer_type_', + 'annotation', + 'sub_fields', + 'sub_fields_mapping', + 'key_field', + 'validators', + 'pre_validators', + 'post_validators', + 'default', + 'default_factory', + 'required', + 'final', + 'model_config', + 'name', + 'alias', + 'has_alias', + 'field_info', + 'discriminator_key', + 'discriminator_alias', + 'validate_always', + 'allow_none', + 'shape', + 'class_validators', + 'parse_json', + ) + + def __init__( + self, + *, + name: str, + type_: Type[Any], + class_validators: Optional[Dict[str, Validator]], + model_config: Type['BaseConfig'], + default: Any = None, + default_factory: Optional[NoArgAnyCallable] = None, + required: 'BoolUndefined' = Undefined, + final: bool = False, + alias: Optional[str] = None, + field_info: Optional[FieldInfo] = None, + ) -> None: + self.name: str = name + self.has_alias: bool = alias is not None + self.alias: str = alias if alias is not None else name + self.annotation = type_ + self.type_: Any = convert_generics(type_) + self.outer_type_: Any = type_ + self.class_validators = class_validators or {} + self.default: Any = default + self.default_factory: Optional[NoArgAnyCallable] = default_factory + self.required: 'BoolUndefined' = required + self.final: bool = final + self.model_config = model_config + self.field_info: FieldInfo = field_info or FieldInfo(default) + self.discriminator_key: Optional[str] = self.field_info.discriminator + self.discriminator_alias: Optional[str] = self.discriminator_key + + self.allow_none: bool = False + self.validate_always: bool = False + self.sub_fields: Optional[List[ModelField]] = None + self.sub_fields_mapping: Optional[Dict[str, 'ModelField']] = None # used for discriminated union + self.key_field: Optional[ModelField] = None + self.validators: 'ValidatorsList' = [] + self.pre_validators: Optional['ValidatorsList'] = None + self.post_validators: Optional['ValidatorsList'] = None + self.parse_json: bool = False + self.shape: int = SHAPE_SINGLETON + self.model_config.prepare_field(self) + self.prepare() + + def get_default(self) -> Any: + return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory() + + @staticmethod + def _get_field_info( + field_name: str, annotation: Any, value: Any, config: Type['BaseConfig'] + ) -> Tuple[FieldInfo, Any]: + """ + Get a FieldInfo from a root typing.Annotated annotation, value, or config default. + + The FieldInfo may be set in typing.Annotated or the value, but not both. If neither contain + a FieldInfo, a new one will be created using the config. + + :param field_name: name of the field for use in error messages + :param annotation: a type hint such as `str` or `Annotated[str, Field(..., min_length=5)]` + :param value: the field's assigned value + :param config: the model's config object + :return: the FieldInfo contained in the `annotation`, the value, or a new one from the config. + """ + field_info_from_config = config.get_field_info(field_name) + + field_info = None + if get_origin(annotation) is Annotated: + field_infos = [arg for arg in get_args(annotation)[1:] if isinstance(arg, FieldInfo)] + if len(field_infos) > 1: + raise ValueError(f'cannot specify multiple `Annotated` `Field`s for {field_name!r}') + field_info = next(iter(field_infos), None) + if field_info is not None: + field_info = copy.copy(field_info) + field_info.update_from_config(field_info_from_config) + if field_info.default not in (Undefined, Required): + raise ValueError(f'`Field` default cannot be set in `Annotated` for {field_name!r}') + if value is not Undefined and value is not Required: + # check also `Required` because of `validate_arguments` that sets `...` as default value + field_info.default = value + + if isinstance(value, FieldInfo): + if field_info is not None: + raise ValueError(f'cannot specify `Annotated` and value `Field`s together for {field_name!r}') + field_info = value + field_info.update_from_config(field_info_from_config) + elif field_info is None: + field_info = FieldInfo(value, **field_info_from_config) + value = None if field_info.default_factory is not None else field_info.default + field_info._validate() + return field_info, value + + @classmethod + def infer( + cls, + *, + name: str, + value: Any, + annotation: Any, + class_validators: Optional[Dict[str, Validator]], + config: Type['BaseConfig'], + ) -> 'ModelField': + from pydantic.v1.schema import get_annotation_from_field_info + + field_info, value = cls._get_field_info(name, annotation, value, config) + required: 'BoolUndefined' = Undefined + if value is Required: + required = True + value = None + elif value is not Undefined: + required = False + annotation = get_annotation_from_field_info(annotation, field_info, name, config.validate_assignment) + + return cls( + name=name, + type_=annotation, + alias=field_info.alias, + class_validators=class_validators, + default=value, + default_factory=field_info.default_factory, + required=required, + model_config=config, + field_info=field_info, + ) + + def set_config(self, config: Type['BaseConfig']) -> None: + self.model_config = config + info_from_config = config.get_field_info(self.name) + config.prepare_field(self) + new_alias = info_from_config.get('alias') + new_alias_priority = info_from_config.get('alias_priority') or 0 + if new_alias and new_alias_priority >= (self.field_info.alias_priority or 0): + self.field_info.alias = new_alias + self.field_info.alias_priority = new_alias_priority + self.alias = new_alias + new_exclude = info_from_config.get('exclude') + if new_exclude is not None: + self.field_info.exclude = ValueItems.merge(self.field_info.exclude, new_exclude) + new_include = info_from_config.get('include') + if new_include is not None: + self.field_info.include = ValueItems.merge(self.field_info.include, new_include, intersect=True) + + @property + def alt_alias(self) -> bool: + return self.name != self.alias + + def prepare(self) -> None: + """ + Prepare the field but inspecting self.default, self.type_ etc. + + Note: this method is **not** idempotent (because _type_analysis is not idempotent), + e.g. calling it it multiple times may modify the field and configure it incorrectly. + """ + self._set_default_and_type() + if self.type_.__class__ is ForwardRef or self.type_.__class__ is DeferredType: + # self.type_ is currently a ForwardRef and there's nothing we can do now, + # user will need to call model.update_forward_refs() + return + + self._type_analysis() + if self.required is Undefined: + self.required = True + if self.default is Undefined and self.default_factory is None: + self.default = None + self.populate_validators() + + def _set_default_and_type(self) -> None: + """ + Set the default value, infer the type if needed and check if `None` value is valid. + """ + if self.default_factory is not None: + if self.type_ is Undefined: + raise errors_.ConfigError( + f'you need to set the type of field {self.name!r} when using `default_factory`' + ) + return + + default_value = self.get_default() + + if default_value is not None and self.type_ is Undefined: + self.type_ = default_value.__class__ + self.outer_type_ = self.type_ + self.annotation = self.type_ + + if self.type_ is Undefined: + raise errors_.ConfigError(f'unable to infer type for attribute "{self.name}"') + + if self.required is False and default_value is None: + self.allow_none = True + + def _type_analysis(self) -> None: # noqa: C901 (ignore complexity) + # typing interface is horrible, we have to do some ugly checks + if lenient_issubclass(self.type_, JsonWrapper): + self.type_ = self.type_.inner_type + self.parse_json = True + elif lenient_issubclass(self.type_, Json): + self.type_ = Any + self.parse_json = True + elif isinstance(self.type_, TypeVar): + if self.type_.__bound__: + self.type_ = self.type_.__bound__ + elif self.type_.__constraints__: + self.type_ = Union[self.type_.__constraints__] + else: + self.type_ = Any + elif is_new_type(self.type_): + self.type_ = new_type_supertype(self.type_) + + if self.type_ is Any or self.type_ is object: + if self.required is Undefined: + self.required = False + self.allow_none = True + return + elif self.type_ is Pattern or self.type_ is re.Pattern: + # python 3.7 only, Pattern is a typing object but without sub fields + return + elif is_literal_type(self.type_): + return + elif is_typeddict(self.type_): + return + + if is_finalvar(self.type_): + self.final = True + + if self.type_ is Final: + self.type_ = Any + else: + self.type_ = get_args(self.type_)[0] + + self._type_analysis() + return + + origin = get_origin(self.type_) + + if origin is Annotated or is_typeddict_special(origin): + self.type_ = get_args(self.type_)[0] + self._type_analysis() + return + + if self.discriminator_key is not None and not is_union(origin): + raise TypeError('`discriminator` can only be used with `Union` type with more than one variant') + + # add extra check for `collections.abc.Hashable` for python 3.10+ where origin is not `None` + if origin is None or origin is CollectionsHashable: + # field is not "typing" object eg. Union, Dict, List etc. + # allow None for virtual superclasses of NoneType, e.g. Hashable + if isinstance(self.type_, type) and isinstance(None, self.type_): + self.allow_none = True + return + elif origin is Callable: + return + elif is_union(origin): + types_ = [] + for type_ in get_args(self.type_): + if is_none_type(type_) or type_ is Any or type_ is object: + if self.required is Undefined: + self.required = False + self.allow_none = True + if is_none_type(type_): + continue + types_.append(type_) + + if len(types_) == 1: + # Optional[] + self.type_ = types_[0] + # this is the one case where the "outer type" isn't just the original type + self.outer_type_ = self.type_ + # re-run to correctly interpret the new self.type_ + self._type_analysis() + else: + self.sub_fields = [self._create_sub_type(t, f'{self.name}_{display_as_type(t)}') for t in types_] + + if self.discriminator_key is not None: + self.prepare_discriminated_union_sub_fields() + return + elif issubclass(origin, Tuple): # type: ignore + # origin == Tuple without item type + args = get_args(self.type_) + if not args: # plain tuple + self.type_ = Any + self.shape = SHAPE_TUPLE_ELLIPSIS + elif len(args) == 2 and args[1] is Ellipsis: # e.g. Tuple[int, ...] + self.type_ = args[0] + self.shape = SHAPE_TUPLE_ELLIPSIS + self.sub_fields = [self._create_sub_type(args[0], f'{self.name}_0')] + elif args == ((),): # Tuple[()] means empty tuple + self.shape = SHAPE_TUPLE + self.type_ = Any + self.sub_fields = [] + else: + self.shape = SHAPE_TUPLE + self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(args)] + return + elif issubclass(origin, List): + # Create self validators + get_validators = getattr(self.type_, '__get_validators__', None) + if get_validators: + self.class_validators.update( + {f'list_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())} + ) + + self.type_ = get_args(self.type_)[0] + self.shape = SHAPE_LIST + elif issubclass(origin, Set): + # Create self validators + get_validators = getattr(self.type_, '__get_validators__', None) + if get_validators: + self.class_validators.update( + {f'set_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())} + ) + + self.type_ = get_args(self.type_)[0] + self.shape = SHAPE_SET + elif issubclass(origin, FrozenSet): + # Create self validators + get_validators = getattr(self.type_, '__get_validators__', None) + if get_validators: + self.class_validators.update( + {f'frozenset_{i}': Validator(validator, pre=True) for i, validator in enumerate(get_validators())} + ) + + self.type_ = get_args(self.type_)[0] + self.shape = SHAPE_FROZENSET + elif issubclass(origin, Deque): + self.type_ = get_args(self.type_)[0] + self.shape = SHAPE_DEQUE + elif issubclass(origin, Sequence): + self.type_ = get_args(self.type_)[0] + self.shape = SHAPE_SEQUENCE + # priority to most common mapping: dict + elif origin is dict or origin is Dict: + self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) + self.type_ = get_args(self.type_)[1] + self.shape = SHAPE_DICT + elif issubclass(origin, DefaultDict): + self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) + self.type_ = get_args(self.type_)[1] + self.shape = SHAPE_DEFAULTDICT + elif issubclass(origin, Counter): + self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) + self.type_ = int + self.shape = SHAPE_COUNTER + elif issubclass(origin, Mapping): + self.key_field = self._create_sub_type(get_args(self.type_)[0], 'key_' + self.name, for_keys=True) + self.type_ = get_args(self.type_)[1] + self.shape = SHAPE_MAPPING + # Equality check as almost everything inherits form Iterable, including str + # check for Iterable and CollectionsIterable, as it could receive one even when declared with the other + elif origin in {Iterable, CollectionsIterable}: + self.type_ = get_args(self.type_)[0] + self.shape = SHAPE_ITERABLE + self.sub_fields = [self._create_sub_type(self.type_, f'{self.name}_type')] + elif issubclass(origin, Type): # type: ignore + return + elif hasattr(origin, '__get_validators__') or self.model_config.arbitrary_types_allowed: + # Is a Pydantic-compatible generic that handles itself + # or we have arbitrary_types_allowed = True + self.shape = SHAPE_GENERIC + self.sub_fields = [self._create_sub_type(t, f'{self.name}_{i}') for i, t in enumerate(get_args(self.type_))] + self.type_ = origin + return + else: + raise TypeError(f'Fields of type "{origin}" are not supported.') + + # type_ has been refined eg. as the type of a List and sub_fields needs to be populated + self.sub_fields = [self._create_sub_type(self.type_, '_' + self.name)] + + def prepare_discriminated_union_sub_fields(self) -> None: + """ + Prepare the mapping -> and update `sub_fields` + Note that this process can be aborted if a `ForwardRef` is encountered + """ + assert self.discriminator_key is not None + + if self.type_.__class__ is DeferredType: + return + + assert self.sub_fields is not None + sub_fields_mapping: Dict[str, 'ModelField'] = {} + all_aliases: Set[str] = set() + + for sub_field in self.sub_fields: + t = sub_field.type_ + if t.__class__ is ForwardRef: + # Stopping everything...will need to call `update_forward_refs` + return + + alias, discriminator_values = get_discriminator_alias_and_values(t, self.discriminator_key) + all_aliases.add(alias) + for discriminator_value in discriminator_values: + sub_fields_mapping[discriminator_value] = sub_field + + self.sub_fields_mapping = sub_fields_mapping + self.discriminator_alias = get_unique_discriminator_alias(all_aliases, self.discriminator_key) + + def _create_sub_type(self, type_: Type[Any], name: str, *, for_keys: bool = False) -> 'ModelField': + if for_keys: + class_validators = None + else: + # validators for sub items should not have `each_item` as we want to check only the first sublevel + class_validators = { + k: Validator( + func=v.func, + pre=v.pre, + each_item=False, + always=v.always, + check_fields=v.check_fields, + skip_on_failure=v.skip_on_failure, + ) + for k, v in self.class_validators.items() + if v.each_item + } + + field_info, _ = self._get_field_info(name, type_, None, self.model_config) + + return self.__class__( + type_=type_, + name=name, + class_validators=class_validators, + model_config=self.model_config, + field_info=field_info, + ) + + def populate_validators(self) -> None: + """ + Prepare self.pre_validators, self.validators, and self.post_validators based on self.type_'s __get_validators__ + and class validators. This method should be idempotent, e.g. it should be safe to call multiple times + without mis-configuring the field. + """ + self.validate_always = getattr(self.type_, 'validate_always', False) or any( + v.always for v in self.class_validators.values() + ) + + class_validators_ = self.class_validators.values() + if not self.sub_fields or self.shape == SHAPE_GENERIC: + get_validators = getattr(self.type_, '__get_validators__', None) + v_funcs = ( + *[v.func for v in class_validators_ if v.each_item and v.pre], + *(get_validators() if get_validators else list(find_validators(self.type_, self.model_config))), + *[v.func for v in class_validators_ if v.each_item and not v.pre], + ) + self.validators = prep_validators(v_funcs) + + self.pre_validators = [] + self.post_validators = [] + + if self.field_info and self.field_info.const: + self.post_validators.append(make_generic_validator(constant_validator)) + + if class_validators_: + self.pre_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and v.pre) + self.post_validators += prep_validators(v.func for v in class_validators_ if not v.each_item and not v.pre) + + if self.parse_json: + self.pre_validators.append(make_generic_validator(validate_json)) + + self.pre_validators = self.pre_validators or None + self.post_validators = self.post_validators or None + + def validate( + self, v: Any, values: Dict[str, Any], *, loc: 'LocStr', cls: Optional['ModelOrDc'] = None + ) -> 'ValidateReturn': + assert self.type_.__class__ is not DeferredType + + if self.type_.__class__ is ForwardRef: + assert cls is not None + raise ConfigError( + f'field "{self.name}" not yet prepared so type is still a ForwardRef, ' + f'you might need to call {cls.__name__}.update_forward_refs().' + ) + + errors: Optional['ErrorList'] + if self.pre_validators: + v, errors = self._apply_validators(v, values, loc, cls, self.pre_validators) + if errors: + return v, errors + + if v is None: + if is_none_type(self.type_): + # keep validating + pass + elif self.allow_none: + if self.post_validators: + return self._apply_validators(v, values, loc, cls, self.post_validators) + else: + return None, None + else: + return v, ErrorWrapper(NoneIsNotAllowedError(), loc) + + if self.shape == SHAPE_SINGLETON: + v, errors = self._validate_singleton(v, values, loc, cls) + elif self.shape in MAPPING_LIKE_SHAPES: + v, errors = self._validate_mapping_like(v, values, loc, cls) + elif self.shape == SHAPE_TUPLE: + v, errors = self._validate_tuple(v, values, loc, cls) + elif self.shape == SHAPE_ITERABLE: + v, errors = self._validate_iterable(v, values, loc, cls) + elif self.shape == SHAPE_GENERIC: + v, errors = self._apply_validators(v, values, loc, cls, self.validators) + else: + # sequence, list, set, generator, tuple with ellipsis, frozen set + v, errors = self._validate_sequence_like(v, values, loc, cls) + + if not errors and self.post_validators: + v, errors = self._apply_validators(v, values, loc, cls, self.post_validators) + return v, errors + + def _validate_sequence_like( # noqa: C901 (ignore complexity) + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] + ) -> 'ValidateReturn': + """ + Validate sequence-like containers: lists, tuples, sets and generators + Note that large if-else blocks are necessary to enable Cython + optimization, which is why we disable the complexity check above. + """ + if not sequence_like(v): + e: errors_.PydanticTypeError + if self.shape == SHAPE_LIST: + e = errors_.ListError() + elif self.shape in (SHAPE_TUPLE, SHAPE_TUPLE_ELLIPSIS): + e = errors_.TupleError() + elif self.shape == SHAPE_SET: + e = errors_.SetError() + elif self.shape == SHAPE_FROZENSET: + e = errors_.FrozenSetError() + else: + e = errors_.SequenceError() + return v, ErrorWrapper(e, loc) + + loc = loc if isinstance(loc, tuple) else (loc,) + result = [] + errors: List[ErrorList] = [] + for i, v_ in enumerate(v): + v_loc = *loc, i + r, ee = self._validate_singleton(v_, values, v_loc, cls) + if ee: + errors.append(ee) + else: + result.append(r) + + if errors: + return v, errors + + converted: Union[List[Any], Set[Any], FrozenSet[Any], Tuple[Any, ...], Iterator[Any], Deque[Any]] = result + + if self.shape == SHAPE_SET: + converted = set(result) + elif self.shape == SHAPE_FROZENSET: + converted = frozenset(result) + elif self.shape == SHAPE_TUPLE_ELLIPSIS: + converted = tuple(result) + elif self.shape == SHAPE_DEQUE: + converted = deque(result, maxlen=getattr(v, 'maxlen', None)) + elif self.shape == SHAPE_SEQUENCE: + if isinstance(v, tuple): + converted = tuple(result) + elif isinstance(v, set): + converted = set(result) + elif isinstance(v, Generator): + converted = iter(result) + elif isinstance(v, deque): + converted = deque(result, maxlen=getattr(v, 'maxlen', None)) + return converted, None + + def _validate_iterable( + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] + ) -> 'ValidateReturn': + """ + Validate Iterables. + + This intentionally doesn't validate values to allow infinite generators. + """ + + try: + iterable = iter(v) + except TypeError: + return v, ErrorWrapper(errors_.IterableError(), loc) + return iterable, None + + def _validate_tuple( + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] + ) -> 'ValidateReturn': + e: Optional[Exception] = None + if not sequence_like(v): + e = errors_.TupleError() + else: + actual_length, expected_length = len(v), len(self.sub_fields) # type: ignore + if actual_length != expected_length: + e = errors_.TupleLengthError(actual_length=actual_length, expected_length=expected_length) + + if e: + return v, ErrorWrapper(e, loc) + + loc = loc if isinstance(loc, tuple) else (loc,) + result = [] + errors: List[ErrorList] = [] + for i, (v_, field) in enumerate(zip(v, self.sub_fields)): # type: ignore + v_loc = *loc, i + r, ee = field.validate(v_, values, loc=v_loc, cls=cls) + if ee: + errors.append(ee) + else: + result.append(r) + + if errors: + return v, errors + else: + return tuple(result), None + + def _validate_mapping_like( + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] + ) -> 'ValidateReturn': + try: + v_iter = dict_validator(v) + except TypeError as exc: + return v, ErrorWrapper(exc, loc) + + loc = loc if isinstance(loc, tuple) else (loc,) + result, errors = {}, [] + for k, v_ in v_iter.items(): + v_loc = *loc, '__key__' + key_result, key_errors = self.key_field.validate(k, values, loc=v_loc, cls=cls) # type: ignore + if key_errors: + errors.append(key_errors) + continue + + v_loc = *loc, k + value_result, value_errors = self._validate_singleton(v_, values, v_loc, cls) + if value_errors: + errors.append(value_errors) + continue + + result[key_result] = value_result + if errors: + return v, errors + elif self.shape == SHAPE_DICT: + return result, None + elif self.shape == SHAPE_DEFAULTDICT: + return defaultdict(self.type_, result), None + elif self.shape == SHAPE_COUNTER: + return CollectionCounter(result), None + else: + return self._get_mapping_value(v, result), None + + def _get_mapping_value(self, original: T, converted: Dict[Any, Any]) -> Union[T, Dict[Any, Any]]: + """ + When type is `Mapping[KT, KV]` (or another unsupported mapping), we try to avoid + coercing to `dict` unwillingly. + """ + original_cls = original.__class__ + + if original_cls == dict or original_cls == Dict: + return converted + elif original_cls in {defaultdict, DefaultDict}: + return defaultdict(self.type_, converted) + else: + try: + # Counter, OrderedDict, UserDict, ... + return original_cls(converted) # type: ignore + except TypeError: + raise RuntimeError(f'Could not convert dictionary to {original_cls.__name__!r}') from None + + def _validate_singleton( + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] + ) -> 'ValidateReturn': + if self.sub_fields: + if self.discriminator_key is not None: + return self._validate_discriminated_union(v, values, loc, cls) + + errors = [] + + if self.model_config.smart_union and is_union(get_origin(self.type_)): + # 1st pass: check if the value is an exact instance of one of the Union types + # (e.g. to avoid coercing a bool into an int) + for field in self.sub_fields: + if v.__class__ is field.outer_type_: + return v, None + + # 2nd pass: check if the value is an instance of any subclass of the Union types + for field in self.sub_fields: + # This whole logic will be improved later on to support more complex `isinstance` checks + # It will probably be done once a strict mode is added and be something like: + # ``` + # value, error = field.validate(v, values, strict=True) + # if error is None: + # return value, None + # ``` + try: + if isinstance(v, field.outer_type_): + return v, None + except TypeError: + # compound type + if lenient_isinstance(v, get_origin(field.outer_type_)): + value, error = field.validate(v, values, loc=loc, cls=cls) + if not error: + return value, None + + # 1st pass by default or 3rd pass with `smart_union` enabled: + # check if the value can be coerced into one of the Union types + for field in self.sub_fields: + value, error = field.validate(v, values, loc=loc, cls=cls) + if error: + errors.append(error) + else: + return value, None + return v, errors + else: + return self._apply_validators(v, values, loc, cls, self.validators) + + def _validate_discriminated_union( + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'] + ) -> 'ValidateReturn': + assert self.discriminator_key is not None + assert self.discriminator_alias is not None + + try: + try: + discriminator_value = v[self.discriminator_alias] + except KeyError: + if self.model_config.allow_population_by_field_name: + discriminator_value = v[self.discriminator_key] + else: + raise + except KeyError: + return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc) + except TypeError: + try: + # BaseModel or dataclass + discriminator_value = getattr(v, self.discriminator_key) + except (AttributeError, TypeError): + return v, ErrorWrapper(MissingDiscriminator(discriminator_key=self.discriminator_key), loc) + + if self.sub_fields_mapping is None: + assert cls is not None + raise ConfigError( + f'field "{self.name}" not yet prepared so type is still a ForwardRef, ' + f'you might need to call {cls.__name__}.update_forward_refs().' + ) + + try: + sub_field = self.sub_fields_mapping[discriminator_value] + except (KeyError, TypeError): + # KeyError: `discriminator_value` is not in the dictionary. + # TypeError: `discriminator_value` is unhashable. + assert self.sub_fields_mapping is not None + return v, ErrorWrapper( + InvalidDiscriminator( + discriminator_key=self.discriminator_key, + discriminator_value=discriminator_value, + allowed_values=list(self.sub_fields_mapping), + ), + loc, + ) + else: + if not isinstance(loc, tuple): + loc = (loc,) + return sub_field.validate(v, values, loc=(*loc, display_as_type(sub_field.type_)), cls=cls) + + def _apply_validators( + self, v: Any, values: Dict[str, Any], loc: 'LocStr', cls: Optional['ModelOrDc'], validators: 'ValidatorsList' + ) -> 'ValidateReturn': + for validator in validators: + try: + v = validator(cls, v, values, self, self.model_config) + except (ValueError, TypeError, AssertionError) as exc: + return v, ErrorWrapper(exc, loc) + return v, None + + def is_complex(self) -> bool: + """ + Whether the field is "complex" eg. env variables should be parsed as JSON. + """ + from pydantic.v1.main import BaseModel + + return ( + self.shape != SHAPE_SINGLETON + or hasattr(self.type_, '__pydantic_model__') + or lenient_issubclass(self.type_, (BaseModel, list, set, frozenset, dict)) + ) + + def _type_display(self) -> PyObjectStr: + t = display_as_type(self.type_) + + if self.shape in MAPPING_LIKE_SHAPES: + t = f'Mapping[{display_as_type(self.key_field.type_)}, {t}]' # type: ignore + elif self.shape == SHAPE_TUPLE: + t = 'Tuple[{}]'.format(', '.join(display_as_type(f.type_) for f in self.sub_fields)) # type: ignore + elif self.shape == SHAPE_GENERIC: + assert self.sub_fields + t = '{}[{}]'.format( + display_as_type(self.type_), ', '.join(display_as_type(f.type_) for f in self.sub_fields) + ) + elif self.shape != SHAPE_SINGLETON: + t = SHAPE_NAME_LOOKUP[self.shape].format(t) + + if self.allow_none and (self.shape != SHAPE_SINGLETON or not self.sub_fields): + t = f'Optional[{t}]' + return PyObjectStr(t) + + def __repr_args__(self) -> 'ReprArgs': + args = [('name', self.name), ('type', self._type_display()), ('required', self.required)] + + if not self.required: + if self.default_factory is not None: + args.append(('default_factory', f'')) + else: + args.append(('default', self.default)) + + if self.alt_alias: + args.append(('alias', self.alias)) + return args + + +class ModelPrivateAttr(Representation): + __slots__ = ('default', 'default_factory') + + def __init__(self, default: Any = Undefined, *, default_factory: Optional[NoArgAnyCallable] = None) -> None: + self.default = default + self.default_factory = default_factory + + def get_default(self) -> Any: + return smart_deepcopy(self.default) if self.default_factory is None else self.default_factory() + + def __eq__(self, other: Any) -> bool: + return isinstance(other, self.__class__) and (self.default, self.default_factory) == ( + other.default, + other.default_factory, + ) + + +def PrivateAttr( + default: Any = Undefined, + *, + default_factory: Optional[NoArgAnyCallable] = None, +) -> Any: + """ + Indicates that attribute is only used internally and never mixed with regular fields. + + Types or values of private attrs are not checked by pydantic and it's up to you to keep them relevant. + + Private attrs are stored in model __slots__. + + :param default: the attribute’s default value + :param default_factory: callable that will be called when a default value is needed for this attribute + If both `default` and `default_factory` are set, an error is raised. + """ + if default is not Undefined and default_factory is not None: + raise ValueError('cannot specify both default and default_factory') + + return ModelPrivateAttr( + default, + default_factory=default_factory, + ) + + +class DeferredType: + """ + Used to postpone field preparation, while creating recursive generic models. + """ + + +def is_finalvar_with_default_val(type_: Type[Any], val: Any) -> bool: + return is_finalvar(type_) and val is not Undefined and not isinstance(val, FieldInfo) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/generics.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/generics.py new file mode 100644 index 0000000000000000000000000000000000000000..fa1dcec42d7fc6310f0f243dd1a50f296d0146f4 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/generics.py @@ -0,0 +1,400 @@ +import functools +import operator +import sys +import types +import typing +from typing import ( + TYPE_CHECKING, + Any, + ClassVar, + Dict, + ForwardRef, + Generic, + Iterator, + List, + Mapping, + Optional, + Tuple, + Type, + TypeVar, + Union, + cast, +) +from weakref import WeakKeyDictionary, WeakValueDictionary + +from typing_extensions import Annotated, Literal as ExtLiteral + +from pydantic.v1.class_validators import gather_all_validators +from pydantic.v1.fields import DeferredType +from pydantic.v1.main import BaseModel, create_model +from pydantic.v1.types import JsonWrapper +from pydantic.v1.typing import display_as_type, get_all_type_hints, get_args, get_origin, typing_base +from pydantic.v1.utils import all_identical, lenient_issubclass + +if sys.version_info >= (3, 8): + from typing import Literal + +GenericModelT = TypeVar('GenericModelT', bound='GenericModel') +TypeVarType = Any # since mypy doesn't allow the use of TypeVar as a type + +CacheKey = Tuple[Type[Any], Any, Tuple[Any, ...]] +Parametrization = Mapping[TypeVarType, Type[Any]] + +# weak dictionaries allow the dynamically created parametrized versions of generic models to get collected +# once they are no longer referenced by the caller. +if sys.version_info >= (3, 9): # Typing for weak dictionaries available at 3.9 + GenericTypesCache = WeakValueDictionary[CacheKey, Type[BaseModel]] + AssignedParameters = WeakKeyDictionary[Type[BaseModel], Parametrization] +else: + GenericTypesCache = WeakValueDictionary + AssignedParameters = WeakKeyDictionary + +# _generic_types_cache is a Mapping from __class_getitem__ arguments to the parametrized version of generic models. +# This ensures multiple calls of e.g. A[B] return always the same class. +_generic_types_cache = GenericTypesCache() + +# _assigned_parameters is a Mapping from parametrized version of generic models to assigned types of parametrizations +# as captured during construction of the class (not instances). +# E.g., for generic model `Model[A, B]`, when parametrized model `Model[int, str]` is created, +# `Model[int, str]`: {A: int, B: str}` will be stored in `_assigned_parameters`. +# (This information is only otherwise available after creation from the class name string). +_assigned_parameters = AssignedParameters() + + +class GenericModel(BaseModel): + __slots__ = () + __concrete__: ClassVar[bool] = False + + if TYPE_CHECKING: + # Putting this in a TYPE_CHECKING block allows us to replace `if Generic not in cls.__bases__` with + # `not hasattr(cls, "__parameters__")`. This means we don't need to force non-concrete subclasses of + # `GenericModel` to also inherit from `Generic`, which would require changes to the use of `create_model` below. + __parameters__: ClassVar[Tuple[TypeVarType, ...]] + + # Setting the return type as Type[Any] instead of Type[BaseModel] prevents PyCharm warnings + def __class_getitem__(cls: Type[GenericModelT], params: Union[Type[Any], Tuple[Type[Any], ...]]) -> Type[Any]: + """Instantiates a new class from a generic class `cls` and type variables `params`. + + :param params: Tuple of types the class . Given a generic class + `Model` with 2 type variables and a concrete model `Model[str, int]`, + the value `(str, int)` would be passed to `params`. + :return: New model class inheriting from `cls` with instantiated + types described by `params`. If no parameters are given, `cls` is + returned as is. + + """ + + def _cache_key(_params: Any) -> CacheKey: + args = get_args(_params) + # python returns a list for Callables, which is not hashable + if len(args) == 2 and isinstance(args[0], list): + args = (tuple(args[0]), args[1]) + return cls, _params, args + + cached = _generic_types_cache.get(_cache_key(params)) + if cached is not None: + return cached + if cls.__concrete__ and Generic not in cls.__bases__: + raise TypeError('Cannot parameterize a concrete instantiation of a generic model') + if not isinstance(params, tuple): + params = (params,) + if cls is GenericModel and any(isinstance(param, TypeVar) for param in params): + raise TypeError('Type parameters should be placed on typing.Generic, not GenericModel') + if not hasattr(cls, '__parameters__'): + raise TypeError(f'Type {cls.__name__} must inherit from typing.Generic before being parameterized') + + check_parameters_count(cls, params) + # Build map from generic typevars to passed params + typevars_map: Dict[TypeVarType, Type[Any]] = dict(zip(cls.__parameters__, params)) + if all_identical(typevars_map.keys(), typevars_map.values()) and typevars_map: + return cls # if arguments are equal to parameters it's the same object + + # Create new model with original model as parent inserting fields with DeferredType. + model_name = cls.__concrete_name__(params) + validators = gather_all_validators(cls) + + type_hints = get_all_type_hints(cls).items() + instance_type_hints = {k: v for k, v in type_hints if get_origin(v) is not ClassVar} + + fields = {k: (DeferredType(), cls.__fields__[k].field_info) for k in instance_type_hints if k in cls.__fields__} + + model_module, called_globally = get_caller_frame_info() + created_model = cast( + Type[GenericModel], # casting ensures mypy is aware of the __concrete__ and __parameters__ attributes + create_model( + model_name, + __module__=model_module or cls.__module__, + __base__=(cls,) + tuple(cls.__parameterized_bases__(typevars_map)), + __config__=None, + __validators__=validators, + __cls_kwargs__=None, + **fields, + ), + ) + + _assigned_parameters[created_model] = typevars_map + + if called_globally: # create global reference and therefore allow pickling + object_by_reference = None + reference_name = model_name + reference_module_globals = sys.modules[created_model.__module__].__dict__ + while object_by_reference is not created_model: + object_by_reference = reference_module_globals.setdefault(reference_name, created_model) + reference_name += '_' + + created_model.Config = cls.Config + + # Find any typevars that are still present in the model. + # If none are left, the model is fully "concrete", otherwise the new + # class is a generic class as well taking the found typevars as + # parameters. + new_params = tuple( + {param: None for param in iter_contained_typevars(typevars_map.values())} + ) # use dict as ordered set + created_model.__concrete__ = not new_params + if new_params: + created_model.__parameters__ = new_params + + # Save created model in cache so we don't end up creating duplicate + # models that should be identical. + _generic_types_cache[_cache_key(params)] = created_model + if len(params) == 1: + _generic_types_cache[_cache_key(params[0])] = created_model + + # Recursively walk class type hints and replace generic typevars + # with concrete types that were passed. + _prepare_model_fields(created_model, fields, instance_type_hints, typevars_map) + + return created_model + + @classmethod + def __concrete_name__(cls: Type[Any], params: Tuple[Type[Any], ...]) -> str: + """Compute class name for child classes. + + :param params: Tuple of types the class . Given a generic class + `Model` with 2 type variables and a concrete model `Model[str, int]`, + the value `(str, int)` would be passed to `params`. + :return: String representing a the new class where `params` are + passed to `cls` as type variables. + + This method can be overridden to achieve a custom naming scheme for GenericModels. + """ + param_names = [display_as_type(param) for param in params] + params_component = ', '.join(param_names) + return f'{cls.__name__}[{params_component}]' + + @classmethod + def __parameterized_bases__(cls, typevars_map: Parametrization) -> Iterator[Type[Any]]: + """ + Returns unbound bases of cls parameterised to given type variables + + :param typevars_map: Dictionary of type applications for binding subclasses. + Given a generic class `Model` with 2 type variables [S, T] + and a concrete model `Model[str, int]`, + the value `{S: str, T: int}` would be passed to `typevars_map`. + :return: an iterator of generic sub classes, parameterised by `typevars_map` + and other assigned parameters of `cls` + + e.g.: + ``` + class A(GenericModel, Generic[T]): + ... + + class B(A[V], Generic[V]): + ... + + assert A[int] in B.__parameterized_bases__({V: int}) + ``` + """ + + def build_base_model( + base_model: Type[GenericModel], mapped_types: Parametrization + ) -> Iterator[Type[GenericModel]]: + base_parameters = tuple(mapped_types[param] for param in base_model.__parameters__) + parameterized_base = base_model.__class_getitem__(base_parameters) + if parameterized_base is base_model or parameterized_base is cls: + # Avoid duplication in MRO + return + yield parameterized_base + + for base_model in cls.__bases__: + if not issubclass(base_model, GenericModel): + # not a class that can be meaningfully parameterized + continue + elif not getattr(base_model, '__parameters__', None): + # base_model is "GenericModel" (and has no __parameters__) + # or + # base_model is already concrete, and will be included transitively via cls. + continue + elif cls in _assigned_parameters: + if base_model in _assigned_parameters: + # cls is partially parameterised but not from base_model + # e.g. cls = B[S], base_model = A[S] + # B[S][int] should subclass A[int], (and will be transitively via B[int]) + # but it's not viable to consistently subclass types with arbitrary construction + # So don't attempt to include A[S][int] + continue + else: # base_model not in _assigned_parameters: + # cls is partially parameterized, base_model is original generic + # e.g. cls = B[str, T], base_model = B[S, T] + # Need to determine the mapping for the base_model parameters + mapped_types: Parametrization = { + key: typevars_map.get(value, value) for key, value in _assigned_parameters[cls].items() + } + yield from build_base_model(base_model, mapped_types) + else: + # cls is base generic, so base_class has a distinct base + # can construct the Parameterised base model using typevars_map directly + yield from build_base_model(base_model, typevars_map) + + +def replace_types(type_: Any, type_map: Mapping[Any, Any]) -> Any: + """Return type with all occurrences of `type_map` keys recursively replaced with their values. + + :param type_: Any type, class or generic alias + :param type_map: Mapping from `TypeVar` instance to concrete types. + :return: New type representing the basic structure of `type_` with all + `typevar_map` keys recursively replaced. + + >>> replace_types(Tuple[str, Union[List[str], float]], {str: int}) + Tuple[int, Union[List[int], float]] + + """ + if not type_map: + return type_ + + type_args = get_args(type_) + origin_type = get_origin(type_) + + if origin_type is Annotated: + annotated_type, *annotations = type_args + return Annotated[replace_types(annotated_type, type_map), tuple(annotations)] + + if (origin_type is ExtLiteral) or (sys.version_info >= (3, 8) and origin_type is Literal): + return type_map.get(type_, type_) + # Having type args is a good indicator that this is a typing module + # class instantiation or a generic alias of some sort. + if type_args: + resolved_type_args = tuple(replace_types(arg, type_map) for arg in type_args) + if all_identical(type_args, resolved_type_args): + # If all arguments are the same, there is no need to modify the + # type or create a new object at all + return type_ + if ( + origin_type is not None + and isinstance(type_, typing_base) + and not isinstance(origin_type, typing_base) + and getattr(type_, '_name', None) is not None + ): + # In python < 3.9 generic aliases don't exist so any of these like `list`, + # `type` or `collections.abc.Callable` need to be translated. + # See: https://www.python.org/dev/peps/pep-0585 + origin_type = getattr(typing, type_._name) + assert origin_type is not None + # PEP-604 syntax (Ex.: list | str) is represented with a types.UnionType object that does not have __getitem__. + # We also cannot use isinstance() since we have to compare types. + if sys.version_info >= (3, 10) and origin_type is types.UnionType: # noqa: E721 + return functools.reduce(operator.or_, resolved_type_args) + return origin_type[resolved_type_args] + + # We handle pydantic generic models separately as they don't have the same + # semantics as "typing" classes or generic aliases + if not origin_type and lenient_issubclass(type_, GenericModel) and not type_.__concrete__: + type_args = type_.__parameters__ + resolved_type_args = tuple(replace_types(t, type_map) for t in type_args) + if all_identical(type_args, resolved_type_args): + return type_ + return type_[resolved_type_args] + + # Handle special case for typehints that can have lists as arguments. + # `typing.Callable[[int, str], int]` is an example for this. + if isinstance(type_, (List, list)): + resolved_list = list(replace_types(element, type_map) for element in type_) + if all_identical(type_, resolved_list): + return type_ + return resolved_list + + # For JsonWrapperValue, need to handle its inner type to allow correct parsing + # of generic Json arguments like Json[T] + if not origin_type and lenient_issubclass(type_, JsonWrapper): + type_.inner_type = replace_types(type_.inner_type, type_map) + return type_ + + # If all else fails, we try to resolve the type directly and otherwise just + # return the input with no modifications. + new_type = type_map.get(type_, type_) + # Convert string to ForwardRef + if isinstance(new_type, str): + return ForwardRef(new_type) + else: + return new_type + + +def check_parameters_count(cls: Type[GenericModel], parameters: Tuple[Any, ...]) -> None: + actual = len(parameters) + expected = len(cls.__parameters__) + if actual != expected: + description = 'many' if actual > expected else 'few' + raise TypeError(f'Too {description} parameters for {cls.__name__}; actual {actual}, expected {expected}') + + +DictValues: Type[Any] = {}.values().__class__ + + +def iter_contained_typevars(v: Any) -> Iterator[TypeVarType]: + """Recursively iterate through all subtypes and type args of `v` and yield any typevars that are found.""" + if isinstance(v, TypeVar): + yield v + elif hasattr(v, '__parameters__') and not get_origin(v) and lenient_issubclass(v, GenericModel): + yield from v.__parameters__ + elif isinstance(v, (DictValues, list)): + for var in v: + yield from iter_contained_typevars(var) + else: + args = get_args(v) + for arg in args: + yield from iter_contained_typevars(arg) + + +def get_caller_frame_info() -> Tuple[Optional[str], bool]: + """ + Used inside a function to check whether it was called globally + + Will only work against non-compiled code, therefore used only in pydantic.generics + + :returns Tuple[module_name, called_globally] + """ + try: + previous_caller_frame = sys._getframe(2) + except ValueError as e: + raise RuntimeError('This function must be used inside another function') from e + except AttributeError: # sys module does not have _getframe function, so there's nothing we can do about it + return None, False + frame_globals = previous_caller_frame.f_globals + return frame_globals.get('__name__'), previous_caller_frame.f_locals is frame_globals + + +def _prepare_model_fields( + created_model: Type[GenericModel], + fields: Mapping[str, Any], + instance_type_hints: Mapping[str, type], + typevars_map: Mapping[Any, type], +) -> None: + """ + Replace DeferredType fields with concrete type hints and prepare them. + """ + + for key, field in created_model.__fields__.items(): + if key not in fields: + assert field.type_.__class__ is not DeferredType + # https://github.com/nedbat/coveragepy/issues/198 + continue # pragma: no cover + + assert field.type_.__class__ is DeferredType, field.type_.__class__ + + field_type_hint = instance_type_hints[key] + concrete_type = replace_types(field_type_hint, typevars_map) + field.type_ = concrete_type + field.outer_type_ = concrete_type + field.prepare() + created_model.__annotations__[key] = concrete_type diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/json.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/json.py new file mode 100644 index 0000000000000000000000000000000000000000..41d0d5fcae0d946401c2d089b69143f540618b90 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/json.py @@ -0,0 +1,112 @@ +import datetime +from collections import deque +from decimal import Decimal +from enum import Enum +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from pathlib import Path +from re import Pattern +from types import GeneratorType +from typing import Any, Callable, Dict, Type, Union +from uuid import UUID + +from pydantic.v1.color import Color +from pydantic.v1.networks import NameEmail +from pydantic.v1.types import SecretBytes, SecretStr + +__all__ = 'pydantic_encoder', 'custom_pydantic_encoder', 'timedelta_isoformat' + + +def isoformat(o: Union[datetime.date, datetime.time]) -> str: + return o.isoformat() + + +def decimal_encoder(dec_value: Decimal) -> Union[int, float]: + """ + Encodes a Decimal as int of there's no exponent, otherwise float + + This is useful when we use ConstrainedDecimal to represent Numeric(x,0) + where a integer (but not int typed) is used. Encoding this as a float + results in failed round-tripping between encode and parse. + Our Id type is a prime example of this. + + >>> decimal_encoder(Decimal("1.0")) + 1.0 + + >>> decimal_encoder(Decimal("1")) + 1 + """ + if dec_value.as_tuple().exponent >= 0: + return int(dec_value) + else: + return float(dec_value) + + +ENCODERS_BY_TYPE: Dict[Type[Any], Callable[[Any], Any]] = { + bytes: lambda o: o.decode(), + Color: str, + datetime.date: isoformat, + datetime.datetime: isoformat, + datetime.time: isoformat, + datetime.timedelta: lambda td: td.total_seconds(), + Decimal: decimal_encoder, + Enum: lambda o: o.value, + frozenset: list, + deque: list, + GeneratorType: list, + IPv4Address: str, + IPv4Interface: str, + IPv4Network: str, + IPv6Address: str, + IPv6Interface: str, + IPv6Network: str, + NameEmail: str, + Path: str, + Pattern: lambda o: o.pattern, + SecretBytes: str, + SecretStr: str, + set: list, + UUID: str, +} + + +def pydantic_encoder(obj: Any) -> Any: + from dataclasses import asdict, is_dataclass + + from pydantic.v1.main import BaseModel + + if isinstance(obj, BaseModel): + return obj.dict() + elif is_dataclass(obj): + return asdict(obj) + + # Check the class type and its superclasses for a matching encoder + for base in obj.__class__.__mro__[:-1]: + try: + encoder = ENCODERS_BY_TYPE[base] + except KeyError: + continue + return encoder(obj) + else: # We have exited the for loop without finding a suitable encoder + raise TypeError(f"Object of type '{obj.__class__.__name__}' is not JSON serializable") + + +def custom_pydantic_encoder(type_encoders: Dict[Any, Callable[[Type[Any]], Any]], obj: Any) -> Any: + # Check the class type and its superclasses for a matching encoder + for base in obj.__class__.__mro__[:-1]: + try: + encoder = type_encoders[base] + except KeyError: + continue + + return encoder(obj) + else: # We have exited the for loop without finding a suitable encoder + return pydantic_encoder(obj) + + +def timedelta_isoformat(td: datetime.timedelta) -> str: + """ + ISO 8601 encoding for Python timedelta object. + """ + minutes, seconds = divmod(td.seconds, 60) + hours, minutes = divmod(minutes, 60) + return f'{"-" if td.days < 0 else ""}P{abs(td.days)}DT{hours:d}H{minutes:d}M{seconds:d}.{td.microseconds:06d}S' diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/main.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/main.py new file mode 100644 index 0000000000000000000000000000000000000000..36e9c44c98735ece2d784f5ad1d60b783e39ce59 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/main.py @@ -0,0 +1,1130 @@ +import sys +import warnings +from abc import ABCMeta +from copy import deepcopy +from enum import Enum +from functools import partial +from pathlib import Path +from types import FunctionType, prepare_class, resolve_bases +from typing import ( + TYPE_CHECKING, + AbstractSet, + Any, + Callable, + ClassVar, + Dict, + List, + Mapping, + Optional, + Tuple, + Type, + TypeVar, + Union, + cast, + no_type_check, + overload, +) + +from typing_extensions import dataclass_transform + +from pydantic.v1.class_validators import ValidatorGroup, extract_root_validators, extract_validators, inherit_validators +from pydantic.v1.config import BaseConfig, Extra, inherit_config, prepare_config +from pydantic.v1.error_wrappers import ErrorWrapper, ValidationError +from pydantic.v1.errors import ConfigError, DictError, ExtraError, MissingError +from pydantic.v1.fields import ( + MAPPING_LIKE_SHAPES, + Field, + ModelField, + ModelPrivateAttr, + PrivateAttr, + Undefined, + is_finalvar_with_default_val, +) +from pydantic.v1.json import custom_pydantic_encoder, pydantic_encoder +from pydantic.v1.parse import Protocol, load_file, load_str_bytes +from pydantic.v1.schema import default_ref_template, model_schema +from pydantic.v1.types import PyObject, StrBytes +from pydantic.v1.typing import ( + AnyCallable, + get_args, + get_origin, + is_classvar, + is_namedtuple, + is_union, + resolve_annotations, + update_model_forward_refs, +) +from pydantic.v1.utils import ( + DUNDER_ATTRIBUTES, + ROOT_KEY, + ClassAttribute, + GetterDict, + Representation, + ValueItems, + generate_model_signature, + is_valid_field, + is_valid_private_name, + lenient_issubclass, + sequence_like, + smart_deepcopy, + unique_list, + validate_field_name, +) + +if TYPE_CHECKING: + from inspect import Signature + + from pydantic.v1.class_validators import ValidatorListDict + from pydantic.v1.types import ModelOrDc + from pydantic.v1.typing import ( + AbstractSetIntStr, + AnyClassMethod, + CallableGenerator, + DictAny, + DictStrAny, + MappingIntStrAny, + ReprArgs, + SetStr, + TupleGenerator, + ) + + Model = TypeVar('Model', bound='BaseModel') + +__all__ = 'BaseModel', 'create_model', 'validate_model' + +_T = TypeVar('_T') + + +def validate_custom_root_type(fields: Dict[str, ModelField]) -> None: + if len(fields) > 1: + raise ValueError(f'{ROOT_KEY} cannot be mixed with other fields') + + +def generate_hash_function(frozen: bool) -> Optional[Callable[[Any], int]]: + def hash_function(self_: Any) -> int: + return hash(self_.__class__) + hash(tuple(self_.__dict__.values())) + + return hash_function if frozen else None + + +# If a field is of type `Callable`, its default value should be a function and cannot to ignored. +ANNOTATED_FIELD_UNTOUCHED_TYPES: Tuple[Any, ...] = (property, type, classmethod, staticmethod) +# When creating a `BaseModel` instance, we bypass all the methods, properties... added to the model +UNTOUCHED_TYPES: Tuple[Any, ...] = (FunctionType,) + ANNOTATED_FIELD_UNTOUCHED_TYPES +# Note `ModelMetaclass` refers to `BaseModel`, but is also used to *create* `BaseModel`, so we need to add this extra +# (somewhat hacky) boolean to keep track of whether we've created the `BaseModel` class yet, and therefore whether it's +# safe to refer to it. If it *hasn't* been created, we assume that the `__new__` call we're in the middle of is for +# the `BaseModel` class, since that's defined immediately after the metaclass. +_is_base_model_class_defined = False + + +@dataclass_transform(kw_only_default=True, field_specifiers=(Field,)) +class ModelMetaclass(ABCMeta): + @no_type_check # noqa C901 + def __new__(mcs, name, bases, namespace, **kwargs): # noqa C901 + fields: Dict[str, ModelField] = {} + config = BaseConfig + validators: 'ValidatorListDict' = {} + + pre_root_validators, post_root_validators = [], [] + private_attributes: Dict[str, ModelPrivateAttr] = {} + base_private_attributes: Dict[str, ModelPrivateAttr] = {} + slots: SetStr = namespace.get('__slots__', ()) + slots = {slots} if isinstance(slots, str) else set(slots) + class_vars: SetStr = set() + hash_func: Optional[Callable[[Any], int]] = None + + for base in reversed(bases): + if _is_base_model_class_defined and issubclass(base, BaseModel) and base != BaseModel: + fields.update(smart_deepcopy(base.__fields__)) + config = inherit_config(base.__config__, config) + validators = inherit_validators(base.__validators__, validators) + pre_root_validators += base.__pre_root_validators__ + post_root_validators += base.__post_root_validators__ + base_private_attributes.update(base.__private_attributes__) + class_vars.update(base.__class_vars__) + hash_func = base.__hash__ + + resolve_forward_refs = kwargs.pop('__resolve_forward_refs__', True) + allowed_config_kwargs: SetStr = { + key + for key in dir(config) + if not (key.startswith('__') and key.endswith('__')) # skip dunder methods and attributes + } + config_kwargs = {key: kwargs.pop(key) for key in kwargs.keys() & allowed_config_kwargs} + config_from_namespace = namespace.get('Config') + if config_kwargs and config_from_namespace: + raise TypeError('Specifying config in two places is ambiguous, use either Config attribute or class kwargs') + config = inherit_config(config_from_namespace, config, **config_kwargs) + + validators = inherit_validators(extract_validators(namespace), validators) + vg = ValidatorGroup(validators) + + for f in fields.values(): + f.set_config(config) + extra_validators = vg.get_validators(f.name) + if extra_validators: + f.class_validators.update(extra_validators) + # re-run prepare to add extra validators + f.populate_validators() + + prepare_config(config, name) + + untouched_types = ANNOTATED_FIELD_UNTOUCHED_TYPES + + def is_untouched(v: Any) -> bool: + return isinstance(v, untouched_types) or v.__class__.__name__ == 'cython_function_or_method' + + if (namespace.get('__module__'), namespace.get('__qualname__')) != ('pydantic.main', 'BaseModel'): + if sys.version_info >= (3, 14): + if '__annotations__' in namespace: + # `from __future__ import annotations` was used in the model's module + raw_annotations = namespace['__annotations__'] + else: + # See https://docs.python.org/3/library/annotationlib.html#using-annotations-in-a-metaclass: + from annotationlib import Format, call_annotate_function, get_annotate_from_class_namespace + + annotate = get_annotate_from_class_namespace(namespace) + if annotate is not None: + raw_annotations = call_annotate_function(annotate, format=Format.FORWARDREF) + else: + raw_annotations = {} + else: + raw_annotations = namespace.get('__annotations__', {}) + + annotations = resolve_annotations(raw_annotations, namespace.get('__module__', None)) + # annotation only fields need to come first in fields + for ann_name, ann_type in annotations.items(): + if is_classvar(ann_type): + class_vars.add(ann_name) + elif is_finalvar_with_default_val(ann_type, namespace.get(ann_name, Undefined)): + class_vars.add(ann_name) + elif is_valid_field(ann_name): + validate_field_name(bases, ann_name) + value = namespace.get(ann_name, Undefined) + allowed_types = get_args(ann_type) if is_union(get_origin(ann_type)) else (ann_type,) + if ( + is_untouched(value) + and ann_type != PyObject + and not any( + lenient_issubclass(get_origin(allowed_type), Type) for allowed_type in allowed_types + ) + ): + continue + fields[ann_name] = ModelField.infer( + name=ann_name, + value=value, + annotation=ann_type, + class_validators=vg.get_validators(ann_name), + config=config, + ) + elif ann_name not in namespace and config.underscore_attrs_are_private: + private_attributes[ann_name] = PrivateAttr() + + untouched_types = UNTOUCHED_TYPES + config.keep_untouched + for var_name, value in namespace.items(): + can_be_changed = var_name not in class_vars and not is_untouched(value) + if isinstance(value, ModelPrivateAttr): + if not is_valid_private_name(var_name): + raise NameError( + f'Private attributes "{var_name}" must not be a valid field name; ' + f'Use sunder or dunder names, e. g. "_{var_name}" or "__{var_name}__"' + ) + private_attributes[var_name] = value + elif config.underscore_attrs_are_private and is_valid_private_name(var_name) and can_be_changed: + private_attributes[var_name] = PrivateAttr(default=value) + elif is_valid_field(var_name) and var_name not in annotations and can_be_changed: + validate_field_name(bases, var_name) + inferred = ModelField.infer( + name=var_name, + value=value, + annotation=annotations.get(var_name, Undefined), + class_validators=vg.get_validators(var_name), + config=config, + ) + if var_name in fields: + if lenient_issubclass(inferred.type_, fields[var_name].type_): + inferred.type_ = fields[var_name].type_ + else: + raise TypeError( + f'The type of {name}.{var_name} differs from the new default value; ' + f'if you wish to change the type of this field, please use a type annotation' + ) + fields[var_name] = inferred + + _custom_root_type = ROOT_KEY in fields + if _custom_root_type: + validate_custom_root_type(fields) + vg.check_for_unused() + if config.json_encoders: + json_encoder = partial(custom_pydantic_encoder, config.json_encoders) + else: + json_encoder = pydantic_encoder + pre_rv_new, post_rv_new = extract_root_validators(namespace) + + if hash_func is None: + hash_func = generate_hash_function(config.frozen) + + exclude_from_namespace = fields | private_attributes.keys() | {'__slots__'} + new_namespace = { + '__config__': config, + '__fields__': fields, + '__exclude_fields__': { + name: field.field_info.exclude for name, field in fields.items() if field.field_info.exclude is not None + } + or None, + '__include_fields__': { + name: field.field_info.include for name, field in fields.items() if field.field_info.include is not None + } + or None, + '__validators__': vg.validators, + '__pre_root_validators__': unique_list( + pre_root_validators + pre_rv_new, + name_factory=lambda v: v.__name__, + ), + '__post_root_validators__': unique_list( + post_root_validators + post_rv_new, + name_factory=lambda skip_on_failure_and_v: skip_on_failure_and_v[1].__name__, + ), + '__schema_cache__': {}, + '__json_encoder__': staticmethod(json_encoder), + '__custom_root_type__': _custom_root_type, + '__private_attributes__': {**base_private_attributes, **private_attributes}, + '__slots__': slots | private_attributes.keys(), + '__hash__': hash_func, + '__class_vars__': class_vars, + **{n: v for n, v in namespace.items() if n not in exclude_from_namespace}, + } + + cls = super().__new__(mcs, name, bases, new_namespace, **kwargs) + # set __signature__ attr only for model class, but not for its instances + cls.__signature__ = ClassAttribute('__signature__', generate_model_signature(cls.__init__, fields, config)) + + if not _is_base_model_class_defined: + # Cython does not understand the `if TYPE_CHECKING:` condition in the + # BaseModel's body (where annotations are set), so clear them manually: + getattr(cls, '__annotations__', {}).clear() + + if resolve_forward_refs: + cls.__try_update_forward_refs__() + + # preserve `__set_name__` protocol defined in https://peps.python.org/pep-0487 + # for attributes not in `new_namespace` (e.g. private attributes) + for name, obj in namespace.items(): + if name not in new_namespace: + set_name = getattr(obj, '__set_name__', None) + if callable(set_name): + set_name(cls, name) + + return cls + + def __instancecheck__(self, instance: Any) -> bool: + """ + Avoid calling ABC _abc_subclasscheck unless we're pretty sure. + + See #3829 and python/cpython#92810 + """ + return hasattr(instance, '__post_root_validators__') and super().__instancecheck__(instance) + + +object_setattr = object.__setattr__ + + +class BaseModel(Representation, metaclass=ModelMetaclass): + if TYPE_CHECKING: + # populated by the metaclass, defined here to help IDEs only + __fields__: ClassVar[Dict[str, ModelField]] = {} + __include_fields__: ClassVar[Optional[Mapping[str, Any]]] = None + __exclude_fields__: ClassVar[Optional[Mapping[str, Any]]] = None + __validators__: ClassVar[Dict[str, AnyCallable]] = {} + __pre_root_validators__: ClassVar[List[AnyCallable]] + __post_root_validators__: ClassVar[List[Tuple[bool, AnyCallable]]] + __config__: ClassVar[Type[BaseConfig]] = BaseConfig + __json_encoder__: ClassVar[Callable[[Any], Any]] = lambda x: x + __schema_cache__: ClassVar['DictAny'] = {} + __custom_root_type__: ClassVar[bool] = False + __signature__: ClassVar['Signature'] + __private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] + __class_vars__: ClassVar[SetStr] + __fields_set__: ClassVar[SetStr] = set() + + Config = BaseConfig + __slots__ = ('__dict__', '__fields_set__') + __doc__ = '' # Null out the Representation docstring + + def __init__(__pydantic_self__, **data: Any) -> None: + """ + Create a new model by parsing and validating input data from keyword arguments. + + Raises ValidationError if the input data cannot be parsed to form a valid model. + """ + # Uses something other than `self` the first arg to allow "self" as a settable attribute + values, fields_set, validation_error = validate_model(__pydantic_self__.__class__, data) + if validation_error: + raise validation_error + try: + object_setattr(__pydantic_self__, '__dict__', values) + except TypeError as e: + raise TypeError( + 'Model values must be a dict; you may not have returned a dictionary from a root validator' + ) from e + object_setattr(__pydantic_self__, '__fields_set__', fields_set) + __pydantic_self__._init_private_attributes() + + @no_type_check + def __setattr__(self, name, value): # noqa: C901 (ignore complexity) + if name in self.__private_attributes__ or name in DUNDER_ATTRIBUTES: + return object_setattr(self, name, value) + + if self.__config__.extra is not Extra.allow and name not in self.__fields__: + raise ValueError(f'"{self.__class__.__name__}" object has no field "{name}"') + elif not self.__config__.allow_mutation or self.__config__.frozen: + raise TypeError(f'"{self.__class__.__name__}" is immutable and does not support item assignment') + elif name in self.__fields__ and self.__fields__[name].final: + raise TypeError( + f'"{self.__class__.__name__}" object "{name}" field is final and does not support reassignment' + ) + elif self.__config__.validate_assignment: + new_values = {**self.__dict__, name: value} + + for validator in self.__pre_root_validators__: + try: + new_values = validator(self.__class__, new_values) + except (ValueError, TypeError, AssertionError) as exc: + raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], self.__class__) + + known_field = self.__fields__.get(name, None) + if known_field: + # We want to + # - make sure validators are called without the current value for this field inside `values` + # - keep other values (e.g. submodels) untouched (using `BaseModel.dict()` will change them into dicts) + # - keep the order of the fields + if not known_field.field_info.allow_mutation: + raise TypeError(f'"{known_field.name}" has allow_mutation set to False and cannot be assigned') + dict_without_original_value = {k: v for k, v in self.__dict__.items() if k != name} + value, error_ = known_field.validate(value, dict_without_original_value, loc=name, cls=self.__class__) + if error_: + raise ValidationError([error_], self.__class__) + else: + new_values[name] = value + + errors = [] + for skip_on_failure, validator in self.__post_root_validators__: + if skip_on_failure and errors: + continue + try: + new_values = validator(self.__class__, new_values) + except (ValueError, TypeError, AssertionError) as exc: + errors.append(ErrorWrapper(exc, loc=ROOT_KEY)) + if errors: + raise ValidationError(errors, self.__class__) + + # update the whole __dict__ as other values than just `value` + # may be changed (e.g. with `root_validator`) + object_setattr(self, '__dict__', new_values) + else: + self.__dict__[name] = value + + self.__fields_set__.add(name) + + def __getstate__(self) -> 'DictAny': + private_attrs = ((k, getattr(self, k, Undefined)) for k in self.__private_attributes__) + return { + '__dict__': self.__dict__, + '__fields_set__': self.__fields_set__, + '__private_attribute_values__': {k: v for k, v in private_attrs if v is not Undefined}, + } + + def __setstate__(self, state: 'DictAny') -> None: + object_setattr(self, '__dict__', state['__dict__']) + object_setattr(self, '__fields_set__', state['__fields_set__']) + for name, value in state.get('__private_attribute_values__', {}).items(): + object_setattr(self, name, value) + + def _init_private_attributes(self) -> None: + for name, private_attr in self.__private_attributes__.items(): + default = private_attr.get_default() + if default is not Undefined: + object_setattr(self, name, default) + + def dict( + self, + *, + include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + by_alias: bool = False, + skip_defaults: Optional[bool] = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + ) -> 'DictStrAny': + """ + Generate a dictionary representation of the model, optionally specifying which fields to include or exclude. + + """ + if skip_defaults is not None: + warnings.warn( + f'{self.__class__.__name__}.dict(): "skip_defaults" is deprecated and replaced by "exclude_unset"', + DeprecationWarning, + ) + exclude_unset = skip_defaults + + return dict( + self._iter( + to_dict=True, + by_alias=by_alias, + include=include, + exclude=exclude, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + ) + + def json( + self, + *, + include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + by_alias: bool = False, + skip_defaults: Optional[bool] = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + encoder: Optional[Callable[[Any], Any]] = None, + models_as_dict: bool = True, + **dumps_kwargs: Any, + ) -> str: + """ + Generate a JSON representation of the model, `include` and `exclude` arguments as per `dict()`. + + `encoder` is an optional function to supply as `default` to json.dumps(), other arguments as per `json.dumps()`. + """ + if skip_defaults is not None: + warnings.warn( + f'{self.__class__.__name__}.json(): "skip_defaults" is deprecated and replaced by "exclude_unset"', + DeprecationWarning, + ) + exclude_unset = skip_defaults + encoder = cast(Callable[[Any], Any], encoder or self.__json_encoder__) + + # We don't directly call `self.dict()`, which does exactly this with `to_dict=True` + # because we want to be able to keep raw `BaseModel` instances and not as `dict`. + # This allows users to write custom JSON encoders for given `BaseModel` classes. + data = dict( + self._iter( + to_dict=models_as_dict, + by_alias=by_alias, + include=include, + exclude=exclude, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + ) + if self.__custom_root_type__: + data = data[ROOT_KEY] + return self.__config__.json_dumps(data, default=encoder, **dumps_kwargs) + + @classmethod + def _enforce_dict_if_root(cls, obj: Any) -> Any: + if cls.__custom_root_type__ and ( + not (isinstance(obj, dict) and obj.keys() == {ROOT_KEY}) + and not (isinstance(obj, BaseModel) and obj.__fields__.keys() == {ROOT_KEY}) + or cls.__fields__[ROOT_KEY].shape in MAPPING_LIKE_SHAPES + ): + return {ROOT_KEY: obj} + else: + return obj + + @classmethod + def parse_obj(cls: Type['Model'], obj: Any) -> 'Model': + obj = cls._enforce_dict_if_root(obj) + if not isinstance(obj, dict): + try: + obj = dict(obj) + except (TypeError, ValueError) as e: + exc = TypeError(f'{cls.__name__} expected dict not {obj.__class__.__name__}') + raise ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls) from e + return cls(**obj) + + @classmethod + def parse_raw( + cls: Type['Model'], + b: StrBytes, + *, + content_type: str = None, + encoding: str = 'utf8', + proto: Protocol = None, + allow_pickle: bool = False, + ) -> 'Model': + try: + obj = load_str_bytes( + b, + proto=proto, + content_type=content_type, + encoding=encoding, + allow_pickle=allow_pickle, + json_loads=cls.__config__.json_loads, + ) + except (ValueError, TypeError, UnicodeDecodeError) as e: + raise ValidationError([ErrorWrapper(e, loc=ROOT_KEY)], cls) + return cls.parse_obj(obj) + + @classmethod + def parse_file( + cls: Type['Model'], + path: Union[str, Path], + *, + content_type: str = None, + encoding: str = 'utf8', + proto: Protocol = None, + allow_pickle: bool = False, + ) -> 'Model': + obj = load_file( + path, + proto=proto, + content_type=content_type, + encoding=encoding, + allow_pickle=allow_pickle, + json_loads=cls.__config__.json_loads, + ) + return cls.parse_obj(obj) + + @classmethod + def from_orm(cls: Type['Model'], obj: Any) -> 'Model': + if not cls.__config__.orm_mode: + raise ConfigError('You must have the config attribute orm_mode=True to use from_orm') + obj = {ROOT_KEY: obj} if cls.__custom_root_type__ else cls._decompose_class(obj) + m = cls.__new__(cls) + values, fields_set, validation_error = validate_model(cls, obj) + if validation_error: + raise validation_error + object_setattr(m, '__dict__', values) + object_setattr(m, '__fields_set__', fields_set) + m._init_private_attributes() + return m + + @classmethod + def construct(cls: Type['Model'], _fields_set: Optional['SetStr'] = None, **values: Any) -> 'Model': + """ + Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. + Default values are respected, but no other validation is performed. + Behaves as if `Config.extra = 'allow'` was set since it adds all passed values + """ + m = cls.__new__(cls) + fields_values: Dict[str, Any] = {} + for name, field in cls.__fields__.items(): + if field.alt_alias and field.alias in values: + fields_values[name] = values[field.alias] + elif name in values: + fields_values[name] = values[name] + elif not field.required: + fields_values[name] = field.get_default() + fields_values.update(values) + object_setattr(m, '__dict__', fields_values) + if _fields_set is None: + _fields_set = set(values.keys()) + object_setattr(m, '__fields_set__', _fields_set) + m._init_private_attributes() + return m + + def _copy_and_set_values(self: 'Model', values: 'DictStrAny', fields_set: 'SetStr', *, deep: bool) -> 'Model': + if deep: + # chances of having empty dict here are quite low for using smart_deepcopy + values = deepcopy(values) + + cls = self.__class__ + m = cls.__new__(cls) + object_setattr(m, '__dict__', values) + object_setattr(m, '__fields_set__', fields_set) + for name in self.__private_attributes__: + value = getattr(self, name, Undefined) + if value is not Undefined: + if deep: + value = deepcopy(value) + object_setattr(m, name, value) + + return m + + def copy( + self: 'Model', + *, + include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + update: Optional['DictStrAny'] = None, + deep: bool = False, + ) -> 'Model': + """ + Duplicate a model, optionally choose which fields to include, exclude and change. + + :param include: fields to include in new model + :param exclude: fields to exclude from new model, as with values this takes precedence over include + :param update: values to change/add in the new model. Note: the data is not validated before creating + the new model: you should trust this data + :param deep: set to `True` to make a deep copy of the model + :return: new model instance + """ + + values = dict( + self._iter(to_dict=False, by_alias=False, include=include, exclude=exclude, exclude_unset=False), + **(update or {}), + ) + + # new `__fields_set__` can have unset optional fields with a set value in `update` kwarg + if update: + fields_set = self.__fields_set__ | update.keys() + else: + fields_set = set(self.__fields_set__) + + return self._copy_and_set_values(values, fields_set, deep=deep) + + @classmethod + def schema(cls, by_alias: bool = True, ref_template: str = default_ref_template) -> 'DictStrAny': + cached = cls.__schema_cache__.get((by_alias, ref_template)) + if cached is not None: + return cached + s = model_schema(cls, by_alias=by_alias, ref_template=ref_template) + cls.__schema_cache__[(by_alias, ref_template)] = s + return s + + @classmethod + def schema_json( + cls, *, by_alias: bool = True, ref_template: str = default_ref_template, **dumps_kwargs: Any + ) -> str: + from pydantic.v1.json import pydantic_encoder + + return cls.__config__.json_dumps( + cls.schema(by_alias=by_alias, ref_template=ref_template), default=pydantic_encoder, **dumps_kwargs + ) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls: Type['Model'], value: Any) -> 'Model': + if isinstance(value, cls): + copy_on_model_validation = cls.__config__.copy_on_model_validation + # whether to deep or shallow copy the model on validation, None means do not copy + deep_copy: Optional[bool] = None + if copy_on_model_validation not in {'deep', 'shallow', 'none'}: + # Warn about deprecated behavior + warnings.warn( + "`copy_on_model_validation` should be a string: 'deep', 'shallow' or 'none'", DeprecationWarning + ) + if copy_on_model_validation: + deep_copy = False + + if copy_on_model_validation == 'shallow': + # shallow copy + deep_copy = False + elif copy_on_model_validation == 'deep': + # deep copy + deep_copy = True + + if deep_copy is None: + return value + else: + return value._copy_and_set_values(value.__dict__, value.__fields_set__, deep=deep_copy) + + value = cls._enforce_dict_if_root(value) + + if isinstance(value, dict): + return cls(**value) + elif cls.__config__.orm_mode: + return cls.from_orm(value) + else: + try: + value_as_dict = dict(value) + except (TypeError, ValueError) as e: + raise DictError() from e + return cls(**value_as_dict) + + @classmethod + def _decompose_class(cls: Type['Model'], obj: Any) -> GetterDict: + if isinstance(obj, GetterDict): + return obj + return cls.__config__.getter_dict(obj) + + @classmethod + @no_type_check + def _get_value( + cls, + v: Any, + to_dict: bool, + by_alias: bool, + include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']], + exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']], + exclude_unset: bool, + exclude_defaults: bool, + exclude_none: bool, + ) -> Any: + if isinstance(v, BaseModel): + if to_dict: + v_dict = v.dict( + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + include=include, + exclude=exclude, + exclude_none=exclude_none, + ) + if ROOT_KEY in v_dict: + return v_dict[ROOT_KEY] + return v_dict + else: + return v.copy(include=include, exclude=exclude) + + value_exclude = ValueItems(v, exclude) if exclude else None + value_include = ValueItems(v, include) if include else None + + if isinstance(v, dict): + return { + k_: cls._get_value( + v_, + to_dict=to_dict, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + include=value_include and value_include.for_element(k_), + exclude=value_exclude and value_exclude.for_element(k_), + exclude_none=exclude_none, + ) + for k_, v_ in v.items() + if (not value_exclude or not value_exclude.is_excluded(k_)) + and (not value_include or value_include.is_included(k_)) + } + + elif sequence_like(v): + seq_args = ( + cls._get_value( + v_, + to_dict=to_dict, + by_alias=by_alias, + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + include=value_include and value_include.for_element(i), + exclude=value_exclude and value_exclude.for_element(i), + exclude_none=exclude_none, + ) + for i, v_ in enumerate(v) + if (not value_exclude or not value_exclude.is_excluded(i)) + and (not value_include or value_include.is_included(i)) + ) + + return v.__class__(*seq_args) if is_namedtuple(v.__class__) else v.__class__(seq_args) + + elif isinstance(v, Enum) and getattr(cls.Config, 'use_enum_values', False): + return v.value + + else: + return v + + @classmethod + def __try_update_forward_refs__(cls, **localns: Any) -> None: + """ + Same as update_forward_refs but will not raise exception + when forward references are not defined. + """ + update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns, (NameError,)) + + @classmethod + def update_forward_refs(cls, **localns: Any) -> None: + """ + Try to update ForwardRefs on fields based on this Model, globalns and localns. + """ + update_model_forward_refs(cls, cls.__fields__.values(), cls.__config__.json_encoders, localns) + + def __iter__(self) -> 'TupleGenerator': + """ + so `dict(model)` works + """ + yield from self.__dict__.items() + + def _iter( + self, + to_dict: bool = False, + by_alias: bool = False, + include: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + exclude: Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']] = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + ) -> 'TupleGenerator': + # Merge field set excludes with explicit exclude parameter with explicit overriding field set options. + # The extra "is not None" guards are not logically necessary but optimizes performance for the simple case. + if exclude is not None or self.__exclude_fields__ is not None: + exclude = ValueItems.merge(self.__exclude_fields__, exclude) + + if include is not None or self.__include_fields__ is not None: + include = ValueItems.merge(self.__include_fields__, include, intersect=True) + + allowed_keys = self._calculate_keys( + include=include, exclude=exclude, exclude_unset=exclude_unset # type: ignore + ) + if allowed_keys is None and not (to_dict or by_alias or exclude_unset or exclude_defaults or exclude_none): + # huge boost for plain _iter() + yield from self.__dict__.items() + return + + value_exclude = ValueItems(self, exclude) if exclude is not None else None + value_include = ValueItems(self, include) if include is not None else None + + for field_key, v in self.__dict__.items(): + if (allowed_keys is not None and field_key not in allowed_keys) or (exclude_none and v is None): + continue + + if exclude_defaults: + model_field = self.__fields__.get(field_key) + if not getattr(model_field, 'required', True) and getattr(model_field, 'default', _missing) == v: + continue + + if by_alias and field_key in self.__fields__: + dict_key = self.__fields__[field_key].alias + else: + dict_key = field_key + + if to_dict or value_include or value_exclude: + v = self._get_value( + v, + to_dict=to_dict, + by_alias=by_alias, + include=value_include and value_include.for_element(field_key), + exclude=value_exclude and value_exclude.for_element(field_key), + exclude_unset=exclude_unset, + exclude_defaults=exclude_defaults, + exclude_none=exclude_none, + ) + yield dict_key, v + + def _calculate_keys( + self, + include: Optional['MappingIntStrAny'], + exclude: Optional['MappingIntStrAny'], + exclude_unset: bool, + update: Optional['DictStrAny'] = None, + ) -> Optional[AbstractSet[str]]: + if include is None and exclude is None and exclude_unset is False: + return None + + keys: AbstractSet[str] + if exclude_unset: + keys = self.__fields_set__.copy() + else: + keys = self.__dict__.keys() + + if include is not None: + keys &= include.keys() + + if update: + keys -= update.keys() + + if exclude: + keys -= {k for k, v in exclude.items() if ValueItems.is_true(v)} + + return keys + + def __eq__(self, other: Any) -> bool: + if isinstance(other, BaseModel): + return self.dict() == other.dict() + else: + return self.dict() == other + + def __repr_args__(self) -> 'ReprArgs': + return [ + (k, v) + for k, v in self.__dict__.items() + if k not in DUNDER_ATTRIBUTES and (k not in self.__fields__ or self.__fields__[k].field_info.repr) + ] + + +_is_base_model_class_defined = True + + +@overload +def create_model( + __model_name: str, + *, + __config__: Optional[Type[BaseConfig]] = None, + __base__: None = None, + __module__: str = __name__, + __validators__: Dict[str, 'AnyClassMethod'] = None, + __cls_kwargs__: Dict[str, Any] = None, + **field_definitions: Any, +) -> Type['BaseModel']: + ... + + +@overload +def create_model( + __model_name: str, + *, + __config__: Optional[Type[BaseConfig]] = None, + __base__: Union[Type['Model'], Tuple[Type['Model'], ...]], + __module__: str = __name__, + __validators__: Dict[str, 'AnyClassMethod'] = None, + __cls_kwargs__: Dict[str, Any] = None, + **field_definitions: Any, +) -> Type['Model']: + ... + + +def create_model( + __model_name: str, + *, + __config__: Optional[Type[BaseConfig]] = None, + __base__: Union[None, Type['Model'], Tuple[Type['Model'], ...]] = None, + __module__: str = __name__, + __validators__: Dict[str, 'AnyClassMethod'] = None, + __cls_kwargs__: Dict[str, Any] = None, + __slots__: Optional[Tuple[str, ...]] = None, + **field_definitions: Any, +) -> Type['Model']: + """ + Dynamically create a model. + :param __model_name: name of the created model + :param __config__: config class to use for the new model + :param __base__: base class for the new model to inherit from + :param __module__: module of the created model + :param __validators__: a dict of method names and @validator class methods + :param __cls_kwargs__: a dict for class creation + :param __slots__: Deprecated, `__slots__` should not be passed to `create_model` + :param field_definitions: fields of the model (or extra fields if a base is supplied) + in the format `=(, )` or `=, e.g. + `foobar=(str, ...)` or `foobar=123`, or, for complex use-cases, in the format + `=` or `=(, )`, e.g. + `foo=Field(datetime, default_factory=datetime.utcnow, alias='bar')` or + `foo=(str, FieldInfo(title='Foo'))` + """ + if __slots__ is not None: + # __slots__ will be ignored from here on + warnings.warn('__slots__ should not be passed to create_model', RuntimeWarning) + + if __base__ is not None: + if __config__ is not None: + raise ConfigError('to avoid confusion __config__ and __base__ cannot be used together') + if not isinstance(__base__, tuple): + __base__ = (__base__,) + else: + __base__ = (cast(Type['Model'], BaseModel),) + + __cls_kwargs__ = __cls_kwargs__ or {} + + fields = {} + annotations = {} + + for f_name, f_def in field_definitions.items(): + if not is_valid_field(f_name): + warnings.warn(f'fields may not start with an underscore, ignoring "{f_name}"', RuntimeWarning) + if isinstance(f_def, tuple): + try: + f_annotation, f_value = f_def + except ValueError as e: + raise ConfigError( + 'field definitions should either be a tuple of (, ) or just a ' + 'default value, unfortunately this means tuples as ' + 'default values are not allowed' + ) from e + else: + f_annotation, f_value = None, f_def + + if f_annotation: + annotations[f_name] = f_annotation + fields[f_name] = f_value + + namespace: 'DictStrAny' = {'__annotations__': annotations, '__module__': __module__} + if __validators__: + namespace.update(__validators__) + namespace.update(fields) + if __config__: + namespace['Config'] = inherit_config(__config__, BaseConfig) + resolved_bases = resolve_bases(__base__) + meta, ns, kwds = prepare_class(__model_name, resolved_bases, kwds=__cls_kwargs__) + if resolved_bases is not __base__: + ns['__orig_bases__'] = __base__ + namespace.update(ns) + return meta(__model_name, resolved_bases, namespace, **kwds) + + +_missing = object() + + +def validate_model( # noqa: C901 (ignore complexity) + model: Type[BaseModel], input_data: 'DictStrAny', cls: 'ModelOrDc' = None +) -> Tuple['DictStrAny', 'SetStr', Optional[ValidationError]]: + """ + validate data against a model. + """ + values = {} + errors = [] + # input_data names, possibly alias + names_used = set() + # field names, never aliases + fields_set = set() + config = model.__config__ + check_extra = config.extra is not Extra.ignore + cls_ = cls or model + + for validator in model.__pre_root_validators__: + try: + input_data = validator(cls_, input_data) + except (ValueError, TypeError, AssertionError) as exc: + return {}, set(), ValidationError([ErrorWrapper(exc, loc=ROOT_KEY)], cls_) + + for name, field in model.__fields__.items(): + value = input_data.get(field.alias, _missing) + using_name = False + if value is _missing and config.allow_population_by_field_name and field.alt_alias: + value = input_data.get(field.name, _missing) + using_name = True + + if value is _missing: + if field.required: + errors.append(ErrorWrapper(MissingError(), loc=field.alias)) + continue + + value = field.get_default() + + if not config.validate_all and not field.validate_always: + values[name] = value + continue + else: + fields_set.add(name) + if check_extra: + names_used.add(field.name if using_name else field.alias) + + v_, errors_ = field.validate(value, values, loc=field.alias, cls=cls_) + if isinstance(errors_, ErrorWrapper): + errors.append(errors_) + elif isinstance(errors_, list): + errors.extend(errors_) + else: + values[name] = v_ + + if check_extra: + if isinstance(input_data, GetterDict): + extra = input_data.extra_keys() - names_used + else: + extra = input_data.keys() - names_used + if extra: + fields_set |= extra + if config.extra is Extra.allow: + for f in extra: + values[f] = input_data[f] + else: + for f in sorted(extra): + errors.append(ErrorWrapper(ExtraError(), loc=f)) + + for skip_on_failure, validator in model.__post_root_validators__: + if skip_on_failure and errors: + continue + try: + values = validator(cls_, values) + except (ValueError, TypeError, AssertionError) as exc: + errors.append(ErrorWrapper(exc, loc=ROOT_KEY)) + + if errors: + return values, fields_set, ValidationError(errors, cls_) + else: + return values, fields_set, None diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/mypy.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/mypy.py new file mode 100644 index 0000000000000000000000000000000000000000..0a77569218ce6f832e7221b1f557d811318402e7 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/mypy.py @@ -0,0 +1,949 @@ +import sys +from configparser import ConfigParser +from typing import Any, Callable, Dict, List, Optional, Set, Tuple, Type as TypingType, Union + +from mypy.errorcodes import ErrorCode +from mypy.nodes import ( + ARG_NAMED, + ARG_NAMED_OPT, + ARG_OPT, + ARG_POS, + ARG_STAR2, + MDEF, + Argument, + AssignmentStmt, + Block, + CallExpr, + ClassDef, + Context, + Decorator, + EllipsisExpr, + FuncBase, + FuncDef, + JsonDict, + MemberExpr, + NameExpr, + PassStmt, + PlaceholderNode, + RefExpr, + StrExpr, + SymbolNode, + SymbolTableNode, + TempNode, + TypeInfo, + TypeVarExpr, + Var, +) +from mypy.options import Options +from mypy.plugin import ( + CheckerPluginInterface, + ClassDefContext, + FunctionContext, + MethodContext, + Plugin, + ReportConfigContext, + SemanticAnalyzerPluginInterface, +) +from mypy.plugins import dataclasses +from mypy.semanal import set_callable_name # type: ignore +from mypy.server.trigger import make_wildcard_trigger +from mypy.types import ( + AnyType, + CallableType, + Instance, + NoneType, + Overloaded, + ProperType, + Type, + TypeOfAny, + TypeType, + TypeVarId, + TypeVarType, + UnionType, + get_proper_type, +) +from mypy.typevars import fill_typevars +from mypy.util import get_unique_redefinition_name +from mypy.version import __version__ as mypy_version + +from pydantic.v1.utils import is_valid_field + +try: + from mypy.types import TypeVarDef # type: ignore[attr-defined] +except ImportError: # pragma: no cover + # Backward-compatible with TypeVarDef from Mypy 0.910. + from mypy.types import TypeVarType as TypeVarDef + +CONFIGFILE_KEY = 'pydantic-mypy' +METADATA_KEY = 'pydantic-mypy-metadata' +_NAMESPACE = __name__[:-5] # 'pydantic' in 1.10.X, 'pydantic.v1' in v2.X +BASEMODEL_FULLNAME = f'{_NAMESPACE}.main.BaseModel' +BASESETTINGS_FULLNAME = f'{_NAMESPACE}.env_settings.BaseSettings' +MODEL_METACLASS_FULLNAME = f'{_NAMESPACE}.main.ModelMetaclass' +FIELD_FULLNAME = f'{_NAMESPACE}.fields.Field' +DATACLASS_FULLNAME = f'{_NAMESPACE}.dataclasses.dataclass' + + +def parse_mypy_version(version: str) -> Tuple[int, ...]: + return tuple(map(int, version.partition('+')[0].split('.'))) + + +MYPY_VERSION_TUPLE = parse_mypy_version(mypy_version) +BUILTINS_NAME = 'builtins' if MYPY_VERSION_TUPLE >= (0, 930) else '__builtins__' + +# Increment version if plugin changes and mypy caches should be invalidated +__version__ = 2 + + +def plugin(version: str) -> 'TypingType[Plugin]': + """ + `version` is the mypy version string + + We might want to use this to print a warning if the mypy version being used is + newer, or especially older, than we expect (or need). + """ + return PydanticPlugin + + +class PydanticPlugin(Plugin): + def __init__(self, options: Options) -> None: + self.plugin_config = PydanticPluginConfig(options) + self._plugin_data = self.plugin_config.to_data() + super().__init__(options) + + def get_base_class_hook(self, fullname: str) -> 'Optional[Callable[[ClassDefContext], None]]': + sym = self.lookup_fully_qualified(fullname) + if sym and isinstance(sym.node, TypeInfo): # pragma: no branch + # No branching may occur if the mypy cache has not been cleared + if any(get_fullname(base) == BASEMODEL_FULLNAME for base in sym.node.mro): + return self._pydantic_model_class_maker_callback + return None + + def get_metaclass_hook(self, fullname: str) -> Optional[Callable[[ClassDefContext], None]]: + if fullname == MODEL_METACLASS_FULLNAME: + return self._pydantic_model_metaclass_marker_callback + return None + + def get_function_hook(self, fullname: str) -> 'Optional[Callable[[FunctionContext], Type]]': + sym = self.lookup_fully_qualified(fullname) + if sym and sym.fullname == FIELD_FULLNAME: + return self._pydantic_field_callback + return None + + def get_method_hook(self, fullname: str) -> Optional[Callable[[MethodContext], Type]]: + if fullname.endswith('.from_orm'): + return from_orm_callback + return None + + def get_class_decorator_hook(self, fullname: str) -> Optional[Callable[[ClassDefContext], None]]: + """Mark pydantic.dataclasses as dataclass. + + Mypy version 1.1.1 added support for `@dataclass_transform` decorator. + """ + if fullname == DATACLASS_FULLNAME and MYPY_VERSION_TUPLE < (1, 1): + return dataclasses.dataclass_class_maker_callback # type: ignore[return-value] + return None + + def report_config_data(self, ctx: ReportConfigContext) -> Dict[str, Any]: + """Return all plugin config data. + + Used by mypy to determine if cache needs to be discarded. + """ + return self._plugin_data + + def _pydantic_model_class_maker_callback(self, ctx: ClassDefContext) -> None: + transformer = PydanticModelTransformer(ctx, self.plugin_config) + transformer.transform() + + def _pydantic_model_metaclass_marker_callback(self, ctx: ClassDefContext) -> None: + """Reset dataclass_transform_spec attribute of ModelMetaclass. + + Let the plugin handle it. This behavior can be disabled + if 'debug_dataclass_transform' is set to True', for testing purposes. + """ + if self.plugin_config.debug_dataclass_transform: + return + info_metaclass = ctx.cls.info.declared_metaclass + assert info_metaclass, "callback not passed from 'get_metaclass_hook'" + if getattr(info_metaclass.type, 'dataclass_transform_spec', None): + info_metaclass.type.dataclass_transform_spec = None # type: ignore[attr-defined] + + def _pydantic_field_callback(self, ctx: FunctionContext) -> 'Type': + """ + Extract the type of the `default` argument from the Field function, and use it as the return type. + + In particular: + * Check whether the default and default_factory argument is specified. + * Output an error if both are specified. + * Retrieve the type of the argument which is specified, and use it as return type for the function. + """ + default_any_type = ctx.default_return_type + + assert ctx.callee_arg_names[0] == 'default', '"default" is no longer first argument in Field()' + assert ctx.callee_arg_names[1] == 'default_factory', '"default_factory" is no longer second argument in Field()' + default_args = ctx.args[0] + default_factory_args = ctx.args[1] + + if default_args and default_factory_args: + error_default_and_default_factory_specified(ctx.api, ctx.context) + return default_any_type + + if default_args: + default_type = ctx.arg_types[0][0] + default_arg = default_args[0] + + # Fallback to default Any type if the field is required + if not isinstance(default_arg, EllipsisExpr): + return default_type + + elif default_factory_args: + default_factory_type = ctx.arg_types[1][0] + + # Functions which use `ParamSpec` can be overloaded, exposing the callable's types as a parameter + # Pydantic calls the default factory without any argument, so we retrieve the first item + if isinstance(default_factory_type, Overloaded): + if MYPY_VERSION_TUPLE > (0, 910): + default_factory_type = default_factory_type.items[0] + else: + # Mypy0.910 exposes the items of overloaded types in a function + default_factory_type = default_factory_type.items()[0] # type: ignore[operator] + + if isinstance(default_factory_type, CallableType): + ret_type = get_proper_type(default_factory_type.ret_type) + if ( + isinstance(ret_type, Instance) + and ret_type.args + and all(isinstance(arg, TypeVarType) for arg in ret_type.args) + ): + # Looks like the default factory is a type like `list` or `dict`, replace all args with `Any` + ret_type = ret_type.copy_modified(args=[default_any_type] * len(ret_type.args)) + return ret_type + + return default_any_type + + +class PydanticPluginConfig: + __slots__ = ( + 'init_forbid_extra', + 'init_typed', + 'warn_required_dynamic_aliases', + 'warn_untyped_fields', + 'debug_dataclass_transform', + ) + init_forbid_extra: bool + init_typed: bool + warn_required_dynamic_aliases: bool + warn_untyped_fields: bool + debug_dataclass_transform: bool # undocumented + + def __init__(self, options: Options) -> None: + if options.config_file is None: # pragma: no cover + return + + toml_config = parse_toml(options.config_file) + if toml_config is not None: + config = toml_config.get('tool', {}).get('pydantic-mypy', {}) + for key in self.__slots__: + setting = config.get(key, False) + if not isinstance(setting, bool): + raise ValueError(f'Configuration value must be a boolean for key: {key}') + setattr(self, key, setting) + else: + plugin_config = ConfigParser() + plugin_config.read(options.config_file) + for key in self.__slots__: + setting = plugin_config.getboolean(CONFIGFILE_KEY, key, fallback=False) + setattr(self, key, setting) + + def to_data(self) -> Dict[str, Any]: + return {key: getattr(self, key) for key in self.__slots__} + + +def from_orm_callback(ctx: MethodContext) -> Type: + """ + Raise an error if orm_mode is not enabled + """ + model_type: Instance + ctx_type = ctx.type + if isinstance(ctx_type, TypeType): + ctx_type = ctx_type.item + if isinstance(ctx_type, CallableType) and isinstance(ctx_type.ret_type, Instance): + model_type = ctx_type.ret_type # called on the class + elif isinstance(ctx_type, Instance): + model_type = ctx_type # called on an instance (unusual, but still valid) + else: # pragma: no cover + detail = f'ctx.type: {ctx_type} (of type {ctx_type.__class__.__name__})' + error_unexpected_behavior(detail, ctx.api, ctx.context) + return ctx.default_return_type + pydantic_metadata = model_type.type.metadata.get(METADATA_KEY) + if pydantic_metadata is None: + return ctx.default_return_type + orm_mode = pydantic_metadata.get('config', {}).get('orm_mode') + if orm_mode is not True: + error_from_orm(get_name(model_type.type), ctx.api, ctx.context) + return ctx.default_return_type + + +class PydanticModelTransformer: + tracked_config_fields: Set[str] = { + 'extra', + 'allow_mutation', + 'frozen', + 'orm_mode', + 'allow_population_by_field_name', + 'alias_generator', + } + + def __init__(self, ctx: ClassDefContext, plugin_config: PydanticPluginConfig) -> None: + self._ctx = ctx + self.plugin_config = plugin_config + + def transform(self) -> None: + """ + Configures the BaseModel subclass according to the plugin settings. + + In particular: + * determines the model config and fields, + * adds a fields-aware signature for the initializer and construct methods + * freezes the class if allow_mutation = False or frozen = True + * stores the fields, config, and if the class is settings in the mypy metadata for access by subclasses + """ + ctx = self._ctx + info = ctx.cls.info + + self.adjust_validator_signatures() + config = self.collect_config() + fields = self.collect_fields(config) + is_settings = any(get_fullname(base) == BASESETTINGS_FULLNAME for base in info.mro[:-1]) + self.add_initializer(fields, config, is_settings) + self.add_construct_method(fields) + self.set_frozen(fields, frozen=config.allow_mutation is False or config.frozen is True) + info.metadata[METADATA_KEY] = { + 'fields': {field.name: field.serialize() for field in fields}, + 'config': config.set_values_dict(), + } + + def adjust_validator_signatures(self) -> None: + """When we decorate a function `f` with `pydantic.validator(...), mypy sees + `f` as a regular method taking a `self` instance, even though pydantic + internally wraps `f` with `classmethod` if necessary. + + Teach mypy this by marking any function whose outermost decorator is a + `validator()` call as a classmethod. + """ + for name, sym in self._ctx.cls.info.names.items(): + if isinstance(sym.node, Decorator): + first_dec = sym.node.original_decorators[0] + if ( + isinstance(first_dec, CallExpr) + and isinstance(first_dec.callee, NameExpr) + and first_dec.callee.fullname == f'{_NAMESPACE}.class_validators.validator' + ): + sym.node.func.is_class = True + + def collect_config(self) -> 'ModelConfigData': + """ + Collects the values of the config attributes that are used by the plugin, accounting for parent classes. + """ + ctx = self._ctx + cls = ctx.cls + config = ModelConfigData() + for stmt in cls.defs.body: + if not isinstance(stmt, ClassDef): + continue + if stmt.name == 'Config': + for substmt in stmt.defs.body: + if not isinstance(substmt, AssignmentStmt): + continue + config.update(self.get_config_update(substmt)) + if ( + config.has_alias_generator + and not config.allow_population_by_field_name + and self.plugin_config.warn_required_dynamic_aliases + ): + error_required_dynamic_aliases(ctx.api, stmt) + for info in cls.info.mro[1:]: # 0 is the current class + if METADATA_KEY not in info.metadata: + continue + + # Each class depends on the set of fields in its ancestors + ctx.api.add_plugin_dependency(make_wildcard_trigger(get_fullname(info))) + for name, value in info.metadata[METADATA_KEY]['config'].items(): + config.setdefault(name, value) + return config + + def collect_fields(self, model_config: 'ModelConfigData') -> List['PydanticModelField']: + """ + Collects the fields for the model, accounting for parent classes + """ + # First, collect fields belonging to the current class. + ctx = self._ctx + cls = self._ctx.cls + fields = [] # type: List[PydanticModelField] + known_fields = set() # type: Set[str] + for stmt in cls.defs.body: + if not isinstance(stmt, AssignmentStmt): # `and stmt.new_syntax` to require annotation + continue + + lhs = stmt.lvalues[0] + if not isinstance(lhs, NameExpr) or not is_valid_field(lhs.name): + continue + + if not stmt.new_syntax and self.plugin_config.warn_untyped_fields: + error_untyped_fields(ctx.api, stmt) + + # if lhs.name == '__config__': # BaseConfig not well handled; I'm not sure why yet + # continue + + sym = cls.info.names.get(lhs.name) + if sym is None: # pragma: no cover + # This is likely due to a star import (see the dataclasses plugin for a more detailed explanation) + # This is the same logic used in the dataclasses plugin + continue + + node = sym.node + if isinstance(node, PlaceholderNode): # pragma: no cover + # See the PlaceholderNode docstring for more detail about how this can occur + # Basically, it is an edge case when dealing with complex import logic + # This is the same logic used in the dataclasses plugin + continue + if not isinstance(node, Var): # pragma: no cover + # Don't know if this edge case still happens with the `is_valid_field` check above + # but better safe than sorry + continue + + # x: ClassVar[int] is ignored by dataclasses. + if node.is_classvar: + continue + + is_required = self.get_is_required(cls, stmt, lhs) + alias, has_dynamic_alias = self.get_alias_info(stmt) + if ( + has_dynamic_alias + and not model_config.allow_population_by_field_name + and self.plugin_config.warn_required_dynamic_aliases + ): + error_required_dynamic_aliases(ctx.api, stmt) + fields.append( + PydanticModelField( + name=lhs.name, + is_required=is_required, + alias=alias, + has_dynamic_alias=has_dynamic_alias, + line=stmt.line, + column=stmt.column, + ) + ) + known_fields.add(lhs.name) + all_fields = fields.copy() + for info in cls.info.mro[1:]: # 0 is the current class, -2 is BaseModel, -1 is object + if METADATA_KEY not in info.metadata: + continue + + superclass_fields = [] + # Each class depends on the set of fields in its ancestors + ctx.api.add_plugin_dependency(make_wildcard_trigger(get_fullname(info))) + + for name, data in info.metadata[METADATA_KEY]['fields'].items(): + if name not in known_fields: + field = PydanticModelField.deserialize(info, data) + known_fields.add(name) + superclass_fields.append(field) + else: + (field,) = (a for a in all_fields if a.name == name) + all_fields.remove(field) + superclass_fields.append(field) + all_fields = superclass_fields + all_fields + return all_fields + + def add_initializer(self, fields: List['PydanticModelField'], config: 'ModelConfigData', is_settings: bool) -> None: + """ + Adds a fields-aware `__init__` method to the class. + + The added `__init__` will be annotated with types vs. all `Any` depending on the plugin settings. + """ + ctx = self._ctx + typed = self.plugin_config.init_typed + use_alias = config.allow_population_by_field_name is not True + force_all_optional = is_settings or bool( + config.has_alias_generator and not config.allow_population_by_field_name + ) + init_arguments = self.get_field_arguments( + fields, typed=typed, force_all_optional=force_all_optional, use_alias=use_alias + ) + if not self.should_init_forbid_extra(fields, config): + var = Var('kwargs') + init_arguments.append(Argument(var, AnyType(TypeOfAny.explicit), None, ARG_STAR2)) + + if '__init__' not in ctx.cls.info.names: + add_method(ctx, '__init__', init_arguments, NoneType()) + + def add_construct_method(self, fields: List['PydanticModelField']) -> None: + """ + Adds a fully typed `construct` classmethod to the class. + + Similar to the fields-aware __init__ method, but always uses the field names (not aliases), + and does not treat settings fields as optional. + """ + ctx = self._ctx + set_str = ctx.api.named_type(f'{BUILTINS_NAME}.set', [ctx.api.named_type(f'{BUILTINS_NAME}.str')]) + optional_set_str = UnionType([set_str, NoneType()]) + fields_set_argument = Argument(Var('_fields_set', optional_set_str), optional_set_str, None, ARG_OPT) + construct_arguments = self.get_field_arguments(fields, typed=True, force_all_optional=False, use_alias=False) + construct_arguments = [fields_set_argument] + construct_arguments + + obj_type = ctx.api.named_type(f'{BUILTINS_NAME}.object') + self_tvar_name = '_PydanticBaseModel' # Make sure it does not conflict with other names in the class + tvar_fullname = ctx.cls.fullname + '.' + self_tvar_name + if MYPY_VERSION_TUPLE >= (1, 4): + tvd = TypeVarType( + self_tvar_name, + tvar_fullname, + ( + TypeVarId(-1, namespace=ctx.cls.fullname + '.construct') + if MYPY_VERSION_TUPLE >= (1, 11) + else TypeVarId(-1) + ), + [], + obj_type, + AnyType(TypeOfAny.from_omitted_generics), # type: ignore[arg-type] + ) + self_tvar_expr = TypeVarExpr( + self_tvar_name, + tvar_fullname, + [], + obj_type, + AnyType(TypeOfAny.from_omitted_generics), # type: ignore[arg-type] + ) + else: + tvd = TypeVarDef(self_tvar_name, tvar_fullname, -1, [], obj_type) + self_tvar_expr = TypeVarExpr(self_tvar_name, tvar_fullname, [], obj_type) + ctx.cls.info.names[self_tvar_name] = SymbolTableNode(MDEF, self_tvar_expr) + + # Backward-compatible with TypeVarDef from Mypy 0.910. + if isinstance(tvd, TypeVarType): + self_type = tvd + else: + self_type = TypeVarType(tvd) + + add_method( + ctx, + 'construct', + construct_arguments, + return_type=self_type, + self_type=self_type, + tvar_def=tvd, + is_classmethod=True, + ) + + def set_frozen(self, fields: List['PydanticModelField'], frozen: bool) -> None: + """ + Marks all fields as properties so that attempts to set them trigger mypy errors. + + This is the same approach used by the attrs and dataclasses plugins. + """ + ctx = self._ctx + info = ctx.cls.info + for field in fields: + sym_node = info.names.get(field.name) + if sym_node is not None: + var = sym_node.node + if isinstance(var, Var): + var.is_property = frozen + elif isinstance(var, PlaceholderNode) and not ctx.api.final_iteration: + # See https://github.com/pydantic/pydantic/issues/5191 to hit this branch for test coverage + ctx.api.defer() + else: # pragma: no cover + # I don't know whether it's possible to hit this branch, but I've added it for safety + try: + var_str = str(var) + except TypeError: + # This happens for PlaceholderNode; perhaps it will happen for other types in the future.. + var_str = repr(var) + detail = f'sym_node.node: {var_str} (of type {var.__class__})' + error_unexpected_behavior(detail, ctx.api, ctx.cls) + else: + var = field.to_var(info, use_alias=False) + var.info = info + var.is_property = frozen + var._fullname = get_fullname(info) + '.' + get_name(var) + info.names[get_name(var)] = SymbolTableNode(MDEF, var) + + def get_config_update(self, substmt: AssignmentStmt) -> Optional['ModelConfigData']: + """ + Determines the config update due to a single statement in the Config class definition. + + Warns if a tracked config attribute is set to a value the plugin doesn't know how to interpret (e.g., an int) + """ + lhs = substmt.lvalues[0] + if not (isinstance(lhs, NameExpr) and lhs.name in self.tracked_config_fields): + return None + if lhs.name == 'extra': + if isinstance(substmt.rvalue, StrExpr): + forbid_extra = substmt.rvalue.value == 'forbid' + elif isinstance(substmt.rvalue, MemberExpr): + forbid_extra = substmt.rvalue.name == 'forbid' + else: + error_invalid_config_value(lhs.name, self._ctx.api, substmt) + return None + return ModelConfigData(forbid_extra=forbid_extra) + if lhs.name == 'alias_generator': + has_alias_generator = True + if isinstance(substmt.rvalue, NameExpr) and substmt.rvalue.fullname == 'builtins.None': + has_alias_generator = False + return ModelConfigData(has_alias_generator=has_alias_generator) + if isinstance(substmt.rvalue, NameExpr) and substmt.rvalue.fullname in ('builtins.True', 'builtins.False'): + return ModelConfigData(**{lhs.name: substmt.rvalue.fullname == 'builtins.True'}) + error_invalid_config_value(lhs.name, self._ctx.api, substmt) + return None + + @staticmethod + def get_is_required(cls: ClassDef, stmt: AssignmentStmt, lhs: NameExpr) -> bool: + """ + Returns a boolean indicating whether the field defined in `stmt` is a required field. + """ + expr = stmt.rvalue + if isinstance(expr, TempNode): + # TempNode means annotation-only, so only non-required if Optional + value_type = get_proper_type(cls.info[lhs.name].type) + return not PydanticModelTransformer.type_has_implicit_default(value_type) + if isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME: + # The "default value" is a call to `Field`; at this point, the field is + # only required if default is Ellipsis (i.e., `field_name: Annotation = Field(...)`) or if default_factory + # is specified. + for arg, name in zip(expr.args, expr.arg_names): + # If name is None, then this arg is the default because it is the only positional argument. + if name is None or name == 'default': + return arg.__class__ is EllipsisExpr + if name == 'default_factory': + return False + # In this case, default and default_factory are not specified, so we need to look at the annotation + value_type = get_proper_type(cls.info[lhs.name].type) + return not PydanticModelTransformer.type_has_implicit_default(value_type) + # Only required if the "default value" is Ellipsis (i.e., `field_name: Annotation = ...`) + return isinstance(expr, EllipsisExpr) + + @staticmethod + def type_has_implicit_default(type_: Optional[ProperType]) -> bool: + """ + Returns True if the passed type will be given an implicit default value. + + In pydantic v1, this is the case for Optional types and Any (with default value None). + """ + if isinstance(type_, AnyType): + # Annotated as Any + return True + if isinstance(type_, UnionType) and any( + isinstance(item, NoneType) or isinstance(item, AnyType) for item in type_.items + ): + # Annotated as Optional, or otherwise having NoneType or AnyType in the union + return True + return False + + @staticmethod + def get_alias_info(stmt: AssignmentStmt) -> Tuple[Optional[str], bool]: + """ + Returns a pair (alias, has_dynamic_alias), extracted from the declaration of the field defined in `stmt`. + + `has_dynamic_alias` is True if and only if an alias is provided, but not as a string literal. + If `has_dynamic_alias` is True, `alias` will be None. + """ + expr = stmt.rvalue + if isinstance(expr, TempNode): + # TempNode means annotation-only + return None, False + + if not ( + isinstance(expr, CallExpr) and isinstance(expr.callee, RefExpr) and expr.callee.fullname == FIELD_FULLNAME + ): + # Assigned value is not a call to pydantic.fields.Field + return None, False + + for i, arg_name in enumerate(expr.arg_names): + if arg_name != 'alias': + continue + arg = expr.args[i] + if isinstance(arg, StrExpr): + return arg.value, False + else: + return None, True + return None, False + + def get_field_arguments( + self, fields: List['PydanticModelField'], typed: bool, force_all_optional: bool, use_alias: bool + ) -> List[Argument]: + """ + Helper function used during the construction of the `__init__` and `construct` method signatures. + + Returns a list of mypy Argument instances for use in the generated signatures. + """ + info = self._ctx.cls.info + arguments = [ + field.to_argument(info, typed=typed, force_optional=force_all_optional, use_alias=use_alias) + for field in fields + if not (use_alias and field.has_dynamic_alias) + ] + return arguments + + def should_init_forbid_extra(self, fields: List['PydanticModelField'], config: 'ModelConfigData') -> bool: + """ + Indicates whether the generated `__init__` should get a `**kwargs` at the end of its signature + + We disallow arbitrary kwargs if the extra config setting is "forbid", or if the plugin config says to, + *unless* a required dynamic alias is present (since then we can't determine a valid signature). + """ + if not config.allow_population_by_field_name: + if self.is_dynamic_alias_present(fields, bool(config.has_alias_generator)): + return False + if config.forbid_extra: + return True + return self.plugin_config.init_forbid_extra + + @staticmethod + def is_dynamic_alias_present(fields: List['PydanticModelField'], has_alias_generator: bool) -> bool: + """ + Returns whether any fields on the model have a "dynamic alias", i.e., an alias that cannot be + determined during static analysis. + """ + for field in fields: + if field.has_dynamic_alias: + return True + if has_alias_generator: + for field in fields: + if field.alias is None: + return True + return False + + +class PydanticModelField: + def __init__( + self, name: str, is_required: bool, alias: Optional[str], has_dynamic_alias: bool, line: int, column: int + ): + self.name = name + self.is_required = is_required + self.alias = alias + self.has_dynamic_alias = has_dynamic_alias + self.line = line + self.column = column + + def to_var(self, info: TypeInfo, use_alias: bool) -> Var: + name = self.name + if use_alias and self.alias is not None: + name = self.alias + return Var(name, info[self.name].type) + + def to_argument(self, info: TypeInfo, typed: bool, force_optional: bool, use_alias: bool) -> Argument: + if typed and info[self.name].type is not None: + type_annotation = info[self.name].type + else: + type_annotation = AnyType(TypeOfAny.explicit) + return Argument( + variable=self.to_var(info, use_alias), + type_annotation=type_annotation, + initializer=None, + kind=ARG_NAMED_OPT if force_optional or not self.is_required else ARG_NAMED, + ) + + def serialize(self) -> JsonDict: + return self.__dict__ + + @classmethod + def deserialize(cls, info: TypeInfo, data: JsonDict) -> 'PydanticModelField': + return cls(**data) + + +class ModelConfigData: + def __init__( + self, + forbid_extra: Optional[bool] = None, + allow_mutation: Optional[bool] = None, + frozen: Optional[bool] = None, + orm_mode: Optional[bool] = None, + allow_population_by_field_name: Optional[bool] = None, + has_alias_generator: Optional[bool] = None, + ): + self.forbid_extra = forbid_extra + self.allow_mutation = allow_mutation + self.frozen = frozen + self.orm_mode = orm_mode + self.allow_population_by_field_name = allow_population_by_field_name + self.has_alias_generator = has_alias_generator + + def set_values_dict(self) -> Dict[str, Any]: + return {k: v for k, v in self.__dict__.items() if v is not None} + + def update(self, config: Optional['ModelConfigData']) -> None: + if config is None: + return + for k, v in config.set_values_dict().items(): + setattr(self, k, v) + + def setdefault(self, key: str, value: Any) -> None: + if getattr(self, key) is None: + setattr(self, key, value) + + +ERROR_ORM = ErrorCode('pydantic-orm', 'Invalid from_orm call', 'Pydantic') +ERROR_CONFIG = ErrorCode('pydantic-config', 'Invalid config value', 'Pydantic') +ERROR_ALIAS = ErrorCode('pydantic-alias', 'Dynamic alias disallowed', 'Pydantic') +ERROR_UNEXPECTED = ErrorCode('pydantic-unexpected', 'Unexpected behavior', 'Pydantic') +ERROR_UNTYPED = ErrorCode('pydantic-field', 'Untyped field disallowed', 'Pydantic') +ERROR_FIELD_DEFAULTS = ErrorCode('pydantic-field', 'Invalid Field defaults', 'Pydantic') + + +def error_from_orm(model_name: str, api: CheckerPluginInterface, context: Context) -> None: + api.fail(f'"{model_name}" does not have orm_mode=True', context, code=ERROR_ORM) + + +def error_invalid_config_value(name: str, api: SemanticAnalyzerPluginInterface, context: Context) -> None: + api.fail(f'Invalid value for "Config.{name}"', context, code=ERROR_CONFIG) + + +def error_required_dynamic_aliases(api: SemanticAnalyzerPluginInterface, context: Context) -> None: + api.fail('Required dynamic aliases disallowed', context, code=ERROR_ALIAS) + + +def error_unexpected_behavior( + detail: str, api: Union[CheckerPluginInterface, SemanticAnalyzerPluginInterface], context: Context +) -> None: # pragma: no cover + # Can't think of a good way to test this, but I confirmed it renders as desired by adding to a non-error path + link = 'https://github.com/pydantic/pydantic/issues/new/choose' + full_message = f'The pydantic mypy plugin ran into unexpected behavior: {detail}\n' + full_message += f'Please consider reporting this bug at {link} so we can try to fix it!' + api.fail(full_message, context, code=ERROR_UNEXPECTED) + + +def error_untyped_fields(api: SemanticAnalyzerPluginInterface, context: Context) -> None: + api.fail('Untyped fields disallowed', context, code=ERROR_UNTYPED) + + +def error_default_and_default_factory_specified(api: CheckerPluginInterface, context: Context) -> None: + api.fail('Field default and default_factory cannot be specified together', context, code=ERROR_FIELD_DEFAULTS) + + +def add_method( + ctx: ClassDefContext, + name: str, + args: List[Argument], + return_type: Type, + self_type: Optional[Type] = None, + tvar_def: Optional[TypeVarDef] = None, + is_classmethod: bool = False, + is_new: bool = False, + # is_staticmethod: bool = False, +) -> None: + """ + Adds a new method to a class. + + This can be dropped if/when https://github.com/python/mypy/issues/7301 is merged + """ + info = ctx.cls.info + + # First remove any previously generated methods with the same name + # to avoid clashes and problems in the semantic analyzer. + if name in info.names: + sym = info.names[name] + if sym.plugin_generated and isinstance(sym.node, FuncDef): + ctx.cls.defs.body.remove(sym.node) # pragma: no cover + + self_type = self_type or fill_typevars(info) + if is_classmethod or is_new: + first = [Argument(Var('_cls'), TypeType.make_normalized(self_type), None, ARG_POS)] + # elif is_staticmethod: + # first = [] + else: + self_type = self_type or fill_typevars(info) + first = [Argument(Var('__pydantic_self__'), self_type, None, ARG_POS)] + args = first + args + arg_types, arg_names, arg_kinds = [], [], [] + for arg in args: + assert arg.type_annotation, 'All arguments must be fully typed.' + arg_types.append(arg.type_annotation) + arg_names.append(get_name(arg.variable)) + arg_kinds.append(arg.kind) + + function_type = ctx.api.named_type(f'{BUILTINS_NAME}.function') + signature = CallableType( + arg_types, arg_kinds, arg_names, return_type, function_type, variables=[tvar_def] if tvar_def else None + ) + + func = FuncDef(name, args, Block([PassStmt()])) + func.info = info + func.type = set_callable_name(signature, func) + func.is_class = is_classmethod + # func.is_static = is_staticmethod + func._fullname = get_fullname(info) + '.' + name + func.line = info.line + + # NOTE: we would like the plugin generated node to dominate, but we still + # need to keep any existing definitions so they get semantically analyzed. + if name in info.names: + # Get a nice unique name instead. + r_name = get_unique_redefinition_name(name, info.names) + info.names[r_name] = info.names[name] + + if is_classmethod: # or is_staticmethod: + func.is_decorated = True + v = Var(name, func.type) + v.info = info + v._fullname = func._fullname + # if is_classmethod: + v.is_classmethod = True + dec = Decorator(func, [NameExpr('classmethod')], v) + # else: + # v.is_staticmethod = True + # dec = Decorator(func, [NameExpr('staticmethod')], v) + + dec.line = info.line + sym = SymbolTableNode(MDEF, dec) + else: + sym = SymbolTableNode(MDEF, func) + sym.plugin_generated = True + + info.names[name] = sym + info.defn.defs.body.append(func) + + +def get_fullname(x: Union[FuncBase, SymbolNode]) -> str: + """ + Used for compatibility with mypy 0.740; can be dropped once support for 0.740 is dropped. + """ + fn = x.fullname + if callable(fn): # pragma: no cover + return fn() + return fn + + +def get_name(x: Union[FuncBase, SymbolNode]) -> str: + """ + Used for compatibility with mypy 0.740; can be dropped once support for 0.740 is dropped. + """ + fn = x.name + if callable(fn): # pragma: no cover + return fn() + return fn + + +def parse_toml(config_file: str) -> Optional[Dict[str, Any]]: + if not config_file.endswith('.toml'): + return None + + read_mode = 'rb' + if sys.version_info >= (3, 11): + import tomllib as toml_ + else: + try: + import tomli as toml_ + except ImportError: + # older versions of mypy have toml as a dependency, not tomli + read_mode = 'r' + try: + import toml as toml_ # type: ignore[no-redef] + except ImportError: # pragma: no cover + import warnings + + warnings.warn('No TOML parser installed, cannot read configuration from `pyproject.toml`.') + return None + + with open(config_file, read_mode) as rf: + return toml_.load(rf) # type: ignore[arg-type] diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/networks.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/networks.py new file mode 100644 index 0000000000000000000000000000000000000000..ba07b74867b83beaaabc3afe2e19d77d71544338 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/networks.py @@ -0,0 +1,747 @@ +import re +from ipaddress import ( + IPv4Address, + IPv4Interface, + IPv4Network, + IPv6Address, + IPv6Interface, + IPv6Network, + _BaseAddress, + _BaseNetwork, +) +from typing import ( + TYPE_CHECKING, + Any, + Collection, + Dict, + Generator, + List, + Match, + Optional, + Pattern, + Set, + Tuple, + Type, + Union, + cast, + no_type_check, +) + +from pydantic.v1 import errors +from pydantic.v1.utils import Representation, update_not_none +from pydantic.v1.validators import constr_length_validator, str_validator + +if TYPE_CHECKING: + import email_validator + from typing_extensions import TypedDict + + from pydantic.v1.config import BaseConfig + from pydantic.v1.fields import ModelField + from pydantic.v1.typing import AnyCallable + + CallableGenerator = Generator[AnyCallable, None, None] + + class Parts(TypedDict, total=False): + scheme: str + user: Optional[str] + password: Optional[str] + ipv4: Optional[str] + ipv6: Optional[str] + domain: Optional[str] + port: Optional[str] + path: Optional[str] + query: Optional[str] + fragment: Optional[str] + + class HostParts(TypedDict, total=False): + host: str + tld: Optional[str] + host_type: Optional[str] + port: Optional[str] + rebuild: bool + +else: + email_validator = None + + class Parts(dict): + pass + + +NetworkType = Union[str, bytes, int, Tuple[Union[str, bytes, int], Union[str, int]]] + +__all__ = [ + 'AnyUrl', + 'AnyHttpUrl', + 'FileUrl', + 'HttpUrl', + 'stricturl', + 'EmailStr', + 'NameEmail', + 'IPvAnyAddress', + 'IPvAnyInterface', + 'IPvAnyNetwork', + 'PostgresDsn', + 'CockroachDsn', + 'AmqpDsn', + 'RedisDsn', + 'MongoDsn', + 'KafkaDsn', + 'validate_email', +] + +_url_regex_cache = None +_multi_host_url_regex_cache = None +_ascii_domain_regex_cache = None +_int_domain_regex_cache = None +_host_regex_cache = None + +_host_regex = ( + r'(?:' + r'(?P(?:\d{1,3}\.){3}\d{1,3})(?=$|[/:#?])|' # ipv4 + r'(?P\[[A-F0-9]*:[A-F0-9:]+\])(?=$|[/:#?])|' # ipv6 + r'(?P[^\s/:?#]+)' # domain, validation occurs later + r')?' + r'(?::(?P\d+))?' # port +) +_scheme_regex = r'(?:(?P[a-z][a-z0-9+\-.]+)://)?' # scheme https://tools.ietf.org/html/rfc3986#appendix-A +_user_info_regex = r'(?:(?P[^\s:/]*)(?::(?P[^\s/]*))?@)?' +_path_regex = r'(?P/[^\s?#]*)?' +_query_regex = r'(?:\?(?P[^\s#]*))?' +_fragment_regex = r'(?:#(?P[^\s#]*))?' + + +def url_regex() -> Pattern[str]: + global _url_regex_cache + if _url_regex_cache is None: + _url_regex_cache = re.compile( + rf'{_scheme_regex}{_user_info_regex}{_host_regex}{_path_regex}{_query_regex}{_fragment_regex}', + re.IGNORECASE, + ) + return _url_regex_cache + + +def multi_host_url_regex() -> Pattern[str]: + """ + Compiled multi host url regex. + + Additionally to `url_regex` it allows to match multiple hosts. + E.g. host1.db.net,host2.db.net + """ + global _multi_host_url_regex_cache + if _multi_host_url_regex_cache is None: + _multi_host_url_regex_cache = re.compile( + rf'{_scheme_regex}{_user_info_regex}' + r'(?P([^/]*))' # validation occurs later + rf'{_path_regex}{_query_regex}{_fragment_regex}', + re.IGNORECASE, + ) + return _multi_host_url_regex_cache + + +def ascii_domain_regex() -> Pattern[str]: + global _ascii_domain_regex_cache + if _ascii_domain_regex_cache is None: + ascii_chunk = r'[_0-9a-z](?:[-_0-9a-z]{0,61}[_0-9a-z])?' + ascii_domain_ending = r'(?P\.[a-z]{2,63})?\.?' + _ascii_domain_regex_cache = re.compile( + fr'(?:{ascii_chunk}\.)*?{ascii_chunk}{ascii_domain_ending}', re.IGNORECASE + ) + return _ascii_domain_regex_cache + + +def int_domain_regex() -> Pattern[str]: + global _int_domain_regex_cache + if _int_domain_regex_cache is None: + int_chunk = r'[_0-9a-\U00040000](?:[-_0-9a-\U00040000]{0,61}[_0-9a-\U00040000])?' + int_domain_ending = r'(?P(\.[^\W\d_]{2,63})|(\.(?:xn--)[_0-9a-z-]{2,63}))?\.?' + _int_domain_regex_cache = re.compile(fr'(?:{int_chunk}\.)*?{int_chunk}{int_domain_ending}', re.IGNORECASE) + return _int_domain_regex_cache + + +def host_regex() -> Pattern[str]: + global _host_regex_cache + if _host_regex_cache is None: + _host_regex_cache = re.compile( + _host_regex, + re.IGNORECASE, + ) + return _host_regex_cache + + +class AnyUrl(str): + strip_whitespace = True + min_length = 1 + max_length = 2**16 + allowed_schemes: Optional[Collection[str]] = None + tld_required: bool = False + user_required: bool = False + host_required: bool = True + hidden_parts: Set[str] = set() + + __slots__ = ('scheme', 'user', 'password', 'host', 'tld', 'host_type', 'port', 'path', 'query', 'fragment') + + @no_type_check + def __new__(cls, url: Optional[str], **kwargs) -> object: + return str.__new__(cls, cls.build(**kwargs) if url is None else url) + + def __init__( + self, + url: str, + *, + scheme: str, + user: Optional[str] = None, + password: Optional[str] = None, + host: Optional[str] = None, + tld: Optional[str] = None, + host_type: str = 'domain', + port: Optional[str] = None, + path: Optional[str] = None, + query: Optional[str] = None, + fragment: Optional[str] = None, + ) -> None: + str.__init__(url) + self.scheme = scheme + self.user = user + self.password = password + self.host = host + self.tld = tld + self.host_type = host_type + self.port = port + self.path = path + self.query = query + self.fragment = fragment + + @classmethod + def build( + cls, + *, + scheme: str, + user: Optional[str] = None, + password: Optional[str] = None, + host: str, + port: Optional[str] = None, + path: Optional[str] = None, + query: Optional[str] = None, + fragment: Optional[str] = None, + **_kwargs: str, + ) -> str: + parts = Parts( + scheme=scheme, + user=user, + password=password, + host=host, + port=port, + path=path, + query=query, + fragment=fragment, + **_kwargs, # type: ignore[misc] + ) + + url = scheme + '://' + if user: + url += user + if password: + url += ':' + password + if user or password: + url += '@' + url += host + if port and ('port' not in cls.hidden_parts or cls.get_default_parts(parts).get('port') != port): + url += ':' + port + if path: + url += path + if query: + url += '?' + query + if fragment: + url += '#' + fragment + return url + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length, format='uri') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, value: Any, field: 'ModelField', config: 'BaseConfig') -> 'AnyUrl': + if value.__class__ == cls: + return value + value = str_validator(value) + if cls.strip_whitespace: + value = value.strip() + url: str = cast(str, constr_length_validator(value, field, config)) + + m = cls._match_url(url) + # the regex should always match, if it doesn't please report with details of the URL tried + assert m, 'URL regex failed unexpectedly' + + original_parts = cast('Parts', m.groupdict()) + parts = cls.apply_default_parts(original_parts) + parts = cls.validate_parts(parts) + + if m.end() != len(url): + raise errors.UrlExtraError(extra=url[m.end() :]) + + return cls._build_url(m, url, parts) + + @classmethod + def _build_url(cls, m: Match[str], url: str, parts: 'Parts') -> 'AnyUrl': + """ + Validate hosts and build the AnyUrl object. Split from `validate` so this method + can be altered in `MultiHostDsn`. + """ + host, tld, host_type, rebuild = cls.validate_host(parts) + + return cls( + None if rebuild else url, + scheme=parts['scheme'], + user=parts['user'], + password=parts['password'], + host=host, + tld=tld, + host_type=host_type, + port=parts['port'], + path=parts['path'], + query=parts['query'], + fragment=parts['fragment'], + ) + + @staticmethod + def _match_url(url: str) -> Optional[Match[str]]: + return url_regex().match(url) + + @staticmethod + def _validate_port(port: Optional[str]) -> None: + if port is not None and int(port) > 65_535: + raise errors.UrlPortError() + + @classmethod + def validate_parts(cls, parts: 'Parts', validate_port: bool = True) -> 'Parts': + """ + A method used to validate parts of a URL. + Could be overridden to set default values for parts if missing + """ + scheme = parts['scheme'] + if scheme is None: + raise errors.UrlSchemeError() + + if cls.allowed_schemes and scheme.lower() not in cls.allowed_schemes: + raise errors.UrlSchemePermittedError(set(cls.allowed_schemes)) + + if validate_port: + cls._validate_port(parts['port']) + + user = parts['user'] + if cls.user_required and user is None: + raise errors.UrlUserInfoError() + + return parts + + @classmethod + def validate_host(cls, parts: 'Parts') -> Tuple[str, Optional[str], str, bool]: + tld, host_type, rebuild = None, None, False + for f in ('domain', 'ipv4', 'ipv6'): + host = parts[f] # type: ignore[literal-required] + if host: + host_type = f + break + + if host is None: + if cls.host_required: + raise errors.UrlHostError() + elif host_type == 'domain': + is_international = False + d = ascii_domain_regex().fullmatch(host) + if d is None: + d = int_domain_regex().fullmatch(host) + if d is None: + raise errors.UrlHostError() + is_international = True + + tld = d.group('tld') + if tld is None and not is_international: + d = int_domain_regex().fullmatch(host) + assert d is not None + tld = d.group('tld') + is_international = True + + if tld is not None: + tld = tld[1:] + elif cls.tld_required: + raise errors.UrlHostTldError() + + if is_international: + host_type = 'int_domain' + rebuild = True + host = host.encode('idna').decode('ascii') + if tld is not None: + tld = tld.encode('idna').decode('ascii') + + return host, tld, host_type, rebuild # type: ignore + + @staticmethod + def get_default_parts(parts: 'Parts') -> 'Parts': + return {} + + @classmethod + def apply_default_parts(cls, parts: 'Parts') -> 'Parts': + for key, value in cls.get_default_parts(parts).items(): + if not parts[key]: # type: ignore[literal-required] + parts[key] = value # type: ignore[literal-required] + return parts + + def __repr__(self) -> str: + extra = ', '.join(f'{n}={getattr(self, n)!r}' for n in self.__slots__ if getattr(self, n) is not None) + return f'{self.__class__.__name__}({super().__repr__()}, {extra})' + + +class AnyHttpUrl(AnyUrl): + allowed_schemes = {'http', 'https'} + + __slots__ = () + + +class HttpUrl(AnyHttpUrl): + tld_required = True + # https://stackoverflow.com/questions/417142/what-is-the-maximum-length-of-a-url-in-different-browsers + max_length = 2083 + hidden_parts = {'port'} + + @staticmethod + def get_default_parts(parts: 'Parts') -> 'Parts': + return {'port': '80' if parts['scheme'] == 'http' else '443'} + + +class FileUrl(AnyUrl): + allowed_schemes = {'file'} + host_required = False + + __slots__ = () + + +class MultiHostDsn(AnyUrl): + __slots__ = AnyUrl.__slots__ + ('hosts',) + + def __init__(self, *args: Any, hosts: Optional[List['HostParts']] = None, **kwargs: Any): + super().__init__(*args, **kwargs) + self.hosts = hosts + + @staticmethod + def _match_url(url: str) -> Optional[Match[str]]: + return multi_host_url_regex().match(url) + + @classmethod + def validate_parts(cls, parts: 'Parts', validate_port: bool = True) -> 'Parts': + return super().validate_parts(parts, validate_port=False) + + @classmethod + def _build_url(cls, m: Match[str], url: str, parts: 'Parts') -> 'MultiHostDsn': + hosts_parts: List['HostParts'] = [] + host_re = host_regex() + for host in m.groupdict()['hosts'].split(','): + d: Parts = host_re.match(host).groupdict() # type: ignore + host, tld, host_type, rebuild = cls.validate_host(d) + port = d.get('port') + cls._validate_port(port) + hosts_parts.append( + { + 'host': host, + 'host_type': host_type, + 'tld': tld, + 'rebuild': rebuild, + 'port': port, + } + ) + + if len(hosts_parts) > 1: + return cls( + None if any([hp['rebuild'] for hp in hosts_parts]) else url, + scheme=parts['scheme'], + user=parts['user'], + password=parts['password'], + path=parts['path'], + query=parts['query'], + fragment=parts['fragment'], + host_type=None, + hosts=hosts_parts, + ) + else: + # backwards compatibility with single host + host_part = hosts_parts[0] + return cls( + None if host_part['rebuild'] else url, + scheme=parts['scheme'], + user=parts['user'], + password=parts['password'], + host=host_part['host'], + tld=host_part['tld'], + host_type=host_part['host_type'], + port=host_part.get('port'), + path=parts['path'], + query=parts['query'], + fragment=parts['fragment'], + ) + + +class PostgresDsn(MultiHostDsn): + allowed_schemes = { + 'postgres', + 'postgresql', + 'postgresql+asyncpg', + 'postgresql+pg8000', + 'postgresql+psycopg', + 'postgresql+psycopg2', + 'postgresql+psycopg2cffi', + 'postgresql+py-postgresql', + 'postgresql+pygresql', + } + user_required = True + + __slots__ = () + + +class CockroachDsn(AnyUrl): + allowed_schemes = { + 'cockroachdb', + 'cockroachdb+psycopg2', + 'cockroachdb+asyncpg', + } + user_required = True + + +class AmqpDsn(AnyUrl): + allowed_schemes = {'amqp', 'amqps'} + host_required = False + + +class RedisDsn(AnyUrl): + __slots__ = () + allowed_schemes = {'redis', 'rediss'} + host_required = False + + @staticmethod + def get_default_parts(parts: 'Parts') -> 'Parts': + return { + 'domain': 'localhost' if not (parts['ipv4'] or parts['ipv6']) else '', + 'port': '6379', + 'path': '/0', + } + + +class MongoDsn(AnyUrl): + allowed_schemes = {'mongodb'} + + # TODO: Needed to generic "Parts" for "Replica Set", "Sharded Cluster", and other mongodb deployment modes + @staticmethod + def get_default_parts(parts: 'Parts') -> 'Parts': + return { + 'port': '27017', + } + + +class KafkaDsn(AnyUrl): + allowed_schemes = {'kafka'} + + @staticmethod + def get_default_parts(parts: 'Parts') -> 'Parts': + return { + 'domain': 'localhost', + 'port': '9092', + } + + +def stricturl( + *, + strip_whitespace: bool = True, + min_length: int = 1, + max_length: int = 2**16, + tld_required: bool = True, + host_required: bool = True, + allowed_schemes: Optional[Collection[str]] = None, +) -> Type[AnyUrl]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict( + strip_whitespace=strip_whitespace, + min_length=min_length, + max_length=max_length, + tld_required=tld_required, + host_required=host_required, + allowed_schemes=allowed_schemes, + ) + return type('UrlValue', (AnyUrl,), namespace) + + +def import_email_validator() -> None: + global email_validator + try: + import email_validator + except ImportError as e: + raise ImportError('email-validator is not installed, run `pip install pydantic[email]`') from e + + +class EmailStr(str): + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='email') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + # included here and below so the error happens straight away + import_email_validator() + + yield str_validator + yield cls.validate + + @classmethod + def validate(cls, value: Union[str]) -> str: + return validate_email(value)[1] + + +class NameEmail(Representation): + __slots__ = 'name', 'email' + + def __init__(self, name: str, email: str): + self.name = name + self.email = email + + def __eq__(self, other: Any) -> bool: + return isinstance(other, NameEmail) and (self.name, self.email) == (other.name, other.email) + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='name-email') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + import_email_validator() + + yield cls.validate + + @classmethod + def validate(cls, value: Any) -> 'NameEmail': + if value.__class__ == cls: + return value + value = str_validator(value) + return cls(*validate_email(value)) + + def __str__(self) -> str: + return f'{self.name} <{self.email}>' + + +class IPvAnyAddress(_BaseAddress): + __slots__ = () + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='ipvanyaddress') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, value: Union[str, bytes, int]) -> Union[IPv4Address, IPv6Address]: + try: + return IPv4Address(value) + except ValueError: + pass + + try: + return IPv6Address(value) + except ValueError: + raise errors.IPvAnyAddressError() + + +class IPvAnyInterface(_BaseAddress): + __slots__ = () + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='ipvanyinterface') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, value: NetworkType) -> Union[IPv4Interface, IPv6Interface]: + try: + return IPv4Interface(value) + except ValueError: + pass + + try: + return IPv6Interface(value) + except ValueError: + raise errors.IPvAnyInterfaceError() + + +class IPvAnyNetwork(_BaseNetwork): # type: ignore + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='ipvanynetwork') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, value: NetworkType) -> Union[IPv4Network, IPv6Network]: + # Assume IP Network is defined with a default value for ``strict`` argument. + # Define your own class if you want to specify network address check strictness. + try: + return IPv4Network(value) + except ValueError: + pass + + try: + return IPv6Network(value) + except ValueError: + raise errors.IPvAnyNetworkError() + + +pretty_email_regex = re.compile(r'([\w ]*?) *<(.*)> *') +MAX_EMAIL_LENGTH = 2048 +"""Maximum length for an email. +A somewhat arbitrary but very generous number compared to what is allowed by most implementations. +""" + + +def validate_email(value: Union[str]) -> Tuple[str, str]: + """ + Email address validation using https://pypi.org/project/email-validator/ + Notes: + * raw ip address (literal) domain parts are not allowed. + * "John Doe " style "pretty" email addresses are processed + * spaces are striped from the beginning and end of addresses but no error is raised + """ + if email_validator is None: + import_email_validator() + + if len(value) > MAX_EMAIL_LENGTH: + raise errors.EmailError() + + m = pretty_email_regex.fullmatch(value) + name: Union[str, None] = None + if m: + name, value = m.groups() + email = value.strip() + try: + parts = email_validator.validate_email(email, check_deliverability=False) + except email_validator.EmailNotValidError as e: + raise errors.EmailError from e + + if hasattr(parts, 'normalized'): + # email-validator >= 2 + email = parts.normalized + assert email is not None + name = name or parts.local_part + return name, email + else: + # email-validator >1, <2 + at_index = email.index('@') + local_part = email[:at_index] # RFC 5321, local part must be case-sensitive. + global_part = email[at_index:].lower() + + return name or local_part, local_part + global_part diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/parse.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/parse.py new file mode 100644 index 0000000000000000000000000000000000000000..431d75a6406f3cf79ae4fcd26a2ad35fb5030526 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/parse.py @@ -0,0 +1,66 @@ +import json +import pickle +from enum import Enum +from pathlib import Path +from typing import Any, Callable, Union + +from pydantic.v1.types import StrBytes + + +class Protocol(str, Enum): + json = 'json' + pickle = 'pickle' + + +def load_str_bytes( + b: StrBytes, + *, + content_type: str = None, + encoding: str = 'utf8', + proto: Protocol = None, + allow_pickle: bool = False, + json_loads: Callable[[str], Any] = json.loads, +) -> Any: + if proto is None and content_type: + if content_type.endswith(('json', 'javascript')): + pass + elif allow_pickle and content_type.endswith('pickle'): + proto = Protocol.pickle + else: + raise TypeError(f'Unknown content-type: {content_type}') + + proto = proto or Protocol.json + + if proto == Protocol.json: + if isinstance(b, bytes): + b = b.decode(encoding) + return json_loads(b) + elif proto == Protocol.pickle: + if not allow_pickle: + raise RuntimeError('Trying to decode with pickle with allow_pickle=False') + bb = b if isinstance(b, bytes) else b.encode() + return pickle.loads(bb) + else: + raise TypeError(f'Unknown protocol: {proto}') + + +def load_file( + path: Union[str, Path], + *, + content_type: str = None, + encoding: str = 'utf8', + proto: Protocol = None, + allow_pickle: bool = False, + json_loads: Callable[[str], Any] = json.loads, +) -> Any: + path = Path(path) + b = path.read_bytes() + if content_type is None: + if path.suffix in ('.js', '.json'): + proto = Protocol.json + elif path.suffix == '.pkl': + proto = Protocol.pickle + + return load_str_bytes( + b, proto=proto, content_type=content_type, encoding=encoding, allow_pickle=allow_pickle, json_loads=json_loads + ) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/py.typed b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/schema.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/schema.py new file mode 100644 index 0000000000000000000000000000000000000000..a91fe2cd4ae12e619a0ca34baf14400ded5f14cf --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/schema.py @@ -0,0 +1,1163 @@ +import re +import warnings +from collections import defaultdict +from dataclasses import is_dataclass +from datetime import date, datetime, time, timedelta +from decimal import Decimal +from enum import Enum +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from pathlib import Path +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Dict, + ForwardRef, + FrozenSet, + Generic, + Iterable, + List, + Optional, + Pattern, + Sequence, + Set, + Tuple, + Type, + TypeVar, + Union, + cast, +) +from uuid import UUID + +from typing_extensions import Annotated, Literal + +from pydantic.v1.fields import ( + MAPPING_LIKE_SHAPES, + SHAPE_DEQUE, + SHAPE_FROZENSET, + SHAPE_GENERIC, + SHAPE_ITERABLE, + SHAPE_LIST, + SHAPE_SEQUENCE, + SHAPE_SET, + SHAPE_SINGLETON, + SHAPE_TUPLE, + SHAPE_TUPLE_ELLIPSIS, + FieldInfo, + ModelField, +) +from pydantic.v1.json import pydantic_encoder +from pydantic.v1.networks import AnyUrl, EmailStr +from pydantic.v1.types import ( + ConstrainedDecimal, + ConstrainedFloat, + ConstrainedFrozenSet, + ConstrainedInt, + ConstrainedList, + ConstrainedSet, + ConstrainedStr, + SecretBytes, + SecretStr, + StrictBytes, + StrictStr, + conbytes, + condecimal, + confloat, + confrozenset, + conint, + conlist, + conset, + constr, +) +from pydantic.v1.typing import ( + all_literal_values, + get_args, + get_origin, + get_sub_types, + is_callable_type, + is_literal_type, + is_namedtuple, + is_none_type, + is_union, +) +from pydantic.v1.utils import ROOT_KEY, get_model, lenient_issubclass + +if TYPE_CHECKING: + from pydantic.v1.dataclasses import Dataclass + from pydantic.v1.main import BaseModel + +default_prefix = '#/definitions/' +default_ref_template = '#/definitions/{model}' + +TypeModelOrEnum = Union[Type['BaseModel'], Type[Enum]] +TypeModelSet = Set[TypeModelOrEnum] + + +def _apply_modify_schema( + modify_schema: Callable[..., None], field: Optional[ModelField], field_schema: Dict[str, Any] +) -> None: + from inspect import signature + + sig = signature(modify_schema) + args = set(sig.parameters.keys()) + if 'field' in args or 'kwargs' in args: + modify_schema(field_schema, field=field) + else: + modify_schema(field_schema) + + +def schema( + models: Sequence[Union[Type['BaseModel'], Type['Dataclass']]], + *, + by_alias: bool = True, + title: Optional[str] = None, + description: Optional[str] = None, + ref_prefix: Optional[str] = None, + ref_template: str = default_ref_template, +) -> Dict[str, Any]: + """ + Process a list of models and generate a single JSON Schema with all of them defined in the ``definitions`` + top-level JSON key, including their sub-models. + + :param models: a list of models to include in the generated JSON Schema + :param by_alias: generate the schemas using the aliases defined, if any + :param title: title for the generated schema that includes the definitions + :param description: description for the generated schema + :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the + default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere + else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the + top-level key ``definitions``, so you can extract them from there. But all the references will have the set + prefix. + :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful + for references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For + a sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``. + :return: dict with the JSON Schema with a ``definitions`` top-level key including the schema definitions for + the models and sub-models passed in ``models``. + """ + clean_models = [get_model(model) for model in models] + flat_models = get_flat_models_from_models(clean_models) + model_name_map = get_model_name_map(flat_models) + definitions = {} + output_schema: Dict[str, Any] = {} + if title: + output_schema['title'] = title + if description: + output_schema['description'] = description + for model in clean_models: + m_schema, m_definitions, m_nested_models = model_process_schema( + model, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + ) + definitions.update(m_definitions) + model_name = model_name_map[model] + definitions[model_name] = m_schema + if definitions: + output_schema['definitions'] = definitions + return output_schema + + +def model_schema( + model: Union[Type['BaseModel'], Type['Dataclass']], + by_alias: bool = True, + ref_prefix: Optional[str] = None, + ref_template: str = default_ref_template, +) -> Dict[str, Any]: + """ + Generate a JSON Schema for one model. With all the sub-models defined in the ``definitions`` top-level + JSON key. + + :param model: a Pydantic model (a class that inherits from BaseModel) + :param by_alias: generate the schemas using the aliases defined, if any + :param ref_prefix: the JSON Pointer prefix for schema references with ``$ref``, if None, will be set to the + default of ``#/definitions/``. Update it if you want the schemas to reference the definitions somewhere + else, e.g. for OpenAPI use ``#/components/schemas/``. The resulting generated schemas will still be at the + top-level key ``definitions``, so you can extract them from there. But all the references will have the set + prefix. + :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for + references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a + sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``. + :return: dict with the JSON Schema for the passed ``model`` + """ + model = get_model(model) + flat_models = get_flat_models_from_model(model) + model_name_map = get_model_name_map(flat_models) + model_name = model_name_map[model] + m_schema, m_definitions, nested_models = model_process_schema( + model, by_alias=by_alias, model_name_map=model_name_map, ref_prefix=ref_prefix, ref_template=ref_template + ) + if model_name in nested_models: + # model_name is in Nested models, it has circular references + m_definitions[model_name] = m_schema + m_schema = get_schema_ref(model_name, ref_prefix, ref_template, False) + if m_definitions: + m_schema.update({'definitions': m_definitions}) + return m_schema + + +def get_field_info_schema(field: ModelField, schema_overrides: bool = False) -> Tuple[Dict[str, Any], bool]: + # If no title is explicitly set, we don't set title in the schema for enums. + # The behaviour is the same as `BaseModel` reference, where the default title + # is in the definitions part of the schema. + schema_: Dict[str, Any] = {} + if field.field_info.title or not lenient_issubclass(field.type_, Enum): + schema_['title'] = field.field_info.title or field.alias.title().replace('_', ' ') + + if field.field_info.title: + schema_overrides = True + + if field.field_info.description: + schema_['description'] = field.field_info.description + schema_overrides = True + + if not field.required and field.default is not None and not is_callable_type(field.outer_type_): + schema_['default'] = encode_default(field.default) + schema_overrides = True + + return schema_, schema_overrides + + +def field_schema( + field: ModelField, + *, + by_alias: bool = True, + model_name_map: Dict[TypeModelOrEnum, str], + ref_prefix: Optional[str] = None, + ref_template: str = default_ref_template, + known_models: Optional[TypeModelSet] = None, +) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]: + """ + Process a Pydantic field and return a tuple with a JSON Schema for it as the first item. + Also return a dictionary of definitions with models as keys and their schemas as values. If the passed field + is a model and has sub-models, and those sub-models don't have overrides (as ``title``, ``default``, etc), they + will be included in the definitions and referenced in the schema instead of included recursively. + + :param field: a Pydantic ``ModelField`` + :param by_alias: use the defined alias (if any) in the returned schema + :param model_name_map: used to generate the JSON Schema references to other models included in the definitions + :param ref_prefix: the JSON Pointer prefix to use for references to other schemas, if None, the default of + #/definitions/ will be used + :param ref_template: Use a ``string.format()`` template for ``$ref`` instead of a prefix. This can be useful for + references that cannot be represented by ``ref_prefix`` such as a definition stored in another file. For a + sibling json file in a ``/schemas`` directory use ``"/schemas/${model}.json#"``. + :param known_models: used to solve circular references + :return: tuple of the schema for this field and additional definitions + """ + s, schema_overrides = get_field_info_schema(field) + + validation_schema = get_field_schema_validations(field) + if validation_schema: + s.update(validation_schema) + schema_overrides = True + + f_schema, f_definitions, f_nested_models = field_type_schema( + field, + by_alias=by_alias, + model_name_map=model_name_map, + schema_overrides=schema_overrides, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models or set(), + ) + + # $ref will only be returned when there are no schema_overrides + if '$ref' in f_schema: + return f_schema, f_definitions, f_nested_models + else: + s.update(f_schema) + return s, f_definitions, f_nested_models + + +numeric_types = (int, float, Decimal) +_str_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = ( + ('max_length', numeric_types, 'maxLength'), + ('min_length', numeric_types, 'minLength'), + ('regex', str, 'pattern'), +) + +_numeric_types_attrs: Tuple[Tuple[str, Union[type, Tuple[type, ...]], str], ...] = ( + ('gt', numeric_types, 'exclusiveMinimum'), + ('lt', numeric_types, 'exclusiveMaximum'), + ('ge', numeric_types, 'minimum'), + ('le', numeric_types, 'maximum'), + ('multiple_of', numeric_types, 'multipleOf'), +) + + +def get_field_schema_validations(field: ModelField) -> Dict[str, Any]: + """ + Get the JSON Schema validation keywords for a ``field`` with an annotation of + a Pydantic ``FieldInfo`` with validation arguments. + """ + f_schema: Dict[str, Any] = {} + + if lenient_issubclass(field.type_, Enum): + # schema is already updated by `enum_process_schema`; just update with field extra + if field.field_info.extra: + f_schema.update(field.field_info.extra) + return f_schema + + if lenient_issubclass(field.type_, (str, bytes)): + for attr_name, t, keyword in _str_types_attrs: + attr = getattr(field.field_info, attr_name, None) + if isinstance(attr, t): + f_schema[keyword] = attr + if lenient_issubclass(field.type_, numeric_types) and not issubclass(field.type_, bool): + for attr_name, t, keyword in _numeric_types_attrs: + attr = getattr(field.field_info, attr_name, None) + if isinstance(attr, t): + f_schema[keyword] = attr + if field.field_info is not None and field.field_info.const: + f_schema['const'] = field.default + if field.field_info.extra: + f_schema.update(field.field_info.extra) + modify_schema = getattr(field.outer_type_, '__modify_schema__', None) + if modify_schema: + _apply_modify_schema(modify_schema, field, f_schema) + return f_schema + + +def get_model_name_map(unique_models: TypeModelSet) -> Dict[TypeModelOrEnum, str]: + """ + Process a set of models and generate unique names for them to be used as keys in the JSON Schema + definitions. By default the names are the same as the class name. But if two models in different Python + modules have the same name (e.g. "users.Model" and "items.Model"), the generated names will be + based on the Python module path for those conflicting models to prevent name collisions. + + :param unique_models: a Python set of models + :return: dict mapping models to names + """ + name_model_map = {} + conflicting_names: Set[str] = set() + for model in unique_models: + model_name = normalize_name(model.__name__) + if model_name in conflicting_names: + model_name = get_long_model_name(model) + name_model_map[model_name] = model + elif model_name in name_model_map: + conflicting_names.add(model_name) + conflicting_model = name_model_map.pop(model_name) + name_model_map[get_long_model_name(conflicting_model)] = conflicting_model + name_model_map[get_long_model_name(model)] = model + else: + name_model_map[model_name] = model + return {v: k for k, v in name_model_map.items()} + + +def get_flat_models_from_model(model: Type['BaseModel'], known_models: Optional[TypeModelSet] = None) -> TypeModelSet: + """ + Take a single ``model`` and generate a set with itself and all the sub-models in the tree. I.e. if you pass + model ``Foo`` (subclass of Pydantic ``BaseModel``) as ``model``, and it has a field of type ``Bar`` (also + subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also subclass of ``BaseModel``), + the return value will be ``set([Foo, Bar, Baz])``. + + :param model: a Pydantic ``BaseModel`` subclass + :param known_models: used to solve circular references + :return: a set with the initial model and all its sub-models + """ + known_models = known_models or set() + flat_models: TypeModelSet = set() + flat_models.add(model) + known_models |= flat_models + fields = cast(Sequence[ModelField], model.__fields__.values()) + flat_models |= get_flat_models_from_fields(fields, known_models=known_models) + return flat_models + + +def get_flat_models_from_field(field: ModelField, known_models: TypeModelSet) -> TypeModelSet: + """ + Take a single Pydantic ``ModelField`` (from a model) that could have been declared as a subclass of BaseModel + (so, it could be a submodel), and generate a set with its model and all the sub-models in the tree. + I.e. if you pass a field that was declared to be of type ``Foo`` (subclass of BaseModel) as ``field``, and that + model ``Foo`` has a field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of + type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. + + :param field: a Pydantic ``ModelField`` + :param known_models: used to solve circular references + :return: a set with the model used in the declaration for this field, if any, and all its sub-models + """ + from pydantic.v1.main import BaseModel + + flat_models: TypeModelSet = set() + + field_type = field.type_ + if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel): + field_type = field_type.__pydantic_model__ + + if field.sub_fields and not lenient_issubclass(field_type, BaseModel): + flat_models |= get_flat_models_from_fields(field.sub_fields, known_models=known_models) + elif lenient_issubclass(field_type, BaseModel) and field_type not in known_models: + flat_models |= get_flat_models_from_model(field_type, known_models=known_models) + elif lenient_issubclass(field_type, Enum): + flat_models.add(field_type) + return flat_models + + +def get_flat_models_from_fields(fields: Sequence[ModelField], known_models: TypeModelSet) -> TypeModelSet: + """ + Take a list of Pydantic ``ModelField``s (from a model) that could have been declared as subclasses of ``BaseModel`` + (so, any of them could be a submodel), and generate a set with their models and all the sub-models in the tree. + I.e. if you pass a the fields of a model ``Foo`` (subclass of ``BaseModel``) as ``fields``, and on of them has a + field of type ``Bar`` (also subclass of ``BaseModel``) and that model ``Bar`` has a field of type ``Baz`` (also + subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. + + :param fields: a list of Pydantic ``ModelField``s + :param known_models: used to solve circular references + :return: a set with any model declared in the fields, and all their sub-models + """ + flat_models: TypeModelSet = set() + for field in fields: + flat_models |= get_flat_models_from_field(field, known_models=known_models) + return flat_models + + +def get_flat_models_from_models(models: Sequence[Type['BaseModel']]) -> TypeModelSet: + """ + Take a list of ``models`` and generate a set with them and all their sub-models in their trees. I.e. if you pass + a list of two models, ``Foo`` and ``Bar``, both subclasses of Pydantic ``BaseModel`` as models, and ``Bar`` has + a field of type ``Baz`` (also subclass of ``BaseModel``), the return value will be ``set([Foo, Bar, Baz])``. + """ + flat_models: TypeModelSet = set() + for model in models: + flat_models |= get_flat_models_from_model(model) + return flat_models + + +def get_long_model_name(model: TypeModelOrEnum) -> str: + return f'{model.__module__}__{model.__qualname__}'.replace('.', '__') + + +def field_type_schema( + field: ModelField, + *, + by_alias: bool, + model_name_map: Dict[TypeModelOrEnum, str], + ref_template: str, + schema_overrides: bool = False, + ref_prefix: Optional[str] = None, + known_models: TypeModelSet, +) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]: + """ + Used by ``field_schema()``, you probably should be using that function. + + Take a single ``field`` and generate the schema for its type only, not including additional + information as title, etc. Also return additional schema definitions, from sub-models. + """ + from pydantic.v1.main import BaseModel # noqa: F811 + + definitions = {} + nested_models: Set[str] = set() + f_schema: Dict[str, Any] + if field.shape in { + SHAPE_LIST, + SHAPE_TUPLE_ELLIPSIS, + SHAPE_SEQUENCE, + SHAPE_SET, + SHAPE_FROZENSET, + SHAPE_ITERABLE, + SHAPE_DEQUE, + }: + items_schema, f_definitions, f_nested_models = field_singleton_schema( + field, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + definitions.update(f_definitions) + nested_models.update(f_nested_models) + f_schema = {'type': 'array', 'items': items_schema} + if field.shape in {SHAPE_SET, SHAPE_FROZENSET}: + f_schema['uniqueItems'] = True + + elif field.shape in MAPPING_LIKE_SHAPES: + f_schema = {'type': 'object'} + key_field = cast(ModelField, field.key_field) + regex = getattr(key_field.type_, 'regex', None) + items_schema, f_definitions, f_nested_models = field_singleton_schema( + field, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + definitions.update(f_definitions) + nested_models.update(f_nested_models) + if regex: + # Dict keys have a regex pattern + # items_schema might be a schema or empty dict, add it either way + f_schema['patternProperties'] = {ConstrainedStr._get_pattern(regex): items_schema} + if items_schema: + # The dict values are not simply Any, so they need a schema + f_schema['additionalProperties'] = items_schema + elif field.shape == SHAPE_TUPLE or (field.shape == SHAPE_GENERIC and not issubclass(field.type_, BaseModel)): + sub_schema = [] + sub_fields = cast(List[ModelField], field.sub_fields) + for sf in sub_fields: + sf_schema, sf_definitions, sf_nested_models = field_type_schema( + sf, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + definitions.update(sf_definitions) + nested_models.update(sf_nested_models) + sub_schema.append(sf_schema) + + sub_fields_len = len(sub_fields) + if field.shape == SHAPE_GENERIC: + all_of_schemas = sub_schema[0] if sub_fields_len == 1 else {'type': 'array', 'items': sub_schema} + f_schema = {'allOf': [all_of_schemas]} + else: + f_schema = { + 'type': 'array', + 'minItems': sub_fields_len, + 'maxItems': sub_fields_len, + } + if sub_fields_len >= 1: + f_schema['items'] = sub_schema + else: + assert field.shape in {SHAPE_SINGLETON, SHAPE_GENERIC}, field.shape + f_schema, f_definitions, f_nested_models = field_singleton_schema( + field, + by_alias=by_alias, + model_name_map=model_name_map, + schema_overrides=schema_overrides, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + definitions.update(f_definitions) + nested_models.update(f_nested_models) + + # check field type to avoid repeated calls to the same __modify_schema__ method + if field.type_ != field.outer_type_: + if field.shape == SHAPE_GENERIC: + field_type = field.type_ + else: + field_type = field.outer_type_ + modify_schema = getattr(field_type, '__modify_schema__', None) + if modify_schema: + _apply_modify_schema(modify_schema, field, f_schema) + return f_schema, definitions, nested_models + + +def model_process_schema( + model: TypeModelOrEnum, + *, + by_alias: bool = True, + model_name_map: Dict[TypeModelOrEnum, str], + ref_prefix: Optional[str] = None, + ref_template: str = default_ref_template, + known_models: Optional[TypeModelSet] = None, + field: Optional[ModelField] = None, +) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]: + """ + Used by ``model_schema()``, you probably should be using that function. + + Take a single ``model`` and generate its schema. Also return additional schema definitions, from sub-models. The + sub-models of the returned schema will be referenced, but their definitions will not be included in the schema. All + the definitions are returned as the second value. + """ + from inspect import getdoc, signature + + known_models = known_models or set() + if lenient_issubclass(model, Enum): + model = cast(Type[Enum], model) + s = enum_process_schema(model, field=field) + return s, {}, set() + model = cast(Type['BaseModel'], model) + s = {'title': model.__config__.title or model.__name__} + doc = getdoc(model) + if doc: + s['description'] = doc + known_models.add(model) + m_schema, m_definitions, nested_models = model_type_schema( + model, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + s.update(m_schema) + schema_extra = model.__config__.schema_extra + if callable(schema_extra): + if len(signature(schema_extra).parameters) == 1: + schema_extra(s) + else: + schema_extra(s, model) + else: + s.update(schema_extra) + return s, m_definitions, nested_models + + +def model_type_schema( + model: Type['BaseModel'], + *, + by_alias: bool, + model_name_map: Dict[TypeModelOrEnum, str], + ref_template: str, + ref_prefix: Optional[str] = None, + known_models: TypeModelSet, +) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]: + """ + You probably should be using ``model_schema()``, this function is indirectly used by that function. + + Take a single ``model`` and generate the schema for its type only, not including additional + information as title, etc. Also return additional schema definitions, from sub-models. + """ + properties = {} + required = [] + definitions: Dict[str, Any] = {} + nested_models: Set[str] = set() + for k, f in model.__fields__.items(): + try: + f_schema, f_definitions, f_nested_models = field_schema( + f, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + except SkipField as skip: + warnings.warn(skip.message, UserWarning) + continue + definitions.update(f_definitions) + nested_models.update(f_nested_models) + if by_alias: + properties[f.alias] = f_schema + if f.required: + required.append(f.alias) + else: + properties[k] = f_schema + if f.required: + required.append(k) + if ROOT_KEY in properties: + out_schema = properties[ROOT_KEY] + out_schema['title'] = model.__config__.title or model.__name__ + else: + out_schema = {'type': 'object', 'properties': properties} + if required: + out_schema['required'] = required + if model.__config__.extra == 'forbid': + out_schema['additionalProperties'] = False + return out_schema, definitions, nested_models + + +def enum_process_schema(enum: Type[Enum], *, field: Optional[ModelField] = None) -> Dict[str, Any]: + """ + Take a single `enum` and generate its schema. + + This is similar to the `model_process_schema` function, but applies to ``Enum`` objects. + """ + import inspect + + schema_: Dict[str, Any] = { + 'title': enum.__name__, + # Python assigns all enums a default docstring value of 'An enumeration', so + # all enums will have a description field even if not explicitly provided. + 'description': inspect.cleandoc(enum.__doc__ or 'An enumeration.'), + # Add enum values and the enum field type to the schema. + 'enum': [item.value for item in cast(Iterable[Enum], enum)], + } + + add_field_type_to_schema(enum, schema_) + + modify_schema = getattr(enum, '__modify_schema__', None) + if modify_schema: + _apply_modify_schema(modify_schema, field, schema_) + + return schema_ + + +def field_singleton_sub_fields_schema( + field: ModelField, + *, + by_alias: bool, + model_name_map: Dict[TypeModelOrEnum, str], + ref_template: str, + schema_overrides: bool = False, + ref_prefix: Optional[str] = None, + known_models: TypeModelSet, +) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]: + """ + This function is indirectly used by ``field_schema()``, you probably should be using that function. + + Take a list of Pydantic ``ModelField`` from the declaration of a type with parameters, and generate their + schema. I.e., fields used as "type parameters", like ``str`` and ``int`` in ``Tuple[str, int]``. + """ + sub_fields = cast(List[ModelField], field.sub_fields) + definitions = {} + nested_models: Set[str] = set() + if len(sub_fields) == 1: + return field_type_schema( + sub_fields[0], + by_alias=by_alias, + model_name_map=model_name_map, + schema_overrides=schema_overrides, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + else: + s: Dict[str, Any] = {} + # https://github.com/OAI/OpenAPI-Specification/blob/master/versions/3.0.2.md#discriminator-object + field_has_discriminator: bool = field.discriminator_key is not None + if field_has_discriminator: + assert field.sub_fields_mapping is not None + + discriminator_models_refs: Dict[str, Union[str, Dict[str, Any]]] = {} + + for discriminator_value, sub_field in field.sub_fields_mapping.items(): + if isinstance(discriminator_value, Enum): + discriminator_value = str(discriminator_value.value) + # sub_field is either a `BaseModel` or directly an `Annotated` `Union` of many + if is_union(get_origin(sub_field.type_)): + sub_models = get_sub_types(sub_field.type_) + discriminator_models_refs[discriminator_value] = { + model_name_map[sub_model]: get_schema_ref( + model_name_map[sub_model], ref_prefix, ref_template, False + ) + for sub_model in sub_models + } + else: + sub_field_type = sub_field.type_ + if hasattr(sub_field_type, '__pydantic_model__'): + sub_field_type = sub_field_type.__pydantic_model__ + + discriminator_model_name = model_name_map[sub_field_type] + discriminator_model_ref = get_schema_ref(discriminator_model_name, ref_prefix, ref_template, False) + discriminator_models_refs[discriminator_value] = discriminator_model_ref['$ref'] + + s['discriminator'] = { + 'propertyName': field.discriminator_alias if by_alias else field.discriminator_key, + 'mapping': discriminator_models_refs, + } + + sub_field_schemas = [] + for sf in sub_fields: + sub_schema, sub_definitions, sub_nested_models = field_type_schema( + sf, + by_alias=by_alias, + model_name_map=model_name_map, + schema_overrides=schema_overrides, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + definitions.update(sub_definitions) + if schema_overrides and 'allOf' in sub_schema: + # if the sub_field is a referenced schema we only need the referenced + # object. Otherwise we will end up with several allOf inside anyOf/oneOf. + # See https://github.com/pydantic/pydantic/issues/1209 + sub_schema = sub_schema['allOf'][0] + + if sub_schema.keys() == {'discriminator', 'oneOf'}: + # we don't want discriminator information inside oneOf choices, this is dealt with elsewhere + sub_schema.pop('discriminator') + sub_field_schemas.append(sub_schema) + nested_models.update(sub_nested_models) + s['oneOf' if field_has_discriminator else 'anyOf'] = sub_field_schemas + return s, definitions, nested_models + + +# Order is important, e.g. subclasses of str must go before str +# this is used only for standard library types, custom types should use __modify_schema__ instead +field_class_to_schema: Tuple[Tuple[Any, Dict[str, Any]], ...] = ( + (Path, {'type': 'string', 'format': 'path'}), + (datetime, {'type': 'string', 'format': 'date-time'}), + (date, {'type': 'string', 'format': 'date'}), + (time, {'type': 'string', 'format': 'time'}), + (timedelta, {'type': 'number', 'format': 'time-delta'}), + (IPv4Network, {'type': 'string', 'format': 'ipv4network'}), + (IPv6Network, {'type': 'string', 'format': 'ipv6network'}), + (IPv4Interface, {'type': 'string', 'format': 'ipv4interface'}), + (IPv6Interface, {'type': 'string', 'format': 'ipv6interface'}), + (IPv4Address, {'type': 'string', 'format': 'ipv4'}), + (IPv6Address, {'type': 'string', 'format': 'ipv6'}), + (Pattern, {'type': 'string', 'format': 'regex'}), + (str, {'type': 'string'}), + (bytes, {'type': 'string', 'format': 'binary'}), + (bool, {'type': 'boolean'}), + (int, {'type': 'integer'}), + (float, {'type': 'number'}), + (Decimal, {'type': 'number'}), + (UUID, {'type': 'string', 'format': 'uuid'}), + (dict, {'type': 'object'}), + (list, {'type': 'array', 'items': {}}), + (tuple, {'type': 'array', 'items': {}}), + (set, {'type': 'array', 'items': {}, 'uniqueItems': True}), + (frozenset, {'type': 'array', 'items': {}, 'uniqueItems': True}), +) + +json_scheme = {'type': 'string', 'format': 'json-string'} + + +def add_field_type_to_schema(field_type: Any, schema_: Dict[str, Any]) -> None: + """ + Update the given `schema` with the type-specific metadata for the given `field_type`. + + This function looks through `field_class_to_schema` for a class that matches the given `field_type`, + and then modifies the given `schema` with the information from that type. + """ + for type_, t_schema in field_class_to_schema: + # Fallback for `typing.Pattern` and `re.Pattern` as they are not a valid class + if lenient_issubclass(field_type, type_) or field_type is type_ is Pattern: + schema_.update(t_schema) + break + + +def get_schema_ref(name: str, ref_prefix: Optional[str], ref_template: str, schema_overrides: bool) -> Dict[str, Any]: + if ref_prefix: + schema_ref = {'$ref': ref_prefix + name} + else: + schema_ref = {'$ref': ref_template.format(model=name)} + return {'allOf': [schema_ref]} if schema_overrides else schema_ref + + +def field_singleton_schema( # noqa: C901 (ignore complexity) + field: ModelField, + *, + by_alias: bool, + model_name_map: Dict[TypeModelOrEnum, str], + ref_template: str, + schema_overrides: bool = False, + ref_prefix: Optional[str] = None, + known_models: TypeModelSet, +) -> Tuple[Dict[str, Any], Dict[str, Any], Set[str]]: + """ + This function is indirectly used by ``field_schema()``, you should probably be using that function. + + Take a single Pydantic ``ModelField``, and return its schema and any additional definitions from sub-models. + """ + from pydantic.v1.main import BaseModel + + definitions: Dict[str, Any] = {} + nested_models: Set[str] = set() + field_type = field.type_ + + # Recurse into this field if it contains sub_fields and is NOT a + # BaseModel OR that BaseModel is a const + if field.sub_fields and ( + (field.field_info and field.field_info.const) or not lenient_issubclass(field_type, BaseModel) + ): + return field_singleton_sub_fields_schema( + field, + by_alias=by_alias, + model_name_map=model_name_map, + schema_overrides=schema_overrides, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + if field_type is Any or field_type is object or field_type.__class__ == TypeVar or get_origin(field_type) is type: + return {}, definitions, nested_models # no restrictions + if is_none_type(field_type): + return {'type': 'null'}, definitions, nested_models + if is_callable_type(field_type): + raise SkipField(f'Callable {field.name} was excluded from schema since JSON schema has no equivalent type.') + f_schema: Dict[str, Any] = {} + if field.field_info is not None and field.field_info.const: + f_schema['const'] = field.default + + if is_literal_type(field_type): + values = tuple(x.value if isinstance(x, Enum) else x for x in all_literal_values(field_type)) + + if len({v.__class__ for v in values}) > 1: + return field_schema( + multitypes_literal_field_for_schema(values, field), + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + ) + + # All values have the same type + field_type = values[0].__class__ + f_schema['enum'] = list(values) + add_field_type_to_schema(field_type, f_schema) + elif lenient_issubclass(field_type, Enum): + enum_name = model_name_map[field_type] + f_schema, schema_overrides = get_field_info_schema(field, schema_overrides) + f_schema.update(get_schema_ref(enum_name, ref_prefix, ref_template, schema_overrides)) + definitions[enum_name] = enum_process_schema(field_type, field=field) + elif is_namedtuple(field_type): + sub_schema, *_ = model_process_schema( + field_type.__pydantic_model__, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + field=field, + ) + items_schemas = list(sub_schema['properties'].values()) + f_schema.update( + { + 'type': 'array', + 'items': items_schemas, + 'minItems': len(items_schemas), + 'maxItems': len(items_schemas), + } + ) + elif not hasattr(field_type, '__pydantic_model__'): + add_field_type_to_schema(field_type, f_schema) + + modify_schema = getattr(field_type, '__modify_schema__', None) + if modify_schema: + _apply_modify_schema(modify_schema, field, f_schema) + + if f_schema: + return f_schema, definitions, nested_models + + # Handle dataclass-based models + if lenient_issubclass(getattr(field_type, '__pydantic_model__', None), BaseModel): + field_type = field_type.__pydantic_model__ + + if issubclass(field_type, BaseModel): + model_name = model_name_map[field_type] + if field_type not in known_models: + sub_schema, sub_definitions, sub_nested_models = model_process_schema( + field_type, + by_alias=by_alias, + model_name_map=model_name_map, + ref_prefix=ref_prefix, + ref_template=ref_template, + known_models=known_models, + field=field, + ) + definitions.update(sub_definitions) + definitions[model_name] = sub_schema + nested_models.update(sub_nested_models) + else: + nested_models.add(model_name) + schema_ref = get_schema_ref(model_name, ref_prefix, ref_template, schema_overrides) + return schema_ref, definitions, nested_models + + # For generics with no args + args = get_args(field_type) + if args is not None and not args and Generic in field_type.__bases__: + return f_schema, definitions, nested_models + + raise ValueError(f'Value not declarable with JSON Schema, field: {field}') + + +def multitypes_literal_field_for_schema(values: Tuple[Any, ...], field: ModelField) -> ModelField: + """ + To support `Literal` with values of different types, we split it into multiple `Literal` with same type + e.g. `Literal['qwe', 'asd', 1, 2]` becomes `Union[Literal['qwe', 'asd'], Literal[1, 2]]` + """ + literal_distinct_types = defaultdict(list) + for v in values: + literal_distinct_types[v.__class__].append(v) + distinct_literals = (Literal[tuple(same_type_values)] for same_type_values in literal_distinct_types.values()) + + return ModelField( + name=field.name, + type_=Union[tuple(distinct_literals)], # type: ignore + class_validators=field.class_validators, + model_config=field.model_config, + default=field.default, + required=field.required, + alias=field.alias, + field_info=field.field_info, + ) + + +def encode_default(dft: Any) -> Any: + from pydantic.v1.main import BaseModel + + if isinstance(dft, BaseModel) or is_dataclass(dft): + dft = cast('dict[str, Any]', pydantic_encoder(dft)) + + if isinstance(dft, dict): + return {encode_default(k): encode_default(v) for k, v in dft.items()} + elif isinstance(dft, Enum): + return dft.value + elif isinstance(dft, (int, float, str)): + return dft + elif isinstance(dft, (list, tuple)): + t = dft.__class__ + seq_args = (encode_default(v) for v in dft) + return t(*seq_args) if is_namedtuple(t) else t(seq_args) + elif dft is None: + return None + else: + return pydantic_encoder(dft) + + +_map_types_constraint: Dict[Any, Callable[..., type]] = {int: conint, float: confloat, Decimal: condecimal} + + +def get_annotation_from_field_info( + annotation: Any, field_info: FieldInfo, field_name: str, validate_assignment: bool = False +) -> Type[Any]: + """ + Get an annotation with validation implemented for numbers and strings based on the field_info. + :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr`` + :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema + :param field_name: name of the field for use in error messages + :param validate_assignment: default False, flag for BaseModel Config value of validate_assignment + :return: the same ``annotation`` if unmodified or a new annotation with validation in place + """ + constraints = field_info.get_constraints() + used_constraints: Set[str] = set() + if constraints: + annotation, used_constraints = get_annotation_with_constraints(annotation, field_info) + if validate_assignment: + used_constraints.add('allow_mutation') + + unused_constraints = constraints - used_constraints + if unused_constraints: + raise ValueError( + f'On field "{field_name}" the following field constraints are set but not enforced: ' + f'{", ".join(unused_constraints)}. ' + f'\nFor more details see https://docs.pydantic.dev/usage/schema/#unenforced-field-constraints' + ) + + return annotation + + +def get_annotation_with_constraints(annotation: Any, field_info: FieldInfo) -> Tuple[Type[Any], Set[str]]: # noqa: C901 + """ + Get an annotation with used constraints implemented for numbers and strings based on the field_info. + + :param annotation: an annotation from a field specification, as ``str``, ``ConstrainedStr`` + :param field_info: an instance of FieldInfo, possibly with declarations for validations and JSON Schema + :return: the same ``annotation`` if unmodified or a new annotation along with the used constraints. + """ + used_constraints: Set[str] = set() + + def go(type_: Any) -> Type[Any]: + if ( + is_literal_type(type_) + or isinstance(type_, ForwardRef) + or lenient_issubclass(type_, (ConstrainedList, ConstrainedSet, ConstrainedFrozenSet)) + ): + return type_ + origin = get_origin(type_) + if origin is not None: + args: Tuple[Any, ...] = get_args(type_) + if any(isinstance(a, ForwardRef) for a in args): + # forward refs cause infinite recursion below + return type_ + + if origin is Annotated: + return go(args[0]) + if is_union(origin): + return Union[tuple(go(a) for a in args)] # type: ignore + + if issubclass(origin, List) and ( + field_info.min_items is not None + or field_info.max_items is not None + or field_info.unique_items is not None + ): + used_constraints.update({'min_items', 'max_items', 'unique_items'}) + return conlist( + go(args[0]), + min_items=field_info.min_items, + max_items=field_info.max_items, + unique_items=field_info.unique_items, + ) + + if issubclass(origin, Set) and (field_info.min_items is not None or field_info.max_items is not None): + used_constraints.update({'min_items', 'max_items'}) + return conset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items) + + if issubclass(origin, FrozenSet) and (field_info.min_items is not None or field_info.max_items is not None): + used_constraints.update({'min_items', 'max_items'}) + return confrozenset(go(args[0]), min_items=field_info.min_items, max_items=field_info.max_items) + + for t in (Tuple, List, Set, FrozenSet, Sequence): + if issubclass(origin, t): # type: ignore + return t[tuple(go(a) for a in args)] # type: ignore + + if issubclass(origin, Dict): + return Dict[args[0], go(args[1])] # type: ignore + + attrs: Optional[Tuple[str, ...]] = None + constraint_func: Optional[Callable[..., type]] = None + if isinstance(type_, type): + if issubclass(type_, (SecretStr, SecretBytes)): + attrs = ('max_length', 'min_length') + + def constraint_func(**kw: Any) -> Type[Any]: # noqa: F811 + return type(type_.__name__, (type_,), kw) + + elif issubclass(type_, str) and not issubclass(type_, (EmailStr, AnyUrl)): + attrs = ('max_length', 'min_length', 'regex') + if issubclass(type_, StrictStr): + + def constraint_func(**kw: Any) -> Type[Any]: + return type(type_.__name__, (type_,), kw) + + else: + constraint_func = constr + elif issubclass(type_, bytes): + attrs = ('max_length', 'min_length', 'regex') + if issubclass(type_, StrictBytes): + + def constraint_func(**kw: Any) -> Type[Any]: + return type(type_.__name__, (type_,), kw) + + else: + constraint_func = conbytes + elif issubclass(type_, numeric_types) and not issubclass( + type_, + ( + ConstrainedInt, + ConstrainedFloat, + ConstrainedDecimal, + ConstrainedList, + ConstrainedSet, + ConstrainedFrozenSet, + bool, + ), + ): + # Is numeric type + attrs = ('gt', 'lt', 'ge', 'le', 'multiple_of') + if issubclass(type_, float): + attrs += ('allow_inf_nan',) + if issubclass(type_, Decimal): + attrs += ('max_digits', 'decimal_places') + numeric_type = next(t for t in numeric_types if issubclass(type_, t)) # pragma: no branch + constraint_func = _map_types_constraint[numeric_type] + + if attrs: + used_constraints.update(set(attrs)) + kwargs = { + attr_name: attr + for attr_name, attr in ((attr_name, getattr(field_info, attr_name)) for attr_name in attrs) + if attr is not None + } + if kwargs: + constraint_func = cast(Callable[..., type], constraint_func) + return constraint_func(**kwargs) + return type_ + + return go(annotation), used_constraints + + +def normalize_name(name: str) -> str: + """ + Normalizes the given name. This can be applied to either a model *or* enum. + """ + return re.sub(r'[^a-zA-Z0-9.\-_]', '_', name) + + +class SkipField(Exception): + """ + Utility exception used to exclude fields from schema. + """ + + def __init__(self, message: str) -> None: + self.message = message diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/tools.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..6838a23ecc7419f0478449d9d680c230a99c20e0 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/tools.py @@ -0,0 +1,92 @@ +import json +from functools import lru_cache +from pathlib import Path +from typing import TYPE_CHECKING, Any, Callable, Optional, Type, TypeVar, Union + +from pydantic.v1.parse import Protocol, load_file, load_str_bytes +from pydantic.v1.types import StrBytes +from pydantic.v1.typing import display_as_type + +__all__ = ('parse_file_as', 'parse_obj_as', 'parse_raw_as', 'schema_of', 'schema_json_of') + +NameFactory = Union[str, Callable[[Type[Any]], str]] + +if TYPE_CHECKING: + from pydantic.v1.typing import DictStrAny + + +def _generate_parsing_type_name(type_: Any) -> str: + return f'ParsingModel[{display_as_type(type_)}]' + + +@lru_cache(maxsize=2048) +def _get_parsing_type(type_: Any, *, type_name: Optional[NameFactory] = None) -> Any: + from pydantic.v1.main import create_model + + if type_name is None: + type_name = _generate_parsing_type_name + if not isinstance(type_name, str): + type_name = type_name(type_) + return create_model(type_name, __root__=(type_, ...)) + + +T = TypeVar('T') + + +def parse_obj_as(type_: Type[T], obj: Any, *, type_name: Optional[NameFactory] = None) -> T: + model_type = _get_parsing_type(type_, type_name=type_name) # type: ignore[arg-type] + return model_type(__root__=obj).__root__ + + +def parse_file_as( + type_: Type[T], + path: Union[str, Path], + *, + content_type: str = None, + encoding: str = 'utf8', + proto: Protocol = None, + allow_pickle: bool = False, + json_loads: Callable[[str], Any] = json.loads, + type_name: Optional[NameFactory] = None, +) -> T: + obj = load_file( + path, + proto=proto, + content_type=content_type, + encoding=encoding, + allow_pickle=allow_pickle, + json_loads=json_loads, + ) + return parse_obj_as(type_, obj, type_name=type_name) + + +def parse_raw_as( + type_: Type[T], + b: StrBytes, + *, + content_type: str = None, + encoding: str = 'utf8', + proto: Protocol = None, + allow_pickle: bool = False, + json_loads: Callable[[str], Any] = json.loads, + type_name: Optional[NameFactory] = None, +) -> T: + obj = load_str_bytes( + b, + proto=proto, + content_type=content_type, + encoding=encoding, + allow_pickle=allow_pickle, + json_loads=json_loads, + ) + return parse_obj_as(type_, obj, type_name=type_name) + + +def schema_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_kwargs: Any) -> 'DictStrAny': + """Generate a JSON schema (as dict) for the passed model or dynamically generated one""" + return _get_parsing_type(type_, type_name=title).schema(**schema_kwargs) + + +def schema_json_of(type_: Any, *, title: Optional[NameFactory] = None, **schema_json_kwargs: Any) -> str: + """Generate a JSON schema (as JSON) for the passed model or dynamically generated one""" + return _get_parsing_type(type_, type_name=title).schema_json(**schema_json_kwargs) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/types.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/types.py new file mode 100644 index 0000000000000000000000000000000000000000..e1840d99f79683412aebfc23ced893d0716c1400 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/types.py @@ -0,0 +1,1205 @@ +import abc +import math +import re +import warnings +from datetime import date +from decimal import Decimal, InvalidOperation +from enum import Enum +from pathlib import Path +from types import new_class +from typing import ( + TYPE_CHECKING, + Any, + Callable, + ClassVar, + Dict, + FrozenSet, + List, + Optional, + Pattern, + Set, + Tuple, + Type, + TypeVar, + Union, + cast, + overload, +) +from uuid import UUID +from weakref import WeakSet + +from pydantic.v1 import errors +from pydantic.v1.datetime_parse import parse_date +from pydantic.v1.utils import import_string, update_not_none +from pydantic.v1.validators import ( + bytes_validator, + constr_length_validator, + constr_lower, + constr_strip_whitespace, + constr_upper, + decimal_validator, + float_finite_validator, + float_validator, + frozenset_validator, + int_validator, + list_validator, + number_multiple_validator, + number_size_validator, + path_exists_validator, + path_validator, + set_validator, + str_validator, + strict_bytes_validator, + strict_float_validator, + strict_int_validator, + strict_str_validator, +) + +__all__ = [ + 'NoneStr', + 'NoneBytes', + 'StrBytes', + 'NoneStrBytes', + 'StrictStr', + 'ConstrainedBytes', + 'conbytes', + 'ConstrainedList', + 'conlist', + 'ConstrainedSet', + 'conset', + 'ConstrainedFrozenSet', + 'confrozenset', + 'ConstrainedStr', + 'constr', + 'PyObject', + 'ConstrainedInt', + 'conint', + 'PositiveInt', + 'NegativeInt', + 'NonNegativeInt', + 'NonPositiveInt', + 'ConstrainedFloat', + 'confloat', + 'PositiveFloat', + 'NegativeFloat', + 'NonNegativeFloat', + 'NonPositiveFloat', + 'FiniteFloat', + 'ConstrainedDecimal', + 'condecimal', + 'UUID1', + 'UUID3', + 'UUID4', + 'UUID5', + 'FilePath', + 'DirectoryPath', + 'Json', + 'JsonWrapper', + 'SecretField', + 'SecretStr', + 'SecretBytes', + 'StrictBool', + 'StrictBytes', + 'StrictInt', + 'StrictFloat', + 'PaymentCardNumber', + 'ByteSize', + 'PastDate', + 'FutureDate', + 'ConstrainedDate', + 'condate', +] + +NoneStr = Optional[str] +NoneBytes = Optional[bytes] +StrBytes = Union[str, bytes] +NoneStrBytes = Optional[StrBytes] +OptionalInt = Optional[int] +OptionalIntFloat = Union[OptionalInt, float] +OptionalIntFloatDecimal = Union[OptionalIntFloat, Decimal] +OptionalDate = Optional[date] +StrIntFloat = Union[str, int, float] + +if TYPE_CHECKING: + from typing_extensions import Annotated + + from pydantic.v1.dataclasses import Dataclass + from pydantic.v1.main import BaseModel + from pydantic.v1.typing import CallableGenerator + + ModelOrDc = Type[Union[BaseModel, Dataclass]] + +T = TypeVar('T') +_DEFINED_TYPES: 'WeakSet[type]' = WeakSet() + + +@overload +def _registered(typ: Type[T]) -> Type[T]: + pass + + +@overload +def _registered(typ: 'ConstrainedNumberMeta') -> 'ConstrainedNumberMeta': + pass + + +def _registered(typ: Union[Type[T], 'ConstrainedNumberMeta']) -> Union[Type[T], 'ConstrainedNumberMeta']: + # In order to generate valid examples of constrained types, Hypothesis needs + # to inspect the type object - so we keep a weakref to each contype object + # until it can be registered. When (or if) our Hypothesis plugin is loaded, + # it monkeypatches this function. + # If Hypothesis is never used, the total effect is to keep a weak reference + # which has minimal memory usage and doesn't even affect garbage collection. + _DEFINED_TYPES.add(typ) + return typ + + +class ConstrainedNumberMeta(type): + def __new__(cls, name: str, bases: Any, dct: Dict[str, Any]) -> 'ConstrainedInt': # type: ignore + new_cls = cast('ConstrainedInt', type.__new__(cls, name, bases, dct)) + + if new_cls.gt is not None and new_cls.ge is not None: + raise errors.ConfigError('bounds gt and ge cannot be specified at the same time') + if new_cls.lt is not None and new_cls.le is not None: + raise errors.ConfigError('bounds lt and le cannot be specified at the same time') + + return _registered(new_cls) # type: ignore + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BOOLEAN TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +if TYPE_CHECKING: + StrictBool = bool +else: + + class StrictBool(int): + """ + StrictBool to allow for bools which are not type-coerced. + """ + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='boolean') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, value: Any) -> bool: + """ + Ensure that we only allow bools. + """ + if isinstance(value, bool): + return value + + raise errors.StrictBoolError() + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ INTEGER TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class ConstrainedInt(int, metaclass=ConstrainedNumberMeta): + strict: bool = False + gt: OptionalInt = None + ge: OptionalInt = None + lt: OptionalInt = None + le: OptionalInt = None + multiple_of: OptionalInt = None + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none( + field_schema, + exclusiveMinimum=cls.gt, + exclusiveMaximum=cls.lt, + minimum=cls.ge, + maximum=cls.le, + multipleOf=cls.multiple_of, + ) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield strict_int_validator if cls.strict else int_validator + yield number_size_validator + yield number_multiple_validator + + +def conint( + *, + strict: bool = False, + gt: Optional[int] = None, + ge: Optional[int] = None, + lt: Optional[int] = None, + le: Optional[int] = None, + multiple_of: Optional[int] = None, +) -> Type[int]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of) + return type('ConstrainedIntValue', (ConstrainedInt,), namespace) + + +if TYPE_CHECKING: + PositiveInt = int + NegativeInt = int + NonPositiveInt = int + NonNegativeInt = int + StrictInt = int +else: + + class PositiveInt(ConstrainedInt): + gt = 0 + + class NegativeInt(ConstrainedInt): + lt = 0 + + class NonPositiveInt(ConstrainedInt): + le = 0 + + class NonNegativeInt(ConstrainedInt): + ge = 0 + + class StrictInt(ConstrainedInt): + strict = True + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ FLOAT TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class ConstrainedFloat(float, metaclass=ConstrainedNumberMeta): + strict: bool = False + gt: OptionalIntFloat = None + ge: OptionalIntFloat = None + lt: OptionalIntFloat = None + le: OptionalIntFloat = None + multiple_of: OptionalIntFloat = None + allow_inf_nan: Optional[bool] = None + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none( + field_schema, + exclusiveMinimum=cls.gt, + exclusiveMaximum=cls.lt, + minimum=cls.ge, + maximum=cls.le, + multipleOf=cls.multiple_of, + ) + # Modify constraints to account for differences between IEEE floats and JSON + if field_schema.get('exclusiveMinimum') == -math.inf: + del field_schema['exclusiveMinimum'] + if field_schema.get('minimum') == -math.inf: + del field_schema['minimum'] + if field_schema.get('exclusiveMaximum') == math.inf: + del field_schema['exclusiveMaximum'] + if field_schema.get('maximum') == math.inf: + del field_schema['maximum'] + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield strict_float_validator if cls.strict else float_validator + yield number_size_validator + yield number_multiple_validator + yield float_finite_validator + + +def confloat( + *, + strict: bool = False, + gt: float = None, + ge: float = None, + lt: float = None, + le: float = None, + multiple_of: float = None, + allow_inf_nan: Optional[bool] = None, +) -> Type[float]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict(strict=strict, gt=gt, ge=ge, lt=lt, le=le, multiple_of=multiple_of, allow_inf_nan=allow_inf_nan) + return type('ConstrainedFloatValue', (ConstrainedFloat,), namespace) + + +if TYPE_CHECKING: + PositiveFloat = float + NegativeFloat = float + NonPositiveFloat = float + NonNegativeFloat = float + StrictFloat = float + FiniteFloat = float +else: + + class PositiveFloat(ConstrainedFloat): + gt = 0 + + class NegativeFloat(ConstrainedFloat): + lt = 0 + + class NonPositiveFloat(ConstrainedFloat): + le = 0 + + class NonNegativeFloat(ConstrainedFloat): + ge = 0 + + class StrictFloat(ConstrainedFloat): + strict = True + + class FiniteFloat(ConstrainedFloat): + allow_inf_nan = False + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTES TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class ConstrainedBytes(bytes): + strip_whitespace = False + to_upper = False + to_lower = False + min_length: OptionalInt = None + max_length: OptionalInt = None + strict: bool = False + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none(field_schema, minLength=cls.min_length, maxLength=cls.max_length) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield strict_bytes_validator if cls.strict else bytes_validator + yield constr_strip_whitespace + yield constr_upper + yield constr_lower + yield constr_length_validator + + +def conbytes( + *, + strip_whitespace: bool = False, + to_upper: bool = False, + to_lower: bool = False, + min_length: Optional[int] = None, + max_length: Optional[int] = None, + strict: bool = False, +) -> Type[bytes]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict( + strip_whitespace=strip_whitespace, + to_upper=to_upper, + to_lower=to_lower, + min_length=min_length, + max_length=max_length, + strict=strict, + ) + return _registered(type('ConstrainedBytesValue', (ConstrainedBytes,), namespace)) + + +if TYPE_CHECKING: + StrictBytes = bytes +else: + + class StrictBytes(ConstrainedBytes): + strict = True + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ STRING TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class ConstrainedStr(str): + strip_whitespace = False + to_upper = False + to_lower = False + min_length: OptionalInt = None + max_length: OptionalInt = None + curtail_length: OptionalInt = None + regex: Optional[Union[str, Pattern[str]]] = None + strict = False + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none( + field_schema, + minLength=cls.min_length, + maxLength=cls.max_length, + pattern=cls.regex and cls._get_pattern(cls.regex), + ) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield strict_str_validator if cls.strict else str_validator + yield constr_strip_whitespace + yield constr_upper + yield constr_lower + yield constr_length_validator + yield cls.validate + + @classmethod + def validate(cls, value: Union[str]) -> Union[str]: + if cls.curtail_length and len(value) > cls.curtail_length: + value = value[: cls.curtail_length] + + if cls.regex: + if not re.match(cls.regex, value): + raise errors.StrRegexError(pattern=cls._get_pattern(cls.regex)) + + return value + + @staticmethod + def _get_pattern(regex: Union[str, Pattern[str]]) -> str: + return regex if isinstance(regex, str) else regex.pattern + + +def constr( + *, + strip_whitespace: bool = False, + to_upper: bool = False, + to_lower: bool = False, + strict: bool = False, + min_length: Optional[int] = None, + max_length: Optional[int] = None, + curtail_length: Optional[int] = None, + regex: Optional[str] = None, +) -> Type[str]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict( + strip_whitespace=strip_whitespace, + to_upper=to_upper, + to_lower=to_lower, + strict=strict, + min_length=min_length, + max_length=max_length, + curtail_length=curtail_length, + regex=regex and re.compile(regex), + ) + return _registered(type('ConstrainedStrValue', (ConstrainedStr,), namespace)) + + +if TYPE_CHECKING: + StrictStr = str +else: + + class StrictStr(ConstrainedStr): + strict = True + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +# This types superclass should be Set[T], but cython chokes on that... +class ConstrainedSet(set): # type: ignore + # Needed for pydantic to detect that this is a set + __origin__ = set + __args__: Set[Type[T]] # type: ignore + + min_items: Optional[int] = None + max_items: Optional[int] = None + item_type: Type[T] # type: ignore + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.set_length_validator + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items) + + @classmethod + def set_length_validator(cls, v: 'Optional[Set[T]]') -> 'Optional[Set[T]]': + if v is None: + return None + + v = set_validator(v) + v_len = len(v) + + if cls.min_items is not None and v_len < cls.min_items: + raise errors.SetMinLengthError(limit_value=cls.min_items) + + if cls.max_items is not None and v_len > cls.max_items: + raise errors.SetMaxLengthError(limit_value=cls.max_items) + + return v + + +def conset(item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None) -> Type[Set[T]]: + # __args__ is needed to conform to typing generics api + namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': [item_type]} + # We use new_class to be able to deal with Generic types + return new_class('ConstrainedSetValue', (ConstrainedSet,), {}, lambda ns: ns.update(namespace)) + + +# This types superclass should be FrozenSet[T], but cython chokes on that... +class ConstrainedFrozenSet(frozenset): # type: ignore + # Needed for pydantic to detect that this is a set + __origin__ = frozenset + __args__: FrozenSet[Type[T]] # type: ignore + + min_items: Optional[int] = None + max_items: Optional[int] = None + item_type: Type[T] # type: ignore + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.frozenset_length_validator + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items) + + @classmethod + def frozenset_length_validator(cls, v: 'Optional[FrozenSet[T]]') -> 'Optional[FrozenSet[T]]': + if v is None: + return None + + v = frozenset_validator(v) + v_len = len(v) + + if cls.min_items is not None and v_len < cls.min_items: + raise errors.FrozenSetMinLengthError(limit_value=cls.min_items) + + if cls.max_items is not None and v_len > cls.max_items: + raise errors.FrozenSetMaxLengthError(limit_value=cls.max_items) + + return v + + +def confrozenset( + item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None +) -> Type[FrozenSet[T]]: + # __args__ is needed to conform to typing generics api + namespace = {'min_items': min_items, 'max_items': max_items, 'item_type': item_type, '__args__': [item_type]} + # We use new_class to be able to deal with Generic types + return new_class('ConstrainedFrozenSetValue', (ConstrainedFrozenSet,), {}, lambda ns: ns.update(namespace)) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ LIST TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +# This types superclass should be List[T], but cython chokes on that... +class ConstrainedList(list): # type: ignore + # Needed for pydantic to detect that this is a list + __origin__ = list + __args__: Tuple[Type[T], ...] # type: ignore + + min_items: Optional[int] = None + max_items: Optional[int] = None + unique_items: Optional[bool] = None + item_type: Type[T] # type: ignore + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.list_length_validator + if cls.unique_items: + yield cls.unique_items_validator + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none(field_schema, minItems=cls.min_items, maxItems=cls.max_items, uniqueItems=cls.unique_items) + + @classmethod + def list_length_validator(cls, v: 'Optional[List[T]]') -> 'Optional[List[T]]': + if v is None: + return None + + v = list_validator(v) + v_len = len(v) + + if cls.min_items is not None and v_len < cls.min_items: + raise errors.ListMinLengthError(limit_value=cls.min_items) + + if cls.max_items is not None and v_len > cls.max_items: + raise errors.ListMaxLengthError(limit_value=cls.max_items) + + return v + + @classmethod + def unique_items_validator(cls, v: 'Optional[List[T]]') -> 'Optional[List[T]]': + if v is None: + return None + + for i, value in enumerate(v, start=1): + if value in v[i:]: + raise errors.ListUniqueItemsError() + + return v + + +def conlist( + item_type: Type[T], *, min_items: Optional[int] = None, max_items: Optional[int] = None, unique_items: bool = None +) -> Type[List[T]]: + # __args__ is needed to conform to typing generics api + namespace = dict( + min_items=min_items, max_items=max_items, unique_items=unique_items, item_type=item_type, __args__=(item_type,) + ) + # We use new_class to be able to deal with Generic types + return new_class('ConstrainedListValue', (ConstrainedList,), {}, lambda ns: ns.update(namespace)) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PYOBJECT TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +if TYPE_CHECKING: + PyObject = Callable[..., Any] +else: + + class PyObject: + validate_always = True + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, value: Any) -> Any: + if isinstance(value, Callable): + return value + + try: + value = str_validator(value) + except errors.StrError: + raise errors.PyObjectError(error_message='value is neither a valid import path not a valid callable') + + try: + return import_string(value) + except ImportError as e: + raise errors.PyObjectError(error_message=str(e)) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DECIMAL TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class ConstrainedDecimal(Decimal, metaclass=ConstrainedNumberMeta): + gt: OptionalIntFloatDecimal = None + ge: OptionalIntFloatDecimal = None + lt: OptionalIntFloatDecimal = None + le: OptionalIntFloatDecimal = None + max_digits: OptionalInt = None + decimal_places: OptionalInt = None + multiple_of: OptionalIntFloatDecimal = None + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none( + field_schema, + exclusiveMinimum=cls.gt, + exclusiveMaximum=cls.lt, + minimum=cls.ge, + maximum=cls.le, + multipleOf=cls.multiple_of, + ) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield decimal_validator + yield number_size_validator + yield number_multiple_validator + yield cls.validate + + @classmethod + def validate(cls, value: Decimal) -> Decimal: + try: + normalized_value = value.normalize() + except InvalidOperation: + normalized_value = value + digit_tuple, exponent = normalized_value.as_tuple()[1:] + if exponent in {'F', 'n', 'N'}: + raise errors.DecimalIsNotFiniteError() + + if exponent >= 0: + # A positive exponent adds that many trailing zeros. + digits = len(digit_tuple) + exponent + decimals = 0 + else: + # If the absolute value of the negative exponent is larger than the + # number of digits, then it's the same as the number of digits, + # because it'll consume all of the digits in digit_tuple and then + # add abs(exponent) - len(digit_tuple) leading zeros after the + # decimal point. + if abs(exponent) > len(digit_tuple): + digits = decimals = abs(exponent) + else: + digits = len(digit_tuple) + decimals = abs(exponent) + whole_digits = digits - decimals + + if cls.max_digits is not None and digits > cls.max_digits: + raise errors.DecimalMaxDigitsError(max_digits=cls.max_digits) + + if cls.decimal_places is not None and decimals > cls.decimal_places: + raise errors.DecimalMaxPlacesError(decimal_places=cls.decimal_places) + + if cls.max_digits is not None and cls.decimal_places is not None: + expected = cls.max_digits - cls.decimal_places + if whole_digits > expected: + raise errors.DecimalWholeDigitsError(whole_digits=expected) + + return value + + +def condecimal( + *, + gt: Decimal = None, + ge: Decimal = None, + lt: Decimal = None, + le: Decimal = None, + max_digits: Optional[int] = None, + decimal_places: Optional[int] = None, + multiple_of: Decimal = None, +) -> Type[Decimal]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict( + gt=gt, ge=ge, lt=lt, le=le, max_digits=max_digits, decimal_places=decimal_places, multiple_of=multiple_of + ) + return type('ConstrainedDecimalValue', (ConstrainedDecimal,), namespace) + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ UUID TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +if TYPE_CHECKING: + UUID1 = UUID + UUID3 = UUID + UUID4 = UUID + UUID5 = UUID +else: + + class UUID1(UUID): + _required_version = 1 + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format=f'uuid{cls._required_version}') + + class UUID3(UUID1): + _required_version = 3 + + class UUID4(UUID1): + _required_version = 4 + + class UUID5(UUID1): + _required_version = 5 + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PATH TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +if TYPE_CHECKING: + FilePath = Path + DirectoryPath = Path +else: + + class FilePath(Path): + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(format='file-path') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield path_validator + yield path_exists_validator + yield cls.validate + + @classmethod + def validate(cls, value: Path) -> Path: + if not value.is_file(): + raise errors.PathNotAFileError(path=value) + + return value + + class DirectoryPath(Path): + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(format='directory-path') + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield path_validator + yield path_exists_validator + yield cls.validate + + @classmethod + def validate(cls, value: Path) -> Path: + if not value.is_dir(): + raise errors.PathNotADirectoryError(path=value) + + return value + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ JSON TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class JsonWrapper: + pass + + +class JsonMeta(type): + def __getitem__(self, t: Type[Any]) -> Type[JsonWrapper]: + if t is Any: + return Json # allow Json[Any] to replicate plain Json + return _registered(type('JsonWrapperValue', (JsonWrapper,), {'inner_type': t})) + + +if TYPE_CHECKING: + Json = Annotated[T, ...] # Json[list[str]] will be recognized by type checkers as list[str] + +else: + + class Json(metaclass=JsonMeta): + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + field_schema.update(type='string', format='json-string') + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SECRET TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class SecretField(abc.ABC): + """ + Note: this should be implemented as a generic like `SecretField(ABC, Generic[T])`, + the `__init__()` should be part of the abstract class and the + `get_secret_value()` method should use the generic `T` type. + + However Cython doesn't support very well generics at the moment and + the generated code fails to be imported (see + https://github.com/cython/cython/issues/2753). + """ + + def __eq__(self, other: Any) -> bool: + return isinstance(other, self.__class__) and self.get_secret_value() == other.get_secret_value() + + def __str__(self) -> str: + return '**********' if self.get_secret_value() else '' + + def __hash__(self) -> int: + return hash(self.get_secret_value()) + + @abc.abstractmethod + def get_secret_value(self) -> Any: # pragma: no cover + ... + + +class SecretStr(SecretField): + min_length: OptionalInt = None + max_length: OptionalInt = None + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none( + field_schema, + type='string', + writeOnly=True, + format='password', + minLength=cls.min_length, + maxLength=cls.max_length, + ) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + yield constr_length_validator + + @classmethod + def validate(cls, value: Any) -> 'SecretStr': + if isinstance(value, cls): + return value + value = str_validator(value) + return cls(value) + + def __init__(self, value: str): + self._secret_value = value + + def __repr__(self) -> str: + return f"SecretStr('{self}')" + + def __len__(self) -> int: + return len(self._secret_value) + + def display(self) -> str: + warnings.warn('`secret_str.display()` is deprecated, use `str(secret_str)` instead', DeprecationWarning) + return str(self) + + def get_secret_value(self) -> str: + return self._secret_value + + +class SecretBytes(SecretField): + min_length: OptionalInt = None + max_length: OptionalInt = None + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none( + field_schema, + type='string', + writeOnly=True, + format='password', + minLength=cls.min_length, + maxLength=cls.max_length, + ) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + yield constr_length_validator + + @classmethod + def validate(cls, value: Any) -> 'SecretBytes': + if isinstance(value, cls): + return value + value = bytes_validator(value) + return cls(value) + + def __init__(self, value: bytes): + self._secret_value = value + + def __repr__(self) -> str: + return f"SecretBytes(b'{self}')" + + def __len__(self) -> int: + return len(self._secret_value) + + def display(self) -> str: + warnings.warn('`secret_bytes.display()` is deprecated, use `str(secret_bytes)` instead', DeprecationWarning) + return str(self) + + def get_secret_value(self) -> bytes: + return self._secret_value + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ PAYMENT CARD TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + + +class PaymentCardBrand(str, Enum): + # If you add another card type, please also add it to the + # Hypothesis strategy in `pydantic._hypothesis_plugin`. + amex = 'American Express' + mastercard = 'Mastercard' + visa = 'Visa' + other = 'other' + + def __str__(self) -> str: + return self.value + + +class PaymentCardNumber(str): + """ + Based on: https://en.wikipedia.org/wiki/Payment_card_number + """ + + strip_whitespace: ClassVar[bool] = True + min_length: ClassVar[int] = 12 + max_length: ClassVar[int] = 19 + bin: str + last4: str + brand: PaymentCardBrand + + def __init__(self, card_number: str): + self.bin = card_number[:6] + self.last4 = card_number[-4:] + self.brand = self._get_brand(card_number) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield str_validator + yield constr_strip_whitespace + yield constr_length_validator + yield cls.validate_digits + yield cls.validate_luhn_check_digit + yield cls + yield cls.validate_length_for_brand + + @property + def masked(self) -> str: + num_masked = len(self) - 10 # len(bin) + len(last4) == 10 + return f'{self.bin}{"*" * num_masked}{self.last4}' + + @classmethod + def validate_digits(cls, card_number: str) -> str: + if not card_number.isdigit(): + raise errors.NotDigitError + return card_number + + @classmethod + def validate_luhn_check_digit(cls, card_number: str) -> str: + """ + Based on: https://en.wikipedia.org/wiki/Luhn_algorithm + """ + sum_ = int(card_number[-1]) + length = len(card_number) + parity = length % 2 + for i in range(length - 1): + digit = int(card_number[i]) + if i % 2 == parity: + digit *= 2 + if digit > 9: + digit -= 9 + sum_ += digit + valid = sum_ % 10 == 0 + if not valid: + raise errors.LuhnValidationError + return card_number + + @classmethod + def validate_length_for_brand(cls, card_number: 'PaymentCardNumber') -> 'PaymentCardNumber': + """ + Validate length based on BIN for major brands: + https://en.wikipedia.org/wiki/Payment_card_number#Issuer_identification_number_(IIN) + """ + required_length: Union[None, int, str] = None + if card_number.brand in PaymentCardBrand.mastercard: + required_length = 16 + valid = len(card_number) == required_length + elif card_number.brand == PaymentCardBrand.visa: + required_length = '13, 16 or 19' + valid = len(card_number) in {13, 16, 19} + elif card_number.brand == PaymentCardBrand.amex: + required_length = 15 + valid = len(card_number) == required_length + else: + valid = True + if not valid: + raise errors.InvalidLengthForBrand(brand=card_number.brand, required_length=required_length) + return card_number + + @staticmethod + def _get_brand(card_number: str) -> PaymentCardBrand: + if card_number[0] == '4': + brand = PaymentCardBrand.visa + elif 51 <= int(card_number[:2]) <= 55: + brand = PaymentCardBrand.mastercard + elif card_number[:2] in {'34', '37'}: + brand = PaymentCardBrand.amex + else: + brand = PaymentCardBrand.other + return brand + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ BYTE SIZE TYPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +BYTE_SIZES = { + 'b': 1, + 'kb': 10**3, + 'mb': 10**6, + 'gb': 10**9, + 'tb': 10**12, + 'pb': 10**15, + 'eb': 10**18, + 'kib': 2**10, + 'mib': 2**20, + 'gib': 2**30, + 'tib': 2**40, + 'pib': 2**50, + 'eib': 2**60, +} +BYTE_SIZES.update({k.lower()[0]: v for k, v in BYTE_SIZES.items() if 'i' not in k}) +byte_string_re = re.compile(r'^\s*(\d*\.?\d+)\s*(\w+)?', re.IGNORECASE) + + +class ByteSize(int): + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield cls.validate + + @classmethod + def validate(cls, v: StrIntFloat) -> 'ByteSize': + try: + return cls(int(v)) + except ValueError: + pass + + str_match = byte_string_re.match(str(v)) + if str_match is None: + raise errors.InvalidByteSize() + + scalar, unit = str_match.groups() + if unit is None: + unit = 'b' + + try: + unit_mult = BYTE_SIZES[unit.lower()] + except KeyError: + raise errors.InvalidByteSizeUnit(unit=unit) + + return cls(int(float(scalar) * unit_mult)) + + def human_readable(self, decimal: bool = False) -> str: + if decimal: + divisor = 1000 + units = ['B', 'KB', 'MB', 'GB', 'TB', 'PB'] + final_unit = 'EB' + else: + divisor = 1024 + units = ['B', 'KiB', 'MiB', 'GiB', 'TiB', 'PiB'] + final_unit = 'EiB' + + num = float(self) + for unit in units: + if abs(num) < divisor: + return f'{num:0.1f}{unit}' + num /= divisor + + return f'{num:0.1f}{final_unit}' + + def to(self, unit: str) -> float: + try: + unit_div = BYTE_SIZES[unit.lower()] + except KeyError: + raise errors.InvalidByteSizeUnit(unit=unit) + + return self / unit_div + + +# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DATE TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +if TYPE_CHECKING: + PastDate = date + FutureDate = date +else: + + class PastDate(date): + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield parse_date + yield cls.validate + + @classmethod + def validate(cls, value: date) -> date: + if value >= date.today(): + raise errors.DateNotInThePastError() + + return value + + class FutureDate(date): + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield parse_date + yield cls.validate + + @classmethod + def validate(cls, value: date) -> date: + if value <= date.today(): + raise errors.DateNotInTheFutureError() + + return value + + +class ConstrainedDate(date, metaclass=ConstrainedNumberMeta): + gt: OptionalDate = None + ge: OptionalDate = None + lt: OptionalDate = None + le: OptionalDate = None + + @classmethod + def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None: + update_not_none(field_schema, exclusiveMinimum=cls.gt, exclusiveMaximum=cls.lt, minimum=cls.ge, maximum=cls.le) + + @classmethod + def __get_validators__(cls) -> 'CallableGenerator': + yield parse_date + yield number_size_validator + + +def condate( + *, + gt: date = None, + ge: date = None, + lt: date = None, + le: date = None, +) -> Type[date]: + # use kwargs then define conf in a dict to aid with IDE type hinting + namespace = dict(gt=gt, ge=ge, lt=lt, le=le) + return type('ConstrainedDateValue', (ConstrainedDate,), namespace) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/typing.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/typing.py new file mode 100644 index 0000000000000000000000000000000000000000..c5c59794b9b3d29765e24bfb05880de78e6f5a25 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/typing.py @@ -0,0 +1,627 @@ +import functools +import operator +import sys +import typing +from collections.abc import Callable +from os import PathLike +from typing import ( # type: ignore + TYPE_CHECKING, + AbstractSet, + Any, + Callable as TypingCallable, + ClassVar, + Dict, + ForwardRef, + Generator, + Iterable, + List, + Mapping, + NewType, + Optional, + Sequence, + Set, + Tuple, + Type, + TypeVar, + Union, + _eval_type, + cast, + get_type_hints, +) + +from typing_extensions import ( + Annotated, + Final, + Literal, + NotRequired as TypedDictNotRequired, + Required as TypedDictRequired, +) + +try: + from typing import _TypingBase as typing_base # type: ignore +except ImportError: + from typing import _Final as typing_base # type: ignore + +try: + from typing import GenericAlias as TypingGenericAlias # type: ignore +except ImportError: + # python < 3.9 does not have GenericAlias (list[int], tuple[str, ...] and so on) + TypingGenericAlias = () + +try: + from types import UnionType as TypesUnionType # type: ignore +except ImportError: + # python < 3.10 does not have UnionType (str | int, byte | bool and so on) + TypesUnionType = () + + +if sys.version_info < (3, 9): + + def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any: + return type_._evaluate(globalns, localns) + +elif sys.version_info < (3, 12, 4): + + def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any: + # Even though it is the right signature for python 3.9, mypy complains with + # `error: Too many arguments for "_evaluate" of "ForwardRef"` hence the cast... + # Python 3.13/3.12.4+ made `recursive_guard` a kwarg, so name it explicitly to avoid: + # TypeError: ForwardRef._evaluate() missing 1 required keyword-only argument: 'recursive_guard' + return cast(Any, type_)._evaluate(globalns, localns, recursive_guard=set()) + +elif sys.version_info < (3, 14): + + def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any: + # Pydantic 1.x will not support PEP 695 syntax, but provide `type_params` to avoid + # warnings: + return cast(Any, type_)._evaluate(globalns, localns, type_params=(), recursive_guard=set()) + +else: + + def evaluate_forwardref(type_: ForwardRef, globalns: Any, localns: Any) -> Any: + # Pydantic 1.x will not support PEP 695 syntax, but provide `type_params` to avoid + # warnings: + return typing.evaluate_forward_ref( + type_, + globals=globalns, + locals=localns, + type_params=(), + _recursive_guard=set(), + ) + + +if sys.version_info < (3, 9): + # Ensure we always get all the whole `Annotated` hint, not just the annotated type. + # For 3.7 to 3.8, `get_type_hints` doesn't recognize `typing_extensions.Annotated`, + # so it already returns the full annotation + get_all_type_hints = get_type_hints + +else: + + def get_all_type_hints(obj: Any, globalns: Any = None, localns: Any = None) -> Any: + return get_type_hints(obj, globalns, localns, include_extras=True) + + +_T = TypeVar('_T') + +AnyCallable = TypingCallable[..., Any] +NoArgAnyCallable = TypingCallable[[], Any] + +# workaround for https://github.com/python/mypy/issues/9496 +AnyArgTCallable = TypingCallable[..., _T] + + +# Annotated[...] is implemented by returning an instance of one of these classes, depending on +# python/typing_extensions version. +AnnotatedTypeNames = {'AnnotatedMeta', '_AnnotatedAlias'} + + +LITERAL_TYPES: Set[Any] = {Literal} +if hasattr(typing, 'Literal'): + LITERAL_TYPES.add(typing.Literal) + + +if sys.version_info < (3, 8): + + def get_origin(t: Type[Any]) -> Optional[Type[Any]]: + if type(t).__name__ in AnnotatedTypeNames: + # weirdly this is a runtime requirement, as well as for mypy + return cast(Type[Any], Annotated) + return getattr(t, '__origin__', None) + +else: + from typing import get_origin as _typing_get_origin + + def get_origin(tp: Type[Any]) -> Optional[Type[Any]]: + """ + We can't directly use `typing.get_origin` since we need a fallback to support + custom generic classes like `ConstrainedList` + It should be useless once https://github.com/cython/cython/issues/3537 is + solved and https://github.com/pydantic/pydantic/pull/1753 is merged. + """ + if type(tp).__name__ in AnnotatedTypeNames: + return cast(Type[Any], Annotated) # mypy complains about _SpecialForm + return _typing_get_origin(tp) or getattr(tp, '__origin__', None) + + +if sys.version_info < (3, 8): + from typing import _GenericAlias + + def get_args(t: Type[Any]) -> Tuple[Any, ...]: + """Compatibility version of get_args for python 3.7. + + Mostly compatible with the python 3.8 `typing` module version + and able to handle almost all use cases. + """ + if type(t).__name__ in AnnotatedTypeNames: + return t.__args__ + t.__metadata__ + if isinstance(t, _GenericAlias): + res = t.__args__ + if t.__origin__ is Callable and res and res[0] is not Ellipsis: + res = (list(res[:-1]), res[-1]) + return res + return getattr(t, '__args__', ()) + +else: + from typing import get_args as _typing_get_args + + def _generic_get_args(tp: Type[Any]) -> Tuple[Any, ...]: + """ + In python 3.9, `typing.Dict`, `typing.List`, ... + do have an empty `__args__` by default (instead of the generic ~T for example). + In order to still support `Dict` for example and consider it as `Dict[Any, Any]`, + we retrieve the `_nparams` value that tells us how many parameters it needs. + """ + if hasattr(tp, '_nparams'): + return (Any,) * tp._nparams + # Special case for `tuple[()]`, which used to return ((),) with `typing.Tuple` + # in python 3.10- but now returns () for `tuple` and `Tuple`. + # This will probably be clarified in pydantic v2 + try: + if tp == Tuple[()] or sys.version_info >= (3, 9) and tp == tuple[()]: # type: ignore[misc] + return ((),) + # there is a TypeError when compiled with cython + except TypeError: # pragma: no cover + pass + return () + + def get_args(tp: Type[Any]) -> Tuple[Any, ...]: + """Get type arguments with all substitutions performed. + + For unions, basic simplifications used by Union constructor are performed. + Examples:: + get_args(Dict[str, int]) == (str, int) + get_args(int) == () + get_args(Union[int, Union[T, int], str][int]) == (int, str) + get_args(Union[int, Tuple[T, int]][str]) == (int, Tuple[str, int]) + get_args(Callable[[], T][int]) == ([], int) + """ + if type(tp).__name__ in AnnotatedTypeNames: + return tp.__args__ + tp.__metadata__ + # the fallback is needed for the same reasons as `get_origin` (see above) + return _typing_get_args(tp) or getattr(tp, '__args__', ()) or _generic_get_args(tp) + + +if sys.version_info < (3, 9): + + def convert_generics(tp: Type[Any]) -> Type[Any]: + """Python 3.9 and older only supports generics from `typing` module. + They convert strings to ForwardRef automatically. + + Examples:: + typing.List['Hero'] == typing.List[ForwardRef('Hero')] + """ + return tp + +else: + + def convert_generics(tp: Type[Any]) -> Type[Any]: + """ + Recursively searches for `str` type hints and replaces them with ForwardRef. + + Examples:: + convert_generics(list['Hero']) == list[ForwardRef('Hero')] + convert_generics(dict['Hero', 'Team']) == dict[ForwardRef('Hero'), ForwardRef('Team')] + convert_generics(typing.Dict['Hero', 'Team']) == typing.Dict[ForwardRef('Hero'), ForwardRef('Team')] + convert_generics(list[str | 'Hero'] | int) == list[str | ForwardRef('Hero')] | int + """ + origin = get_origin(tp) + if not origin or not hasattr(tp, '__args__'): + return tp + + args = get_args(tp) + + # typing.Annotated needs special treatment + if origin is Annotated: + return Annotated[(convert_generics(args[0]), *args[1:])] # type: ignore + + # recursively replace `str` instances inside of `GenericAlias` with `ForwardRef(arg)` + converted = tuple( + ForwardRef(arg) if isinstance(arg, str) and isinstance(tp, TypingGenericAlias) else convert_generics(arg) + for arg in args + ) + + if converted == args: + return tp + elif isinstance(tp, TypingGenericAlias): + return TypingGenericAlias(origin, converted) + elif isinstance(tp, TypesUnionType): + # recreate types.UnionType (PEP604, Python >= 3.10) + return functools.reduce(operator.or_, converted) # type: ignore + else: + try: + setattr(tp, '__args__', converted) + except AttributeError: + pass + return tp + + +if sys.version_info < (3, 10): + + def is_union(tp: Optional[Type[Any]]) -> bool: + return tp is Union + + WithArgsTypes = (TypingGenericAlias,) + +else: + import types + import typing + + def is_union(tp: Optional[Type[Any]]) -> bool: + return tp is Union or tp is types.UnionType # noqa: E721 + + WithArgsTypes = (typing._GenericAlias, types.GenericAlias, types.UnionType) + + +StrPath = Union[str, PathLike] + + +if TYPE_CHECKING: + from pydantic.v1.fields import ModelField + + TupleGenerator = Generator[Tuple[str, Any], None, None] + DictStrAny = Dict[str, Any] + DictAny = Dict[Any, Any] + SetStr = Set[str] + ListStr = List[str] + IntStr = Union[int, str] + AbstractSetIntStr = AbstractSet[IntStr] + DictIntStrAny = Dict[IntStr, Any] + MappingIntStrAny = Mapping[IntStr, Any] + CallableGenerator = Generator[AnyCallable, None, None] + ReprArgs = Sequence[Tuple[Optional[str], Any]] + + MYPY = False + if MYPY: + AnyClassMethod = classmethod[Any] + else: + # classmethod[TargetType, CallableParamSpecType, CallableReturnType] + AnyClassMethod = classmethod[Any, Any, Any] + +__all__ = ( + 'AnyCallable', + 'NoArgAnyCallable', + 'NoneType', + 'is_none_type', + 'display_as_type', + 'resolve_annotations', + 'is_callable_type', + 'is_literal_type', + 'all_literal_values', + 'is_namedtuple', + 'is_typeddict', + 'is_typeddict_special', + 'is_new_type', + 'new_type_supertype', + 'is_classvar', + 'is_finalvar', + 'update_field_forward_refs', + 'update_model_forward_refs', + 'TupleGenerator', + 'DictStrAny', + 'DictAny', + 'SetStr', + 'ListStr', + 'IntStr', + 'AbstractSetIntStr', + 'DictIntStrAny', + 'CallableGenerator', + 'ReprArgs', + 'AnyClassMethod', + 'CallableGenerator', + 'WithArgsTypes', + 'get_args', + 'get_origin', + 'get_sub_types', + 'typing_base', + 'get_all_type_hints', + 'is_union', + 'StrPath', + 'MappingIntStrAny', +) + + +NoneType = None.__class__ + + +NONE_TYPES: Tuple[Any, Any, Any] = (None, NoneType, Literal[None]) + + +if sys.version_info < (3, 8): + # Even though this implementation is slower, we need it for python 3.7: + # In python 3.7 "Literal" is not a builtin type and uses a different + # mechanism. + # for this reason `Literal[None] is Literal[None]` evaluates to `False`, + # breaking the faster implementation used for the other python versions. + + def is_none_type(type_: Any) -> bool: + return type_ in NONE_TYPES + +elif sys.version_info[:2] == (3, 8): + + def is_none_type(type_: Any) -> bool: + for none_type in NONE_TYPES: + if type_ is none_type: + return True + # With python 3.8, specifically 3.8.10, Literal "is" check sare very flakey + # can change on very subtle changes like use of types in other modules, + # hopefully this check avoids that issue. + if is_literal_type(type_): # pragma: no cover + return all_literal_values(type_) == (None,) + return False + +else: + + def is_none_type(type_: Any) -> bool: + return type_ in NONE_TYPES + + +def display_as_type(v: Type[Any]) -> str: + if not isinstance(v, typing_base) and not isinstance(v, WithArgsTypes) and not isinstance(v, type): + v = v.__class__ + + if is_union(get_origin(v)): + return f'Union[{", ".join(map(display_as_type, get_args(v)))}]' + + if isinstance(v, WithArgsTypes): + # Generic alias are constructs like `list[int]` + return str(v).replace('typing.', '') + + try: + return v.__name__ + except AttributeError: + # happens with typing objects + return str(v).replace('typing.', '') + + +def resolve_annotations(raw_annotations: Dict[str, Type[Any]], module_name: Optional[str]) -> Dict[str, Type[Any]]: + """ + Partially taken from typing.get_type_hints. + + Resolve string or ForwardRef annotations into type objects if possible. + """ + base_globals: Optional[Dict[str, Any]] = None + if module_name: + try: + module = sys.modules[module_name] + except KeyError: + # happens occasionally, see https://github.com/pydantic/pydantic/issues/2363 + pass + else: + base_globals = module.__dict__ + + annotations = {} + for name, value in raw_annotations.items(): + if isinstance(value, str): + if (3, 10) > sys.version_info >= (3, 9, 8) or sys.version_info >= (3, 10, 1): + value = ForwardRef(value, is_argument=False, is_class=True) + else: + value = ForwardRef(value, is_argument=False) + try: + if sys.version_info >= (3, 13): + value = _eval_type(value, base_globals, None, type_params=()) + else: + value = _eval_type(value, base_globals, None) + except NameError: + # this is ok, it can be fixed with update_forward_refs + pass + annotations[name] = value + return annotations + + +def is_callable_type(type_: Type[Any]) -> bool: + return type_ is Callable or get_origin(type_) is Callable + + +def is_literal_type(type_: Type[Any]) -> bool: + return Literal is not None and get_origin(type_) in LITERAL_TYPES + + +def literal_values(type_: Type[Any]) -> Tuple[Any, ...]: + return get_args(type_) + + +def all_literal_values(type_: Type[Any]) -> Tuple[Any, ...]: + """ + This method is used to retrieve all Literal values as + Literal can be used recursively (see https://www.python.org/dev/peps/pep-0586) + e.g. `Literal[Literal[Literal[1, 2, 3], "foo"], 5, None]` + """ + if not is_literal_type(type_): + return (type_,) + + values = literal_values(type_) + return tuple(x for value in values for x in all_literal_values(value)) + + +def is_namedtuple(type_: Type[Any]) -> bool: + """ + Check if a given class is a named tuple. + It can be either a `typing.NamedTuple` or `collections.namedtuple` + """ + from pydantic.v1.utils import lenient_issubclass + + return lenient_issubclass(type_, tuple) and hasattr(type_, '_fields') + + +def is_typeddict(type_: Type[Any]) -> bool: + """ + Check if a given class is a typed dict (from `typing` or `typing_extensions`) + In 3.10, there will be a public method (https://docs.python.org/3.10/library/typing.html#typing.is_typeddict) + """ + from pydantic.v1.utils import lenient_issubclass + + return lenient_issubclass(type_, dict) and hasattr(type_, '__total__') + + +def _check_typeddict_special(type_: Any) -> bool: + return type_ is TypedDictRequired or type_ is TypedDictNotRequired + + +def is_typeddict_special(type_: Any) -> bool: + """ + Check if type is a TypedDict special form (Required or NotRequired). + """ + return _check_typeddict_special(type_) or _check_typeddict_special(get_origin(type_)) + + +test_type = NewType('test_type', str) + + +def is_new_type(type_: Type[Any]) -> bool: + """ + Check whether type_ was created using typing.NewType + """ + return isinstance(type_, test_type.__class__) and hasattr(type_, '__supertype__') # type: ignore + + +def new_type_supertype(type_: Type[Any]) -> Type[Any]: + while hasattr(type_, '__supertype__'): + type_ = type_.__supertype__ + return type_ + + +def _check_classvar(v: Optional[Type[Any]]) -> bool: + if v is None: + return False + + return v.__class__ == ClassVar.__class__ and getattr(v, '_name', None) == 'ClassVar' + + +def _check_finalvar(v: Optional[Type[Any]]) -> bool: + """ + Check if a given type is a `typing.Final` type. + """ + if v is None: + return False + + return v.__class__ == Final.__class__ and (sys.version_info < (3, 8) or getattr(v, '_name', None) == 'Final') + + +def is_classvar(ann_type: Type[Any]) -> bool: + if _check_classvar(ann_type) or _check_classvar(get_origin(ann_type)): + return True + + # this is an ugly workaround for class vars that contain forward references and are therefore themselves + # forward references, see #3679 + if ann_type.__class__ == ForwardRef and ann_type.__forward_arg__.startswith('ClassVar['): + return True + + return False + + +def is_finalvar(ann_type: Type[Any]) -> bool: + return _check_finalvar(ann_type) or _check_finalvar(get_origin(ann_type)) + + +def update_field_forward_refs(field: 'ModelField', globalns: Any, localns: Any) -> None: + """ + Try to update ForwardRefs on fields based on this ModelField, globalns and localns. + """ + prepare = False + if field.type_.__class__ == ForwardRef: + prepare = True + field.type_ = evaluate_forwardref(field.type_, globalns, localns or None) + if field.outer_type_.__class__ == ForwardRef: + prepare = True + field.outer_type_ = evaluate_forwardref(field.outer_type_, globalns, localns or None) + if prepare: + field.prepare() + + if field.sub_fields: + for sub_f in field.sub_fields: + update_field_forward_refs(sub_f, globalns=globalns, localns=localns) + + if field.discriminator_key is not None: + field.prepare_discriminated_union_sub_fields() + + +def update_model_forward_refs( + model: Type[Any], + fields: Iterable['ModelField'], + json_encoders: Dict[Union[Type[Any], str, ForwardRef], AnyCallable], + localns: 'DictStrAny', + exc_to_suppress: Tuple[Type[BaseException], ...] = (), +) -> None: + """ + Try to update model fields ForwardRefs based on model and localns. + """ + if model.__module__ in sys.modules: + globalns = sys.modules[model.__module__].__dict__.copy() + else: + globalns = {} + + globalns.setdefault(model.__name__, model) + + for f in fields: + try: + update_field_forward_refs(f, globalns=globalns, localns=localns) + except exc_to_suppress: + pass + + for key in set(json_encoders.keys()): + if isinstance(key, str): + fr: ForwardRef = ForwardRef(key) + elif isinstance(key, ForwardRef): + fr = key + else: + continue + + try: + new_key = evaluate_forwardref(fr, globalns, localns or None) + except exc_to_suppress: # pragma: no cover + continue + + json_encoders[new_key] = json_encoders.pop(key) + + +def get_class(type_: Type[Any]) -> Union[None, bool, Type[Any]]: + """ + Tries to get the class of a Type[T] annotation. Returns True if Type is used + without brackets. Otherwise returns None. + """ + if type_ is type: + return True + + if get_origin(type_) is None: + return None + + args = get_args(type_) + if not args or not isinstance(args[0], type): + return True + else: + return args[0] + + +def get_sub_types(tp: Any) -> List[Any]: + """ + Return all the types that are allowed by type `tp` + `tp` can be a `Union` of allowed types or an `Annotated` type + """ + origin = get_origin(tp) + if origin is Annotated: + return get_sub_types(get_args(tp)[0]) + elif is_union(origin): + return [x for t in get_args(tp) for x in get_sub_types(t)] + else: + return [tp] diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/utils.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..2094e84fad951d6620635cf86f7f5fb647c5c20b --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/utils.py @@ -0,0 +1,807 @@ +import keyword +import warnings +import weakref +from collections import OrderedDict, defaultdict, deque +from copy import deepcopy +from itertools import islice, zip_longest +from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType +from typing import ( + TYPE_CHECKING, + AbstractSet, + Any, + Callable, + Collection, + Dict, + Generator, + Iterable, + Iterator, + List, + Mapping, + NoReturn, + Optional, + Set, + Tuple, + Type, + TypeVar, + Union, +) + +from typing_extensions import Annotated + +from pydantic.v1.errors import ConfigError +from pydantic.v1.typing import ( + NoneType, + WithArgsTypes, + all_literal_values, + display_as_type, + get_args, + get_origin, + is_literal_type, + is_union, +) +from pydantic.v1.version import version_info + +if TYPE_CHECKING: + from inspect import Signature + from pathlib import Path + + from pydantic.v1.config import BaseConfig + from pydantic.v1.dataclasses import Dataclass + from pydantic.v1.fields import ModelField + from pydantic.v1.main import BaseModel + from pydantic.v1.typing import AbstractSetIntStr, DictIntStrAny, IntStr, MappingIntStrAny, ReprArgs + + RichReprResult = Iterable[Union[Any, Tuple[Any], Tuple[str, Any], Tuple[str, Any, Any]]] + +__all__ = ( + 'import_string', + 'sequence_like', + 'validate_field_name', + 'lenient_isinstance', + 'lenient_issubclass', + 'in_ipython', + 'is_valid_identifier', + 'deep_update', + 'update_not_none', + 'almost_equal_floats', + 'get_model', + 'to_camel', + 'to_lower_camel', + 'is_valid_field', + 'smart_deepcopy', + 'PyObjectStr', + 'Representation', + 'GetterDict', + 'ValueItems', + 'version_info', # required here to match behaviour in v1.3 + 'ClassAttribute', + 'path_type', + 'ROOT_KEY', + 'get_unique_discriminator_alias', + 'get_discriminator_alias_and_values', + 'DUNDER_ATTRIBUTES', +) + +ROOT_KEY = '__root__' +# these are types that are returned unchanged by deepcopy +IMMUTABLE_NON_COLLECTIONS_TYPES: Set[Type[Any]] = { + int, + float, + complex, + str, + bool, + bytes, + type, + NoneType, + FunctionType, + BuiltinFunctionType, + LambdaType, + weakref.ref, + CodeType, + # note: including ModuleType will differ from behaviour of deepcopy by not producing error. + # It might be not a good idea in general, but considering that this function used only internally + # against default values of fields, this will allow to actually have a field with module as default value + ModuleType, + NotImplemented.__class__, + Ellipsis.__class__, +} + +# these are types that if empty, might be copied with simple copy() instead of deepcopy() +BUILTIN_COLLECTIONS: Set[Type[Any]] = { + list, + set, + tuple, + frozenset, + dict, + OrderedDict, + defaultdict, + deque, +} + + +def import_string(dotted_path: str) -> Any: + """ + Stolen approximately from django. Import a dotted module path and return the attribute/class designated by the + last name in the path. Raise ImportError if the import fails. + """ + from importlib import import_module + + try: + module_path, class_name = dotted_path.strip(' ').rsplit('.', 1) + except ValueError as e: + raise ImportError(f'"{dotted_path}" doesn\'t look like a module path') from e + + module = import_module(module_path) + try: + return getattr(module, class_name) + except AttributeError as e: + raise ImportError(f'Module "{module_path}" does not define a "{class_name}" attribute') from e + + +def truncate(v: Union[str], *, max_len: int = 80) -> str: + """ + Truncate a value and add a unicode ellipsis (three dots) to the end if it was too long + """ + warnings.warn('`truncate` is no-longer used by pydantic and is deprecated', DeprecationWarning) + if isinstance(v, str) and len(v) > (max_len - 2): + # -3 so quote + string + … + quote has correct length + return (v[: (max_len - 3)] + '…').__repr__() + try: + v = v.__repr__() + except TypeError: + v = v.__class__.__repr__(v) # in case v is a type + if len(v) > max_len: + v = v[: max_len - 1] + '…' + return v + + +def sequence_like(v: Any) -> bool: + return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque)) + + +def validate_field_name(bases: Iterable[Type[Any]], field_name: str) -> None: + """ + Ensure that the field's name does not shadow an existing attribute of the model. + """ + for base in bases: + if getattr(base, field_name, None): + raise NameError( + f'Field name "{field_name}" shadows a BaseModel attribute; ' + f'use a different field name with "alias=\'{field_name}\'".' + ) + + +def lenient_isinstance(o: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool: + try: + return isinstance(o, class_or_tuple) # type: ignore[arg-type] + except TypeError: + return False + + +def lenient_issubclass(cls: Any, class_or_tuple: Union[Type[Any], Tuple[Type[Any], ...], None]) -> bool: + try: + return isinstance(cls, type) and issubclass(cls, class_or_tuple) # type: ignore[arg-type] + except TypeError: + if isinstance(cls, WithArgsTypes): + return False + raise # pragma: no cover + + +def in_ipython() -> bool: + """ + Check whether we're in an ipython environment, including jupyter notebooks. + """ + try: + eval('__IPYTHON__') + except NameError: + return False + else: # pragma: no cover + return True + + +def is_valid_identifier(identifier: str) -> bool: + """ + Checks that a string is a valid identifier and not a Python keyword. + :param identifier: The identifier to test. + :return: True if the identifier is valid. + """ + return identifier.isidentifier() and not keyword.iskeyword(identifier) + + +KeyType = TypeVar('KeyType') + + +def deep_update(mapping: Dict[KeyType, Any], *updating_mappings: Dict[KeyType, Any]) -> Dict[KeyType, Any]: + updated_mapping = mapping.copy() + for updating_mapping in updating_mappings: + for k, v in updating_mapping.items(): + if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict): + updated_mapping[k] = deep_update(updated_mapping[k], v) + else: + updated_mapping[k] = v + return updated_mapping + + +def update_not_none(mapping: Dict[Any, Any], **update: Any) -> None: + mapping.update({k: v for k, v in update.items() if v is not None}) + + +def almost_equal_floats(value_1: float, value_2: float, *, delta: float = 1e-8) -> bool: + """ + Return True if two floats are almost equal + """ + return abs(value_1 - value_2) <= delta + + +def generate_model_signature( + init: Callable[..., None], fields: Dict[str, 'ModelField'], config: Type['BaseConfig'] +) -> 'Signature': + """ + Generate signature for model based on its fields + """ + from inspect import Parameter, Signature, signature + + from pydantic.v1.config import Extra + + present_params = signature(init).parameters.values() + merged_params: Dict[str, Parameter] = {} + var_kw = None + use_var_kw = False + + for param in islice(present_params, 1, None): # skip self arg + if param.kind is param.VAR_KEYWORD: + var_kw = param + continue + merged_params[param.name] = param + + if var_kw: # if custom init has no var_kw, fields which are not declared in it cannot be passed through + allow_names = config.allow_population_by_field_name + for field_name, field in fields.items(): + param_name = field.alias + if field_name in merged_params or param_name in merged_params: + continue + elif not is_valid_identifier(param_name): + if allow_names and is_valid_identifier(field_name): + param_name = field_name + else: + use_var_kw = True + continue + + # TODO: replace annotation with actual expected types once #1055 solved + kwargs = {'default': field.default} if not field.required else {} + merged_params[param_name] = Parameter( + param_name, Parameter.KEYWORD_ONLY, annotation=field.annotation, **kwargs + ) + + if config.extra is Extra.allow: + use_var_kw = True + + if var_kw and use_var_kw: + # Make sure the parameter for extra kwargs + # does not have the same name as a field + default_model_signature = [ + ('__pydantic_self__', Parameter.POSITIONAL_OR_KEYWORD), + ('data', Parameter.VAR_KEYWORD), + ] + if [(p.name, p.kind) for p in present_params] == default_model_signature: + # if this is the standard model signature, use extra_data as the extra args name + var_kw_name = 'extra_data' + else: + # else start from var_kw + var_kw_name = var_kw.name + + # generate a name that's definitely unique + while var_kw_name in fields: + var_kw_name += '_' + merged_params[var_kw_name] = var_kw.replace(name=var_kw_name) + + return Signature(parameters=list(merged_params.values()), return_annotation=None) + + +def get_model(obj: Union[Type['BaseModel'], Type['Dataclass']]) -> Type['BaseModel']: + from pydantic.v1.main import BaseModel + + try: + model_cls = obj.__pydantic_model__ # type: ignore + except AttributeError: + model_cls = obj + + if not issubclass(model_cls, BaseModel): + raise TypeError('Unsupported type, must be either BaseModel or dataclass') + return model_cls + + +def to_camel(string: str) -> str: + return ''.join(word.capitalize() for word in string.split('_')) + + +def to_lower_camel(string: str) -> str: + if len(string) >= 1: + pascal_string = to_camel(string) + return pascal_string[0].lower() + pascal_string[1:] + return string.lower() + + +T = TypeVar('T') + + +def unique_list( + input_list: Union[List[T], Tuple[T, ...]], + *, + name_factory: Callable[[T], str] = str, +) -> List[T]: + """ + Make a list unique while maintaining order. + We update the list if another one with the same name is set + (e.g. root validator overridden in subclass) + """ + result: List[T] = [] + result_names: List[str] = [] + for v in input_list: + v_name = name_factory(v) + if v_name not in result_names: + result_names.append(v_name) + result.append(v) + else: + result[result_names.index(v_name)] = v + + return result + + +class PyObjectStr(str): + """ + String class where repr doesn't include quotes. Useful with Representation when you want to return a string + representation of something that valid (or pseudo-valid) python. + """ + + def __repr__(self) -> str: + return str(self) + + +class Representation: + """ + Mixin to provide __str__, __repr__, and __pretty__ methods. See #884 for more details. + + __pretty__ is used by [devtools](https://python-devtools.helpmanual.io/) to provide human readable representations + of objects. + """ + + __slots__: Tuple[str, ...] = tuple() + + def __repr_args__(self) -> 'ReprArgs': + """ + Returns the attributes to show in __str__, __repr__, and __pretty__ this is generally overridden. + + Can either return: + * name - value pairs, e.g.: `[('foo_name', 'foo'), ('bar_name', ['b', 'a', 'r'])]` + * or, just values, e.g.: `[(None, 'foo'), (None, ['b', 'a', 'r'])]` + """ + attrs = ((s, getattr(self, s)) for s in self.__slots__) + return [(a, v) for a, v in attrs if v is not None] + + def __repr_name__(self) -> str: + """ + Name of the instance's class, used in __repr__. + """ + return self.__class__.__name__ + + def __repr_str__(self, join_str: str) -> str: + return join_str.join(repr(v) if a is None else f'{a}={v!r}' for a, v in self.__repr_args__()) + + def __pretty__(self, fmt: Callable[[Any], Any], **kwargs: Any) -> Generator[Any, None, None]: + """ + Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects + """ + yield self.__repr_name__() + '(' + yield 1 + for name, value in self.__repr_args__(): + if name is not None: + yield name + '=' + yield fmt(value) + yield ',' + yield 0 + yield -1 + yield ')' + + def __str__(self) -> str: + return self.__repr_str__(' ') + + def __repr__(self) -> str: + return f'{self.__repr_name__()}({self.__repr_str__(", ")})' + + def __rich_repr__(self) -> 'RichReprResult': + """Get fields for Rich library""" + for name, field_repr in self.__repr_args__(): + if name is None: + yield field_repr + else: + yield name, field_repr + + +class GetterDict(Representation): + """ + Hack to make object's smell just enough like dicts for validate_model. + + We can't inherit from Mapping[str, Any] because it upsets cython so we have to implement all methods ourselves. + """ + + __slots__ = ('_obj',) + + def __init__(self, obj: Any): + self._obj = obj + + def __getitem__(self, key: str) -> Any: + try: + return getattr(self._obj, key) + except AttributeError as e: + raise KeyError(key) from e + + def get(self, key: Any, default: Any = None) -> Any: + return getattr(self._obj, key, default) + + def extra_keys(self) -> Set[Any]: + """ + We don't want to get any other attributes of obj if the model didn't explicitly ask for them + """ + return set() + + def keys(self) -> List[Any]: + """ + Keys of the pseudo dictionary, uses a list not set so order information can be maintained like python + dictionaries. + """ + return list(self) + + def values(self) -> List[Any]: + return [self[k] for k in self] + + def items(self) -> Iterator[Tuple[str, Any]]: + for k in self: + yield k, self.get(k) + + def __iter__(self) -> Iterator[str]: + for name in dir(self._obj): + if not name.startswith('_'): + yield name + + def __len__(self) -> int: + return sum(1 for _ in self) + + def __contains__(self, item: Any) -> bool: + return item in self.keys() + + def __eq__(self, other: Any) -> bool: + return dict(self) == dict(other.items()) + + def __repr_args__(self) -> 'ReprArgs': + return [(None, dict(self))] + + def __repr_name__(self) -> str: + return f'GetterDict[{display_as_type(self._obj)}]' + + +class ValueItems(Representation): + """ + Class for more convenient calculation of excluded or included fields on values. + """ + + __slots__ = ('_items', '_type') + + def __init__(self, value: Any, items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> None: + items = self._coerce_items(items) + + if isinstance(value, (list, tuple)): + items = self._normalize_indexes(items, len(value)) + + self._items: 'MappingIntStrAny' = items + + def is_excluded(self, item: Any) -> bool: + """ + Check if item is fully excluded. + + :param item: key or index of a value + """ + return self.is_true(self._items.get(item)) + + def is_included(self, item: Any) -> bool: + """ + Check if value is contained in self._items + + :param item: key or index of value + """ + return item in self._items + + def for_element(self, e: 'IntStr') -> Optional[Union['AbstractSetIntStr', 'MappingIntStrAny']]: + """ + :param e: key or index of element on value + :return: raw values for element if self._items is dict and contain needed element + """ + + item = self._items.get(e) + return item if not self.is_true(item) else None + + def _normalize_indexes(self, items: 'MappingIntStrAny', v_length: int) -> 'DictIntStrAny': + """ + :param items: dict or set of indexes which will be normalized + :param v_length: length of sequence indexes of which will be + + >>> self._normalize_indexes({0: True, -2: True, -1: True}, 4) + {0: True, 2: True, 3: True} + >>> self._normalize_indexes({'__all__': True}, 4) + {0: True, 1: True, 2: True, 3: True} + """ + + normalized_items: 'DictIntStrAny' = {} + all_items = None + for i, v in items.items(): + if not (isinstance(v, Mapping) or isinstance(v, AbstractSet) or self.is_true(v)): + raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}') + if i == '__all__': + all_items = self._coerce_value(v) + continue + if not isinstance(i, int): + raise TypeError( + 'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: ' + 'expected integer keys or keyword "__all__"' + ) + normalized_i = v_length + i if i < 0 else i + normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i)) + + if not all_items: + return normalized_items + if self.is_true(all_items): + for i in range(v_length): + normalized_items.setdefault(i, ...) + return normalized_items + for i in range(v_length): + normalized_item = normalized_items.setdefault(i, {}) + if not self.is_true(normalized_item): + normalized_items[i] = self.merge(all_items, normalized_item) + return normalized_items + + @classmethod + def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any: + """ + Merge a ``base`` item with an ``override`` item. + + Both ``base`` and ``override`` are converted to dictionaries if possible. + Sets are converted to dictionaries with the sets entries as keys and + Ellipsis as values. + + Each key-value pair existing in ``base`` is merged with ``override``, + while the rest of the key-value pairs are updated recursively with this function. + + Merging takes place based on the "union" of keys if ``intersect`` is + set to ``False`` (default) and on the intersection of keys if + ``intersect`` is set to ``True``. + """ + override = cls._coerce_value(override) + base = cls._coerce_value(base) + if override is None: + return base + if cls.is_true(base) or base is None: + return override + if cls.is_true(override): + return base if intersect else override + + # intersection or union of keys while preserving ordering: + if intersect: + merge_keys = [k for k in base if k in override] + [k for k in override if k in base] + else: + merge_keys = list(base) + [k for k in override if k not in base] + + merged: 'DictIntStrAny' = {} + for k in merge_keys: + merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect) + if merged_item is not None: + merged[k] = merged_item + + return merged + + @staticmethod + def _coerce_items(items: Union['AbstractSetIntStr', 'MappingIntStrAny']) -> 'MappingIntStrAny': + if isinstance(items, Mapping): + pass + elif isinstance(items, AbstractSet): + items = dict.fromkeys(items, ...) + else: + class_name = getattr(items, '__class__', '???') + assert_never( + items, + f'Unexpected type of exclude value {class_name}', + ) + return items + + @classmethod + def _coerce_value(cls, value: Any) -> Any: + if value is None or cls.is_true(value): + return value + return cls._coerce_items(value) + + @staticmethod + def is_true(v: Any) -> bool: + return v is True or v is ... + + def __repr_args__(self) -> 'ReprArgs': + return [(None, self._items)] + + +class ClassAttribute: + """ + Hide class attribute from its instances + """ + + __slots__ = ( + 'name', + 'value', + ) + + def __init__(self, name: str, value: Any) -> None: + self.name = name + self.value = value + + def __get__(self, instance: Any, owner: Type[Any]) -> None: + if instance is None: + return self.value + raise AttributeError(f'{self.name!r} attribute of {owner.__name__!r} is class-only') + + +path_types = { + 'is_dir': 'directory', + 'is_file': 'file', + 'is_mount': 'mount point', + 'is_symlink': 'symlink', + 'is_block_device': 'block device', + 'is_char_device': 'char device', + 'is_fifo': 'FIFO', + 'is_socket': 'socket', +} + + +def path_type(p: 'Path') -> str: + """ + Find out what sort of thing a path is. + """ + assert p.exists(), 'path does not exist' + for method, name in path_types.items(): + if getattr(p, method)(): + return name + + return 'unknown' + + +Obj = TypeVar('Obj') + + +def smart_deepcopy(obj: Obj) -> Obj: + """ + Return type as is for immutable built-in types + Use obj.copy() for built-in empty collections + Use copy.deepcopy() for non-empty collections and unknown objects + """ + + obj_type = obj.__class__ + if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES: + return obj # fastest case: obj is immutable and not collection therefore will not be copied anyway + try: + if not obj and obj_type in BUILTIN_COLLECTIONS: + # faster way for empty collections, no need to copy its members + return obj if obj_type is tuple else obj.copy() # type: ignore # tuple doesn't have copy method + except (TypeError, ValueError, RuntimeError): + # do we really dare to catch ALL errors? Seems a bit risky + pass + + return deepcopy(obj) # slowest way when we actually might need a deepcopy + + +def is_valid_field(name: str) -> bool: + if not name.startswith('_'): + return True + return ROOT_KEY == name + + +DUNDER_ATTRIBUTES = { + '__annotations__', + '__classcell__', + '__doc__', + '__module__', + '__orig_bases__', + '__orig_class__', + '__qualname__', + '__firstlineno__', + '__static_attributes__', + '__classdictcell__', +} + + +def is_valid_private_name(name: str) -> bool: + return not is_valid_field(name) and name not in DUNDER_ATTRIBUTES + + +_EMPTY = object() + + +def all_identical(left: Iterable[Any], right: Iterable[Any]) -> bool: + """ + Check that the items of `left` are the same objects as those in `right`. + + >>> a, b = object(), object() + >>> all_identical([a, b, a], [a, b, a]) + True + >>> all_identical([a, b, [a]], [a, b, [a]]) # new list object, while "equal" is not "identical" + False + """ + for left_item, right_item in zip_longest(left, right, fillvalue=_EMPTY): + if left_item is not right_item: + return False + return True + + +def assert_never(obj: NoReturn, msg: str) -> NoReturn: + """ + Helper to make sure that we have covered all possible types. + + This is mostly useful for ``mypy``, docs: + https://mypy.readthedocs.io/en/latest/literal_types.html#exhaustive-checks + """ + raise TypeError(msg) + + +def get_unique_discriminator_alias(all_aliases: Collection[str], discriminator_key: str) -> str: + """Validate that all aliases are the same and if that's the case return the alias""" + unique_aliases = set(all_aliases) + if len(unique_aliases) > 1: + raise ConfigError( + f'Aliases for discriminator {discriminator_key!r} must be the same (got {", ".join(sorted(all_aliases))})' + ) + return unique_aliases.pop() + + +def get_discriminator_alias_and_values(tp: Any, discriminator_key: str) -> Tuple[str, Tuple[str, ...]]: + """ + Get alias and all valid values in the `Literal` type of the discriminator field + `tp` can be a `BaseModel` class or directly an `Annotated` `Union` of many. + """ + is_root_model = getattr(tp, '__custom_root_type__', False) + + if get_origin(tp) is Annotated: + tp = get_args(tp)[0] + + if hasattr(tp, '__pydantic_model__'): + tp = tp.__pydantic_model__ + + if is_union(get_origin(tp)): + alias, all_values = _get_union_alias_and_all_values(tp, discriminator_key) + return alias, tuple(v for values in all_values for v in values) + elif is_root_model: + union_type = tp.__fields__[ROOT_KEY].type_ + alias, all_values = _get_union_alias_and_all_values(union_type, discriminator_key) + + if len(set(all_values)) > 1: + raise ConfigError( + f'Field {discriminator_key!r} is not the same for all submodels of {display_as_type(tp)!r}' + ) + + return alias, all_values[0] + + else: + try: + t_discriminator_type = tp.__fields__[discriminator_key].type_ + except AttributeError as e: + raise TypeError(f'Type {tp.__name__!r} is not a valid `BaseModel` or `dataclass`') from e + except KeyError as e: + raise ConfigError(f'Model {tp.__name__!r} needs a discriminator field for key {discriminator_key!r}') from e + + if not is_literal_type(t_discriminator_type): + raise ConfigError(f'Field {discriminator_key!r} of model {tp.__name__!r} needs to be a `Literal`') + + return tp.__fields__[discriminator_key].alias, all_literal_values(t_discriminator_type) + + +def _get_union_alias_and_all_values( + union_type: Type[Any], discriminator_key: str +) -> Tuple[str, Tuple[Tuple[str, ...], ...]]: + zipped_aliases_values = [get_discriminator_alias_and_values(t, discriminator_key) for t in get_args(union_type)] + # unzip: [('alias_a',('v1', 'v2)), ('alias_b', ('v3',))] => [('alias_a', 'alias_b'), (('v1', 'v2'), ('v3',))] + all_aliases, all_values = zip(*zipped_aliases_values) + return get_unique_discriminator_alias(all_aliases, discriminator_key), all_values diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/validators.py new file mode 100644 index 0000000000000000000000000000000000000000..c0940e8114a47ceacf21f4df946a6401946b9b8f --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/validators.py @@ -0,0 +1,768 @@ +import math +import re +from collections import OrderedDict, deque +from collections.abc import Hashable as CollectionsHashable +from datetime import date, datetime, time, timedelta +from decimal import Decimal, DecimalException +from enum import Enum, IntEnum +from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network +from pathlib import Path +from typing import ( + TYPE_CHECKING, + Any, + Callable, + Deque, + Dict, + ForwardRef, + FrozenSet, + Generator, + Hashable, + List, + NamedTuple, + Pattern, + Set, + Tuple, + Type, + TypeVar, + Union, +) +from uuid import UUID +from warnings import warn + +from pydantic.v1 import errors +from pydantic.v1.datetime_parse import parse_date, parse_datetime, parse_duration, parse_time +from pydantic.v1.typing import ( + AnyCallable, + all_literal_values, + display_as_type, + get_class, + is_callable_type, + is_literal_type, + is_namedtuple, + is_none_type, + is_typeddict, +) +from pydantic.v1.utils import almost_equal_floats, lenient_issubclass, sequence_like + +if TYPE_CHECKING: + from typing_extensions import Literal, TypedDict + + from pydantic.v1.config import BaseConfig + from pydantic.v1.fields import ModelField + from pydantic.v1.types import ConstrainedDecimal, ConstrainedFloat, ConstrainedInt + + ConstrainedNumber = Union[ConstrainedDecimal, ConstrainedFloat, ConstrainedInt] + AnyOrderedDict = OrderedDict[Any, Any] + Number = Union[int, float, Decimal] + StrBytes = Union[str, bytes] + + +def str_validator(v: Any) -> Union[str]: + if isinstance(v, str): + if isinstance(v, Enum): + return v.value + else: + return v + elif isinstance(v, (float, int, Decimal)): + # is there anything else we want to add here? If you think so, create an issue. + return str(v) + elif isinstance(v, (bytes, bytearray)): + return v.decode() + else: + raise errors.StrError() + + +def strict_str_validator(v: Any) -> Union[str]: + if isinstance(v, str) and not isinstance(v, Enum): + return v + raise errors.StrError() + + +def bytes_validator(v: Any) -> Union[bytes]: + if isinstance(v, bytes): + return v + elif isinstance(v, bytearray): + return bytes(v) + elif isinstance(v, str): + return v.encode() + elif isinstance(v, (float, int, Decimal)): + return str(v).encode() + else: + raise errors.BytesError() + + +def strict_bytes_validator(v: Any) -> Union[bytes]: + if isinstance(v, bytes): + return v + elif isinstance(v, bytearray): + return bytes(v) + else: + raise errors.BytesError() + + +BOOL_FALSE = {0, '0', 'off', 'f', 'false', 'n', 'no'} +BOOL_TRUE = {1, '1', 'on', 't', 'true', 'y', 'yes'} + + +def bool_validator(v: Any) -> bool: + if v is True or v is False: + return v + if isinstance(v, bytes): + v = v.decode() + if isinstance(v, str): + v = v.lower() + try: + if v in BOOL_TRUE: + return True + if v in BOOL_FALSE: + return False + except TypeError: + raise errors.BoolError() + raise errors.BoolError() + + +# matches the default limit cpython, see https://github.com/python/cpython/pull/96500 +max_str_int = 4_300 + + +def int_validator(v: Any) -> int: + if isinstance(v, int) and not (v is True or v is False): + return v + + # see https://github.com/pydantic/pydantic/issues/1477 and in turn, https://github.com/python/cpython/issues/95778 + # this check should be unnecessary once patch releases are out for 3.7, 3.8, 3.9 and 3.10 + # but better to check here until then. + # NOTICE: this does not fully protect user from the DOS risk since the standard library JSON implementation + # (and other std lib modules like xml) use `int()` and are likely called before this, the best workaround is to + # 1. update to the latest patch release of python once released, 2. use a different JSON library like ujson + if isinstance(v, (str, bytes, bytearray)) and len(v) > max_str_int: + raise errors.IntegerError() + + try: + return int(v) + except (TypeError, ValueError, OverflowError): + raise errors.IntegerError() + + +def strict_int_validator(v: Any) -> int: + if isinstance(v, int) and not (v is True or v is False): + return v + raise errors.IntegerError() + + +def float_validator(v: Any) -> float: + if isinstance(v, float): + return v + + try: + return float(v) + except (TypeError, ValueError): + raise errors.FloatError() + + +def strict_float_validator(v: Any) -> float: + if isinstance(v, float): + return v + raise errors.FloatError() + + +def float_finite_validator(v: 'Number', field: 'ModelField', config: 'BaseConfig') -> 'Number': + allow_inf_nan = getattr(field.type_, 'allow_inf_nan', None) + if allow_inf_nan is None: + allow_inf_nan = config.allow_inf_nan + + if allow_inf_nan is False and (math.isnan(v) or math.isinf(v)): + raise errors.NumberNotFiniteError() + return v + + +def number_multiple_validator(v: 'Number', field: 'ModelField') -> 'Number': + field_type: ConstrainedNumber = field.type_ + if field_type.multiple_of is not None: + mod = float(v) / float(field_type.multiple_of) % 1 + if not almost_equal_floats(mod, 0.0) and not almost_equal_floats(mod, 1.0): + raise errors.NumberNotMultipleError(multiple_of=field_type.multiple_of) + return v + + +def number_size_validator(v: 'Number', field: 'ModelField') -> 'Number': + field_type: ConstrainedNumber = field.type_ + if field_type.gt is not None and not v > field_type.gt: + raise errors.NumberNotGtError(limit_value=field_type.gt) + elif field_type.ge is not None and not v >= field_type.ge: + raise errors.NumberNotGeError(limit_value=field_type.ge) + + if field_type.lt is not None and not v < field_type.lt: + raise errors.NumberNotLtError(limit_value=field_type.lt) + if field_type.le is not None and not v <= field_type.le: + raise errors.NumberNotLeError(limit_value=field_type.le) + + return v + + +def constant_validator(v: 'Any', field: 'ModelField') -> 'Any': + """Validate ``const`` fields. + + The value provided for a ``const`` field must be equal to the default value + of the field. This is to support the keyword of the same name in JSON + Schema. + """ + if v != field.default: + raise errors.WrongConstantError(given=v, permitted=[field.default]) + + return v + + +def anystr_length_validator(v: 'StrBytes', config: 'BaseConfig') -> 'StrBytes': + v_len = len(v) + + min_length = config.min_anystr_length + if v_len < min_length: + raise errors.AnyStrMinLengthError(limit_value=min_length) + + max_length = config.max_anystr_length + if max_length is not None and v_len > max_length: + raise errors.AnyStrMaxLengthError(limit_value=max_length) + + return v + + +def anystr_strip_whitespace(v: 'StrBytes') -> 'StrBytes': + return v.strip() + + +def anystr_upper(v: 'StrBytes') -> 'StrBytes': + return v.upper() + + +def anystr_lower(v: 'StrBytes') -> 'StrBytes': + return v.lower() + + +def ordered_dict_validator(v: Any) -> 'AnyOrderedDict': + if isinstance(v, OrderedDict): + return v + + try: + return OrderedDict(v) + except (TypeError, ValueError): + raise errors.DictError() + + +def dict_validator(v: Any) -> Dict[Any, Any]: + if isinstance(v, dict): + return v + + try: + return dict(v) + except (TypeError, ValueError): + raise errors.DictError() + + +def list_validator(v: Any) -> List[Any]: + if isinstance(v, list): + return v + elif sequence_like(v): + return list(v) + else: + raise errors.ListError() + + +def tuple_validator(v: Any) -> Tuple[Any, ...]: + if isinstance(v, tuple): + return v + elif sequence_like(v): + return tuple(v) + else: + raise errors.TupleError() + + +def set_validator(v: Any) -> Set[Any]: + if isinstance(v, set): + return v + elif sequence_like(v): + return set(v) + else: + raise errors.SetError() + + +def frozenset_validator(v: Any) -> FrozenSet[Any]: + if isinstance(v, frozenset): + return v + elif sequence_like(v): + return frozenset(v) + else: + raise errors.FrozenSetError() + + +def deque_validator(v: Any) -> Deque[Any]: + if isinstance(v, deque): + return v + elif sequence_like(v): + return deque(v) + else: + raise errors.DequeError() + + +def enum_member_validator(v: Any, field: 'ModelField', config: 'BaseConfig') -> Enum: + try: + enum_v = field.type_(v) + except ValueError: + # field.type_ should be an enum, so will be iterable + raise errors.EnumMemberError(enum_values=list(field.type_)) + return enum_v.value if config.use_enum_values else enum_v + + +def uuid_validator(v: Any, field: 'ModelField') -> UUID: + try: + if isinstance(v, str): + v = UUID(v) + elif isinstance(v, (bytes, bytearray)): + try: + v = UUID(v.decode()) + except ValueError: + # 16 bytes in big-endian order as the bytes argument fail + # the above check + v = UUID(bytes=v) + except ValueError: + raise errors.UUIDError() + + if not isinstance(v, UUID): + raise errors.UUIDError() + + required_version = getattr(field.type_, '_required_version', None) + if required_version and v.version != required_version: + raise errors.UUIDVersionError(required_version=required_version) + + return v + + +def decimal_validator(v: Any) -> Decimal: + if isinstance(v, Decimal): + return v + elif isinstance(v, (bytes, bytearray)): + v = v.decode() + + v = str(v).strip() + + try: + v = Decimal(v) + except DecimalException: + raise errors.DecimalError() + + if not v.is_finite(): + raise errors.DecimalIsNotFiniteError() + + return v + + +def hashable_validator(v: Any) -> Hashable: + if isinstance(v, Hashable): + return v + + raise errors.HashableError() + + +def ip_v4_address_validator(v: Any) -> IPv4Address: + if isinstance(v, IPv4Address): + return v + + try: + return IPv4Address(v) + except ValueError: + raise errors.IPv4AddressError() + + +def ip_v6_address_validator(v: Any) -> IPv6Address: + if isinstance(v, IPv6Address): + return v + + try: + return IPv6Address(v) + except ValueError: + raise errors.IPv6AddressError() + + +def ip_v4_network_validator(v: Any) -> IPv4Network: + """ + Assume IPv4Network initialised with a default ``strict`` argument + + See more: + https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network + """ + if isinstance(v, IPv4Network): + return v + + try: + return IPv4Network(v) + except ValueError: + raise errors.IPv4NetworkError() + + +def ip_v6_network_validator(v: Any) -> IPv6Network: + """ + Assume IPv6Network initialised with a default ``strict`` argument + + See more: + https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network + """ + if isinstance(v, IPv6Network): + return v + + try: + return IPv6Network(v) + except ValueError: + raise errors.IPv6NetworkError() + + +def ip_v4_interface_validator(v: Any) -> IPv4Interface: + if isinstance(v, IPv4Interface): + return v + + try: + return IPv4Interface(v) + except ValueError: + raise errors.IPv4InterfaceError() + + +def ip_v6_interface_validator(v: Any) -> IPv6Interface: + if isinstance(v, IPv6Interface): + return v + + try: + return IPv6Interface(v) + except ValueError: + raise errors.IPv6InterfaceError() + + +def path_validator(v: Any) -> Path: + if isinstance(v, Path): + return v + + try: + return Path(v) + except TypeError: + raise errors.PathError() + + +def path_exists_validator(v: Any) -> Path: + if not v.exists(): + raise errors.PathNotExistsError(path=v) + + return v + + +def callable_validator(v: Any) -> AnyCallable: + """ + Perform a simple check if the value is callable. + + Note: complete matching of argument type hints and return types is not performed + """ + if callable(v): + return v + + raise errors.CallableError(value=v) + + +def enum_validator(v: Any) -> Enum: + if isinstance(v, Enum): + return v + + raise errors.EnumError(value=v) + + +def int_enum_validator(v: Any) -> IntEnum: + if isinstance(v, IntEnum): + return v + + raise errors.IntEnumError(value=v) + + +def make_literal_validator(type_: Any) -> Callable[[Any], Any]: + permitted_choices = all_literal_values(type_) + + # To have a O(1) complexity and still return one of the values set inside the `Literal`, + # we create a dict with the set values (a set causes some problems with the way intersection works). + # In some cases the set value and checked value can indeed be different (see `test_literal_validator_str_enum`) + allowed_choices = {v: v for v in permitted_choices} + + def literal_validator(v: Any) -> Any: + try: + return allowed_choices[v] + except (KeyError, TypeError): + raise errors.WrongConstantError(given=v, permitted=permitted_choices) + + return literal_validator + + +def constr_length_validator(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes': + v_len = len(v) + + min_length = field.type_.min_length if field.type_.min_length is not None else config.min_anystr_length + if v_len < min_length: + raise errors.AnyStrMinLengthError(limit_value=min_length) + + max_length = field.type_.max_length if field.type_.max_length is not None else config.max_anystr_length + if max_length is not None and v_len > max_length: + raise errors.AnyStrMaxLengthError(limit_value=max_length) + + return v + + +def constr_strip_whitespace(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes': + strip_whitespace = field.type_.strip_whitespace or config.anystr_strip_whitespace + if strip_whitespace: + v = v.strip() + + return v + + +def constr_upper(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes': + upper = field.type_.to_upper or config.anystr_upper + if upper: + v = v.upper() + + return v + + +def constr_lower(v: 'StrBytes', field: 'ModelField', config: 'BaseConfig') -> 'StrBytes': + lower = field.type_.to_lower or config.anystr_lower + if lower: + v = v.lower() + return v + + +def validate_json(v: Any, config: 'BaseConfig') -> Any: + if v is None: + # pass None through to other validators + return v + try: + return config.json_loads(v) # type: ignore + except ValueError: + raise errors.JsonError() + except TypeError: + raise errors.JsonTypeError() + + +T = TypeVar('T') + + +def make_arbitrary_type_validator(type_: Type[T]) -> Callable[[T], T]: + def arbitrary_type_validator(v: Any) -> T: + if isinstance(v, type_): + return v + raise errors.ArbitraryTypeError(expected_arbitrary_type=type_) + + return arbitrary_type_validator + + +def make_class_validator(type_: Type[T]) -> Callable[[Any], Type[T]]: + def class_validator(v: Any) -> Type[T]: + if lenient_issubclass(v, type_): + return v + raise errors.SubclassError(expected_class=type_) + + return class_validator + + +def any_class_validator(v: Any) -> Type[T]: + if isinstance(v, type): + return v + raise errors.ClassError() + + +def none_validator(v: Any) -> 'Literal[None]': + if v is None: + return v + raise errors.NotNoneError() + + +def pattern_validator(v: Any) -> Pattern[str]: + if isinstance(v, Pattern): + return v + + str_value = str_validator(v) + + try: + return re.compile(str_value) + except re.error: + raise errors.PatternError() + + +NamedTupleT = TypeVar('NamedTupleT', bound=NamedTuple) + + +def make_namedtuple_validator( + namedtuple_cls: Type[NamedTupleT], config: Type['BaseConfig'] +) -> Callable[[Tuple[Any, ...]], NamedTupleT]: + from pydantic.v1.annotated_types import create_model_from_namedtuple + + NamedTupleModel = create_model_from_namedtuple( + namedtuple_cls, + __config__=config, + __module__=namedtuple_cls.__module__, + ) + namedtuple_cls.__pydantic_model__ = NamedTupleModel # type: ignore[attr-defined] + + def namedtuple_validator(values: Tuple[Any, ...]) -> NamedTupleT: + annotations = NamedTupleModel.__annotations__ + + if len(values) > len(annotations): + raise errors.ListMaxLengthError(limit_value=len(annotations)) + + dict_values: Dict[str, Any] = dict(zip(annotations, values)) + validated_dict_values: Dict[str, Any] = dict(NamedTupleModel(**dict_values)) + return namedtuple_cls(**validated_dict_values) + + return namedtuple_validator + + +def make_typeddict_validator( + typeddict_cls: Type['TypedDict'], config: Type['BaseConfig'] # type: ignore[valid-type] +) -> Callable[[Any], Dict[str, Any]]: + from pydantic.v1.annotated_types import create_model_from_typeddict + + TypedDictModel = create_model_from_typeddict( + typeddict_cls, + __config__=config, + __module__=typeddict_cls.__module__, + ) + typeddict_cls.__pydantic_model__ = TypedDictModel # type: ignore[attr-defined] + + def typeddict_validator(values: 'TypedDict') -> Dict[str, Any]: # type: ignore[valid-type] + return TypedDictModel.parse_obj(values).dict(exclude_unset=True) + + return typeddict_validator + + +class IfConfig: + def __init__(self, validator: AnyCallable, *config_attr_names: str, ignored_value: Any = False) -> None: + self.validator = validator + self.config_attr_names = config_attr_names + self.ignored_value = ignored_value + + def check(self, config: Type['BaseConfig']) -> bool: + return any(getattr(config, name) not in {None, self.ignored_value} for name in self.config_attr_names) + + +# order is important here, for example: bool is a subclass of int so has to come first, datetime before date same, +# IPv4Interface before IPv4Address, etc +_VALIDATORS: List[Tuple[Type[Any], List[Any]]] = [ + (IntEnum, [int_validator, enum_member_validator]), + (Enum, [enum_member_validator]), + ( + str, + [ + str_validator, + IfConfig(anystr_strip_whitespace, 'anystr_strip_whitespace'), + IfConfig(anystr_upper, 'anystr_upper'), + IfConfig(anystr_lower, 'anystr_lower'), + IfConfig(anystr_length_validator, 'min_anystr_length', 'max_anystr_length'), + ], + ), + ( + bytes, + [ + bytes_validator, + IfConfig(anystr_strip_whitespace, 'anystr_strip_whitespace'), + IfConfig(anystr_upper, 'anystr_upper'), + IfConfig(anystr_lower, 'anystr_lower'), + IfConfig(anystr_length_validator, 'min_anystr_length', 'max_anystr_length'), + ], + ), + (bool, [bool_validator]), + (int, [int_validator]), + (float, [float_validator, IfConfig(float_finite_validator, 'allow_inf_nan', ignored_value=True)]), + (Path, [path_validator]), + (datetime, [parse_datetime]), + (date, [parse_date]), + (time, [parse_time]), + (timedelta, [parse_duration]), + (OrderedDict, [ordered_dict_validator]), + (dict, [dict_validator]), + (list, [list_validator]), + (tuple, [tuple_validator]), + (set, [set_validator]), + (frozenset, [frozenset_validator]), + (deque, [deque_validator]), + (UUID, [uuid_validator]), + (Decimal, [decimal_validator]), + (IPv4Interface, [ip_v4_interface_validator]), + (IPv6Interface, [ip_v6_interface_validator]), + (IPv4Address, [ip_v4_address_validator]), + (IPv6Address, [ip_v6_address_validator]), + (IPv4Network, [ip_v4_network_validator]), + (IPv6Network, [ip_v6_network_validator]), +] + + +def find_validators( # noqa: C901 (ignore complexity) + type_: Type[Any], config: Type['BaseConfig'] +) -> Generator[AnyCallable, None, None]: + from pydantic.v1.dataclasses import is_builtin_dataclass, make_dataclass_validator + + if type_ is Any or type_ is object: + return + type_type = type_.__class__ + if type_type == ForwardRef or type_type == TypeVar: + return + + if is_none_type(type_): + yield none_validator + return + if type_ is Pattern or type_ is re.Pattern: + yield pattern_validator + return + if type_ is Hashable or type_ is CollectionsHashable: + yield hashable_validator + return + if is_callable_type(type_): + yield callable_validator + return + if is_literal_type(type_): + yield make_literal_validator(type_) + return + if is_builtin_dataclass(type_): + yield from make_dataclass_validator(type_, config) + return + if type_ is Enum: + yield enum_validator + return + if type_ is IntEnum: + yield int_enum_validator + return + if is_namedtuple(type_): + yield tuple_validator + yield make_namedtuple_validator(type_, config) + return + if is_typeddict(type_): + yield make_typeddict_validator(type_, config) + return + + class_ = get_class(type_) + if class_ is not None: + if class_ is not Any and isinstance(class_, type): + yield make_class_validator(class_) + else: + yield any_class_validator + return + + for val_type, validators in _VALIDATORS: + try: + if issubclass(type_, val_type): + for v in validators: + if isinstance(v, IfConfig): + if v.check(config): + yield v.validator + else: + yield v + return + except TypeError: + raise RuntimeError(f'error checking inheritance of {type_!r} (type: {display_as_type(type_)})') + + if config.arbitrary_types_allowed: + yield make_arbitrary_type_validator(type_) + else: + if hasattr(type_, '__pydantic_core_schema__'): + warn(f'Mixing V1 and V2 models is not supported. `{type_.__name__}` is a V2 model.', UserWarning) + raise RuntimeError(f'no validator found for {type_}, see `arbitrary_types_allowed` in Config') diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/version.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/version.py new file mode 100644 index 0000000000000000000000000000000000000000..6ba1cb08332b03fc85194d29ee76ab993ea951c9 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/v1/version.py @@ -0,0 +1,38 @@ +__all__ = 'compiled', 'VERSION', 'version_info' + +VERSION = '1.10.26' + +try: + import cython # type: ignore +except ImportError: + compiled: bool = False +else: # pragma: no cover + try: + compiled = cython.compiled + except AttributeError: + compiled = False + + +def version_info() -> str: + import platform + import sys + from importlib import import_module + from pathlib import Path + + optional_deps = [] + for p in ('devtools', 'dotenv', 'email-validator', 'typing-extensions'): + try: + import_module(p.replace('-', '_')) + except ImportError: + continue + optional_deps.append(p) + + info = { + 'pydantic version': VERSION, + 'pydantic compiled': compiled, + 'install path': Path(__file__).resolve().parent, + 'python version': sys.version, + 'platform': platform.platform(), + 'optional deps. installed': optional_deps, + } + return '\n'.join('{:>30} {}'.format(k + ':', str(v).replace('\n', ' ')) for k, v in info.items()) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/validate_call_decorator.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/validate_call_decorator.py new file mode 100644 index 0000000000000000000000000000000000000000..4bc899146d96b216fa7b02b951bc271243bdf3ee --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/validate_call_decorator.py @@ -0,0 +1,116 @@ +"""Decorator for validating function calls.""" + +from __future__ import annotations as _annotations + +import inspect +from functools import partial +from types import BuiltinFunctionType +from typing import TYPE_CHECKING, Any, Callable, TypeVar, cast, overload + +from ._internal import _generate_schema, _typing_extra, _validate_call +from .errors import PydanticUserError + +__all__ = ('validate_call',) + +if TYPE_CHECKING: + from .config import ConfigDict + + AnyCallableT = TypeVar('AnyCallableT', bound=Callable[..., Any]) + + +_INVALID_TYPE_ERROR_CODE = 'validate-call-type' + + +def _check_function_type(function: object) -> None: + """Check if the input function is a supported type for `validate_call`.""" + if isinstance(function, _generate_schema.VALIDATE_CALL_SUPPORTED_TYPES): + try: + _typing_extra.signature_no_eval(cast(_generate_schema.ValidateCallSupportedTypes, function)) + except (ValueError, TypeError): + raise PydanticUserError( + f"Input function `{function}` doesn't have a valid signature", code=_INVALID_TYPE_ERROR_CODE + ) + + if isinstance(function, partial): + try: + assert not isinstance(partial.func, partial), 'Partial of partial' + _check_function_type(function.func) + except PydanticUserError as e: + raise PydanticUserError( + f'Partial of `{function.func}` is invalid because the type of `{function.func}` is not supported by `validate_call`', + code=_INVALID_TYPE_ERROR_CODE, + ) from e + + return + + if isinstance(function, BuiltinFunctionType): + raise PydanticUserError(f'Input built-in function `{function}` is not supported', code=_INVALID_TYPE_ERROR_CODE) + if isinstance(function, (classmethod, staticmethod, property)): + name = type(function).__name__ + raise PydanticUserError( + f'The `@{name}` decorator should be applied after `@validate_call` (put `@{name}` on top)', + code=_INVALID_TYPE_ERROR_CODE, + ) + + if inspect.isclass(function): + raise PydanticUserError( + f'Unable to validate {function}: `validate_call` should be applied to functions, not classes (put `@validate_call` on top of `__init__` or `__new__` instead)', + code=_INVALID_TYPE_ERROR_CODE, + ) + if callable(function): + raise PydanticUserError( + f'Unable to validate {function}: `validate_call` should be applied to functions, not instances or other callables. Use `validate_call` explicitly on `__call__` instead.', + code=_INVALID_TYPE_ERROR_CODE, + ) + + raise PydanticUserError( + f'Unable to validate {function}: `validate_call` should be applied to one of the following: function, method, partial, or lambda', + code=_INVALID_TYPE_ERROR_CODE, + ) + + +@overload +def validate_call( + *, config: ConfigDict | None = None, validate_return: bool = False +) -> Callable[[AnyCallableT], AnyCallableT]: ... + + +@overload +def validate_call(func: AnyCallableT, /) -> AnyCallableT: ... + + +def validate_call( + func: AnyCallableT | None = None, + /, + *, + config: ConfigDict | None = None, + validate_return: bool = False, +) -> AnyCallableT | Callable[[AnyCallableT], AnyCallableT]: + """!!! abstract "Usage Documentation" + [Validation Decorator](../concepts/validation_decorator.md) + + Returns a decorated wrapper around the function that validates the arguments and, optionally, the return value. + + Usage may be either as a plain decorator `@validate_call` or with arguments `@validate_call(...)`. + + Args: + func: The function to be decorated. + config: The configuration dictionary. + validate_return: Whether to validate the return value. + + Returns: + The decorated function. + """ + parent_namespace = _typing_extra.parent_frame_namespace() + + def validate(function: AnyCallableT) -> AnyCallableT: + _check_function_type(function) + validate_call_wrapper = _validate_call.ValidateCallWrapper( + cast(_generate_schema.ValidateCallSupportedTypes, function), config, validate_return, parent_namespace + ) + return _validate_call.update_wrapper_attributes(function, validate_call_wrapper.__call__) # type: ignore + + if func is not None: + return validate(func) + else: + return validate diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/validators.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/validators.py new file mode 100644 index 0000000000000000000000000000000000000000..7921b04f05d5ef0ad20365e7372a48d86724dc51 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/validators.py @@ -0,0 +1,5 @@ +"""The `validators` module is a backport module from V1.""" + +from ._migration import getattr_migration + +__getattr__ = getattr_migration(__name__) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/version.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/version.py new file mode 100644 index 0000000000000000000000000000000000000000..56fbdcda0d2948e081c90079915d6db3d31b7c46 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/version.py @@ -0,0 +1,113 @@ +"""The `version` module holds the version information for Pydantic.""" + +from __future__ import annotations as _annotations + +import sys + +from pydantic_core import __version__ as __pydantic_core_version__ + +__all__ = 'VERSION', 'version_info' + +VERSION = '2.13.3' +"""The version of Pydantic. + +This version specifier is guaranteed to be compliant with the [specification], +introduced by [PEP 440]. + +[specification]: https://packaging.python.org/en/latest/specifications/version-specifiers/ +[PEP 440]: https://peps.python.org/pep-0440/ +""" + +# Keep this in sync with the version constraint in the `pyproject.toml` dependencies: +_COMPATIBLE_PYDANTIC_CORE_VERSION = '2.46.3' + + +def version_short() -> str: + """Return the `major.minor` part of Pydantic version. + + It returns '2.1' if Pydantic version is '2.1.1'. + """ + return '.'.join(VERSION.split('.')[:2]) + + +def version_info() -> str: + """Return complete version information for Pydantic and its dependencies.""" + import importlib.metadata + import platform + from pathlib import Path + + import pydantic_core._pydantic_core as pdc + + from ._internal import _git as git + + # get data about packages that are closely related to pydantic, use pydantic or often conflict with pydantic + package_names = { + 'email-validator', + 'fastapi', + 'mypy', + 'pydantic-extra-types', + 'pydantic-settings', + 'pyright', + 'typing_extensions', + } + related_packages = [] + + for dist in importlib.metadata.distributions(): + name = dist.metadata['Name'] + if name in package_names: + related_packages.append(f'{name}-{dist.version}') + + pydantic_dir = Path(__file__).parents[1].resolve() + most_recent_commit = ( + git.git_revision(pydantic_dir) if git.is_git_repo(pydantic_dir) and git.have_git() else 'unknown' + ) + + info = { + 'pydantic version': VERSION, + 'pydantic-core version': __pydantic_core_version__, + 'pydantic-core build': getattr(pdc, 'build_info', None) or pdc.build_profile, # pyright: ignore[reportPrivateImportUsage] + 'python version': sys.version, + 'platform': platform.platform(), + 'related packages': ' '.join(related_packages), + 'commit': most_recent_commit, + } + return '\n'.join('{:>30} {}'.format(k + ':', str(v).replace('\n', ' ')) for k, v in info.items()) + + +def check_pydantic_core_version() -> bool: + """Check that the installed `pydantic-core` dependency is compatible.""" + return __pydantic_core_version__ == _COMPATIBLE_PYDANTIC_CORE_VERSION + + +def _ensure_pydantic_core_version() -> None: # pragma: no cover + if not check_pydantic_core_version(): + raise_error = True + # Do not raise the error if pydantic is installed in editable mode (i.e. in development): + if sys.version_info >= (3, 13): # origin property added in 3.13 + from importlib.metadata import distribution + + dist = distribution('pydantic') + if getattr(getattr(dist.origin, 'dir_info', None), 'editable', False): + raise_error = False + + if raise_error: + raise SystemError( + f'The installed pydantic-core version ({__pydantic_core_version__}) is incompatible ' + f'with the current pydantic version, which requires {_COMPATIBLE_PYDANTIC_CORE_VERSION}. ' + "If you encounter this error, make sure that you haven't upgraded pydantic-core manually." + ) + + +def parse_mypy_version(version: str) -> tuple[int, int, int]: + """Parse `mypy` string version to a 3-tuple of ints. + + It parses normal version like `1.11.0` and extra info followed by a `+` sign + like `1.11.0+dev.d6d9d8cd4f27c52edac1f537e236ec48a01e54cb.dirty`. + + Args: + version: The mypy version string. + + Returns: + A triple of ints, e.g. `(1, 11, 0)`. + """ + return tuple(map(int, version.partition('+')[0].split('.'))) # pyright: ignore[reportReturnType] diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/warnings.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/warnings.py new file mode 100644 index 0000000000000000000000000000000000000000..02323b8da5924aa3285dd557b2a33533053534bd --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic/warnings.py @@ -0,0 +1,122 @@ +"""Pydantic-specific warnings.""" + +from __future__ import annotations as _annotations + +from .version import version_short + +__all__ = ( + 'PydanticDeprecatedSince20', + 'PydanticDeprecatedSince26', + 'PydanticDeprecatedSince29', + 'PydanticDeprecatedSince210', + 'PydanticDeprecatedSince211', + 'PydanticDeprecatedSince212', + 'PydanticDeprecationWarning', + 'PydanticExperimentalWarning', + 'ArbitraryTypeWarning', + 'UnsupportedFieldAttributeWarning', + 'TypedDictExtraConfigWarning', +) + + +class PydanticDeprecationWarning(DeprecationWarning): + """A Pydantic specific deprecation warning. + + This warning is raised when using deprecated functionality in Pydantic. It provides information on when the + deprecation was introduced and the expected version in which the corresponding functionality will be removed. + + Attributes: + message: Description of the warning. + since: Pydantic version in what the deprecation was introduced. + expected_removal: Pydantic version in what the corresponding functionality expected to be removed. + """ + + message: str + since: tuple[int, int] + expected_removal: tuple[int, int] + + def __init__( + self, message: str, *args: object, since: tuple[int, int], expected_removal: tuple[int, int] | None = None + ) -> None: + super().__init__(message, *args) + self.message = message.rstrip('.') + self.since = since + self.expected_removal = expected_removal if expected_removal is not None else (since[0] + 1, 0) + + def __str__(self) -> str: + message = ( + f'{self.message}. Deprecated in Pydantic V{self.since[0]}.{self.since[1]}' + f' to be removed in V{self.expected_removal[0]}.{self.expected_removal[1]}.' + ) + if self.since == (2, 0): + message += f' See Pydantic V2 Migration Guide at https://errors.pydantic.dev/{version_short()}/migration/' + return message + + +class PydanticDeprecatedSince20(PydanticDeprecationWarning): + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.0.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 0), expected_removal=(3, 0)) + + +class PydanticDeprecatedSince26(PydanticDeprecationWarning): # pragma: no cover + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.6.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 6), expected_removal=(3, 0)) + + +class PydanticDeprecatedSince29(PydanticDeprecationWarning): + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.9.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 9), expected_removal=(3, 0)) + + +class PydanticDeprecatedSince210(PydanticDeprecationWarning): + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.10.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 10), expected_removal=(3, 0)) + + +class PydanticDeprecatedSince211(PydanticDeprecationWarning): + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.11.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 11), expected_removal=(3, 0)) + + +class PydanticDeprecatedSince212(PydanticDeprecationWarning): + """A specific `PydanticDeprecationWarning` subclass defining functionality deprecated since Pydantic 2.12.""" + + def __init__(self, message: str, *args: object) -> None: + super().__init__(message, *args, since=(2, 12), expected_removal=(3, 0)) + + +class GenericBeforeBaseModelWarning(Warning): + pass + + +class PydanticExperimentalWarning(Warning): + """A Pydantic specific experimental functionality warning. + + It is raised to warn users that the functionality may change or be removed in future versions of Pydantic. + """ + + +class CoreSchemaGenerationWarning(UserWarning): + """A warning raised during core schema generation.""" + + +class ArbitraryTypeWarning(CoreSchemaGenerationWarning): + """A warning raised when Pydantic fails to generate a core schema for an arbitrary type.""" + + +class UnsupportedFieldAttributeWarning(CoreSchemaGenerationWarning): + """A warning raised when a `Field()` attribute isn't supported in the context it is used.""" + + +class TypedDictExtraConfigWarning(CoreSchemaGenerationWarning): + """A warning raised when the [`extra`][pydantic.ConfigDict.extra] configuration is incompatible with the `closed` or `extra_items` specification.""" diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/INSTALLER b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..5c69047b2eb8235994febeeae1da4a82365a240a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/INSTALLER @@ -0,0 +1 @@ +uv \ No newline at end of file diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/METADATA b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..787056f757455ecc4e1ae864fa0060aa70b8c262 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/METADATA @@ -0,0 +1,173 @@ +Metadata-Version: 2.4 +Name: pydantic_core +Version: 2.46.3 +Classifier: Development Status :: 3 - Alpha +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Programming Language :: Python :: 3.13 +Classifier: Programming Language :: Python :: 3.14 +Classifier: Programming Language :: Python :: Implementation :: CPython +Classifier: Programming Language :: Python :: Implementation :: PyPy +Classifier: Programming Language :: Python :: Implementation :: GraalPy +Classifier: Programming Language :: Rust +Classifier: Framework :: Pydantic +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: Operating System :: POSIX :: Linux +Classifier: Operating System :: Microsoft :: Windows +Classifier: Operating System :: MacOS +Classifier: Typing :: Typed +Requires-Dist: typing-extensions>=4.14.1 +License-File: LICENSE +Summary: Core functionality for Pydantic validation and serialization +Home-Page: https://github.com/pydantic/pydantic +Author-email: Samuel Colvin , Adrian Garcia Badaracco <1755071+adriangb@users.noreply.github.com>, David Montague , David Hewitt , Sydney Runkle , Victorien Plot +License-Expression: MIT +Requires-Python: >=3.9 +Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM +Project-URL: Funding, https://github.com/sponsors/samuelcolvin +Project-URL: Homepage, https://github.com/pydantic +Project-URL: Source, https://github.com/pydantic/pydantic/tree/main/pydantic-core + +# pydantic-core + +[![CI](https://github.com/pydantic/pydantic-core/workflows/ci/badge.svg?event=push)](https://github.com/pydantic/pydantic-core/actions?query=event%3Apush+branch%3Amain+workflow%3Aci) +[![Coverage](https://codecov.io/gh/pydantic/pydantic-core/branch/main/graph/badge.svg)](https://codecov.io/gh/pydantic/pydantic-core) +[![pypi](https://img.shields.io/pypi/v/pydantic-core.svg)](https://pypi.python.org/pypi/pydantic-core) +[![versions](https://img.shields.io/pypi/pyversions/pydantic-core.svg)](https://github.com/pydantic/pydantic-core) +[![license](https://img.shields.io/github/license/pydantic/pydantic-core.svg)](https://github.com/pydantic/pydantic-core/blob/main/LICENSE) + +This package provides the core functionality for [pydantic](https://docs.pydantic.dev) validation and serialization. + +Pydantic-core is currently around 17x faster than pydantic V1. +See [`tests/benchmarks/`](./tests/benchmarks/) for details. + +## Example of direct usage + +*NOTE: You should not need to use pydantic-core directly; instead, use pydantic, which in turn uses pydantic-core.* + +```py +from pydantic_core import SchemaValidator, ValidationError + + +v = SchemaValidator( + { + 'type': 'typed-dict', + 'fields': { + 'name': { + 'type': 'typed-dict-field', + 'schema': { + 'type': 'str', + }, + }, + 'age': { + 'type': 'typed-dict-field', + 'schema': { + 'type': 'int', + 'ge': 18, + }, + }, + 'is_developer': { + 'type': 'typed-dict-field', + 'schema': { + 'type': 'default', + 'schema': {'type': 'bool'}, + 'default': True, + }, + }, + }, + } +) + +r1 = v.validate_python({'name': 'Samuel', 'age': 35}) +assert r1 == {'name': 'Samuel', 'age': 35, 'is_developer': True} + +# pydantic-core can also validate JSON directly +r2 = v.validate_json('{"name": "Samuel", "age": 35}') +assert r1 == r2 + +try: + v.validate_python({'name': 'Samuel', 'age': 11}) +except ValidationError as e: + print(e) + """ + 1 validation error for model + age + Input should be greater than or equal to 18 + [type=greater_than_equal, context={ge: 18}, input_value=11, input_type=int] + """ +``` + +## Getting Started + +### Prerequisites + +You'll need: + +1. **[Rust](https://rustup.rs/)** - Rust stable (or nightly for coverage) +2. **[uv](https://docs.astral.sh/uv/getting-started/installation/)** - Fast Python package manager (will install Python 3.9+ automatically) +3. **[git](https://git-scm.com/)** - For version control +4. **[make](https://www.gnu.org/software/make/)** - For running development commands (or use `nmake` on Windows) + +### Quick Start + +```bash +# Clone the repository (or from your fork) +git clone git@github.com:pydantic/pydantic-core.git +cd pydantic-core + +# Install all dependencies using uv, setup pre-commit hooks, and build the development version +make install +``` + +Verify your installation by running: + +```bash +make +``` + +This runs a full development cycle: formatting, building, linting, and testing + +### Development Commands + +Run `make help` to see all available commands, or use these common ones: + +```bash +make build-dev # to build the package during development +make build-prod # to perform an optimised build for benchmarking +make test # to run the tests +make testcov # to run the tests and generate a coverage report +make lint # to run the linter +make format # to format python and rust code +make all # to run to run build-dev + format + lint + test +``` + +### Useful Resources + +* [`python/pydantic_core/_pydantic_core.pyi`](./python/pydantic_core/_pydantic_core.pyi) - Python API types +* [`python/pydantic_core/core_schema.py`](./python/pydantic_core/core_schema.py) - Core schema definitions +* [`tests/`](./tests) - Comprehensive usage examples + +## Profiling + +It's possible to profile the code using the [`flamegraph` utility from `flamegraph-rs`](https://github.com/flamegraph-rs/flamegraph). (Tested on Linux.) You can install this with `cargo install flamegraph`. + +Run `make build-profiling` to install a release build with debugging symbols included (needed for profiling). + +Once that is built, you can profile pytest benchmarks with (e.g.): + +```bash +flamegraph -- pytest tests/benchmarks/test_micro_benchmarks.py -k test_list_of_ints_core_py --benchmark-enable +``` + +The `flamegraph` command will produce an interactive SVG at `flamegraph.svg`. + +## Releasing + +TBC (needs to be integrated into `pydantic` repository release process). + diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/RECORD b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..9bdebf2af94f5f66cd30c9cfc6ab0cc23f22ba84 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/RECORD @@ -0,0 +1,12 @@ +pydantic_core-2.46.3.dist-info/INSTALLER,sha256=5hhM4Q4mYTT9z6QB6PGpUAW81PGNFrYrdXMj4oM_6ak,2 +pydantic_core-2.46.3.dist-info/METADATA,sha256=NQwyDYzgXcHFzCmN8IdEMOKIkw0Aqiivt9xmyrIyK3M,6573 +pydantic_core-2.46.3.dist-info/RECORD,, +pydantic_core-2.46.3.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +pydantic_core-2.46.3.dist-info/WHEEL,sha256=K7WEhceKrUcmvtDDTcmMfFZMFbiOevaadPlGHHcaRsQ,147 +pydantic_core-2.46.3.dist-info/licenses/LICENSE,sha256=Kv3TDVS01itvSIprzBVG6E7FBh8T9CCcA9ASNIeDeVo,1080 +pydantic_core-2.46.3.dist-info/sboms/pydantic-core.cyclonedx.json,sha256=HtqpyBvFb64sv7PEJphmsEVw3iV_6UPKJAiYfj0xphw,125577 +pydantic_core/__init__.py,sha256=nK1ikrdSVK9gapcKrpv_blrp8LCAic1jrK-jkbYHlNI,5115 +pydantic_core/_pydantic_core.cpython-313-x86_64-linux-gnu.so,sha256=Fw9prMZPxg2PLnm0QnVYCUp-Bp9M9rNiChw4u9Tkclc,4750664 +pydantic_core/_pydantic_core.pyi,sha256=H_lG8iKiv3Bpfd3rixY7xzs67gE-s0eiAqJaSbnF8Ss,45932 +pydantic_core/core_schema.py,sha256=1PM31yf_kGpm_H7GLnNFRkIPAQGPL9zh6rf2SxohVxg,155574 +pydantic_core/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/REQUESTED b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/WHEEL b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..298751c27dcc9bf0653e67833f5ef649ea8d41cc --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/WHEEL @@ -0,0 +1,5 @@ +Wheel-Version: 1.0 +Generator: maturin (1.13.1) +Root-Is-Purelib: false +Tag: cp313-cp313-manylinux_2_17_x86_64 +Tag: cp313-cp313-manylinux2014_x86_64 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/licenses/LICENSE b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..0716871caabdbbb3e77a0371d49936cef1923ea1 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core-2.46.3.dist-info/licenses/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2022 Samuel Colvin + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. 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a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..d5facd16196818c1cc9a4cf78e4fae6efb273b40 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/__init__.py @@ -0,0 +1,171 @@ +from __future__ import annotations + +import sys as _sys +from typing import Any as _Any + +from typing_extensions import Sentinel + +from ._pydantic_core import ( + ArgsKwargs, + MultiHostUrl, + PydanticCustomError, + PydanticKnownError, + PydanticOmit, + PydanticSerializationError, + PydanticSerializationUnexpectedValue, + PydanticUndefined, + PydanticUndefinedType, + PydanticUseDefault, + SchemaError, + SchemaSerializer, + SchemaValidator, + Some, + TzInfo, + Url, + ValidationError, + __version__, + from_json, + to_json, + to_jsonable_python, +) +from .core_schema import CoreConfig, CoreSchema, CoreSchemaType, ErrorType + +if _sys.version_info < (3, 11): + from typing_extensions import NotRequired as _NotRequired +else: + from typing import NotRequired as _NotRequired + +if _sys.version_info < (3, 12): + from typing_extensions import TypedDict as _TypedDict +else: + from typing import TypedDict as _TypedDict + +__all__ = [ + '__version__', + 'UNSET', + 'CoreConfig', + 'CoreSchema', + 'CoreSchemaType', + 'SchemaValidator', + 'SchemaSerializer', + 'Some', + 'Url', + 'MultiHostUrl', + 'ArgsKwargs', + 'PydanticUndefined', + 'PydanticUndefinedType', + 'SchemaError', + 'ErrorDetails', + 'InitErrorDetails', + 'ValidationError', + 'PydanticCustomError', + 'PydanticKnownError', + 'PydanticOmit', + 'PydanticUseDefault', + 'PydanticSerializationError', + 'PydanticSerializationUnexpectedValue', + 'TzInfo', + 'to_json', + 'from_json', + 'to_jsonable_python', +] + + +class ErrorDetails(_TypedDict): + type: str + """ + The type of error that occurred, this is an identifier designed for + programmatic use that will change rarely or never. + + `type` is unique for each error message, and can hence be used as an identifier to build custom error messages. + """ + loc: tuple[int | str, ...] + """Tuple of strings and ints identifying where in the schema the error occurred.""" + msg: str + """A human readable error message.""" + input: _Any + """The input data at this `loc` that caused the error.""" + ctx: _NotRequired[dict[str, _Any]] + """ + Values which are required to render the error message, and could hence be useful in rendering custom error messages. + Also useful for passing custom error data forward. + """ + url: _NotRequired[str] + """ + The documentation URL giving information about the error. No URL is available if + a [`PydanticCustomError`][pydantic_core.PydanticCustomError] is used. + """ + + +class InitErrorDetails(_TypedDict): + type: str | PydanticCustomError + """The type of error that occurred, this should be a "slug" identifier that changes rarely or never.""" + loc: _NotRequired[tuple[int | str, ...]] + """Tuple of strings and ints identifying where in the schema the error occurred.""" + input: _Any + """The input data at this `loc` that caused the error.""" + ctx: _NotRequired[dict[str, _Any]] + """ + Values which are required to render the error message, and could hence be useful in rendering custom error messages. + Also useful for passing custom error data forward. + """ + + +class ErrorTypeInfo(_TypedDict): + """ + Gives information about errors. + """ + + type: ErrorType + """The type of error that occurred, this should be a "slug" identifier that changes rarely or never.""" + message_template_python: str + """String template to render a human readable error message from using context, when the input is Python.""" + example_message_python: str + """Example of a human readable error message, when the input is Python.""" + message_template_json: _NotRequired[str] + """String template to render a human readable error message from using context, when the input is JSON data.""" + example_message_json: _NotRequired[str] + """Example of a human readable error message, when the input is JSON data.""" + example_context: dict[str, _Any] | None + """Example of context values.""" + + +class MultiHostHost(_TypedDict): + """ + A host part of a multi-host URL. + """ + + username: str | None + """The username part of this host, or `None`.""" + password: str | None + """The password part of this host, or `None`.""" + host: str | None + """The host part of this host, or `None`.""" + port: int | None + """The port part of this host, or `None`.""" + + +MISSING = Sentinel('MISSING') +"""A singleton indicating a field value was not provided during validation. + +This singleton can be used a default value, as an alternative to `None` when it has +an explicit meaning. During serialization, any field with `MISSING` as a value is excluded +from the output. + +Example: + ```python + from pydantic import BaseModel + + from pydantic_core import MISSING + + + class Configuration(BaseModel): + timeout: int | None | MISSING = MISSING + + + # configuration defaults, stored somewhere else: + defaults = {'timeout': 200} + + conf = Configuration.model_validate({...}) + timeout = conf.timeout if timeout.timeout is not MISSING else defaults['timeout'] +""" diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.cpython-313-x86_64-linux-gnu.so b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.cpython-313-x86_64-linux-gnu.so new file mode 100644 index 0000000000000000000000000000000000000000..c7213a987f193380c545562176b4f239a56eeb61 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.cpython-313-x86_64-linux-gnu.so @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:170f69acc64fc60d8f2e79b4427558094a7e069f4cf6b3620a1c38bbd4e47257 +size 4750664 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.pyi b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.pyi new file mode 100644 index 0000000000000000000000000000000000000000..122c86faae3998f4e594c791ae1afab8f332850f --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.pyi @@ -0,0 +1,1056 @@ +import datetime +from collections.abc import Mapping +from typing import Any, Callable, Generic, Literal, TypeVar, final + +from _typeshed import SupportsAllComparisons +from typing_extensions import LiteralString, Self, TypeAlias + +from pydantic_core import ErrorDetails, ErrorTypeInfo, InitErrorDetails, MultiHostHost +from pydantic_core.core_schema import CoreConfig, CoreSchema, ErrorType, ExtraBehavior + +__all__ = [ + '__version__', + 'build_profile', + 'build_info', + '_recursion_limit', + 'ArgsKwargs', + 'SchemaValidator', + 'SchemaSerializer', + 'Url', + 'MultiHostUrl', + 'SchemaError', + 'ValidationError', + 'PydanticCustomError', + 'PydanticKnownError', + 'PydanticOmit', + 'PydanticUseDefault', + 'PydanticSerializationError', + 'PydanticSerializationUnexpectedValue', + 'PydanticUndefined', + 'PydanticUndefinedType', + 'Some', + 'to_json', + 'from_json', + 'to_jsonable_python', + 'list_all_errors', + 'TzInfo', +] +__version__: str +build_profile: str +build_info: str +_recursion_limit: int + +_T = TypeVar('_T', default=Any, covariant=True) + +_StringInput: TypeAlias = 'dict[str, _StringInput]' + +@final +class Some(Generic[_T]): + """ + Similar to Rust's [`Option::Some`](https://doc.rust-lang.org/std/option/enum.Option.html) type, this + identifies a value as being present, and provides a way to access it. + + Generally used in a union with `None` to different between "some value which could be None" and no value. + """ + + __match_args__ = ('value',) + + @property + def value(self) -> _T: + """ + Returns the value wrapped by `Some`. + """ + @classmethod + def __class_getitem__(cls, item: Any, /) -> type[Self]: ... + +@final +class SchemaValidator: + """ + `SchemaValidator` is the Python wrapper for `pydantic-core`'s Rust validation logic, internally it owns one + `CombinedValidator` which may in turn own more `CombinedValidator`s which make up the full schema validator. + """ + + # note: pyo3 currently supports __new__, but not __init__, though we include __init__ stubs + # and docstrings here (and in the following classes) for documentation purposes + + def __init__(self, schema: CoreSchema, config: CoreConfig | None = None, _use_prebuilt: bool = True) -> None: + """Initializes the `SchemaValidator`. + + Arguments: + schema: The `CoreSchema` to use for validation. + config: Optionally a [`CoreConfig`][pydantic_core.core_schema.CoreConfig] to configure validation. + _use_prebuilt: Whether to use pre-built validators (False during rebuilds to avoid stale references). + """ + + def __new__(cls, schema: CoreSchema, config: CoreConfig | None = None, _use_prebuilt: bool = True) -> Self: ... + @property + def title(self) -> str: + """ + The title of the schema, as used in the heading of [`ValidationError.__str__()`][pydantic_core.ValidationError]. + """ + def validate_python( + self, + input: Any, + *, + strict: bool | None = None, + extra: ExtraBehavior | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + self_instance: Any | None = None, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> Any: + """ + Validate a Python object against the schema and return the validated object. + + Arguments: + input: The Python object to validate. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + extra: Whether to ignore, allow, or forbid extra data during model validation. + If `None`, the value of [`CoreConfig.extra_fields_behavior`][pydantic_core.core_schema.CoreConfig] is used. + from_attributes: Whether to validate objects as inputs to models by extracting attributes. + If `None`, the value of [`CoreConfig.from_attributes`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + self_instance: An instance of a model set attributes on from validation, this is used when running + validation from the `__init__` method of a model. + allow_partial: Whether to allow partial validation; if `True` errors in the last element of sequences + and mappings are ignored. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Raises: + ValidationError: If validation fails. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + The validated object. + """ + def isinstance_python( + self, + input: Any, + *, + strict: bool | None = None, + extra: ExtraBehavior | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + self_instance: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> bool: + """ + Similar to [`validate_python()`][pydantic_core.SchemaValidator.validate_python] but returns a boolean. + + Arguments match `validate_python()`. This method will not raise `ValidationError`s but will raise internal + errors. + + Returns: + `True` if validation succeeds, `False` if validation fails. + """ + def validate_json( + self, + input: str | bytes | bytearray, + *, + strict: bool | None = None, + extra: ExtraBehavior | None = None, + context: Any | None = None, + self_instance: Any | None = None, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> Any: + """ + Validate JSON data directly against the schema and return the validated Python object. + + This method should be significantly faster than `validate_python(json.loads(json_data))` as it avoids the + need to create intermediate Python objects + + It also handles constructing the correct Python type even in strict mode, where + `validate_python(json.loads(json_data))` would fail validation. + + Arguments: + input: The JSON data to validate. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + extra: Whether to ignore, allow, or forbid extra data during model validation. + If `None`, the value of [`CoreConfig.extra_fields_behavior`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + self_instance: An instance of a model set attributes on from validation. + allow_partial: Whether to allow partial validation; if `True` incomplete JSON will be parsed successfully + and errors in the last element of sequences and mappings are ignored. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Raises: + ValidationError: If validation fails or if the JSON data is invalid. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + The validated Python object. + """ + def validate_strings( + self, + input: _StringInput, + *, + strict: bool | None = None, + extra: ExtraBehavior | None = None, + context: Any | None = None, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> Any: + """ + Validate a string against the schema and return the validated Python object. + + This is similar to `validate_json` but applies to scenarios where the input will be a string but not + JSON data, e.g. URL fragments, query parameters, etc. + + Arguments: + input: The input as a string, or bytes/bytearray if `strict=False`. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + extra: Whether to ignore, allow, or forbid extra data during model validation. + If `None`, the value of [`CoreConfig.extra_fields_behavior`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + allow_partial: Whether to allow partial validation; if `True` errors in the last element of sequences + and mappings are ignored. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Raises: + ValidationError: If validation fails or if the JSON data is invalid. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + The validated Python object. + """ + def validate_assignment( + self, + obj: Any, + field_name: str, + field_value: Any, + *, + strict: bool | None = None, + extra: ExtraBehavior | None = None, + from_attributes: bool | None = None, + context: Any | None = None, + by_alias: bool | None = None, + by_name: bool | None = None, + ) -> dict[str, Any] | tuple[dict[str, Any], dict[str, Any] | None, set[str]]: + """ + Validate an assignment to a field on a model. + + Arguments: + obj: The model instance being assigned to. + field_name: The name of the field to validate assignment for. + field_value: The value to assign to the field. + strict: Whether to validate the object in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + extra: Whether to ignore, allow, or forbid extra data during model validation. + If `None`, the value of [`CoreConfig.extra_fields_behavior`][pydantic_core.core_schema.CoreConfig] is used. + from_attributes: Whether to validate objects as inputs to models by extracting attributes. + If `None`, the value of [`CoreConfig.from_attributes`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + by_alias: Whether to use the field's alias when validating against the provided input data. + by_name: Whether to use the field's name when validating against the provided input data. + + Raises: + ValidationError: If validation fails. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + Either the model dict or a tuple of `(model_data, model_extra, fields_set)` + """ + def get_default_value(self, *, strict: bool | None = None, context: Any = None) -> Some | None: + """ + Get the default value for the schema, including running default value validation. + + Arguments: + strict: Whether to validate the default value in strict mode. + If `None`, the value of [`CoreConfig.strict`][pydantic_core.core_schema.CoreConfig] is used. + context: The context to use for validation, this is passed to functional validators as + [`info.context`][pydantic_core.core_schema.ValidationInfo.context]. + + Raises: + ValidationError: If validation fails. + Exception: Other error types maybe raised if internal errors occur. + + Returns: + `None` if the schema has no default value, otherwise a [`Some`][pydantic_core.Some] containing the default. + """ + +# In reality, `bool` should be replaced by `Literal[True]` but mypy fails to correctly apply bidirectional type inference +# (e.g. when using `{'a': {'b': True}}`). +_IncEx: TypeAlias = set[int] | set[str] | Mapping[int, _IncEx | bool] | Mapping[str, _IncEx | bool] + +@final +class SchemaSerializer: + """ + `SchemaSerializer` is the Python wrapper for `pydantic-core`'s Rust serialization logic, internally it owns one + `CombinedSerializer` which may in turn own more `CombinedSerializer`s which make up the full schema serializer. + """ + + def __init__(self, schema: CoreSchema, config: CoreConfig | None = None, _use_prebuilt: bool = True) -> None: + """Initializes the `SchemaSerializer`. + + Arguments: + schema: The `CoreSchema` to use for serialization. + config: Optionally a [`CoreConfig`][pydantic_core.core_schema.CoreConfig] to to configure serialization. + _use_prebuilt: Whether to use pre-built validators (False during rebuilds to avoid stale references). + """ + + def __new__(cls, schema: CoreSchema, config: CoreConfig | None = None, _use_prebuilt: bool = True) -> Self: ... + def to_python( + self, + value: Any, + *, + mode: str | None = None, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + context: Any | None = None, + ) -> Any: + """ + Serialize/marshal a Python object to a Python object including transforming and filtering data. + + Arguments: + value: The Python object to serialize. + mode: The serialization mode to use, either `'python'` or `'json'`, defaults to `'python'`. In JSON mode, + all values are converted to JSON compatible types, e.g. `None`, `int`, `float`, `str`, `list`, `dict`. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_unset: Whether to exclude fields that are not set, + e.g. are not included in `__pydantic_fields_set__`. + exclude_defaults: Whether to exclude fields that are equal to their default value. + exclude_none: Whether to exclude fields that have a value of `None`. + exclude_computed_fields: Whether to exclude computed fields. + round_trip: Whether to enable serialization and validation round-trip support. + warnings: How to handle invalid fields. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + The serialized Python object. + """ + def to_json( + self, + value: Any, + *, + indent: int | None = None, + ensure_ascii: bool = False, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + by_alias: bool | None = None, + exclude_unset: bool = False, + exclude_defaults: bool = False, + exclude_none: bool = False, + exclude_computed_fields: bool = False, + round_trip: bool = False, + warnings: bool | Literal['none', 'warn', 'error'] = True, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + context: Any | None = None, + ) -> bytes: + """ + Serialize a Python object to JSON including transforming and filtering data. + + Arguments: + value: The Python object to serialize. + indent: If `None`, the JSON will be compact, otherwise it will be pretty-printed with the indent provided. + ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped. + If `False` (the default), these characters will be output as-is. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_unset: Whether to exclude fields that are not set, + e.g. are not included in `__pydantic_fields_set__`. + exclude_defaults: Whether to exclude fields that are equal to their default value. + exclude_none: Whether to exclude fields that have a value of `None`. + exclude_computed_fields: Whether to exclude computed fields. + round_trip: Whether to enable serialization and validation round-trip support. + warnings: How to handle invalid fields. False/"none" ignores them, True/"warn" logs errors, + "error" raises a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError]. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + JSON bytes. + """ + +def to_json( + value: Any, + *, + indent: int | None = None, + ensure_ascii: bool = False, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + # Note: In Pydantic 2.11, the default value of `by_alias` on `SchemaSerializer` was changed from `True` to `None`, + # to be consistent with the Pydantic "dump" methods. However, the default of `True` was kept here for + # backwards compatibility. In Pydantic V3, `by_alias` is expected to default to `True` everywhere: + by_alias: bool = True, + exclude_none: bool = False, + round_trip: bool = False, + timedelta_mode: Literal['iso8601', 'float'] = 'iso8601', + temporal_mode: Literal['iso8601', 'seconds', 'milliseconds'] = 'iso8601', + bytes_mode: Literal['utf8', 'base64', 'hex'] = 'utf8', + inf_nan_mode: Literal['null', 'constants', 'strings'] = 'constants', + serialize_unknown: bool = False, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + context: Any | None = None, +) -> bytes: + """ + Serialize a Python object to JSON including transforming and filtering data. + + This is effectively a standalone version of [`SchemaSerializer.to_json`][pydantic_core.SchemaSerializer.to_json]. + + Arguments: + value: The Python object to serialize. + indent: If `None`, the JSON will be compact, otherwise it will be pretty-printed with the indent provided. + ensure_ascii: If `True`, the output is guaranteed to have all incoming non-ASCII characters escaped. + If `False` (the default), these characters will be output as-is. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to enable serialization and validation round-trip support. + timedelta_mode: How to serialize `timedelta` objects, either `'iso8601'` or `'float'`. + temporal_mode: How to serialize datetime-like objects (`datetime`, `date`, `time`), either `'iso8601'`, `'seconds'`, or `'milliseconds'`. + `iso8601` returns an ISO 8601 string; `seconds` returns the Unix timestamp in seconds as a float; `milliseconds` returns the Unix timestamp in milliseconds as a float. + + bytes_mode: How to serialize `bytes` objects, either `'utf8'`, `'base64'`, or `'hex'`. + inf_nan_mode: How to serialize `Infinity`, `-Infinity` and `NaN` values, either `'null'`, `'constants'`, or `'strings'`. + serialize_unknown: Attempt to serialize unknown types, `str(value)` will be used, if that fails + `""` will be used. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + JSON bytes. + """ + +def from_json( + data: str | bytes | bytearray, + *, + allow_inf_nan: bool = True, + cache_strings: bool | Literal['all', 'keys', 'none'] = True, + allow_partial: bool | Literal['off', 'on', 'trailing-strings'] = False, +) -> Any: + """ + Deserialize JSON data to a Python object. + + This is effectively a faster version of `json.loads()`, with some extra functionality. + + Arguments: + data: The JSON data to deserialize. + allow_inf_nan: Whether to allow `Infinity`, `-Infinity` and `NaN` values as `json.loads()` does by default. + cache_strings: Whether to cache strings to avoid constructing new Python objects, + this should have a significant impact on performance while increasing memory usage slightly, + `all/True` means cache all strings, `keys` means cache only dict keys, `none/False` means no caching. + allow_partial: Whether to allow partial deserialization, if `True` JSON data is returned if the end of the + input is reached before the full object is deserialized, e.g. `["aa", "bb", "c` would return `['aa', 'bb']`. + `'trailing-strings'` means any final unfinished JSON string is included in the result. + + Raises: + ValueError: If deserialization fails. + + Returns: + The deserialized Python object. + """ + +def to_jsonable_python( + value: Any, + *, + include: _IncEx | None = None, + exclude: _IncEx | None = None, + # Note: In Pydantic 2.11, the default value of `by_alias` on `SchemaSerializer` was changed from `True` to `None`, + # to be consistent with the Pydantic "dump" methods. However, the default of `True` was kept here for + # backwards compatibility. In Pydantic V3, `by_alias` is expected to default to `True` everywhere: + by_alias: bool = True, + exclude_none: bool = False, + round_trip: bool = False, + timedelta_mode: Literal['iso8601', 'float'] = 'iso8601', + temporal_mode: Literal['iso8601', 'seconds', 'milliseconds'] = 'iso8601', + bytes_mode: Literal['utf8', 'base64', 'hex'] = 'utf8', + inf_nan_mode: Literal['null', 'constants', 'strings'] = 'constants', + serialize_unknown: bool = False, + fallback: Callable[[Any], Any] | None = None, + serialize_as_any: bool = False, + polymorphic_serialization: bool | None = None, + context: Any | None = None, +) -> Any: + """ + Serialize/marshal a Python object to a JSON-serializable Python object including transforming and filtering data. + + This is effectively a standalone version of + [`SchemaSerializer.to_python(mode='json')`][pydantic_core.SchemaSerializer.to_python]. + + Args: + value: The Python object to serialize. + include: A set of fields to include, if `None` all fields are included. + exclude: A set of fields to exclude, if `None` no fields are excluded. + by_alias: Whether to use the alias names of fields. + exclude_none: Whether to exclude fields that have a value of `None`. + round_trip: Whether to enable serialization and validation round-trip support. + timedelta_mode: How to serialize `timedelta` objects, either `'iso8601'` or `'float'`. + temporal_mode: How to serialize datetime-like objects (`datetime`, `date`, `time`), either `'iso8601'`, `'seconds'`, or `'milliseconds'`. + `iso8601` returns an ISO 8601 string; `seconds` returns the Unix timestamp in seconds as a float; `milliseconds` returns the Unix timestamp in milliseconds as a float. + + bytes_mode: How to serialize `bytes` objects, either `'utf8'`, `'base64'`, or `'hex'`. + inf_nan_mode: How to serialize `Infinity`, `-Infinity` and `NaN` values, either `'null'`, `'constants'`, or `'strings'`. + serialize_unknown: Attempt to serialize unknown types, `str(value)` will be used, if that fails + `""` will be used. + fallback: A function to call when an unknown value is encountered, + if `None` a [`PydanticSerializationError`][pydantic_core.PydanticSerializationError] error is raised. + serialize_as_any: Whether to serialize fields with duck-typing serialization behavior. + polymorphic_serialization: Whether to use model and dataclass polymorphic serialization for this call. + context: The context to use for serialization, this is passed to functional serializers as + [`info.context`][pydantic_core.core_schema.SerializationInfo.context]. + + Raises: + PydanticSerializationError: If serialization fails and no `fallback` function is provided. + + Returns: + The serialized Python object. + """ + +class Url(SupportsAllComparisons): + """ + A URL type, internal logic uses the [url rust crate](https://docs.rs/url/latest/url/) originally developed + by Mozilla. + """ + + def __init__(self, url: str) -> None: ... + def __new__(cls, url: str) -> Self: ... + @property + def scheme(self) -> str: ... + @property + def username(self) -> str | None: ... + @property + def password(self) -> str | None: ... + @property + def host(self) -> str | None: ... + def unicode_host(self) -> str | None: ... + @property + def port(self) -> int | None: ... + @property + def path(self) -> str | None: ... + @property + def query(self) -> str | None: ... + def query_params(self) -> list[tuple[str, str]]: ... + @property + def fragment(self) -> str | None: ... + def unicode_string(self) -> str: ... + def __repr__(self) -> str: ... + def __str__(self) -> str: ... + def __deepcopy__(self, memo: dict) -> str: ... + @classmethod + def build( + cls, + *, + scheme: str, + username: str | None = None, + password: str | None = None, + host: str, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: ... + +class MultiHostUrl(SupportsAllComparisons): + """ + A URL type with support for multiple hosts, as used by some databases for DSNs, e.g. `https://foo.com,bar.com/path`. + + Internal URL logic uses the [url rust crate](https://docs.rs/url/latest/url/) originally developed + by Mozilla. + """ + + def __init__(self, url: str) -> None: ... + def __new__(cls, url: str) -> Self: ... + @property + def scheme(self) -> str: ... + @property + def path(self) -> str | None: ... + @property + def query(self) -> str | None: ... + def query_params(self) -> list[tuple[str, str]]: ... + @property + def fragment(self) -> str | None: ... + def hosts(self) -> list[MultiHostHost]: ... + def unicode_string(self) -> str: ... + def __repr__(self) -> str: ... + def __str__(self) -> str: ... + def __deepcopy__(self, memo: dict) -> Self: ... + @classmethod + def build( + cls, + *, + scheme: str, + hosts: list[MultiHostHost] | None = None, + username: str | None = None, + password: str | None = None, + host: str | None = None, + port: int | None = None, + path: str | None = None, + query: str | None = None, + fragment: str | None = None, + ) -> Self: ... + +@final +class SchemaError(Exception): + """ + Information about errors that occur while building a [`SchemaValidator`][pydantic_core.SchemaValidator] + or [`SchemaSerializer`][pydantic_core.SchemaSerializer]. + """ + + def error_count(self) -> int: + """ + Returns: + The number of errors in the schema. + """ + def errors(self) -> list[ErrorDetails]: + """ + Returns: + A list of [`ErrorDetails`][pydantic_core.ErrorDetails] for each error in the schema. + """ + +class ValidationError(ValueError): + """ + `ValidationError` is the exception raised by `pydantic-core` when validation fails, it contains a list of errors + which detail why validation failed. + """ + @classmethod + def from_exception_data( + cls, + title: str, + line_errors: list[InitErrorDetails], + input_type: Literal['python', 'json'] = 'python', + hide_input: bool = False, + ) -> Self: + """ + Python constructor for a Validation Error. + + Arguments: + title: The title of the error, as used in the heading of `str(validation_error)` + line_errors: A list of [`InitErrorDetails`][pydantic_core.InitErrorDetails] which contain information + about errors that occurred during validation. + input_type: Whether the error is for a Python object or JSON. + hide_input: Whether to hide the input value in the error message. + """ + @property + def title(self) -> str: + """ + The title of the error, as used in the heading of `str(validation_error)`. + """ + def error_count(self) -> int: + """ + Returns: + The number of errors in the validation error. + """ + def errors( + self, *, include_url: bool = True, include_context: bool = True, include_input: bool = True + ) -> list[ErrorDetails]: + """ + Details about each error in the validation error. + + Args: + include_url: Whether to include a URL to documentation on the error each error. + include_context: Whether to include the context of each error. + include_input: Whether to include the input value of each error. + + Returns: + A list of [`ErrorDetails`][pydantic_core.ErrorDetails] for each error in the validation error. + """ + def json( + self, + *, + indent: int | None = None, + include_url: bool = True, + include_context: bool = True, + include_input: bool = True, + ) -> str: + """ + Same as [`errors()`][pydantic_core.ValidationError.errors] but returns a JSON string. + + Args: + indent: The number of spaces to indent the JSON by, or `None` for no indentation - compact JSON. + include_url: Whether to include a URL to documentation on the error each error. + include_context: Whether to include the context of each error. + include_input: Whether to include the input value of each error. + + Returns: + a JSON string. + """ + + def __repr__(self) -> str: + """ + A string representation of the validation error. + + Whether or not documentation URLs are included in the repr is controlled by the + environment variable `PYDANTIC_ERRORS_INCLUDE_URL` being set to `1` or + `true`; by default, URLs are shown. + + Due to implementation details, this environment variable can only be set once, + before the first validation error is created. + """ + +class PydanticCustomError(ValueError): + """A custom exception providing flexible error handling for Pydantic validators. + + You can raise this error in custom validators when you'd like flexibility in regards to the error type, message, and context. + + Example: + ```py + from pydantic_core import PydanticCustomError + + def custom_validator(v) -> None: + if v <= 10: + raise PydanticCustomError('custom_value_error', 'Value must be greater than {value}', {'value': 10, 'extra_context': 'extra_data'}) + return v + ``` + + Arguments: + error_type: The error type. + message_template: The message template. + context: The data to inject into the message template. + """ + + def __init__( + self, error_type: LiteralString, message_template: LiteralString, context: dict[str, Any] | None = None, / + ) -> None: ... + @property + def context(self) -> dict[str, Any] | None: + """Values which are required to render the error message, and could hence be useful in passing error data forward.""" + + @property + def type(self) -> str: + """The error type associated with the error. For consistency with Pydantic, this is typically a snake_case string.""" + + @property + def message_template(self) -> str: + """The message template associated with the error. This is a string that can be formatted with context variables in `{curly_braces}`.""" + + def message(self) -> str: + """The formatted message associated with the error. This presents as the message template with context variables appropriately injected.""" + +@final +class PydanticKnownError(ValueError): + """A helper class for raising exceptions that mimic Pydantic's built-in exceptions, with more flexibility in regards to context. + + Unlike [`PydanticCustomError`][pydantic_core.PydanticCustomError], the `error_type` argument must be a known `ErrorType`. + + Example: + ```py + from pydantic_core import PydanticKnownError + + def custom_validator(v) -> None: + if v <= 10: + raise PydanticKnownError('greater_than', {'gt': 10}) + return v + ``` + + Arguments: + error_type: The error type. + context: The data to inject into the message template. + """ + + def __init__(self, error_type: ErrorType, context: dict[str, Any] | None = None, /) -> None: ... + @property + def context(self) -> dict[str, Any] | None: + """Values which are required to render the error message, and could hence be useful in passing error data forward.""" + + @property + def type(self) -> ErrorType: + """The type of the error.""" + + @property + def message_template(self) -> str: + """The message template associated with the provided error type. This is a string that can be formatted with context variables in `{curly_braces}`.""" + + def message(self) -> str: + """The formatted message associated with the error. This presents as the message template with context variables appropriately injected.""" + +@final +class PydanticOmit(Exception): + """An exception to signal that a field should be omitted from a generated result. + + This could span from omitting a field from a JSON Schema to omitting a field from a serialized result. + Upcoming: more robust support for using PydanticOmit in custom serializers is still in development. + Right now, this is primarily used in the JSON Schema generation process. + + Example: + ```py + from typing import Callable + + from pydantic_core import PydanticOmit + + from pydantic import BaseModel + from pydantic.json_schema import GenerateJsonSchema, JsonSchemaValue + + + class MyGenerateJsonSchema(GenerateJsonSchema): + def handle_invalid_for_json_schema(self, schema, error_info) -> JsonSchemaValue: + raise PydanticOmit + + + class Predicate(BaseModel): + name: str = 'no-op' + func: Callable = lambda x: x + + + instance_example = Predicate() + + validation_schema = instance_example.model_json_schema(schema_generator=MyGenerateJsonSchema, mode='validation') + print(validation_schema) + ''' + {'properties': {'name': {'default': 'no-op', 'title': 'Name', 'type': 'string'}}, 'title': 'Predicate', 'type': 'object'} + ''' + ``` + + For a more in depth example / explanation, see the [customizing JSON schema](../concepts/json_schema.md#customizing-the-json-schema-generation-process) docs. + """ + + def __new__(cls) -> Self: ... + +@final +class PydanticUseDefault(Exception): + """An exception to signal that standard validation either failed or should be skipped, and the default value should be used instead. + + This warning can be raised in custom validation functions to redirect the flow of validation. + + Example: + ```py + from pydantic_core import PydanticUseDefault + from datetime import datetime + from pydantic import BaseModel, field_validator + + + class Event(BaseModel): + name: str = 'meeting' + time: datetime + + @field_validator('name', mode='plain') + def name_must_be_present(cls, v) -> str: + if not v or not isinstance(v, str): + raise PydanticUseDefault() + return v + + + event1 = Event(name='party', time=datetime(2024, 1, 1, 12, 0, 0)) + print(repr(event1)) + # > Event(name='party', time=datetime.datetime(2024, 1, 1, 12, 0)) + event2 = Event(time=datetime(2024, 1, 1, 12, 0, 0)) + print(repr(event2)) + # > Event(name='meeting', time=datetime.datetime(2024, 1, 1, 12, 0)) + ``` + + For an additional example, see the [validating partial json data](../concepts/json.md#partial-json-parsing) section of the Pydantic documentation. + """ + + def __new__(cls) -> Self: ... + +@final +class PydanticSerializationError(ValueError): + """An error raised when an issue occurs during serialization. + + In custom serializers, this error can be used to indicate that serialization has failed. + + Arguments: + message: The message associated with the error. + """ + + def __init__(self, message: str, /) -> None: ... + +@final +class PydanticSerializationUnexpectedValue(ValueError): + """An error raised when an unexpected value is encountered during serialization. + + This error is often caught and coerced into a warning, as `pydantic-core` generally makes a best attempt + at serializing values, in contrast with validation where errors are eagerly raised. + + Example: + ```py + from pydantic import BaseModel, field_serializer + from pydantic_core import PydanticSerializationUnexpectedValue + + class BasicPoint(BaseModel): + x: int + y: int + + @field_serializer('*') + def serialize(self, v): + if not isinstance(v, int): + raise PydanticSerializationUnexpectedValue(f'Expected type `int`, got {type(v)} with value {v}') + return v + + point = BasicPoint(x=1, y=2) + # some sort of mutation + point.x = 'a' + + print(point.model_dump()) + ''' + UserWarning: Pydantic serializer warnings: + PydanticSerializationUnexpectedValue(Expected type `int`, got with value a) + return self.__pydantic_serializer__.to_python( + {'x': 'a', 'y': 2} + ''' + ``` + + This is often used internally in `pydantic-core` when unexpected types are encountered during serialization, + but it can also be used by users in custom serializers, as seen above. + + Arguments: + message: The message associated with the unexpected value. + """ + + def __init__(self, message: str, /) -> None: ... + +@final +class ArgsKwargs: + """A construct used to store arguments and keyword arguments for a function call. + + This data structure is generally used to store information for core schemas associated with functions (like in an arguments schema). + This data structure is also currently used for some validation against dataclasses. + + Example: + ```py + from pydantic.dataclasses import dataclass + from pydantic import model_validator + + + @dataclass + class Model: + a: int + b: int + + @model_validator(mode="before") + @classmethod + def no_op_validator(cls, values): + print(values) + return values + + Model(1, b=2) + #> ArgsKwargs((1,), {"b": 2}) + + Model(1, 2) + #> ArgsKwargs((1, 2), {}) + + Model(a=1, b=2) + #> ArgsKwargs((), {"a": 1, "b": 2}) + ``` + """ + + def __init__(self, args: tuple[Any, ...], kwargs: dict[str, Any] | None = None) -> None: + """Initializes the `ArgsKwargs`. + + Arguments: + args: The arguments (inherently ordered) for a function call. + kwargs: The keyword arguments for a function call + """ + + def __new__(cls, args: tuple[Any, ...], kwargs: dict[str, Any] | None = None) -> Self: ... + @property + def args(self) -> tuple[Any, ...]: + """The arguments (inherently ordered) for a function call.""" + + @property + def kwargs(self) -> dict[str, Any] | None: + """The keyword arguments for a function call.""" + +@final +class PydanticUndefinedType: + """A type used as a sentinel for undefined values.""" + + def __copy__(self) -> Self: ... + def __deepcopy__(self, memo: Any) -> Self: ... + +PydanticUndefined: PydanticUndefinedType + +def list_all_errors() -> list[ErrorTypeInfo]: + """ + Get information about all built-in errors. + + Returns: + A list of `ErrorTypeInfo` typed dicts. + """ +@final +class TzInfo(datetime.tzinfo): + """An `pydantic-core` implementation of the abstract [`datetime.tzinfo`][] class.""" + + def __init__(self, seconds: float = 0.0) -> None: + """Initializes the `TzInfo`. + + Arguments: + seconds: The offset from UTC in seconds. Defaults to 0.0 (UTC). + """ + + def __new__(cls, seconds: float = 0.0) -> Self: ... + + # Docstrings for attributes sourced from the abstract base class, [`datetime.tzinfo`](https://docs.python.org/3/library/datetime.html#datetime.tzinfo). + + def tzname(self, dt: datetime.datetime | None) -> str | None: + """Return the time zone name corresponding to the [`datetime`][datetime.datetime] object _dt_, as a string. + + For more info, see [`tzinfo.tzname`][datetime.tzinfo.tzname]. + """ + + def utcoffset(self, dt: datetime.datetime | None) -> datetime.timedelta | None: + """Return offset of local time from UTC, as a [`timedelta`][datetime.timedelta] object that is positive east of UTC. If local time is west of UTC, this should be negative. + + More info can be found at [`tzinfo.utcoffset`][datetime.tzinfo.utcoffset]. + """ + + def dst(self, dt: datetime.datetime | None) -> datetime.timedelta | None: + """Return the daylight saving time (DST) adjustment, as a [`timedelta`][datetime.timedelta] object or `None` if DST information isn’t known. + + More info can be found at[`tzinfo.dst`][datetime.tzinfo.dst].""" + + def fromutc(self, dt: datetime.datetime) -> datetime.datetime: + """Adjust the date and time data associated datetime object _dt_, returning an equivalent datetime in self’s local time. + + More info can be found at [`tzinfo.fromutc`][datetime.tzinfo.fromutc].""" + + def __deepcopy__(self, _memo: dict[Any, Any]) -> TzInfo: ... diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/core_schema.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/core_schema.py new file mode 100644 index 0000000000000000000000000000000000000000..6ff202c151423de6fa50a0290e264e8eef83c4fa --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/core_schema.py @@ -0,0 +1,4461 @@ +""" +This module contains definitions to build schemas which `pydantic_core` can +validate and serialize. +""" + +from __future__ import annotations as _annotations + +import sys +import warnings +from collections.abc import Generator, Hashable, Mapping +from datetime import date, datetime, time, timedelta +from decimal import Decimal +from re import Pattern +from typing import TYPE_CHECKING, Any, Callable, Literal, Union + +from typing_extensions import TypeVar, deprecated + +if sys.version_info < (3, 12): + from typing_extensions import TypedDict +else: + from typing import TypedDict + +if sys.version_info < (3, 11): + from typing_extensions import Protocol, Required, TypeAlias +else: + from typing import Protocol, Required, TypeAlias + +if TYPE_CHECKING: + from pydantic_core import PydanticUndefined +else: + # The initial build of pydantic_core requires PydanticUndefined to generate + # the core schema; so we need to conditionally skip it. mypy doesn't like + # this at all, hence the TYPE_CHECKING branch above. + try: + from pydantic_core import PydanticUndefined + except ImportError: + PydanticUndefined = object() + + +ExtraBehavior = Literal['allow', 'forbid', 'ignore'] + + +class CoreConfig(TypedDict, total=False): + """ + Base class for schema configuration options. + + Attributes: + title: The name of the configuration. + strict: Whether the configuration should strictly adhere to specified rules. + extra_fields_behavior: The behavior for handling extra fields. + typed_dict_total: Whether the TypedDict should be considered total. Default is `True`. + from_attributes: Whether to use attributes for models, dataclasses, and tagged union keys. + loc_by_alias: Whether to use the used alias (or first alias for "field required" errors) instead of + `field_names` to construct error `loc`s. Default is `True`. + revalidate_instances: Whether instances of models and dataclasses should re-validate. Default is 'never'. + validate_default: Whether to validate default values during validation. Default is `False`. + str_max_length: The maximum length for string fields. + str_min_length: The minimum length for string fields. + str_strip_whitespace: Whether to strip whitespace from string fields. + str_to_lower: Whether to convert string fields to lowercase. + str_to_upper: Whether to convert string fields to uppercase. + allow_inf_nan: Whether to allow infinity and NaN values for float fields. Default is `True`. + ser_json_timedelta: The serialization option for `timedelta` values. Default is 'iso8601'. + Note that if ser_json_temporal is set, then this param will be ignored. + ser_json_temporal: The serialization option for datetime like values. Default is 'iso8601'. + The types this covers are datetime, date, time and timedelta. + If this is set, it will take precedence over ser_json_timedelta + ser_json_bytes: The serialization option for `bytes` values. Default is 'utf8'. + ser_json_inf_nan: The serialization option for infinity and NaN values + in float fields. Default is 'null'. + val_json_bytes: The validation option for `bytes` values, complementing ser_json_bytes. Default is 'utf8'. + hide_input_in_errors: Whether to hide input data from `ValidationError` representation. + validation_error_cause: Whether to add user-python excs to the __cause__ of a ValidationError. + Requires exceptiongroup backport pre Python 3.11. + coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode). + regex_engine: The regex engine to use for regex pattern validation. Default is 'rust-regex'. See `StringSchema`. + cache_strings: Whether to cache strings. Default is `True`, `True` or `'all'` is required to cache strings + during general validation since validators don't know if they're in a key or a value. + validate_by_alias: Whether to use the field's alias when validating against the provided input data. Default is `True`. + validate_by_name: Whether to use the field's name when validating against the provided input data. Default is `False`. Replacement for `populate_by_name`. + serialize_by_alias: Whether to serialize by alias. Default is `False`, expected to change to `True` in V3. + polymorphic_serialization: Whether to enable polymorphic serialization for models and dataclasses. Default is `False`. + url_preserve_empty_path: Whether to preserve empty URL paths when validating values for a URL type. Defaults to `False`. + """ + + title: str + strict: bool + # settings related to typed dicts, model fields, dataclass fields + extra_fields_behavior: ExtraBehavior + typed_dict_total: bool # default: True + # used for models, dataclasses, and tagged union keys + from_attributes: bool + # whether to use the used alias (or first alias for "field required" errors) instead of field_names + # to construct error `loc`s, default True + loc_by_alias: bool + # whether instances of models and dataclasses (including subclass instances) should re-validate, default 'never' + revalidate_instances: Literal['always', 'never', 'subclass-instances'] + # whether to validate default values during validation, default False + validate_default: bool + # used on typed-dicts and arguments + # fields related to string fields only + str_max_length: int + str_min_length: int + str_strip_whitespace: bool + str_to_lower: bool + str_to_upper: bool + # fields related to float fields only + allow_inf_nan: bool # default: True + # the config options are used to customise serialization to JSON + ser_json_timedelta: Literal['iso8601', 'float'] # default: 'iso8601' + ser_json_temporal: Literal['iso8601', 'seconds', 'milliseconds'] # default: 'iso8601' + ser_json_bytes: Literal['utf8', 'base64', 'hex'] # default: 'utf8' + ser_json_inf_nan: Literal['null', 'constants', 'strings'] # default: 'null' + val_json_bytes: Literal['utf8', 'base64', 'hex'] # default: 'utf8' + # used to hide input data from ValidationError repr + hide_input_in_errors: bool + validation_error_cause: bool # default: False + coerce_numbers_to_str: bool # default: False + regex_engine: Literal['rust-regex', 'python-re'] # default: 'rust-regex' + cache_strings: Union[bool, Literal['all', 'keys', 'none']] # default: 'True' + validate_by_alias: bool # default: True + validate_by_name: bool # default: False + serialize_by_alias: bool # default: False + polymorphic_serialization: bool # default: False + url_preserve_empty_path: bool # default: False + + +IncExCall: TypeAlias = 'set[int | str] | dict[int | str, IncExCall] | None' + +ContextT = TypeVar('ContextT', covariant=True, default='Any | None') + + +class SerializationInfo(Protocol[ContextT]): + """Extra data used during serialization.""" + + @property + def include(self) -> IncExCall: + """The `include` argument set during serialization.""" + ... + + @property + def exclude(self) -> IncExCall: + """The `exclude` argument set during serialization.""" + ... + + @property + def context(self) -> ContextT: + """The current serialization context.""" + ... + + @property + def mode(self) -> Literal['python', 'json'] | str: + """The serialization mode set during serialization.""" + ... + + @property + def by_alias(self) -> bool: + """The `by_alias` argument set during serialization.""" + ... + + @property + def exclude_unset(self) -> bool: + """The `exclude_unset` argument set during serialization.""" + ... + + @property + def exclude_defaults(self) -> bool: + """The `exclude_defaults` argument set during serialization.""" + ... + + @property + def exclude_none(self) -> bool: + """The `exclude_none` argument set during serialization.""" + ... + + @property + def exclude_computed_fields(self) -> bool: + """The `exclude_computed_fields` argument set during serialization.""" + ... + + @property + def serialize_as_any(self) -> bool: + """The `serialize_as_any` argument set during serialization.""" + ... + + @property + def polymorphic_serialization(self) -> bool | None: + """The `polymorphic_serialization` argument set during serialization, if any.""" + ... + + @property + def round_trip(self) -> bool: + """The `round_trip` argument set during serialization.""" + ... + + def mode_is_json(self) -> bool: ... + + def __str__(self) -> str: ... + + def __repr__(self) -> str: ... + + +class FieldSerializationInfo(SerializationInfo[ContextT], Protocol): + """Extra data used during field serialization.""" + + @property + def field_name(self) -> str: + """The name of the current field being serialized.""" + ... + + +class ValidationInfo(Protocol[ContextT]): + """Extra data used during validation.""" + + @property + def context(self) -> ContextT: + """The current validation context.""" + ... + + @property + def config(self) -> CoreConfig | None: + """The CoreConfig that applies to this validation.""" + ... + + @property + def mode(self) -> Literal['python', 'json']: + """The type of input data we are currently validating.""" + ... + + @property + def data(self) -> dict[str, Any]: + """The data being validated for this model.""" + ... + + @property + def field_name(self) -> str | None: + """ + The name of the current field being validated if this validator is + attached to a model field. + """ + ... + + +ExpectedSerializationTypes = Literal[ + 'none', + 'int', + 'bool', + 'float', + 'str', + 'bytes', + 'bytearray', + 'list', + 'tuple', + 'set', + 'frozenset', + 'generator', + 'dict', + 'datetime', + 'date', + 'time', + 'timedelta', + 'url', + 'multi-host-url', + 'json', + 'uuid', + 'any', +] + + +class SimpleSerSchema(TypedDict, total=False): + type: Required[ExpectedSerializationTypes] + + +def simple_ser_schema(type: ExpectedSerializationTypes) -> SimpleSerSchema: + """ + Returns a schema for serialization with a custom type. + + Args: + type: The type to use for serialization + """ + return SimpleSerSchema(type=type) + + +# (input_value: Any, /) -> Any +GeneralPlainNoInfoSerializerFunction = Callable[[Any], Any] +# (input_value: Any, info: FieldSerializationInfo, /) -> Any +GeneralPlainInfoSerializerFunction = Callable[[Any, SerializationInfo[Any]], Any] +# (model: Any, input_value: Any, /) -> Any +FieldPlainNoInfoSerializerFunction = Callable[[Any, Any], Any] +# (model: Any, input_value: Any, info: FieldSerializationInfo, /) -> Any +FieldPlainInfoSerializerFunction = Callable[[Any, Any, FieldSerializationInfo[Any]], Any] +SerializerFunction = Union[ + GeneralPlainNoInfoSerializerFunction, + GeneralPlainInfoSerializerFunction, + FieldPlainNoInfoSerializerFunction, + FieldPlainInfoSerializerFunction, +] + +WhenUsed = Literal['always', 'unless-none', 'json', 'json-unless-none'] +""" +Values have the following meanings: + +* `'always'` means always use +* `'unless-none'` means use unless the value is `None` +* `'json'` means use when serializing to JSON +* `'json-unless-none'` means use when serializing to JSON and the value is not `None` +""" + + +class PlainSerializerFunctionSerSchema(TypedDict, total=False): + type: Required[Literal['function-plain']] + function: Required[SerializerFunction] + is_field_serializer: bool # default False + info_arg: bool # default False + return_schema: CoreSchema # if omitted, AnySchema is used + when_used: WhenUsed # default: 'always' + + +def plain_serializer_function_ser_schema( + function: SerializerFunction, + *, + is_field_serializer: bool | None = None, + info_arg: bool | None = None, + return_schema: CoreSchema | None = None, + when_used: WhenUsed = 'always', +) -> PlainSerializerFunctionSerSchema: + """ + Returns a schema for serialization with a function, can be either a "general" or "field" function. + + Args: + function: The function to use for serialization + is_field_serializer: Whether the serializer is for a field, e.g. takes `model` as the first argument, + and `info` includes `field_name` + info_arg: Whether the function takes an `info` argument + return_schema: Schema to use for serializing return value + when_used: When the function should be called + """ + if when_used == 'always': + # just to avoid extra elements in schema, and to use the actual default defined in rust + when_used = None # type: ignore + return _dict_not_none( + type='function-plain', + function=function, + is_field_serializer=is_field_serializer, + info_arg=info_arg, + return_schema=return_schema, + when_used=when_used, + ) + + +class SerializerFunctionWrapHandler(Protocol): # pragma: no cover + def __call__(self, input_value: Any, index_key: int | str | None = None, /) -> Any: ... + + +# (input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any +GeneralWrapNoInfoSerializerFunction = Callable[[Any, SerializerFunctionWrapHandler], Any] +# (input_value: Any, serializer: SerializerFunctionWrapHandler, info: SerializationInfo, /) -> Any +GeneralWrapInfoSerializerFunction = Callable[[Any, SerializerFunctionWrapHandler, SerializationInfo[Any]], Any] +# (model: Any, input_value: Any, serializer: SerializerFunctionWrapHandler, /) -> Any +FieldWrapNoInfoSerializerFunction = Callable[[Any, Any, SerializerFunctionWrapHandler], Any] +# (model: Any, input_value: Any, serializer: SerializerFunctionWrapHandler, info: FieldSerializationInfo, /) -> Any +FieldWrapInfoSerializerFunction = Callable[[Any, Any, SerializerFunctionWrapHandler, FieldSerializationInfo[Any]], Any] +WrapSerializerFunction = Union[ + GeneralWrapNoInfoSerializerFunction, + GeneralWrapInfoSerializerFunction, + FieldWrapNoInfoSerializerFunction, + FieldWrapInfoSerializerFunction, +] + + +class WrapSerializerFunctionSerSchema(TypedDict, total=False): + type: Required[Literal['function-wrap']] + function: Required[WrapSerializerFunction] + is_field_serializer: bool # default False + info_arg: bool # default False + schema: CoreSchema # if omitted, the schema on which this serializer is defined is used + return_schema: CoreSchema # if omitted, AnySchema is used + when_used: WhenUsed # default: 'always' + + +def wrap_serializer_function_ser_schema( + function: WrapSerializerFunction, + *, + is_field_serializer: bool | None = None, + info_arg: bool | None = None, + schema: CoreSchema | None = None, + return_schema: CoreSchema | None = None, + when_used: WhenUsed = 'always', +) -> WrapSerializerFunctionSerSchema: + """ + Returns a schema for serialization with a wrap function, can be either a "general" or "field" function. + + Args: + function: The function to use for serialization + is_field_serializer: Whether the serializer is for a field, e.g. takes `model` as the first argument, + and `info` includes `field_name` + info_arg: Whether the function takes an `info` argument + schema: The schema to use for the inner serialization + return_schema: Schema to use for serializing return value + when_used: When the function should be called + """ + if when_used == 'always': + # just to avoid extra elements in schema, and to use the actual default defined in rust + when_used = None # type: ignore + return _dict_not_none( + type='function-wrap', + function=function, + is_field_serializer=is_field_serializer, + info_arg=info_arg, + schema=schema, + return_schema=return_schema, + when_used=when_used, + ) + + +class FormatSerSchema(TypedDict, total=False): + type: Required[Literal['format']] + formatting_string: Required[str] + when_used: WhenUsed # default: 'json-unless-none' + + +def format_ser_schema(formatting_string: str, *, when_used: WhenUsed = 'json-unless-none') -> FormatSerSchema: + """ + Returns a schema for serialization using python's `format` method. + + Args: + formatting_string: String defining the format to use + when_used: Same meaning as for [general_function_plain_ser_schema], but with a different default + """ + if when_used == 'json-unless-none': + # just to avoid extra elements in schema, and to use the actual default defined in rust + when_used = None # type: ignore + return _dict_not_none(type='format', formatting_string=formatting_string, when_used=when_used) + + +class ToStringSerSchema(TypedDict, total=False): + type: Required[Literal['to-string']] + when_used: WhenUsed # default: 'json-unless-none' + + +def to_string_ser_schema(*, when_used: WhenUsed = 'json-unless-none') -> ToStringSerSchema: + """ + Returns a schema for serialization using python's `str()` / `__str__` method. + + Args: + when_used: Same meaning as for [general_function_plain_ser_schema], but with a different default + """ + s = dict(type='to-string') + if when_used != 'json-unless-none': + # just to avoid extra elements in schema, and to use the actual default defined in rust + s['when_used'] = when_used + return s # type: ignore + + +class ModelSerSchema(TypedDict, total=False): + type: Required[Literal['model']] + cls: Required[type[Any]] + schema: Required[CoreSchema] + + +def model_ser_schema(cls: type[Any], schema: CoreSchema) -> ModelSerSchema: + """ + Returns a schema for serialization using a model. + + Args: + cls: The expected class type, used to generate warnings if the wrong type is passed + schema: Internal schema to use to serialize the model dict + """ + return ModelSerSchema(type='model', cls=cls, schema=schema) + + +SerSchema = Union[ + SimpleSerSchema, + PlainSerializerFunctionSerSchema, + WrapSerializerFunctionSerSchema, + FormatSerSchema, + ToStringSerSchema, + ModelSerSchema, +] + + +class InvalidSchema(TypedDict, total=False): + type: Required[Literal['invalid']] + ref: str + metadata: dict[str, Any] + # note, we never plan to use this, but include it for type checking purposes to match + # all other CoreSchema union members + serialization: SerSchema + + +def invalid_schema(ref: str | None = None, metadata: dict[str, Any] | None = None) -> InvalidSchema: + """ + Returns an invalid schema, used to indicate that a schema is invalid. + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + + return _dict_not_none(type='invalid', ref=ref, metadata=metadata) + + +class ComputedField(TypedDict, total=False): + type: Required[Literal['computed-field']] + property_name: Required[str] + return_schema: Required[CoreSchema] + alias: str + serialization_exclude_if: Callable[[Any], bool] + metadata: dict[str, Any] + + +def computed_field( + property_name: str, + return_schema: CoreSchema, + *, + alias: str | None = None, + serialization_exclude_if: Callable[[Any], bool] | None = None, + metadata: dict[str, Any] | None = None, +) -> ComputedField: + """ + ComputedFields are properties of a model or dataclass that are included in serialization. + + Args: + property_name: The name of the property on the model or dataclass + return_schema: The schema used for the type returned by the computed field + alias: The name to use in the serialized output + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + return _dict_not_none( + type='computed-field', + property_name=property_name, + return_schema=return_schema, + alias=alias, + serialization_exclude_if=serialization_exclude_if, + metadata=metadata, + ) + + +class AnySchema(TypedDict, total=False): + type: Required[Literal['any']] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def any_schema( + *, ref: str | None = None, metadata: dict[str, Any] | None = None, serialization: SerSchema | None = None +) -> AnySchema: + """ + Returns a schema that matches any value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.any_schema() + v = SchemaValidator(schema) + assert v.validate_python(1) == 1 + ``` + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='any', ref=ref, metadata=metadata, serialization=serialization) + + +class NoneSchema(TypedDict, total=False): + type: Required[Literal['none']] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def none_schema( + *, ref: str | None = None, metadata: dict[str, Any] | None = None, serialization: SerSchema | None = None +) -> NoneSchema: + """ + Returns a schema that matches a None value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.none_schema() + v = SchemaValidator(schema) + assert v.validate_python(None) is None + ``` + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='none', ref=ref, metadata=metadata, serialization=serialization) + + +class BoolSchema(TypedDict, total=False): + type: Required[Literal['bool']] + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def bool_schema( + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BoolSchema: + """ + Returns a schema that matches a bool value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.bool_schema() + v = SchemaValidator(schema) + assert v.validate_python('True') is True + ``` + + Args: + strict: Whether the value should be a bool or a value that can be converted to a bool + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='bool', strict=strict, ref=ref, metadata=metadata, serialization=serialization) + + +class IntSchema(TypedDict, total=False): + type: Required[Literal['int']] + multiple_of: int + le: int + ge: int + lt: int + gt: int + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def int_schema( + *, + multiple_of: int | None = None, + le: int | None = None, + ge: int | None = None, + lt: int | None = None, + gt: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> IntSchema: + """ + Returns a schema that matches a int value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.int_schema(multiple_of=2, le=6, ge=2) + v = SchemaValidator(schema) + assert v.validate_python('4') == 4 + ``` + + Args: + multiple_of: The value must be a multiple of this number + le: The value must be less than or equal to this number + ge: The value must be greater than or equal to this number + lt: The value must be strictly less than this number + gt: The value must be strictly greater than this number + strict: Whether the value should be a int or a value that can be converted to a int + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='int', + multiple_of=multiple_of, + le=le, + ge=ge, + lt=lt, + gt=gt, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class FloatSchema(TypedDict, total=False): + type: Required[Literal['float']] + allow_inf_nan: bool # whether 'NaN', '+inf', '-inf' should be forbidden. default: True + multiple_of: float + le: float + ge: float + lt: float + gt: float + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def float_schema( + *, + allow_inf_nan: bool | None = None, + multiple_of: float | None = None, + le: float | None = None, + ge: float | None = None, + lt: float | None = None, + gt: float | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> FloatSchema: + """ + Returns a schema that matches a float value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.float_schema(le=0.8, ge=0.2) + v = SchemaValidator(schema) + assert v.validate_python('0.5') == 0.5 + ``` + + Args: + allow_inf_nan: Whether to allow inf and nan values + multiple_of: The value must be a multiple of this number + le: The value must be less than or equal to this number + ge: The value must be greater than or equal to this number + lt: The value must be strictly less than this number + gt: The value must be strictly greater than this number + strict: Whether the value should be a float or a value that can be converted to a float + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='float', + allow_inf_nan=allow_inf_nan, + multiple_of=multiple_of, + le=le, + ge=ge, + lt=lt, + gt=gt, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DecimalSchema(TypedDict, total=False): + type: Required[Literal['decimal']] + allow_inf_nan: bool # whether 'NaN', '+inf', '-inf' should be forbidden. default: False + multiple_of: Decimal + le: Decimal + ge: Decimal + lt: Decimal + gt: Decimal + max_digits: int + decimal_places: int + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def decimal_schema( + *, + allow_inf_nan: bool | None = None, + multiple_of: Decimal | None = None, + le: Decimal | None = None, + ge: Decimal | None = None, + lt: Decimal | None = None, + gt: Decimal | None = None, + max_digits: int | None = None, + decimal_places: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DecimalSchema: + """ + Returns a schema that matches a decimal value, e.g.: + + ```py + from decimal import Decimal + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.decimal_schema(le=0.8, ge=0.2) + v = SchemaValidator(schema) + assert v.validate_python('0.5') == Decimal('0.5') + ``` + + Args: + allow_inf_nan: Whether to allow inf and nan values + multiple_of: The value must be a multiple of this number + le: The value must be less than or equal to this number + ge: The value must be greater than or equal to this number + lt: The value must be strictly less than this number + gt: The value must be strictly greater than this number + max_digits: The maximum number of decimal digits allowed + decimal_places: The maximum number of decimal places allowed + strict: Whether the value should be a float or a value that can be converted to a float + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='decimal', + gt=gt, + ge=ge, + lt=lt, + le=le, + max_digits=max_digits, + decimal_places=decimal_places, + multiple_of=multiple_of, + allow_inf_nan=allow_inf_nan, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ComplexSchema(TypedDict, total=False): + type: Required[Literal['complex']] + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def complex_schema( + *, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ComplexSchema: + """ + Returns a schema that matches a complex value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.complex_schema() + v = SchemaValidator(schema) + assert v.validate_python('1+2j') == complex(1, 2) + assert v.validate_python(complex(1, 2)) == complex(1, 2) + ``` + + Args: + strict: Whether the value should be a complex object instance or a value that can be converted to a complex object + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='complex', + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class StringSchema(TypedDict, total=False): + type: Required[Literal['str']] + pattern: Union[str, Pattern[str]] + max_length: int + min_length: int + strip_whitespace: bool + to_lower: bool + to_upper: bool + regex_engine: Literal['rust-regex', 'python-re'] # default: 'rust-regex' + strict: bool + coerce_numbers_to_str: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def str_schema( + *, + pattern: str | Pattern[str] | None = None, + max_length: int | None = None, + min_length: int | None = None, + strip_whitespace: bool | None = None, + to_lower: bool | None = None, + to_upper: bool | None = None, + regex_engine: Literal['rust-regex', 'python-re'] | None = None, + strict: bool | None = None, + coerce_numbers_to_str: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> StringSchema: + """ + Returns a schema that matches a string value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.str_schema(max_length=10, min_length=2) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello' + ``` + + Args: + pattern: A regex pattern that the value must match + max_length: The value must be at most this length + min_length: The value must be at least this length + strip_whitespace: Whether to strip whitespace from the value + to_lower: Whether to convert the value to lowercase + to_upper: Whether to convert the value to uppercase + regex_engine: The regex engine to use for pattern validation. Default is 'rust-regex'. + - `rust-regex` uses the [`regex`](https://docs.rs/regex) Rust + crate, which is non-backtracking and therefore more DDoS + resistant, but does not support all regex features. + - `python-re` use the [`re`](https://docs.python.org/3/library/re.html) module, + which supports all regex features, but may be slower. + strict: Whether the value should be a string or a value that can be converted to a string + coerce_numbers_to_str: Whether to enable coercion of any `Number` type to `str` (not applicable in `strict` mode). + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='str', + pattern=pattern, + max_length=max_length, + min_length=min_length, + strip_whitespace=strip_whitespace, + to_lower=to_lower, + to_upper=to_upper, + regex_engine=regex_engine, + strict=strict, + coerce_numbers_to_str=coerce_numbers_to_str, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class BytesSchema(TypedDict, total=False): + type: Required[Literal['bytes']] + max_length: int + min_length: int + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def bytes_schema( + *, + max_length: int | None = None, + min_length: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BytesSchema: + """ + Returns a schema that matches a bytes value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.bytes_schema(max_length=10, min_length=2) + v = SchemaValidator(schema) + assert v.validate_python(b'hello') == b'hello' + ``` + + Args: + max_length: The value must be at most this length + min_length: The value must be at least this length + strict: Whether the value should be a bytes or a value that can be converted to a bytes + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='bytes', + max_length=max_length, + min_length=min_length, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DateSchema(TypedDict, total=False): + type: Required[Literal['date']] + strict: bool + le: date + ge: date + lt: date + gt: date + now_op: Literal['past', 'future'] + # defaults to current local utc offset from `time.localtime().tm_gmtoff` + # value is restricted to -86_400 < offset < 86_400: + now_utc_offset: int + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def date_schema( + *, + strict: bool | None = None, + le: date | None = None, + ge: date | None = None, + lt: date | None = None, + gt: date | None = None, + now_op: Literal['past', 'future'] | None = None, + now_utc_offset: int | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DateSchema: + """ + Returns a schema that matches a date value, e.g.: + + ```py + from datetime import date + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.date_schema(le=date(2020, 1, 1), ge=date(2019, 1, 1)) + v = SchemaValidator(schema) + assert v.validate_python(date(2019, 6, 1)) == date(2019, 6, 1) + ``` + + Args: + strict: Whether the value should be a date or a value that can be converted to a date + le: The value must be less than or equal to this date + ge: The value must be greater than or equal to this date + lt: The value must be strictly less than this date + gt: The value must be strictly greater than this date + now_op: The value must be in the past or future relative to the current date + now_utc_offset: The value must be in the past or future relative to the current date with this utc offset + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='date', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + now_op=now_op, + now_utc_offset=now_utc_offset, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TimeSchema(TypedDict, total=False): + type: Required[Literal['time']] + strict: bool + le: time + ge: time + lt: time + gt: time + tz_constraint: Union[Literal['aware', 'naive'], int] + microseconds_precision: Literal['truncate', 'error'] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def time_schema( + *, + strict: bool | None = None, + le: time | None = None, + ge: time | None = None, + lt: time | None = None, + gt: time | None = None, + tz_constraint: Literal['aware', 'naive'] | int | None = None, + microseconds_precision: Literal['truncate', 'error'] = 'truncate', + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> TimeSchema: + """ + Returns a schema that matches a time value, e.g.: + + ```py + from datetime import time + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.time_schema(le=time(12, 0, 0), ge=time(6, 0, 0)) + v = SchemaValidator(schema) + assert v.validate_python(time(9, 0, 0)) == time(9, 0, 0) + ``` + + Args: + strict: Whether the value should be a time or a value that can be converted to a time + le: The value must be less than or equal to this time + ge: The value must be greater than or equal to this time + lt: The value must be strictly less than this time + gt: The value must be strictly greater than this time + tz_constraint: The value must be timezone aware or naive, or an int to indicate required tz offset + microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='time', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + tz_constraint=tz_constraint, + microseconds_precision=microseconds_precision, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DatetimeSchema(TypedDict, total=False): + type: Required[Literal['datetime']] + strict: bool + le: datetime + ge: datetime + lt: datetime + gt: datetime + now_op: Literal['past', 'future'] + tz_constraint: Union[Literal['aware', 'naive'], int] + # defaults to current local utc offset from `time.localtime().tm_gmtoff` + # value is restricted to -86_400 < offset < 86_400 by bounds in generate_self_schema.py + now_utc_offset: int + microseconds_precision: Literal['truncate', 'error'] # default: 'truncate' + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def datetime_schema( + *, + strict: bool | None = None, + le: datetime | None = None, + ge: datetime | None = None, + lt: datetime | None = None, + gt: datetime | None = None, + now_op: Literal['past', 'future'] | None = None, + tz_constraint: Literal['aware', 'naive'] | int | None = None, + now_utc_offset: int | None = None, + microseconds_precision: Literal['truncate', 'error'] = 'truncate', + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DatetimeSchema: + """ + Returns a schema that matches a datetime value, e.g.: + + ```py + from datetime import datetime + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.datetime_schema() + v = SchemaValidator(schema) + now = datetime.now() + assert v.validate_python(str(now)) == now + ``` + + Args: + strict: Whether the value should be a datetime or a value that can be converted to a datetime + le: The value must be less than or equal to this datetime + ge: The value must be greater than or equal to this datetime + lt: The value must be strictly less than this datetime + gt: The value must be strictly greater than this datetime + now_op: The value must be in the past or future relative to the current datetime + tz_constraint: The value must be timezone aware or naive, or an int to indicate required tz offset + TODO: use of a tzinfo where offset changes based on the datetime is not yet supported + now_utc_offset: The value must be in the past or future relative to the current datetime with this utc offset + microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='datetime', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + now_op=now_op, + tz_constraint=tz_constraint, + now_utc_offset=now_utc_offset, + microseconds_precision=microseconds_precision, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TimedeltaSchema(TypedDict, total=False): + type: Required[Literal['timedelta']] + strict: bool + le: timedelta + ge: timedelta + lt: timedelta + gt: timedelta + microseconds_precision: Literal['truncate', 'error'] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def timedelta_schema( + *, + strict: bool | None = None, + le: timedelta | None = None, + ge: timedelta | None = None, + lt: timedelta | None = None, + gt: timedelta | None = None, + microseconds_precision: Literal['truncate', 'error'] = 'truncate', + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> TimedeltaSchema: + """ + Returns a schema that matches a timedelta value, e.g.: + + ```py + from datetime import timedelta + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.timedelta_schema(le=timedelta(days=1), ge=timedelta(days=0)) + v = SchemaValidator(schema) + assert v.validate_python(timedelta(hours=12)) == timedelta(hours=12) + ``` + + Args: + strict: Whether the value should be a timedelta or a value that can be converted to a timedelta + le: The value must be less than or equal to this timedelta + ge: The value must be greater than or equal to this timedelta + lt: The value must be strictly less than this timedelta + gt: The value must be strictly greater than this timedelta + microseconds_precision: The behavior when seconds have more than 6 digits or microseconds is too large + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='timedelta', + strict=strict, + le=le, + ge=ge, + lt=lt, + gt=gt, + microseconds_precision=microseconds_precision, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class LiteralSchema(TypedDict, total=False): + type: Required[Literal['literal']] + expected: Required[list[Any]] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def literal_schema( + expected: list[Any], + *, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> LiteralSchema: + """ + Returns a schema that matches a literal value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.literal_schema(['hello', 'world']) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello' + ``` + + Args: + expected: The value must be one of these values + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='literal', expected=expected, ref=ref, metadata=metadata, serialization=serialization) + + +class EnumSchema(TypedDict, total=False): + type: Required[Literal['enum']] + cls: Required[Any] + members: Required[list[Any]] + sub_type: Literal['str', 'int', 'float'] + missing: Callable[[Any], Any] + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def enum_schema( + cls: Any, + members: list[Any], + *, + sub_type: Literal['str', 'int', 'float'] | None = None, + missing: Callable[[Any], Any] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> EnumSchema: + """ + Returns a schema that matches an enum value, e.g.: + + ```py + from enum import Enum + from pydantic_core import SchemaValidator, core_schema + + class Color(Enum): + RED = 1 + GREEN = 2 + BLUE = 3 + + schema = core_schema.enum_schema(Color, list(Color.__members__.values())) + v = SchemaValidator(schema) + assert v.validate_python(2) is Color.GREEN + ``` + + Args: + cls: The enum class + members: The members of the enum, generally `list(MyEnum.__members__.values())` + sub_type: The type of the enum, either 'str' or 'int' or None for plain enums + missing: A function to use when the value is not found in the enum, from `_missing_` + strict: Whether to use strict mode, defaults to False + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='enum', + cls=cls, + members=members, + sub_type=sub_type, + missing=missing, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class MissingSentinelSchema(TypedDict, total=False): + type: Required[Literal['missing-sentinel']] + metadata: dict[str, Any] + serialization: SerSchema + + +def missing_sentinel_schema( + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> MissingSentinelSchema: + """Returns a schema for the `MISSING` sentinel.""" + + return _dict_not_none( + type='missing-sentinel', + metadata=metadata, + serialization=serialization, + ) + + +# must match input/parse_json.rs::JsonType::try_from +JsonType = Literal['null', 'bool', 'int', 'float', 'str', 'list', 'dict'] + + +class IsInstanceSchema(TypedDict, total=False): + type: Required[Literal['is-instance']] + cls: Required[Any] + cls_repr: str + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def is_instance_schema( + cls: Any, + *, + cls_repr: str | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> IsInstanceSchema: + """ + Returns a schema that checks if a value is an instance of a class, equivalent to python's `isinstance` method, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + class A: + pass + + schema = core_schema.is_instance_schema(cls=A) + v = SchemaValidator(schema) + v.validate_python(A()) + ``` + + Args: + cls: The value must be an instance of this class + cls_repr: If provided this string is used in the validator name instead of `repr(cls)` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='is-instance', cls=cls, cls_repr=cls_repr, ref=ref, metadata=metadata, serialization=serialization + ) + + +class IsSubclassSchema(TypedDict, total=False): + type: Required[Literal['is-subclass']] + cls: Required[type[Any]] + cls_repr: str + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def is_subclass_schema( + cls: type[Any], + *, + cls_repr: str | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> IsInstanceSchema: + """ + Returns a schema that checks if a value is a subtype of a class, equivalent to python's `issubclass` method, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + class A: + pass + + class B(A): + pass + + schema = core_schema.is_subclass_schema(cls=A) + v = SchemaValidator(schema) + v.validate_python(B) + ``` + + Args: + cls: The value must be a subclass of this class + cls_repr: If provided this string is used in the validator name instead of `repr(cls)` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='is-subclass', cls=cls, cls_repr=cls_repr, ref=ref, metadata=metadata, serialization=serialization + ) + + +class CallableSchema(TypedDict, total=False): + type: Required[Literal['callable']] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def callable_schema( + *, ref: str | None = None, metadata: dict[str, Any] | None = None, serialization: SerSchema | None = None +) -> CallableSchema: + """ + Returns a schema that checks if a value is callable, equivalent to python's `callable` method, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.callable_schema() + v = SchemaValidator(schema) + v.validate_python(min) + ``` + + Args: + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='callable', ref=ref, metadata=metadata, serialization=serialization) + + +class UuidSchema(TypedDict, total=False): + type: Required[Literal['uuid']] + version: Literal[1, 3, 4, 5, 7] + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def uuid_schema( + *, + version: Literal[1, 3, 4, 5, 6, 7, 8] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> UuidSchema: + return _dict_not_none( + type='uuid', version=version, strict=strict, ref=ref, metadata=metadata, serialization=serialization + ) + + +class IncExSeqSerSchema(TypedDict, total=False): + type: Required[Literal['include-exclude-sequence']] + include: set[int] + exclude: set[int] + + +def filter_seq_schema(*, include: set[int] | None = None, exclude: set[int] | None = None) -> IncExSeqSerSchema: + return _dict_not_none(type='include-exclude-sequence', include=include, exclude=exclude) + + +IncExSeqOrElseSerSchema = Union[IncExSeqSerSchema, SerSchema] + + +class ListSchema(TypedDict, total=False): + type: Required[Literal['list']] + items_schema: CoreSchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: dict[str, Any] + serialization: IncExSeqOrElseSerSchema + + +def list_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> ListSchema: + """ + Returns a schema that matches a list value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.list_schema(core_schema.int_schema(), min_length=0, max_length=10) + v = SchemaValidator(schema) + assert v.validate_python(['4']) == [4] + ``` + + Args: + items_schema: The value must be a list of items that match this schema + min_length: The value must be a list with at least this many items + max_length: The value must be a list with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a list with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='list', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# @deprecated('tuple_positional_schema is deprecated. Use pydantic_core.core_schema.tuple_schema instead.') +def tuple_positional_schema( + items_schema: list[CoreSchema], + *, + extras_schema: CoreSchema | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> TupleSchema: + """ + Returns a schema that matches a tuple of schemas, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.tuple_positional_schema( + [core_schema.int_schema(), core_schema.str_schema()] + ) + v = SchemaValidator(schema) + assert v.validate_python((1, 'hello')) == (1, 'hello') + ``` + + Args: + items_schema: The value must be a tuple with items that match these schemas + extras_schema: The value must be a tuple with items that match this schema + This was inspired by JSON schema's `prefixItems` and `items` fields. + In python's `typing.Tuple`, you can't specify a type for "extra" items -- they must all be the same type + if the length is variable. So this field won't be set from a `typing.Tuple` annotation on a pydantic model. + strict: The value must be a tuple with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + if extras_schema is not None: + variadic_item_index = len(items_schema) + items_schema = items_schema + [extras_schema] + else: + variadic_item_index = None + return tuple_schema( + items_schema=items_schema, + variadic_item_index=variadic_item_index, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# @deprecated('tuple_variable_schema is deprecated. Use pydantic_core.core_schema.tuple_schema instead.') +def tuple_variable_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> TupleSchema: + """ + Returns a schema that matches a tuple of a given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.tuple_variable_schema( + items_schema=core_schema.int_schema(), min_length=0, max_length=10 + ) + v = SchemaValidator(schema) + assert v.validate_python(('1', 2, 3)) == (1, 2, 3) + ``` + + Args: + items_schema: The value must be a tuple with items that match this schema + min_length: The value must be a tuple with at least this many items + max_length: The value must be a tuple with at most this many items + strict: The value must be a tuple with exactly this many items + ref: Optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return tuple_schema( + items_schema=[items_schema or any_schema()], + variadic_item_index=0, + min_length=min_length, + max_length=max_length, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TupleSchema(TypedDict, total=False): + type: Required[Literal['tuple']] + items_schema: Required[list[CoreSchema]] + variadic_item_index: int + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: dict[str, Any] + serialization: IncExSeqOrElseSerSchema + + +def tuple_schema( + items_schema: list[CoreSchema], + *, + variadic_item_index: int | None = None, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> TupleSchema: + """ + Returns a schema that matches a tuple of schemas, with an optional variadic item, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.tuple_schema( + [core_schema.int_schema(), core_schema.str_schema(), core_schema.float_schema()], + variadic_item_index=1, + ) + v = SchemaValidator(schema) + assert v.validate_python((1, 'hello', 'world', 1.5)) == (1, 'hello', 'world', 1.5) + ``` + + Args: + items_schema: The value must be a tuple with items that match these schemas + variadic_item_index: The index of the schema in `items_schema` to be treated as variadic (following PEP 646) + min_length: The value must be a tuple with at least this many items + max_length: The value must be a tuple with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a tuple with exactly this many items + ref: Optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='tuple', + items_schema=items_schema, + variadic_item_index=variadic_item_index, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class SetSchema(TypedDict, total=False): + type: Required[Literal['set']] + items_schema: CoreSchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def set_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> SetSchema: + """ + Returns a schema that matches a set of a given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.set_schema( + items_schema=core_schema.int_schema(), min_length=0, max_length=10 + ) + v = SchemaValidator(schema) + assert v.validate_python({1, '2', 3}) == {1, 2, 3} + ``` + + Args: + items_schema: The value must be a set with items that match this schema + min_length: The value must be a set with at least this many items + max_length: The value must be a set with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a set with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='set', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class FrozenSetSchema(TypedDict, total=False): + type: Required[Literal['frozenset']] + items_schema: CoreSchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def frozenset_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> FrozenSetSchema: + """ + Returns a schema that matches a frozenset of a given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.frozenset_schema( + items_schema=core_schema.int_schema(), min_length=0, max_length=10 + ) + v = SchemaValidator(schema) + assert v.validate_python(frozenset(range(3))) == frozenset({0, 1, 2}) + ``` + + Args: + items_schema: The value must be a frozenset with items that match this schema + min_length: The value must be a frozenset with at least this many items + max_length: The value must be a frozenset with at most this many items + fail_fast: Stop validation on the first error + strict: The value must be a frozenset with exactly this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='frozenset', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class GeneratorSchema(TypedDict, total=False): + type: Required[Literal['generator']] + items_schema: CoreSchema + min_length: int + max_length: int + ref: str + metadata: dict[str, Any] + serialization: IncExSeqOrElseSerSchema + + +def generator_schema( + items_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: IncExSeqOrElseSerSchema | None = None, +) -> GeneratorSchema: + """ + Returns a schema that matches a generator value, e.g.: + + ```py + from typing import Iterator + from pydantic_core import SchemaValidator, core_schema + + def gen() -> Iterator[int]: + yield 1 + + schema = core_schema.generator_schema(items_schema=core_schema.int_schema()) + v = SchemaValidator(schema) + v.validate_python(gen()) + ``` + + Unlike other types, validated generators do not raise ValidationErrors eagerly, + but instead will raise a ValidationError when a violating value is actually read from the generator. + This is to ensure that "validated" generators retain the benefit of lazy evaluation. + + Args: + items_schema: The value must be a generator with items that match this schema + min_length: The value must be a generator that yields at least this many items + max_length: The value must be a generator that yields at most this many items + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='generator', + items_schema=items_schema, + min_length=min_length, + max_length=max_length, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +IncExDict = set[Union[int, str]] + + +class IncExDictSerSchema(TypedDict, total=False): + type: Required[Literal['include-exclude-dict']] + include: IncExDict + exclude: IncExDict + + +def filter_dict_schema(*, include: IncExDict | None = None, exclude: IncExDict | None = None) -> IncExDictSerSchema: + return _dict_not_none(type='include-exclude-dict', include=include, exclude=exclude) + + +IncExDictOrElseSerSchema = Union[IncExDictSerSchema, SerSchema] + + +class DictSchema(TypedDict, total=False): + type: Required[Literal['dict']] + keys_schema: CoreSchema # default: AnySchema + values_schema: CoreSchema # default: AnySchema + min_length: int + max_length: int + fail_fast: bool + strict: bool + ref: str + metadata: dict[str, Any] + serialization: IncExDictOrElseSerSchema + + +def dict_schema( + keys_schema: CoreSchema | None = None, + values_schema: CoreSchema | None = None, + *, + min_length: int | None = None, + max_length: int | None = None, + fail_fast: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DictSchema: + """ + Returns a schema that matches a dict value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.dict_schema( + keys_schema=core_schema.str_schema(), values_schema=core_schema.int_schema() + ) + v = SchemaValidator(schema) + assert v.validate_python({'a': '1', 'b': 2}) == {'a': 1, 'b': 2} + ``` + + Args: + keys_schema: The value must be a dict with keys that match this schema + values_schema: The value must be a dict with values that match this schema + min_length: The value must be a dict with at least this many items + max_length: The value must be a dict with at most this many items + fail_fast: Stop validation on the first error + strict: Whether the keys and values should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='dict', + keys_schema=keys_schema, + values_schema=values_schema, + min_length=min_length, + max_length=max_length, + fail_fast=fail_fast, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +# (input_value: Any, /) -> Any +NoInfoValidatorFunction = Callable[[Any], Any] + + +class NoInfoValidatorFunctionSchema(TypedDict): + type: Literal['no-info'] + function: NoInfoValidatorFunction + + +# (input_value: Any, info: ValidationInfo, /) -> Any +WithInfoValidatorFunction = Callable[[Any, ValidationInfo[Any]], Any] + + +class WithInfoValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['with-info']] + function: Required[WithInfoValidatorFunction] + field_name: str # deprecated + + +ValidationFunction = Union[NoInfoValidatorFunctionSchema, WithInfoValidatorFunctionSchema] + + +class _ValidatorFunctionSchema(TypedDict, total=False): + function: Required[ValidationFunction] + schema: Required[CoreSchema] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +class BeforeValidatorFunctionSchema(_ValidatorFunctionSchema, total=False): + type: Required[Literal['function-before']] + json_schema_input_schema: CoreSchema + + +def no_info_before_validator_function( + function: NoInfoValidatorFunction, + schema: CoreSchema, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BeforeValidatorFunctionSchema: + """ + Returns a schema that calls a validator function before validating, no `info` argument is provided, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: bytes) -> str: + return v.decode() + 'world' + + func_schema = core_schema.no_info_before_validator_function( + function=fn, schema=core_schema.str_schema() + ) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call + schema: The schema to validate the output of the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-before', + function={'type': 'no-info', 'function': function}, + schema=schema, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_before_validator_function( + function: WithInfoValidatorFunction, + schema: CoreSchema, + *, + field_name: str | None = None, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> BeforeValidatorFunctionSchema: + """ + Returns a schema that calls a validator function before validation, the function is called with + an `info` argument, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: bytes, info: core_schema.ValidationInfo) -> str: + assert info.data is not None + assert info.field_name is not None + return v.decode() + 'world' + + func_schema = core_schema.with_info_before_validator_function( + function=fn, schema=core_schema.str_schema() + ) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call + field_name: The name of the field this validator is applied to, if any (deprecated) + schema: The schema to validate the output of the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + if field_name is not None: + warnings.warn( + 'The `field_name` argument on `with_info_before_validator_function` is deprecated, it will be passed to the function through `ValidationState` instead.', + DeprecationWarning, + stacklevel=2, + ) + + return _dict_not_none( + type='function-before', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + schema=schema, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +class AfterValidatorFunctionSchema(_ValidatorFunctionSchema, total=False): + type: Required[Literal['function-after']] + + +def no_info_after_validator_function( + function: NoInfoValidatorFunction, + schema: CoreSchema, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> AfterValidatorFunctionSchema: + """ + Returns a schema that calls a validator function after validating, no `info` argument is provided, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str) -> str: + return v + 'world' + + func_schema = core_schema.no_info_after_validator_function(fn, core_schema.str_schema()) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call after the schema is validated + schema: The schema to validate before the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-after', + function={'type': 'no-info', 'function': function}, + schema=schema, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_after_validator_function( + function: WithInfoValidatorFunction, + schema: CoreSchema, + *, + field_name: str | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> AfterValidatorFunctionSchema: + """ + Returns a schema that calls a validator function after validation, the function is called with + an `info` argument, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert info.data is not None + assert info.field_name is not None + return v + 'world' + + func_schema = core_schema.with_info_after_validator_function( + function=fn, schema=core_schema.str_schema() + ) + schema = core_schema.typed_dict_schema({'a': core_schema.typed_dict_field(func_schema)}) + + v = SchemaValidator(schema) + assert v.validate_python({'a': b'hello '}) == {'a': 'hello world'} + ``` + + Args: + function: The validator function to call after the schema is validated + schema: The schema to validate before the validator function + field_name: The name of the field this validator is applied to, if any (deprecated) + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + if field_name is not None: + warnings.warn( + 'The `field_name` argument on `with_info_after_validator_function` is deprecated, it will be passed to the function through `ValidationState` instead.', + DeprecationWarning, + stacklevel=2, + ) + + return _dict_not_none( + type='function-after', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + schema=schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ValidatorFunctionWrapHandler(Protocol): + def __call__(self, input_value: Any, outer_location: str | int | None = None, /) -> Any: # pragma: no cover + ... + + +# (input_value: Any, validator: ValidatorFunctionWrapHandler, /) -> Any +NoInfoWrapValidatorFunction = Callable[[Any, ValidatorFunctionWrapHandler], Any] + + +class NoInfoWrapValidatorFunctionSchema(TypedDict): + type: Literal['no-info'] + function: NoInfoWrapValidatorFunction + + +# (input_value: Any, validator: ValidatorFunctionWrapHandler, info: ValidationInfo, /) -> Any +WithInfoWrapValidatorFunction = Callable[[Any, ValidatorFunctionWrapHandler, ValidationInfo[Any]], Any] + + +class WithInfoWrapValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['with-info']] + function: Required[WithInfoWrapValidatorFunction] + field_name: str # deprecated + + +WrapValidatorFunction = Union[NoInfoWrapValidatorFunctionSchema, WithInfoWrapValidatorFunctionSchema] + + +class WrapValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['function-wrap']] + function: Required[WrapValidatorFunction] + schema: Required[CoreSchema] + ref: str + json_schema_input_schema: CoreSchema + metadata: dict[str, Any] + serialization: SerSchema + + +def no_info_wrap_validator_function( + function: NoInfoWrapValidatorFunction, + schema: CoreSchema, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> WrapValidatorFunctionSchema: + """ + Returns a schema which calls a function with a `validator` callable argument which can + optionally be used to call inner validation with the function logic, this is much like the + "onion" implementation of middleware in many popular web frameworks, no `info` argument is passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn( + v: str, + validator: core_schema.ValidatorFunctionWrapHandler, + ) -> str: + return validator(input_value=v) + 'world' + + schema = core_schema.no_info_wrap_validator_function( + function=fn, schema=core_schema.str_schema() + ) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + schema: The schema to validate the output of the validator function + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-wrap', + function={'type': 'no-info', 'function': function}, + schema=schema, + json_schema_input_schema=json_schema_input_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_wrap_validator_function( + function: WithInfoWrapValidatorFunction, + schema: CoreSchema, + *, + field_name: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> WrapValidatorFunctionSchema: + """ + Returns a schema which calls a function with a `validator` callable argument which can + optionally be used to call inner validation with the function logic, this is much like the + "onion" implementation of middleware in many popular web frameworks, an `info` argument is also passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn( + v: str, + validator: core_schema.ValidatorFunctionWrapHandler, + info: core_schema.ValidationInfo, + ) -> str: + return validator(input_value=v) + 'world' + + schema = core_schema.with_info_wrap_validator_function( + function=fn, schema=core_schema.str_schema() + ) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + schema: The schema to validate the output of the validator function + field_name: The name of the field this validator is applied to, if any (deprecated) + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + if field_name is not None: + warnings.warn( + 'The `field_name` argument on `with_info_wrap_validator_function` is deprecated, it will be passed to the function through `ValidationState` instead.', + DeprecationWarning, + stacklevel=2, + ) + + return _dict_not_none( + type='function-wrap', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + schema=schema, + json_schema_input_schema=json_schema_input_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class PlainValidatorFunctionSchema(TypedDict, total=False): + type: Required[Literal['function-plain']] + function: Required[ValidationFunction] + ref: str + json_schema_input_schema: CoreSchema + metadata: dict[str, Any] + serialization: SerSchema + + +def no_info_plain_validator_function( + function: NoInfoValidatorFunction, + *, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> PlainValidatorFunctionSchema: + """ + Returns a schema that uses the provided function for validation, no `info` argument is passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str) -> str: + assert 'hello' in v + return v + 'world' + + schema = core_schema.no_info_plain_validator_function(function=fn) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='function-plain', + function={'type': 'no-info', 'function': function}, + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +def with_info_plain_validator_function( + function: WithInfoValidatorFunction, + *, + field_name: str | None = None, + ref: str | None = None, + json_schema_input_schema: CoreSchema | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> PlainValidatorFunctionSchema: + """ + Returns a schema that uses the provided function for validation, an `info` argument is passed, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert 'hello' in v + return v + 'world' + + schema = core_schema.with_info_plain_validator_function(function=fn) + v = SchemaValidator(schema) + assert v.validate_python('hello ') == 'hello world' + ``` + + Args: + function: The validator function to call + field_name: The name of the field this validator is applied to, if any (deprecated) + ref: optional unique identifier of the schema, used to reference the schema in other places + json_schema_input_schema: The core schema to be used to generate the corresponding JSON Schema input type + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + if field_name is not None: + warnings.warn( + 'The `field_name` argument on `with_info_plain_validator_function` is deprecated, it will be passed to the function through `ValidationState` instead.', + DeprecationWarning, + stacklevel=2, + ) + + return _dict_not_none( + type='function-plain', + function=_dict_not_none(type='with-info', function=function, field_name=field_name), + ref=ref, + json_schema_input_schema=json_schema_input_schema, + metadata=metadata, + serialization=serialization, + ) + + +class WithDefaultSchema(TypedDict, total=False): + type: Required[Literal['default']] + schema: Required[CoreSchema] + default: Any + default_factory: Union[Callable[[], Any], Callable[[dict[str, Any]], Any]] + default_factory_takes_data: bool + on_error: Literal['raise', 'omit', 'default'] # default: 'raise' + validate_default: bool # default: False + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def with_default_schema( + schema: CoreSchema, + *, + default: Any = PydanticUndefined, + default_factory: Union[Callable[[], Any], Callable[[dict[str, Any]], Any], None] = None, + default_factory_takes_data: bool | None = None, + on_error: Literal['raise', 'omit', 'default'] | None = None, + validate_default: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> WithDefaultSchema: + """ + Returns a schema that adds a default value to the given schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.with_default_schema(core_schema.str_schema(), default='hello') + wrapper_schema = core_schema.typed_dict_schema( + {'a': core_schema.typed_dict_field(schema)} + ) + v = SchemaValidator(wrapper_schema) + assert v.validate_python({}) == v.validate_python({'a': 'hello'}) + ``` + + Args: + schema: The schema to add a default value to + default: The default value to use + default_factory: A callable that returns the default value to use + default_factory_takes_data: Whether the default factory takes a validated data argument + on_error: What to do if the schema validation fails. One of 'raise', 'omit', 'default' + validate_default: Whether the default value should be validated + strict: Whether the underlying schema should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + s = _dict_not_none( + type='default', + schema=schema, + default_factory=default_factory, + default_factory_takes_data=default_factory_takes_data, + on_error=on_error, + validate_default=validate_default, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + if default is not PydanticUndefined: + s['default'] = default + return s + + +class NullableSchema(TypedDict, total=False): + type: Required[Literal['nullable']] + schema: Required[CoreSchema] + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def nullable_schema( + schema: CoreSchema, + *, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> NullableSchema: + """ + Returns a schema that matches a nullable value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.nullable_schema(core_schema.str_schema()) + v = SchemaValidator(schema) + assert v.validate_python(None) is None + ``` + + Args: + schema: The schema to wrap + strict: Whether the underlying schema should be validated with strict mode + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='nullable', schema=schema, strict=strict, ref=ref, metadata=metadata, serialization=serialization + ) + + +class UnionSchema(TypedDict, total=False): + type: Required[Literal['union']] + choices: Required[list[Union[CoreSchema, tuple[CoreSchema, str]]]] + # default true, whether to automatically collapse unions with one element to the inner validator + auto_collapse: bool + custom_error_type: str + custom_error_message: str + custom_error_context: dict[str, Union[str, int, float]] + mode: Literal['smart', 'left_to_right'] # default: 'smart' + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def union_schema( + choices: list[CoreSchema | tuple[CoreSchema, str]], + *, + auto_collapse: bool | None = None, + custom_error_type: str | None = None, + custom_error_message: str | None = None, + custom_error_context: dict[str, str | int] | None = None, + mode: Literal['smart', 'left_to_right'] | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> UnionSchema: + """ + Returns a schema that matches a union value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.union_schema([core_schema.str_schema(), core_schema.int_schema()]) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello' + assert v.validate_python(1) == 1 + ``` + + Args: + choices: The schemas to match. If a tuple, the second item is used as the label for the case. + auto_collapse: whether to automatically collapse unions with one element to the inner validator, default true + custom_error_type: The custom error type to use if the validation fails + custom_error_message: The custom error message to use if the validation fails + custom_error_context: The custom error context to use if the validation fails + mode: How to select which choice to return + * `smart` (default) will try to return the choice which is the closest match to the input value + * `left_to_right` will return the first choice in `choices` which succeeds validation + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='union', + choices=choices, + auto_collapse=auto_collapse, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + mode=mode, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TaggedUnionSchema(TypedDict, total=False): + type: Required[Literal['tagged-union']] + choices: Required[dict[Hashable, CoreSchema]] + discriminator: Required[Union[str, list[Union[str, int]], list[list[Union[str, int]]], Callable[[Any], Hashable]]] + custom_error_type: str + custom_error_message: str + custom_error_context: dict[str, Union[str, int, float]] + strict: bool + from_attributes: bool # default: True + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def tagged_union_schema( + choices: dict[Any, CoreSchema], + discriminator: str | list[str | int] | list[list[str | int]] | Callable[[Any], Any], + *, + custom_error_type: str | None = None, + custom_error_message: str | None = None, + custom_error_context: dict[str, int | str | float] | None = None, + strict: bool | None = None, + from_attributes: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> TaggedUnionSchema: + """ + Returns a schema that matches a tagged union value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + apple_schema = core_schema.typed_dict_schema( + { + 'foo': core_schema.typed_dict_field(core_schema.str_schema()), + 'bar': core_schema.typed_dict_field(core_schema.int_schema()), + } + ) + banana_schema = core_schema.typed_dict_schema( + { + 'foo': core_schema.typed_dict_field(core_schema.str_schema()), + 'spam': core_schema.typed_dict_field( + core_schema.list_schema(items_schema=core_schema.int_schema()) + ), + } + ) + schema = core_schema.tagged_union_schema( + choices={ + 'apple': apple_schema, + 'banana': banana_schema, + }, + discriminator='foo', + ) + v = SchemaValidator(schema) + assert v.validate_python({'foo': 'apple', 'bar': '123'}) == {'foo': 'apple', 'bar': 123} + assert v.validate_python({'foo': 'banana', 'spam': [1, 2, 3]}) == { + 'foo': 'banana', + 'spam': [1, 2, 3], + } + ``` + + Args: + choices: The schemas to match + When retrieving a schema from `choices` using the discriminator value, if the value is a str, + it should be fed back into the `choices` map until a schema is obtained + (This approach is to prevent multiple ownership of a single schema in Rust) + discriminator: The discriminator to use to determine the schema to use + * If `discriminator` is a str, it is the name of the attribute to use as the discriminator + * If `discriminator` is a list of int/str, it should be used as a "path" to access the discriminator + * If `discriminator` is a list of lists, each inner list is a path, and the first path that exists is used + * If `discriminator` is a callable, it should return the discriminator when called on the value to validate; + the callable can return `None` to indicate that there is no matching discriminator present on the input + custom_error_type: The custom error type to use if the validation fails + custom_error_message: The custom error message to use if the validation fails + custom_error_context: The custom error context to use if the validation fails + strict: Whether the underlying schemas should be validated with strict mode + from_attributes: Whether to use the attributes of the object to retrieve the discriminator value + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='tagged-union', + choices=choices, + discriminator=discriminator, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + strict=strict, + from_attributes=from_attributes, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ChainSchema(TypedDict, total=False): + type: Required[Literal['chain']] + steps: Required[list[CoreSchema]] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def chain_schema( + steps: list[CoreSchema], + *, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ChainSchema: + """ + Returns a schema that chains the provided validation schemas, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert 'hello' in v + return v + ' world' + + fn_schema = core_schema.with_info_plain_validator_function(function=fn) + schema = core_schema.chain_schema( + [fn_schema, fn_schema, fn_schema, core_schema.str_schema()] + ) + v = SchemaValidator(schema) + assert v.validate_python('hello') == 'hello world world world' + ``` + + Args: + steps: The schemas to chain + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='chain', steps=steps, ref=ref, metadata=metadata, serialization=serialization) + + +class LaxOrStrictSchema(TypedDict, total=False): + type: Required[Literal['lax-or-strict']] + lax_schema: Required[CoreSchema] + strict_schema: Required[CoreSchema] + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def lax_or_strict_schema( + lax_schema: CoreSchema, + strict_schema: CoreSchema, + *, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> LaxOrStrictSchema: + """ + Returns a schema that uses the lax or strict schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + def fn(v: str, info: core_schema.ValidationInfo) -> str: + assert 'hello' in v + return v + ' world' + + lax_schema = core_schema.int_schema(strict=False) + strict_schema = core_schema.int_schema(strict=True) + + schema = core_schema.lax_or_strict_schema( + lax_schema=lax_schema, strict_schema=strict_schema, strict=True + ) + v = SchemaValidator(schema) + assert v.validate_python(123) == 123 + + schema = core_schema.lax_or_strict_schema( + lax_schema=lax_schema, strict_schema=strict_schema, strict=False + ) + v = SchemaValidator(schema) + assert v.validate_python('123') == 123 + ``` + + Args: + lax_schema: The lax schema to use + strict_schema: The strict schema to use + strict: Whether the strict schema should be used + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='lax-or-strict', + lax_schema=lax_schema, + strict_schema=strict_schema, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class JsonOrPythonSchema(TypedDict, total=False): + type: Required[Literal['json-or-python']] + json_schema: Required[CoreSchema] + python_schema: Required[CoreSchema] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def json_or_python_schema( + json_schema: CoreSchema, + python_schema: CoreSchema, + *, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> JsonOrPythonSchema: + """ + Returns a schema that uses the Json or Python schema depending on the input: + + ```py + from pydantic_core import SchemaValidator, ValidationError, core_schema + + v = SchemaValidator( + core_schema.json_or_python_schema( + json_schema=core_schema.int_schema(), + python_schema=core_schema.int_schema(strict=True), + ) + ) + + assert v.validate_json('"123"') == 123 + + try: + v.validate_python('123') + except ValidationError: + pass + else: + raise AssertionError('Validation should have failed') + ``` + + Args: + json_schema: The schema to use for Json inputs + python_schema: The schema to use for Python inputs + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='json-or-python', + json_schema=json_schema, + python_schema=python_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class TypedDictField(TypedDict, total=False): + type: Required[Literal['typed-dict-field']] + schema: Required[CoreSchema] + required: bool + validation_alias: Union[str, list[Union[str, int]], list[list[Union[str, int]]]] + serialization_alias: str + serialization_exclude: bool # default: False + metadata: dict[str, Any] + serialization_exclude_if: Callable[[Any], bool] # default None + + +def typed_dict_field( + schema: CoreSchema, + *, + required: bool | None = None, + validation_alias: str | list[str | int] | list[list[str | int]] | None = None, + serialization_alias: str | None = None, + serialization_exclude: bool | None = None, + metadata: dict[str, Any] | None = None, + serialization_exclude_if: Callable[[Any], bool] | None = None, +) -> TypedDictField: + """ + Returns a schema that matches a typed dict field, e.g.: + + ```py + from pydantic_core import core_schema + + field = core_schema.typed_dict_field(schema=core_schema.int_schema(), required=True) + ``` + + Args: + schema: The schema to use for the field + required: Whether the field is required, otherwise uses the value from `total` on the typed dict + validation_alias: The alias(es) to use to find the field in the validation data + serialization_alias: The alias to use as a key when serializing + serialization_exclude: Whether to exclude the field when serializing + serialization_exclude_if: A callable that determines whether to exclude the field when serializing based on its value. + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + return _dict_not_none( + type='typed-dict-field', + schema=schema, + required=required, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + serialization_exclude=serialization_exclude, + serialization_exclude_if=serialization_exclude_if, + metadata=metadata, + ) + + +class TypedDictSchema(TypedDict, total=False): + type: Required[Literal['typed-dict']] + fields: Required[dict[str, TypedDictField]] + cls: type[Any] + cls_name: str + computed_fields: list[ComputedField] + strict: bool + extras_schema: CoreSchema + # all these values can be set via config, equivalent fields have `typed_dict_` prefix + extra_behavior: ExtraBehavior + total: bool # default: True + ref: str + metadata: dict[str, Any] + serialization: SerSchema + config: CoreConfig + + +def typed_dict_schema( + fields: dict[str, TypedDictField], + *, + cls: type[Any] | None = None, + cls_name: str | None = None, + computed_fields: list[ComputedField] | None = None, + strict: bool | None = None, + extras_schema: CoreSchema | None = None, + extra_behavior: ExtraBehavior | None = None, + total: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, + config: CoreConfig | None = None, +) -> TypedDictSchema: + """ + Returns a schema that matches a typed dict, e.g.: + + ```py + from typing_extensions import TypedDict + + from pydantic_core import SchemaValidator, core_schema + + class MyTypedDict(TypedDict): + a: str + + wrapper_schema = core_schema.typed_dict_schema( + {'a': core_schema.typed_dict_field(core_schema.str_schema())}, cls=MyTypedDict + ) + v = SchemaValidator(wrapper_schema) + assert v.validate_python({'a': 'hello'}) == {'a': 'hello'} + ``` + + Args: + fields: The fields to use for the typed dict + cls: The class to use for the typed dict + cls_name: The name to use in error locations. Falls back to `cls.__name__`, or the validator name if no class + is provided. + computed_fields: Computed fields to use when serializing the model, only applies when directly inside a model + strict: Whether the typed dict is strict + extras_schema: The extra validator to use for the typed dict + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + extra_behavior: The extra behavior to use for the typed dict + total: Whether the typed dict is total, otherwise uses `typed_dict_total` from config + serialization: Custom serialization schema + """ + return _dict_not_none( + type='typed-dict', + fields=fields, + cls=cls, + cls_name=cls_name, + computed_fields=computed_fields, + strict=strict, + extras_schema=extras_schema, + extra_behavior=extra_behavior, + total=total, + ref=ref, + metadata=metadata, + serialization=serialization, + config=config, + ) + + +class ModelField(TypedDict, total=False): + type: Required[Literal['model-field']] + schema: Required[CoreSchema] + validation_alias: Union[str, list[Union[str, int]], list[list[Union[str, int]]]] + serialization_alias: str + serialization_exclude: bool # default: False + serialization_exclude_if: Callable[[Any], bool] # default: None + frozen: bool + metadata: dict[str, Any] + + +def model_field( + schema: CoreSchema, + *, + validation_alias: str | list[str | int] | list[list[str | int]] | None = None, + serialization_alias: str | None = None, + serialization_exclude: bool | None = None, + serialization_exclude_if: Callable[[Any], bool] | None = None, + frozen: bool | None = None, + metadata: dict[str, Any] | None = None, +) -> ModelField: + """ + Returns a schema for a model field, e.g.: + + ```py + from pydantic_core import core_schema + + field = core_schema.model_field(schema=core_schema.int_schema()) + ``` + + Args: + schema: The schema to use for the field + validation_alias: The alias(es) to use to find the field in the validation data + serialization_alias: The alias to use as a key when serializing + serialization_exclude: Whether to exclude the field when serializing + serialization_exclude_if: A Callable that determines whether to exclude a field during serialization based on its value. + frozen: Whether the field is frozen + metadata: Any other information you want to include with the schema, not used by pydantic-core + """ + return _dict_not_none( + type='model-field', + schema=schema, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + serialization_exclude=serialization_exclude, + serialization_exclude_if=serialization_exclude_if, + frozen=frozen, + metadata=metadata, + ) + + +class ModelFieldsSchema(TypedDict, total=False): + type: Required[Literal['model-fields']] + fields: Required[dict[str, ModelField]] + model_name: str + computed_fields: list[ComputedField] + strict: bool + extras_schema: CoreSchema + extras_keys_schema: CoreSchema + extra_behavior: ExtraBehavior + from_attributes: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def model_fields_schema( + fields: dict[str, ModelField], + *, + model_name: str | None = None, + computed_fields: list[ComputedField] | None = None, + strict: bool | None = None, + extras_schema: CoreSchema | None = None, + extras_keys_schema: CoreSchema | None = None, + extra_behavior: ExtraBehavior | None = None, + from_attributes: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ModelFieldsSchema: + """ + Returns a schema that matches the fields of a Pydantic model, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + wrapper_schema = core_schema.model_fields_schema( + {'a': core_schema.model_field(core_schema.str_schema())} + ) + v = SchemaValidator(wrapper_schema) + print(v.validate_python({'a': 'hello'})) + #> ({'a': 'hello'}, None, {'a'}) + ``` + + Args: + fields: The fields of the model + model_name: The name of the model, used for error messages, defaults to "Model" + computed_fields: Computed fields to use when serializing the model, only applies when directly inside a model + strict: Whether the model is strict + extras_schema: The schema to use when validating extra input data + extras_keys_schema: The schema to use when validating the keys of extra input data + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + extra_behavior: The extra behavior to use for the model fields + from_attributes: Whether the model fields should be populated from attributes + serialization: Custom serialization schema + """ + return _dict_not_none( + type='model-fields', + fields=fields, + model_name=model_name, + computed_fields=computed_fields, + strict=strict, + extras_schema=extras_schema, + extras_keys_schema=extras_keys_schema, + extra_behavior=extra_behavior, + from_attributes=from_attributes, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ModelSchema(TypedDict, total=False): + type: Required[Literal['model']] + cls: Required[type[Any]] + generic_origin: type[Any] + schema: Required[CoreSchema] + custom_init: bool + root_model: bool + post_init: str + revalidate_instances: Literal['always', 'never', 'subclass-instances'] # default: 'never' + strict: bool + frozen: bool + extra_behavior: ExtraBehavior + config: CoreConfig + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def model_schema( + cls: type[Any], + schema: CoreSchema, + *, + generic_origin: type[Any] | None = None, + custom_init: bool | None = None, + root_model: bool | None = None, + post_init: str | None = None, + revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None, + strict: bool | None = None, + frozen: bool | None = None, + extra_behavior: ExtraBehavior | None = None, + config: CoreConfig | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ModelSchema: + """ + A model schema generally contains a typed-dict schema. + It will run the typed dict validator, then create a new class + and set the dict and fields set returned from the typed dict validator + to `__dict__` and `__pydantic_fields_set__` respectively. + + Example: + + ```py + from pydantic_core import CoreConfig, SchemaValidator, core_schema + + class MyModel: + __slots__ = ( + '__dict__', + '__pydantic_fields_set__', + '__pydantic_extra__', + '__pydantic_private__', + ) + + schema = core_schema.model_schema( + cls=MyModel, + config=CoreConfig(str_max_length=5), + schema=core_schema.model_fields_schema( + fields={'a': core_schema.model_field(core_schema.str_schema())}, + ), + ) + v = SchemaValidator(schema) + assert v.isinstance_python({'a': 'hello'}) is True + assert v.isinstance_python({'a': 'too long'}) is False + ``` + + Args: + cls: The class to use for the model + schema: The schema to use for the model + generic_origin: The origin type used for this model, if it's a parametrized generic. Ex, + if this model schema represents `SomeModel[int]`, generic_origin is `SomeModel` + custom_init: Whether the model has a custom init method + root_model: Whether the model is a `RootModel` + post_init: The call after init to use for the model + revalidate_instances: whether instances of models and dataclasses (including subclass instances) + should re-validate defaults to config.revalidate_instances, else 'never' + strict: Whether the model is strict + frozen: Whether the model is frozen + extra_behavior: The extra behavior to use for the model, used in serialization + config: The config to use for the model + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='model', + cls=cls, + generic_origin=generic_origin, + schema=schema, + custom_init=custom_init, + root_model=root_model, + post_init=post_init, + revalidate_instances=revalidate_instances, + strict=strict, + frozen=frozen, + extra_behavior=extra_behavior, + config=config, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DataclassField(TypedDict, total=False): + type: Required[Literal['dataclass-field']] + name: Required[str] + schema: Required[CoreSchema] + kw_only: bool # default: True + init: bool # default: True + init_only: bool # default: False + frozen: bool # default: False + validation_alias: Union[str, list[Union[str, int]], list[list[Union[str, int]]]] + serialization_alias: str + serialization_exclude: bool # default: False + metadata: dict[str, Any] + serialization_exclude_if: Callable[[Any], bool] # default: None + + +def dataclass_field( + name: str, + schema: CoreSchema, + *, + kw_only: bool | None = None, + init: bool | None = None, + init_only: bool | None = None, + validation_alias: str | list[str | int] | list[list[str | int]] | None = None, + serialization_alias: str | None = None, + serialization_exclude: bool | None = None, + metadata: dict[str, Any] | None = None, + serialization_exclude_if: Callable[[Any], bool] | None = None, + frozen: bool | None = None, +) -> DataclassField: + """ + Returns a schema for a dataclass field, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + field = core_schema.dataclass_field( + name='a', schema=core_schema.str_schema(), kw_only=False + ) + schema = core_schema.dataclass_args_schema('Foobar', [field]) + v = SchemaValidator(schema) + assert v.validate_python({'a': 'hello'}) == ({'a': 'hello'}, None) + ``` + + Args: + name: The name to use for the argument parameter + schema: The schema to use for the argument parameter + kw_only: Whether the field can be set with a positional argument as well as a keyword argument + init: Whether the field should be validated during initialization + init_only: Whether the field should be omitted from `__dict__` and passed to `__post_init__` + validation_alias: The alias(es) to use to find the field in the validation data + serialization_alias: The alias to use as a key when serializing + serialization_exclude: Whether to exclude the field when serializing + serialization_exclude_if: A callable that determines whether to exclude the field when serializing based on its value. + metadata: Any other information you want to include with the schema, not used by pydantic-core + frozen: Whether the field is frozen + """ + return _dict_not_none( + type='dataclass-field', + name=name, + schema=schema, + kw_only=kw_only, + init=init, + init_only=init_only, + validation_alias=validation_alias, + serialization_alias=serialization_alias, + serialization_exclude=serialization_exclude, + serialization_exclude_if=serialization_exclude_if, + metadata=metadata, + frozen=frozen, + ) + + +class DataclassArgsSchema(TypedDict, total=False): + type: Required[Literal['dataclass-args']] + dataclass_name: Required[str] + fields: Required[list[DataclassField]] + computed_fields: list[ComputedField] + collect_init_only: bool # default: False + ref: str + metadata: dict[str, Any] + serialization: SerSchema + extra_behavior: ExtraBehavior + + +def dataclass_args_schema( + dataclass_name: str, + fields: list[DataclassField], + *, + computed_fields: list[ComputedField] | None = None, + collect_init_only: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, + extra_behavior: ExtraBehavior | None = None, +) -> DataclassArgsSchema: + """ + Returns a schema for validating dataclass arguments, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + field_a = core_schema.dataclass_field( + name='a', schema=core_schema.str_schema(), kw_only=False + ) + field_b = core_schema.dataclass_field( + name='b', schema=core_schema.bool_schema(), kw_only=False + ) + schema = core_schema.dataclass_args_schema('Foobar', [field_a, field_b]) + v = SchemaValidator(schema) + assert v.validate_python({'a': 'hello', 'b': True}) == ({'a': 'hello', 'b': True}, None) + ``` + + Args: + dataclass_name: The name of the dataclass being validated + fields: The fields to use for the dataclass + computed_fields: Computed fields to use when serializing the dataclass + collect_init_only: Whether to collect init only fields into a dict to pass to `__post_init__` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + extra_behavior: How to handle extra fields + """ + return _dict_not_none( + type='dataclass-args', + dataclass_name=dataclass_name, + fields=fields, + computed_fields=computed_fields, + collect_init_only=collect_init_only, + ref=ref, + metadata=metadata, + serialization=serialization, + extra_behavior=extra_behavior, + ) + + +class DataclassSchema(TypedDict, total=False): + type: Required[Literal['dataclass']] + cls: Required[type[Any]] + generic_origin: type[Any] + schema: Required[CoreSchema] + fields: Required[list[str]] + cls_name: str + post_init: bool # default: False + revalidate_instances: Literal['always', 'never', 'subclass-instances'] # default: 'never' + strict: bool # default: False + frozen: bool # default False + ref: str + metadata: dict[str, Any] + serialization: SerSchema + slots: bool + config: CoreConfig + + +def dataclass_schema( + cls: type[Any], + schema: CoreSchema, + fields: list[str], + *, + generic_origin: type[Any] | None = None, + cls_name: str | None = None, + post_init: bool | None = None, + revalidate_instances: Literal['always', 'never', 'subclass-instances'] | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, + frozen: bool | None = None, + slots: bool | None = None, + config: CoreConfig | None = None, +) -> DataclassSchema: + """ + Returns a schema for a dataclass. As with `ModelSchema`, this schema can only be used as a field within + another schema, not as the root type. + + Args: + cls: The dataclass type, used to perform subclass checks + schema: The schema to use for the dataclass fields + fields: Fields of the dataclass, this is used in serialization and in validation during re-validation + and while validating assignment + generic_origin: The origin type used for this dataclass, if it's a parametrized generic. Ex, + if this model schema represents `SomeDataclass[int]`, generic_origin is `SomeDataclass` + cls_name: The name to use in error locs, etc; this is useful for generics (default: `cls.__name__`) + post_init: Whether to call `__post_init__` after validation + revalidate_instances: whether instances of models and dataclasses (including subclass instances) + should re-validate defaults to config.revalidate_instances, else 'never' + strict: Whether to require an exact instance of `cls` + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + frozen: Whether the dataclass is frozen + slots: Whether `slots=True` on the dataclass, means each field is assigned independently, rather than + simply setting `__dict__`, default false + """ + return _dict_not_none( + type='dataclass', + cls=cls, + generic_origin=generic_origin, + fields=fields, + cls_name=cls_name, + schema=schema, + post_init=post_init, + revalidate_instances=revalidate_instances, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + frozen=frozen, + slots=slots, + config=config, + ) + + +class ArgumentsParameter(TypedDict, total=False): + name: Required[str] + schema: Required[CoreSchema] + mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] # default positional_or_keyword + alias: Union[str, list[Union[str, int]], list[list[Union[str, int]]]] + + +def arguments_parameter( + name: str, + schema: CoreSchema, + *, + mode: Literal['positional_only', 'positional_or_keyword', 'keyword_only'] | None = None, + alias: str | list[str | int] | list[list[str | int]] | None = None, +) -> ArgumentsParameter: + """ + Returns a schema that matches an argument parameter, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param = core_schema.arguments_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + schema = core_schema.arguments_schema([param]) + v = SchemaValidator(schema) + assert v.validate_python(('hello',)) == (('hello',), {}) + ``` + + Args: + name: The name to use for the argument parameter + schema: The schema to use for the argument parameter + mode: The mode to use for the argument parameter + alias: The alias to use for the argument parameter + """ + return _dict_not_none(name=name, schema=schema, mode=mode, alias=alias) + + +VarKwargsMode: TypeAlias = Literal['uniform', 'unpacked-typed-dict'] + + +class ArgumentsSchema(TypedDict, total=False): + type: Required[Literal['arguments']] + arguments_schema: Required[list[ArgumentsParameter]] + validate_by_name: bool + validate_by_alias: bool + var_args_schema: CoreSchema + var_kwargs_mode: VarKwargsMode + var_kwargs_schema: CoreSchema + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def arguments_schema( + arguments: list[ArgumentsParameter], + *, + validate_by_name: bool | None = None, + validate_by_alias: bool | None = None, + var_args_schema: CoreSchema | None = None, + var_kwargs_mode: VarKwargsMode | None = None, + var_kwargs_schema: CoreSchema | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ArgumentsSchema: + """ + Returns a schema that matches an arguments schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param_a = core_schema.arguments_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + param_b = core_schema.arguments_parameter( + name='b', schema=core_schema.bool_schema(), mode='positional_only' + ) + schema = core_schema.arguments_schema([param_a, param_b]) + v = SchemaValidator(schema) + assert v.validate_python(('hello', True)) == (('hello', True), {}) + ``` + + Args: + arguments: The arguments to use for the arguments schema + validate_by_name: Whether to populate by the parameter names, defaults to `False`. + validate_by_alias: Whether to populate by the parameter aliases, defaults to `True`. + var_args_schema: The variable args schema to use for the arguments schema + var_kwargs_mode: The validation mode to use for variadic keyword arguments. If `'uniform'`, every value of the + keyword arguments will be validated against the `var_kwargs_schema` schema. If `'unpacked-typed-dict'`, + the `var_kwargs_schema` argument must be a [`typed_dict_schema`][pydantic_core.core_schema.typed_dict_schema] + var_kwargs_schema: The variable kwargs schema to use for the arguments schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='arguments', + arguments_schema=arguments, + validate_by_name=validate_by_name, + validate_by_alias=validate_by_alias, + var_args_schema=var_args_schema, + var_kwargs_mode=var_kwargs_mode, + var_kwargs_schema=var_kwargs_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class ArgumentsV3Parameter(TypedDict, total=False): + name: Required[str] + schema: Required[CoreSchema] + mode: Literal[ + 'positional_only', + 'positional_or_keyword', + 'keyword_only', + 'var_args', + 'var_kwargs_uniform', + 'var_kwargs_unpacked_typed_dict', + ] # default positional_or_keyword + alias: Union[str, list[Union[str, int]], list[list[Union[str, int]]]] + + +def arguments_v3_parameter( + name: str, + schema: CoreSchema, + *, + mode: Literal[ + 'positional_only', + 'positional_or_keyword', + 'keyword_only', + 'var_args', + 'var_kwargs_uniform', + 'var_kwargs_unpacked_typed_dict', + ] + | None = None, + alias: str | list[str | int] | list[list[str | int]] | None = None, +) -> ArgumentsV3Parameter: + """ + Returns a schema that matches an argument parameter, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param = core_schema.arguments_v3_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + schema = core_schema.arguments_v3_schema([param]) + v = SchemaValidator(schema) + assert v.validate_python({'a': 'hello'}) == (('hello',), {}) + ``` + + Args: + name: The name to use for the argument parameter + schema: The schema to use for the argument parameter + mode: The mode to use for the argument parameter + alias: The alias to use for the argument parameter + """ + return _dict_not_none(name=name, schema=schema, mode=mode, alias=alias) + + +class ArgumentsV3Schema(TypedDict, total=False): + type: Required[Literal['arguments-v3']] + arguments_schema: Required[list[ArgumentsV3Parameter]] + validate_by_name: bool + validate_by_alias: bool + extra_behavior: Literal['forbid', 'ignore'] # 'allow' doesn't make sense here. + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def arguments_v3_schema( + arguments: list[ArgumentsV3Parameter], + *, + validate_by_name: bool | None = None, + validate_by_alias: bool | None = None, + extra_behavior: Literal['forbid', 'ignore'] | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> ArgumentsV3Schema: + """ + Returns a schema that matches an arguments schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param_a = core_schema.arguments_v3_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + param_b = core_schema.arguments_v3_parameter( + name='kwargs', schema=core_schema.bool_schema(), mode='var_kwargs_uniform' + ) + schema = core_schema.arguments_v3_schema([param_a, param_b]) + v = SchemaValidator(schema) + assert v.validate_python({'a': 'hi', 'kwargs': {'b': True}}) == (('hi',), {'b': True}) + ``` + + This schema is currently not used by other Pydantic components. In V3, it will most likely + become the default arguments schema for the `'call'` schema. + + Args: + arguments: The arguments to use for the arguments schema. + validate_by_name: Whether to populate by the parameter names, defaults to `False`. + validate_by_alias: Whether to populate by the parameter aliases, defaults to `True`. + extra_behavior: The extra behavior to use. + ref: optional unique identifier of the schema, used to reference the schema in other places. + metadata: Any other information you want to include with the schema, not used by pydantic-core. + serialization: Custom serialization schema. + """ + return _dict_not_none( + type='arguments-v3', + arguments_schema=arguments, + validate_by_name=validate_by_name, + validate_by_alias=validate_by_alias, + extra_behavior=extra_behavior, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class CallSchema(TypedDict, total=False): + type: Required[Literal['call']] + arguments_schema: Required[CoreSchema] + function: Required[Callable[..., Any]] + function_name: str # default function.__name__ + return_schema: CoreSchema + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def call_schema( + arguments: CoreSchema, + function: Callable[..., Any], + *, + function_name: str | None = None, + return_schema: CoreSchema | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> CallSchema: + """ + Returns a schema that matches an arguments schema, then calls a function, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + param_a = core_schema.arguments_parameter( + name='a', schema=core_schema.str_schema(), mode='positional_only' + ) + param_b = core_schema.arguments_parameter( + name='b', schema=core_schema.bool_schema(), mode='positional_only' + ) + args_schema = core_schema.arguments_schema([param_a, param_b]) + + schema = core_schema.call_schema( + arguments=args_schema, + function=lambda a, b: a + str(not b), + return_schema=core_schema.str_schema(), + ) + v = SchemaValidator(schema) + assert v.validate_python((('hello', True))) == 'helloFalse' + ``` + + Args: + arguments: The arguments to use for the arguments schema + function: The function to use for the call schema + function_name: The function name to use for the call schema, if not provided `function.__name__` is used + return_schema: The return schema to use for the call schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='call', + arguments_schema=arguments, + function=function, + function_name=function_name, + return_schema=return_schema, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class CustomErrorSchema(TypedDict, total=False): + type: Required[Literal['custom-error']] + schema: Required[CoreSchema] + custom_error_type: Required[str] + custom_error_message: str + custom_error_context: dict[str, Union[str, int, float]] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def custom_error_schema( + schema: CoreSchema, + custom_error_type: str, + *, + custom_error_message: str | None = None, + custom_error_context: dict[str, Any] | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> CustomErrorSchema: + """ + Returns a schema that matches a custom error value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.custom_error_schema( + schema=core_schema.int_schema(), + custom_error_type='MyError', + custom_error_message='Error msg', + ) + v = SchemaValidator(schema) + v.validate_python(1) + ``` + + Args: + schema: The schema to use for the custom error schema + custom_error_type: The custom error type to use for the custom error schema + custom_error_message: The custom error message to use for the custom error schema + custom_error_context: The custom error context to use for the custom error schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='custom-error', + schema=schema, + custom_error_type=custom_error_type, + custom_error_message=custom_error_message, + custom_error_context=custom_error_context, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class JsonSchema(TypedDict, total=False): + type: Required[Literal['json']] + schema: CoreSchema + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def json_schema( + schema: CoreSchema | None = None, + *, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> JsonSchema: + """ + Returns a schema that matches a JSON value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + dict_schema = core_schema.model_fields_schema( + { + 'field_a': core_schema.model_field(core_schema.str_schema()), + 'field_b': core_schema.model_field(core_schema.bool_schema()), + }, + ) + + class MyModel: + __slots__ = ( + '__dict__', + '__pydantic_fields_set__', + '__pydantic_extra__', + '__pydantic_private__', + ) + field_a: str + field_b: bool + + json_schema = core_schema.json_schema(schema=dict_schema) + schema = core_schema.model_schema(cls=MyModel, schema=json_schema) + v = SchemaValidator(schema) + m = v.validate_python('{"field_a": "hello", "field_b": true}') + assert isinstance(m, MyModel) + ``` + + Args: + schema: The schema to use for the JSON schema + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none(type='json', schema=schema, ref=ref, metadata=metadata, serialization=serialization) + + +class UrlSchema(TypedDict, total=False): + type: Required[Literal['url']] + max_length: int + allowed_schemes: list[str] + host_required: bool # default False + default_host: str + default_port: int + default_path: str + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def url_schema( + *, + max_length: int | None = None, + allowed_schemes: list[str] | None = None, + host_required: bool | None = None, + default_host: str | None = None, + default_port: int | None = None, + default_path: str | None = None, + preserve_empty_path: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> UrlSchema: + """ + Returns a schema that matches a URL value, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.url_schema() + v = SchemaValidator(schema) + print(v.validate_python('https://example.com')) + #> https://example.com/ + ``` + + Args: + max_length: The maximum length of the URL + allowed_schemes: The allowed URL schemes + host_required: Whether the URL must have a host + default_host: The default host to use if the URL does not have a host + default_port: The default port to use if the URL does not have a port + default_path: The default path to use if the URL does not have a path + preserve_empty_path: Whether to preserve an empty path or convert it to '/', default False + strict: Whether to use strict URL parsing + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='url', + max_length=max_length, + allowed_schemes=allowed_schemes, + host_required=host_required, + default_host=default_host, + default_port=default_port, + default_path=default_path, + preserve_empty_path=preserve_empty_path, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class MultiHostUrlSchema(TypedDict, total=False): + type: Required[Literal['multi-host-url']] + max_length: int + allowed_schemes: list[str] + host_required: bool # default False + default_host: str + default_port: int + default_path: str + strict: bool + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def multi_host_url_schema( + *, + max_length: int | None = None, + allowed_schemes: list[str] | None = None, + host_required: bool | None = None, + default_host: str | None = None, + default_port: int | None = None, + default_path: str | None = None, + preserve_empty_path: bool | None = None, + strict: bool | None = None, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> MultiHostUrlSchema: + """ + Returns a schema that matches a URL value with possibly multiple hosts, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.multi_host_url_schema() + v = SchemaValidator(schema) + print(v.validate_python('redis://localhost,0.0.0.0,127.0.0.1')) + #> redis://localhost,0.0.0.0,127.0.0.1 + ``` + + Args: + max_length: The maximum length of the URL + allowed_schemes: The allowed URL schemes + host_required: Whether the URL must have a host + default_host: The default host to use if the URL does not have a host + default_port: The default port to use if the URL does not have a port + default_path: The default path to use if the URL does not have a path + preserve_empty_path: Whether to preserve an empty path or convert it to '/', default False + strict: Whether to use strict URL parsing + ref: optional unique identifier of the schema, used to reference the schema in other places + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='multi-host-url', + max_length=max_length, + allowed_schemes=allowed_schemes, + host_required=host_required, + default_host=default_host, + default_port=default_port, + default_path=default_path, + preserve_empty_path=preserve_empty_path, + strict=strict, + ref=ref, + metadata=metadata, + serialization=serialization, + ) + + +class DefinitionsSchema(TypedDict, total=False): + type: Required[Literal['definitions']] + schema: Required[CoreSchema] + definitions: Required[list[CoreSchema]] + metadata: dict[str, Any] + serialization: SerSchema + + +def definitions_schema(schema: CoreSchema, definitions: list[CoreSchema]) -> DefinitionsSchema: + """ + Build a schema that contains both an inner schema and a list of definitions which can be used + within the inner schema. + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema = core_schema.definitions_schema( + core_schema.list_schema(core_schema.definition_reference_schema('foobar')), + [core_schema.int_schema(ref='foobar')], + ) + v = SchemaValidator(schema) + assert v.validate_python([1, 2, '3']) == [1, 2, 3] + ``` + + Args: + schema: The inner schema + definitions: List of definitions which can be referenced within inner schema + """ + return DefinitionsSchema(type='definitions', schema=schema, definitions=definitions) + + +class DefinitionReferenceSchema(TypedDict, total=False): + type: Required[Literal['definition-ref']] + schema_ref: Required[str] + ref: str + metadata: dict[str, Any] + serialization: SerSchema + + +def definition_reference_schema( + schema_ref: str, + ref: str | None = None, + metadata: dict[str, Any] | None = None, + serialization: SerSchema | None = None, +) -> DefinitionReferenceSchema: + """ + Returns a schema that points to a schema stored in "definitions", this is useful for nested recursive + models and also when you want to define validators separately from the main schema, e.g.: + + ```py + from pydantic_core import SchemaValidator, core_schema + + schema_definition = core_schema.definition_reference_schema('list-schema') + schema = core_schema.definitions_schema( + schema=schema_definition, + definitions=[ + core_schema.list_schema(items_schema=schema_definition, ref='list-schema'), + ], + ) + v = SchemaValidator(schema) + assert v.validate_python([()]) == [[]] + ``` + + Args: + schema_ref: The schema ref to use for the definition reference schema + metadata: Any other information you want to include with the schema, not used by pydantic-core + serialization: Custom serialization schema + """ + return _dict_not_none( + type='definition-ref', schema_ref=schema_ref, ref=ref, metadata=metadata, serialization=serialization + ) + + +MYPY = False +# See https://github.com/python/mypy/issues/14034 for details, in summary mypy is extremely slow to process this +# union which kills performance not just for pydantic, but even for code using pydantic +if not MYPY: + CoreSchema = Union[ + InvalidSchema, + AnySchema, + NoneSchema, + BoolSchema, + IntSchema, + FloatSchema, + DecimalSchema, + StringSchema, + BytesSchema, + DateSchema, + TimeSchema, + DatetimeSchema, + TimedeltaSchema, + LiteralSchema, + MissingSentinelSchema, + EnumSchema, + IsInstanceSchema, + IsSubclassSchema, + CallableSchema, + ListSchema, + TupleSchema, + SetSchema, + FrozenSetSchema, + GeneratorSchema, + DictSchema, + AfterValidatorFunctionSchema, + BeforeValidatorFunctionSchema, + WrapValidatorFunctionSchema, + PlainValidatorFunctionSchema, + WithDefaultSchema, + NullableSchema, + UnionSchema, + TaggedUnionSchema, + ChainSchema, + LaxOrStrictSchema, + JsonOrPythonSchema, + TypedDictSchema, + ModelFieldsSchema, + ModelSchema, + DataclassArgsSchema, + DataclassSchema, + ArgumentsSchema, + ArgumentsV3Schema, + CallSchema, + CustomErrorSchema, + JsonSchema, + UrlSchema, + MultiHostUrlSchema, + DefinitionsSchema, + DefinitionReferenceSchema, + UuidSchema, + ComplexSchema, + ] +elif False: + CoreSchema: TypeAlias = Mapping[str, Any] + + +# to update this, call `pytest -k test_core_schema_type_literal` and copy the output +CoreSchemaType = Literal[ + 'invalid', + 'any', + 'none', + 'bool', + 'int', + 'float', + 'decimal', + 'str', + 'bytes', + 'date', + 'time', + 'datetime', + 'timedelta', + 'literal', + 'missing-sentinel', + 'enum', + 'is-instance', + 'is-subclass', + 'callable', + 'list', + 'tuple', + 'set', + 'frozenset', + 'generator', + 'dict', + 'function-after', + 'function-before', + 'function-wrap', + 'function-plain', + 'default', + 'nullable', + 'union', + 'tagged-union', + 'chain', + 'lax-or-strict', + 'json-or-python', + 'typed-dict', + 'model-fields', + 'model', + 'dataclass-args', + 'dataclass', + 'arguments', + 'arguments-v3', + 'call', + 'custom-error', + 'json', + 'url', + 'multi-host-url', + 'definitions', + 'definition-ref', + 'uuid', + 'complex', +] + +CoreSchemaFieldType = Literal['model-field', 'dataclass-field', 'typed-dict-field', 'computed-field'] + + +# used in _pydantic_core.pyi::PydanticKnownError +# to update this, call `pytest -k test_all_errors` and copy the output +ErrorType = Literal[ + 'no_such_attribute', + 'json_invalid', + 'json_type', + 'needs_python_object', + 'recursion_loop', + 'missing', + 'frozen_field', + 'frozen_instance', + 'extra_forbidden', + 'invalid_key', + 'get_attribute_error', + 'model_type', + 'model_attributes_type', + 'dataclass_type', + 'dataclass_exact_type', + 'default_factory_not_called', + 'none_required', + 'greater_than', + 'greater_than_equal', + 'less_than', + 'less_than_equal', + 'multiple_of', + 'finite_number', + 'too_short', + 'too_long', + 'iterable_type', + 'iteration_error', + 'string_type', + 'string_sub_type', + 'string_unicode', + 'string_too_short', + 'string_too_long', + 'string_pattern_mismatch', + 'string_not_ascii', + 'enum', + 'dict_type', + 'mapping_type', + 'list_type', + 'tuple_type', + 'set_type', + 'set_item_not_hashable', + 'bool_type', + 'bool_parsing', + 'int_type', + 'int_parsing', + 'int_parsing_size', + 'int_from_float', + 'float_type', + 'float_parsing', + 'bytes_type', + 'bytes_too_short', + 'bytes_too_long', + 'bytes_invalid_encoding', + 'value_error', + 'assertion_error', + 'literal_error', + 'missing_sentinel_error', + 'date_type', + 'date_parsing', + 'date_from_datetime_parsing', + 'date_from_datetime_inexact', + 'date_past', + 'date_future', + 'time_type', + 'time_parsing', + 'datetime_type', + 'datetime_parsing', + 'datetime_object_invalid', + 'datetime_from_date_parsing', + 'datetime_past', + 'datetime_future', + 'timezone_naive', + 'timezone_aware', + 'timezone_offset', + 'time_delta_type', + 'time_delta_parsing', + 'frozen_set_type', + 'is_instance_of', + 'is_subclass_of', + 'callable_type', + 'union_tag_invalid', + 'union_tag_not_found', + 'arguments_type', + 'missing_argument', + 'unexpected_keyword_argument', + 'missing_keyword_only_argument', + 'unexpected_positional_argument', + 'missing_positional_only_argument', + 'multiple_argument_values', + 'url_type', + 'url_parsing', + 'url_syntax_violation', + 'url_too_long', + 'url_scheme', + 'uuid_type', + 'uuid_parsing', + 'uuid_version', + 'decimal_type', + 'decimal_parsing', + 'decimal_max_digits', + 'decimal_max_places', + 'decimal_whole_digits', + 'complex_type', + 'complex_str_parsing', +] + + +def _dict_not_none(**kwargs: Any) -> Any: + return {k: v for k, v in kwargs.items() if v is not None} + + +def iter_union_choices(union_schema: UnionSchema) -> Generator[CoreSchema]: + """Iterate over the choices of a `'union'` schema.""" + for choice in union_schema['choices']: + if isinstance(choice, tuple): + yield choice[0] + else: + yield choice + + +############################################################################### +# All this stuff is deprecated by #980 and will be removed eventually +# They're kept because some code external code will be using them + + +@deprecated('`field_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.') +def field_before_validator_function(function: WithInfoValidatorFunction, field_name: str, schema: CoreSchema, **kwargs): + warnings.warn( + '`field_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.', + DeprecationWarning, + ) + return with_info_before_validator_function(function, schema, field_name=field_name, **kwargs) + + +@deprecated('`general_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.') +def general_before_validator_function(*args, **kwargs): + warnings.warn( + '`general_before_validator_function` is deprecated, use `with_info_before_validator_function` instead.', + DeprecationWarning, + ) + return with_info_before_validator_function(*args, **kwargs) + + +@deprecated('`field_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.') +def field_after_validator_function(function: WithInfoValidatorFunction, field_name: str, schema: CoreSchema, **kwargs): + warnings.warn( + '`field_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.', + DeprecationWarning, + ) + return with_info_after_validator_function(function, schema, field_name=field_name, **kwargs) + + +@deprecated('`general_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.') +def general_after_validator_function(*args, **kwargs): + warnings.warn( + '`general_after_validator_function` is deprecated, use `with_info_after_validator_function` instead.', + DeprecationWarning, + ) + return with_info_after_validator_function(*args, **kwargs) + + +@deprecated('`field_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.') +def field_wrap_validator_function( + function: WithInfoWrapValidatorFunction, field_name: str, schema: CoreSchema, **kwargs +): + warnings.warn( + '`field_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.', + DeprecationWarning, + ) + return with_info_wrap_validator_function(function, schema, field_name=field_name, **kwargs) + + +@deprecated('`general_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.') +def general_wrap_validator_function(*args, **kwargs): + warnings.warn( + '`general_wrap_validator_function` is deprecated, use `with_info_wrap_validator_function` instead.', + DeprecationWarning, + ) + return with_info_wrap_validator_function(*args, **kwargs) + + +@deprecated('`field_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.') +def field_plain_validator_function(function: WithInfoValidatorFunction, field_name: str, **kwargs): + warnings.warn( + '`field_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.', + DeprecationWarning, + ) + return with_info_plain_validator_function(function, field_name=field_name, **kwargs) + + +@deprecated('`general_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.') +def general_plain_validator_function(*args, **kwargs): + warnings.warn( + '`general_plain_validator_function` is deprecated, use `with_info_plain_validator_function` instead.', + DeprecationWarning, + ) + return with_info_plain_validator_function(*args, **kwargs) + + +_deprecated_import_lookup = { + 'FieldValidationInfo': ValidationInfo, + 'FieldValidatorFunction': WithInfoValidatorFunction, + 'GeneralValidatorFunction': WithInfoValidatorFunction, + 'FieldWrapValidatorFunction': WithInfoWrapValidatorFunction, +} + +if TYPE_CHECKING: + FieldValidationInfo = ValidationInfo + + +def __getattr__(attr_name: str) -> object: + new_attr = _deprecated_import_lookup.get(attr_name) + if new_attr is None: + raise AttributeError(f"module 'pydantic_core' has no attribute '{attr_name}'") + else: + import warnings + + msg = f'`{attr_name}` is deprecated, use `{new_attr.__name__}` instead.' + warnings.warn(msg, DeprecationWarning, stacklevel=1) + return new_attr diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/py.typed b/personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..3a5ba538b05cab44263485e2ba88ef466f230259 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/__init__.py @@ -0,0 +1,82 @@ +""" + Pygments + ~~~~~~~~ + + Pygments is a syntax highlighting package written in Python. + + It is a generic syntax highlighter for general use in all kinds of software + such as forum systems, wikis or other applications that need to prettify + source code. Highlights are: + + * a wide range of common languages and markup formats is supported + * special attention is paid to details, increasing quality by a fair amount + * support for new languages and formats are added easily + * a number of output formats, presently HTML, LaTeX, RTF, SVG, all image + formats that PIL supports, and ANSI sequences + * it is usable as a command-line tool and as a library + * ... and it highlights even Brainfuck! + + The `Pygments master branch`_ is installable with ``easy_install Pygments==dev``. + + .. _Pygments master branch: + https://github.com/pygments/pygments/archive/master.zip#egg=Pygments-dev + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" +from io import StringIO, BytesIO + +__version__ = '2.20.0' +__docformat__ = 'restructuredtext' + +__all__ = ['lex', 'format', 'highlight'] + + +def lex(code, lexer): + """ + Lex `code` with the `lexer` (must be a `Lexer` instance) + and return an iterable of tokens. Currently, this only calls + `lexer.get_tokens()`. + """ + try: + return lexer.get_tokens(code) + except TypeError: + # Heuristic to catch a common mistake. + from pygments.lexer import RegexLexer + if isinstance(lexer, type) and issubclass(lexer, RegexLexer): + raise TypeError('lex() argument must be a lexer instance, ' + 'not a class') + raise + + +def format(tokens, formatter, outfile=None): # pylint: disable=redefined-builtin + """ + Format ``tokens`` (an iterable of tokens) with the formatter ``formatter`` + (a `Formatter` instance). + + If ``outfile`` is given and a valid file object (an object with a + ``write`` method), the result will be written to it, otherwise it + is returned as a string. + """ + try: + if not outfile: + realoutfile = getattr(formatter, 'encoding', None) and BytesIO() or StringIO() + formatter.format(tokens, realoutfile) + return realoutfile.getvalue() + else: + formatter.format(tokens, outfile) + except TypeError: + # Heuristic to catch a common mistake. + from pygments.formatter import Formatter + if isinstance(formatter, type) and issubclass(formatter, Formatter): + raise TypeError('format() argument must be a formatter instance, ' + 'not a class') + raise + + +def highlight(code, lexer, formatter, outfile=None): + """ + This is the most high-level highlighting function. It combines `lex` and + `format` in one function. + """ + return format(lex(code, lexer), formatter, outfile) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/__main__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/__main__.py new file mode 100644 index 0000000000000000000000000000000000000000..818bfc3ae6ac4b79e029f714c56101ab2b1076ea --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/__main__.py @@ -0,0 +1,17 @@ +""" + pygments.__main__ + ~~~~~~~~~~~~~~~~~ + + Main entry point for ``python -m pygments``. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import sys +import pygments.cmdline + +try: + sys.exit(pygments.cmdline.main(sys.argv)) +except KeyboardInterrupt: + sys.exit(1) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/cmdline.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/cmdline.py new file mode 100644 index 0000000000000000000000000000000000000000..bd4a1fb43926b7f7bdd7ef5028dbe19a9137515a --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/cmdline.py @@ -0,0 +1,668 @@ +""" + pygments.cmdline + ~~~~~~~~~~~~~~~~ + + Command line interface. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import os +import sys +import shutil +import argparse +from textwrap import dedent + +from pygments import __version__, highlight +from pygments.util import ClassNotFound, OptionError, docstring_headline, \ + guess_decode, guess_decode_from_terminal, terminal_encoding, \ + UnclosingTextIOWrapper +from pygments.lexers import get_all_lexers, get_lexer_by_name, guess_lexer, \ + load_lexer_from_file, get_lexer_for_filename, find_lexer_class_for_filename +from pygments.lexers.special import TextLexer +from pygments.formatters.latex import LatexEmbeddedLexer, LatexFormatter +from pygments.formatters import get_all_formatters, get_formatter_by_name, \ + load_formatter_from_file, get_formatter_for_filename, find_formatter_class +from pygments.formatters.terminal import TerminalFormatter +from pygments.formatters.terminal256 import Terminal256Formatter, TerminalTrueColorFormatter +from pygments.filters import get_all_filters, find_filter_class +from pygments.styles import get_all_styles, get_style_by_name + + +def _parse_options(o_strs): + opts = {} + if not o_strs: + return opts + for o_str in o_strs: + if not o_str.strip(): + continue + o_args = o_str.split(',') + for o_arg in o_args: + o_arg = o_arg.strip() + try: + o_key, o_val = o_arg.split('=', 1) + o_key = o_key.strip() + o_val = o_val.strip() + except ValueError: + opts[o_arg] = True + else: + opts[o_key] = o_val + return opts + + +def _parse_filters(f_strs): + filters = [] + if not f_strs: + return filters + for f_str in f_strs: + if ':' in f_str: + fname, fopts = f_str.split(':', 1) + filters.append((fname, _parse_options([fopts]))) + else: + filters.append((f_str, {})) + return filters + + +def _print_help(what, name): + try: + if what == 'lexer': + cls = get_lexer_by_name(name) + print(f"Help on the {cls.name} lexer:") + print(dedent(cls.__doc__)) + elif what == 'formatter': + cls = find_formatter_class(name) + print(f"Help on the {cls.name} formatter:") + print(dedent(cls.__doc__)) + elif what == 'filter': + cls = find_filter_class(name) + print(f"Help on the {name} filter:") + print(dedent(cls.__doc__)) + return 0 + except (AttributeError, ValueError): + print(f"{what} not found!", file=sys.stderr) + return 1 + + +def _print_list(what): + if what == 'lexer': + print() + print("Lexers:") + print("~~~~~~~") + + info = [] + for fullname, names, exts, _ in get_all_lexers(): + tup = (', '.join(names)+':', fullname, + exts and '(filenames ' + ', '.join(exts) + ')' or '') + info.append(tup) + info.sort() + for i in info: + print(('* {}\n {} {}').format(*i)) + + elif what == 'formatter': + print() + print("Formatters:") + print("~~~~~~~~~~~") + + info = [] + for cls in get_all_formatters(): + doc = docstring_headline(cls) + tup = (', '.join(cls.aliases) + ':', doc, cls.filenames and + '(filenames ' + ', '.join(cls.filenames) + ')' or '') + info.append(tup) + info.sort() + for i in info: + print(('* {}\n {} {}').format(*i)) + + elif what == 'filter': + print() + print("Filters:") + print("~~~~~~~~") + + for name in get_all_filters(): + cls = find_filter_class(name) + print("* " + name + ':') + print(f" {docstring_headline(cls)}") + + elif what == 'style': + print() + print("Styles:") + print("~~~~~~~") + + for name in get_all_styles(): + cls = get_style_by_name(name) + print("* " + name + ':') + print(f" {docstring_headline(cls)}") + + +def _print_list_as_json(requested_items): + import json + result = {} + if 'lexer' in requested_items: + info = {} + for fullname, names, filenames, mimetypes in get_all_lexers(): + info[fullname] = { + 'aliases': names, + 'filenames': filenames, + 'mimetypes': mimetypes + } + result['lexers'] = info + + if 'formatter' in requested_items: + info = {} + for cls in get_all_formatters(): + doc = docstring_headline(cls) + info[cls.name] = { + 'aliases': cls.aliases, + 'filenames': cls.filenames, + 'doc': doc + } + result['formatters'] = info + + if 'filter' in requested_items: + info = {} + for name in get_all_filters(): + cls = find_filter_class(name) + info[name] = { + 'doc': docstring_headline(cls) + } + result['filters'] = info + + if 'style' in requested_items: + info = {} + for name in get_all_styles(): + cls = get_style_by_name(name) + info[name] = { + 'doc': docstring_headline(cls) + } + result['styles'] = info + + json.dump(result, sys.stdout) + +def main_inner(parser, argns): + if argns.help: + parser.print_help() + return 0 + + if argns.V: + print(f'Pygments version {__version__}, (c) 2006-present by Georg Brandl, Matthäus ' + 'Chajdas and contributors.') + return 0 + + def is_only_option(opt): + return not any(v for (k, v) in vars(argns).items() if k != opt) + + # handle ``pygmentize -L`` + if argns.L is not None: + arg_set = set() + for k, v in vars(argns).items(): + if v: + arg_set.add(k) + + arg_set.discard('L') + arg_set.discard('json') + + if arg_set: + parser.print_help(sys.stderr) + return 2 + + # print version + if not argns.json: + main(['', '-V']) + allowed_types = {'lexer', 'formatter', 'filter', 'style'} + largs = [arg.rstrip('s') for arg in argns.L] + if any(arg not in allowed_types for arg in largs): + parser.print_help(sys.stderr) + return 0 + if not largs: + largs = allowed_types + if not argns.json: + for arg in largs: + _print_list(arg) + else: + _print_list_as_json(largs) + return 0 + + # handle ``pygmentize -H`` + if argns.H: + if not is_only_option('H'): + parser.print_help(sys.stderr) + return 2 + what, name = argns.H + if what not in ('lexer', 'formatter', 'filter'): + parser.print_help(sys.stderr) + return 2 + return _print_help(what, name) + + # parse -O options + parsed_opts = _parse_options(argns.O or []) + + # parse -P options + for p_opt in argns.P or []: + try: + name, value = p_opt.split('=', 1) + except ValueError: + parsed_opts[p_opt] = True + else: + parsed_opts[name] = value + + # encodings + inencoding = parsed_opts.get('inencoding', parsed_opts.get('encoding')) + outencoding = parsed_opts.get('outencoding', parsed_opts.get('encoding')) + + # handle ``pygmentize -N`` + if argns.N: + lexer = find_lexer_class_for_filename(argns.N) + if lexer is None: + lexer = TextLexer + + print(lexer.aliases[0]) + return 0 + + # handle ``pygmentize -C`` + if argns.C: + inp = sys.stdin.buffer.read() + try: + lexer = guess_lexer(inp, inencoding=inencoding) + except ClassNotFound: + lexer = TextLexer + + print(lexer.aliases[0]) + return 0 + + # handle ``pygmentize -S`` + S_opt = argns.S + a_opt = argns.a + if S_opt is not None: + f_opt = argns.f + if not f_opt: + parser.print_help(sys.stderr) + return 2 + if argns.l or argns.INPUTFILE: + parser.print_help(sys.stderr) + return 2 + + try: + parsed_opts['style'] = S_opt + fmter = get_formatter_by_name(f_opt, **parsed_opts) + except ClassNotFound as err: + print(err, file=sys.stderr) + return 1 + + print(fmter.get_style_defs(a_opt or '')) + return 0 + + # if no -S is given, -a is not allowed + if argns.a is not None: + parser.print_help(sys.stderr) + return 2 + + # parse -F options + F_opts = _parse_filters(argns.F or []) + + # -x: allow custom (eXternal) lexers and formatters + allow_custom_lexer_formatter = bool(argns.x) + + # select lexer + lexer = None + + # given by name? + lexername = argns.l + if lexername: + # custom lexer, located relative to user's cwd + if allow_custom_lexer_formatter and '.py' in lexername: + try: + filename = None + name = None + if ':' in lexername: + filename, name = lexername.rsplit(':', 1) + + if '.py' in name: + # This can happen on Windows: If the lexername is + # C:\lexer.py -- return to normal load path in that case + name = None + + if filename and name: + lexer = load_lexer_from_file(filename, name, + **parsed_opts) + else: + lexer = load_lexer_from_file(lexername, **parsed_opts) + except ClassNotFound as err: + print('Error:', err, file=sys.stderr) + return 1 + else: + try: + lexer = get_lexer_by_name(lexername, **parsed_opts) + except (OptionError, ClassNotFound) as err: + print('Error:', err, file=sys.stderr) + return 1 + + # read input code + code = None + + if argns.INPUTFILE: + if argns.s: + print('Error: -s option not usable when input file specified', + file=sys.stderr) + return 2 + + infn = argns.INPUTFILE + try: + with open(infn, 'rb') as infp: + code = infp.read() + except Exception as err: + print('Error: cannot read infile:', err, file=sys.stderr) + return 1 + if not inencoding: + code, inencoding = guess_decode(code) + + # do we have to guess the lexer? + if not lexer: + try: + lexer = get_lexer_for_filename(infn, code, **parsed_opts) + except ClassNotFound as err: + if argns.g: + try: + lexer = guess_lexer(code, **parsed_opts) + except ClassNotFound: + lexer = TextLexer(**parsed_opts) + else: + print('Error:', err, file=sys.stderr) + return 1 + except OptionError as err: + print('Error:', err, file=sys.stderr) + return 1 + + elif not argns.s: # treat stdin as full file (-s support is later) + # read code from terminal, always in binary mode since we want to + # decode ourselves and be tolerant with it + code = sys.stdin.buffer.read() # use .buffer to get a binary stream + if not inencoding: + code, inencoding = guess_decode_from_terminal(code, sys.stdin) + # else the lexer will do the decoding + if not lexer: + try: + lexer = guess_lexer(code, **parsed_opts) + except ClassNotFound: + lexer = TextLexer(**parsed_opts) + + else: # -s option needs a lexer with -l + if not lexer: + print('Error: when using -s a lexer has to be selected with -l', + file=sys.stderr) + return 2 + + # process filters + for fname, fopts in F_opts: + try: + lexer.add_filter(fname, **fopts) + except ClassNotFound as err: + print('Error:', err, file=sys.stderr) + return 1 + + # select formatter + outfn = argns.o + fmter = argns.f + if fmter: + # custom formatter, located relative to user's cwd + if allow_custom_lexer_formatter and '.py' in fmter: + try: + filename = None + name = None + if ':' in fmter: + # Same logic as above for custom lexer + filename, name = fmter.rsplit(':', 1) + + if '.py' in name: + name = None + + if filename and name: + fmter = load_formatter_from_file(filename, name, + **parsed_opts) + else: + fmter = load_formatter_from_file(fmter, **parsed_opts) + except ClassNotFound as err: + print('Error:', err, file=sys.stderr) + return 1 + else: + try: + fmter = get_formatter_by_name(fmter, **parsed_opts) + except (OptionError, ClassNotFound) as err: + print('Error:', err, file=sys.stderr) + return 1 + + if outfn: + if not fmter: + try: + fmter = get_formatter_for_filename(outfn, **parsed_opts) + except (OptionError, ClassNotFound) as err: + print('Error:', err, file=sys.stderr) + return 1 + try: + outfile = open(outfn, 'wb') + except Exception as err: + print('Error: cannot open outfile:', err, file=sys.stderr) + return 1 + else: + if not fmter: + if os.environ.get('COLORTERM','') in ('truecolor', '24bit'): + fmter = TerminalTrueColorFormatter(**parsed_opts) + elif '256' in os.environ.get('TERM', ''): + fmter = Terminal256Formatter(**parsed_opts) + else: + fmter = TerminalFormatter(**parsed_opts) + outfile = sys.stdout.buffer + + # determine output encoding if not explicitly selected + if not outencoding: + if outfn: + # output file? use lexer encoding for now (can still be None) + fmter.encoding = inencoding + else: + # else use terminal encoding + fmter.encoding = terminal_encoding(sys.stdout) + + # provide coloring under Windows, if possible + if not outfn and sys.platform in ('win32', 'cygwin') and \ + fmter.name in ('Terminal', 'Terminal256'): # pragma: no cover + # unfortunately colorama doesn't support binary streams on Py3 + outfile = UnclosingTextIOWrapper(outfile, encoding=fmter.encoding) + fmter.encoding = None + try: + import colorama.initialise + except ImportError: + pass + else: + outfile = colorama.initialise.wrap_stream( + outfile, convert=None, strip=None, autoreset=False, wrap=True) + + # When using the LaTeX formatter and the option `escapeinside` is + # specified, we need a special lexer which collects escaped text + # before running the chosen language lexer. + escapeinside = parsed_opts.get('escapeinside', '') + if len(escapeinside) == 2 and isinstance(fmter, LatexFormatter): + left = escapeinside[0] + right = escapeinside[1] + lexer = LatexEmbeddedLexer(left, right, lexer) + + # ... and do it! + if not argns.s: + # process whole input as per normal... + try: + highlight(code, lexer, fmter, outfile) + finally: + if outfn: + outfile.close() + return 0 + else: + # line by line processing of stdin (eg: for 'tail -f')... + try: + while 1: + line = sys.stdin.buffer.readline() + if not line: + break + if not inencoding: + line = guess_decode_from_terminal(line, sys.stdin)[0] + highlight(line, lexer, fmter, outfile) + if hasattr(outfile, 'flush'): + outfile.flush() + return 0 + except KeyboardInterrupt: # pragma: no cover + return 0 + finally: + if outfn: + outfile.close() + + +class HelpFormatter(argparse.HelpFormatter): + def __init__(self, prog, indent_increment=2, max_help_position=16, width=None): + if width is None: + try: + width = shutil.get_terminal_size().columns - 2 + except Exception: + pass + argparse.HelpFormatter.__init__(self, prog, indent_increment, + max_help_position, width) + + +def main(args=sys.argv): + """ + Main command line entry point. + """ + desc = "Highlight an input file and write the result to an output file." + parser = argparse.ArgumentParser(description=desc, add_help=False, + formatter_class=HelpFormatter) + + operation = parser.add_argument_group('Main operation') + lexersel = operation.add_mutually_exclusive_group() + lexersel.add_argument( + '-l', metavar='LEXER', + help='Specify the lexer to use. (Query names with -L.) If not ' + 'given and -g is not present, the lexer is guessed from the filename.') + lexersel.add_argument( + '-g', action='store_true', + help='Guess the lexer from the file contents, or pass through ' + 'as plain text if nothing can be guessed.') + operation.add_argument( + '-F', metavar='FILTER[:options]', action='append', + help='Add a filter to the token stream. (Query names with -L.) ' + 'Filter options are given after a colon if necessary.') + operation.add_argument( + '-f', metavar='FORMATTER', + help='Specify the formatter to use. (Query names with -L.) ' + 'If not given, the formatter is guessed from the output filename, ' + 'and defaults to the terminal formatter if the output is to the ' + 'terminal or an unknown file extension.') + operation.add_argument( + '-O', metavar='OPTION=value[,OPTION=value,...]', action='append', + help='Give options to the lexer and formatter as a comma-separated ' + 'list of key-value pairs. ' + 'Example: `-O bg=light,python=cool`.') + operation.add_argument( + '-P', metavar='OPTION=value', action='append', + help='Give a single option to the lexer and formatter - with this ' + 'you can pass options whose value contains commas and equal signs. ' + 'Example: `-P "heading=Pygments, the Python highlighter"`.') + operation.add_argument( + '-o', metavar='OUTPUTFILE', + help='Where to write the output. Defaults to standard output.') + + operation.add_argument( + 'INPUTFILE', nargs='?', + help='Where to read the input. Defaults to standard input.') + + flags = parser.add_argument_group('Operation flags') + flags.add_argument( + '-v', action='store_true', + help='Print a detailed traceback on unhandled exceptions, which ' + 'is useful for debugging and bug reports.') + flags.add_argument( + '-s', action='store_true', + help='Process lines one at a time until EOF, rather than waiting to ' + 'process the entire file. This only works for stdin, only for lexers ' + 'with no line-spanning constructs, and is intended for streaming ' + 'input such as you get from `tail -f`. ' + 'Example usage: `tail -f sql.log | pygmentize -s -l sql`.') + flags.add_argument( + '-x', action='store_true', + help='Allow custom lexers and formatters to be loaded from a .py file ' + 'relative to the current working directory. For example, ' + '`-l ./customlexer.py -x`. By default, this option expects a file ' + 'with a class named CustomLexer or CustomFormatter; you can also ' + 'specify your own class name with a colon (`-l ./lexer.py:MyLexer`). ' + 'Users should be very careful not to use this option with untrusted ' + 'files, because it will import and run them.') + flags.add_argument('--json', help='Output as JSON. This can ' + 'be only used in conjunction with -L.', + default=False, + action='store_true') + + special_modes_group = parser.add_argument_group( + 'Special modes - do not do any highlighting') + special_modes = special_modes_group.add_mutually_exclusive_group() + special_modes.add_argument( + '-S', metavar='STYLE -f formatter', + help='Print style definitions for STYLE for a formatter ' + 'given with -f. The argument given by -a is formatter ' + 'dependent.') + special_modes.add_argument( + '-L', nargs='*', metavar='WHAT', + help='List lexers, formatters, styles or filters -- ' + 'give additional arguments for the thing(s) you want to list ' + '(e.g. "styles"), or omit them to list everything.') + special_modes.add_argument( + '-N', metavar='FILENAME', + help='Guess and print out a lexer name based solely on the given ' + 'filename. Does not take input or highlight anything. If no specific ' + 'lexer can be determined, "text" is printed.') + special_modes.add_argument( + '-C', action='store_true', + help='Like -N, but print out a lexer name based solely on ' + 'a given content from standard input.') + special_modes.add_argument( + '-H', action='store', nargs=2, metavar=('NAME', 'TYPE'), + help='Print detailed help for the object of type , ' + 'where is one of "lexer", "formatter" or "filter".') + special_modes.add_argument( + '-V', action='store_true', + help='Print the package version.') + special_modes.add_argument( + '-h', '--help', action='store_true', + help='Print this help.') + special_modes_group.add_argument( + '-a', metavar='ARG', + help='Formatter-specific additional argument for the -S (print ' + 'style sheet) mode.') + + argns = parser.parse_args(args[1:]) + + try: + return main_inner(parser, argns) + except BrokenPipeError: + # someone closed our stdout, e.g. by quitting a pager. + return 0 + except Exception: + if argns.v: + print(file=sys.stderr) + print('*' * 65, file=sys.stderr) + print('An unhandled exception occurred while highlighting.', + file=sys.stderr) + print('Please report the whole traceback to the issue tracker at', + file=sys.stderr) + print('.', + file=sys.stderr) + print('*' * 65, file=sys.stderr) + print(file=sys.stderr) + raise + import traceback + info = traceback.format_exception(*sys.exc_info()) + msg = info[-1].strip() + if len(info) >= 3: + # extract relevant file and position info + msg += '\n (f{})'.format(info[-2].split('\n')[0].strip()[1:]) + print(file=sys.stderr) + print('*** Error while highlighting:', file=sys.stderr) + print(msg, file=sys.stderr) + print('*** If this is a bug you want to report, please rerun with -v.', + file=sys.stderr) + return 1 diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/console.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/console.py new file mode 100644 index 0000000000000000000000000000000000000000..d9009168cadbe25ba0bb1fc5427a5db0f1582830 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/console.py @@ -0,0 +1,70 @@ +""" + pygments.console + ~~~~~~~~~~~~~~~~ + + Format colored console output. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +esc = "\x1b[" + +codes = {} +codes[""] = "" +codes["reset"] = esc + "39;49;00m" + +codes["bold"] = esc + "01m" +codes["faint"] = esc + "02m" +codes["standout"] = esc + "03m" +codes["underline"] = esc + "04m" +codes["blink"] = esc + "05m" +codes["overline"] = esc + "06m" + +dark_colors = ["black", "red", "green", "yellow", "blue", + "magenta", "cyan", "gray"] +light_colors = ["brightblack", "brightred", "brightgreen", "brightyellow", "brightblue", + "brightmagenta", "brightcyan", "white"] + +x = 30 +for dark, light in zip(dark_colors, light_colors): + codes[dark] = esc + "%im" % x + codes[light] = esc + "%im" % (60 + x) + x += 1 + +del dark, light, x + +codes["white"] = codes["bold"] + + +def reset_color(): + return codes["reset"] + + +def colorize(color_key, text): + return codes[color_key] + text + codes["reset"] + + +def ansiformat(attr, text): + """ + Format ``text`` with a color and/or some attributes:: + + color normal color + *color* bold color + _color_ underlined color + +color+ blinking color + """ + result = [] + if attr[:1] == attr[-1:] == '+': + result.append(codes['blink']) + attr = attr[1:-1] + if attr[:1] == attr[-1:] == '*': + result.append(codes['bold']) + attr = attr[1:-1] + if attr[:1] == attr[-1:] == '_': + result.append(codes['underline']) + attr = attr[1:-1] + result.append(codes[attr]) + result.append(text) + result.append(codes['reset']) + return ''.join(result) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/filter.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/filter.py new file mode 100644 index 0000000000000000000000000000000000000000..6c926c26cfcf0ca7579f05affcab46c389595b71 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/filter.py @@ -0,0 +1,70 @@ +""" + pygments.filter + ~~~~~~~~~~~~~~~ + + Module that implements the default filter. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + + +def apply_filters(stream, filters, lexer=None): + """ + Use this method to apply an iterable of filters to + a stream. If lexer is given it's forwarded to the + filter, otherwise the filter receives `None`. + """ + def _apply(filter_, stream): + yield from filter_.filter(lexer, stream) + for filter_ in filters: + stream = _apply(filter_, stream) + return stream + + +def simplefilter(f): + """ + Decorator that converts a function into a filter:: + + @simplefilter + def lowercase(self, lexer, stream, options): + for ttype, value in stream: + yield ttype, value.lower() + """ + return type(f.__name__, (FunctionFilter,), { + '__module__': getattr(f, '__module__'), + '__doc__': f.__doc__, + 'function': f, + }) + + +class Filter: + """ + Default filter. Subclass this class or use the `simplefilter` + decorator to create own filters. + """ + + def __init__(self, **options): + self.options = options + + def filter(self, lexer, stream): + raise NotImplementedError() + + +class FunctionFilter(Filter): + """ + Abstract class used by `simplefilter` to create simple + function filters on the fly. The `simplefilter` decorator + automatically creates subclasses of this class for + functions passed to it. + """ + function = None + + def __init__(self, **options): + if not hasattr(self, 'function'): + raise TypeError(f'{self.__class__.__name__!r} used without bound function') + Filter.__init__(self, **options) + + def filter(self, lexer, stream): + # pylint: disable=not-callable + yield from self.function(lexer, stream, self.options) diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/filters/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/filters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..c7ba6adb5b75ac3bf8e9e94868575e6ce770e6c1 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/filters/__init__.py @@ -0,0 +1,942 @@ +""" + pygments.filters + ~~~~~~~~~~~~~~~~ + + Module containing filter lookup functions and default + filters. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import re + +from pygments.token import String, Comment, Keyword, Name, Error, Whitespace, \ + string_to_tokentype +from pygments.filter import Filter +from pygments.util import get_list_opt, get_int_opt, get_bool_opt, \ + get_choice_opt, ClassNotFound, OptionError +from pygments.plugin import find_plugin_filters + + +def find_filter_class(filtername): + """Lookup a filter by name. Return None if not found.""" + if filtername in FILTERS: + return FILTERS[filtername] + for name, cls in find_plugin_filters(): + if name == filtername: + return cls + return None + + +def get_filter_by_name(filtername, **options): + """Return an instantiated filter. + + Options are passed to the filter initializer if wanted. + Raise a ClassNotFound if not found. + """ + cls = find_filter_class(filtername) + if cls: + return cls(**options) + else: + raise ClassNotFound(f'filter {filtername!r} not found') + + +def get_all_filters(): + """Return a generator of all filter names.""" + yield from FILTERS + for name, _ in find_plugin_filters(): + yield name + + +def _replace_special(ttype, value, regex, specialttype, + replacefunc=lambda x: x): + last = 0 + for match in regex.finditer(value): + start, end = match.start(), match.end() + if start != last: + yield ttype, value[last:start] + yield specialttype, replacefunc(value[start:end]) + last = end + if last != len(value): + yield ttype, value[last:] + + +class CodeTagFilter(Filter): + """Highlight special code tags in comments and docstrings. + + Options accepted: + + `codetags` : list of strings + A list of strings that are flagged as code tags. The default is to + highlight ``XXX``, ``TODO``, ``FIXME``, ``BUG`` and ``NOTE``. + + .. versionchanged:: 2.13 + Now recognizes ``FIXME`` by default. + """ + + def __init__(self, **options): + Filter.__init__(self, **options) + tags = get_list_opt(options, 'codetags', + ['XXX', 'TODO', 'FIXME', 'BUG', 'NOTE']) + self.tag_re = re.compile(r'\b({})\b'.format('|'.join([ + re.escape(tag) for tag in tags if tag + ]))) + + def filter(self, lexer, stream): + regex = self.tag_re + for ttype, value in stream: + if ttype in String.Doc or \ + ttype in Comment and \ + ttype not in Comment.Preproc: + yield from _replace_special(ttype, value, regex, Comment.Special) + else: + yield ttype, value + + +class SymbolFilter(Filter): + """Convert mathematical symbols into Unicode characters. + + Examples are ``\\`` in Isabelle or + ``\\longrightarrow`` in LaTeX. + + This is mostly useful for HTML or console output when you want to + approximate the source rendering you'd see in an IDE. + + Options accepted: + + `lang` : string + The symbol language. Must be one of ``'isabelle'`` or + ``'latex'``. The default is ``'isabelle'``. + """ + + latex_symbols = { + '\\alpha' : '\U000003b1', + '\\beta' : '\U000003b2', + '\\gamma' : '\U000003b3', + '\\delta' : '\U000003b4', + '\\varepsilon' : '\U000003b5', + '\\zeta' : '\U000003b6', + '\\eta' : '\U000003b7', + '\\vartheta' : '\U000003b8', + '\\iota' : '\U000003b9', + '\\kappa' : '\U000003ba', + '\\lambda' : '\U000003bb', + '\\mu' : '\U000003bc', + '\\nu' : '\U000003bd', + '\\xi' : '\U000003be', + '\\pi' : '\U000003c0', + '\\varrho' : '\U000003c1', + '\\sigma' : '\U000003c3', + '\\tau' : '\U000003c4', + '\\upsilon' : '\U000003c5', + '\\varphi' : '\U000003c6', + '\\chi' : '\U000003c7', + '\\psi' : '\U000003c8', + '\\omega' : '\U000003c9', + '\\Gamma' : '\U00000393', + '\\Delta' : '\U00000394', + '\\Theta' : '\U00000398', + '\\Lambda' : '\U0000039b', + '\\Xi' : '\U0000039e', + '\\Pi' : '\U000003a0', + '\\Sigma' : '\U000003a3', + '\\Upsilon' : '\U000003a5', + '\\Phi' : '\U000003a6', + '\\Psi' : '\U000003a8', + '\\Omega' : '\U000003a9', + '\\leftarrow' : '\U00002190', + '\\longleftarrow' : '\U000027f5', + '\\rightarrow' : '\U00002192', + '\\longrightarrow' : '\U000027f6', + '\\Leftarrow' : '\U000021d0', + '\\Longleftarrow' : '\U000027f8', + '\\Rightarrow' : '\U000021d2', + '\\Longrightarrow' : '\U000027f9', + '\\leftrightarrow' : '\U00002194', + '\\longleftrightarrow' : '\U000027f7', + '\\Leftrightarrow' : '\U000021d4', + '\\Longleftrightarrow' : '\U000027fa', + '\\mapsto' : '\U000021a6', + '\\longmapsto' : '\U000027fc', + '\\relbar' : '\U00002500', + '\\Relbar' : '\U00002550', + '\\hookleftarrow' : '\U000021a9', + '\\hookrightarrow' : '\U000021aa', + '\\leftharpoondown' : '\U000021bd', + '\\rightharpoondown' : '\U000021c1', + '\\leftharpoonup' : '\U000021bc', + '\\rightharpoonup' : '\U000021c0', + '\\rightleftharpoons' : '\U000021cc', + '\\leadsto' : '\U0000219d', + '\\downharpoonleft' : '\U000021c3', + '\\downharpoonright' : '\U000021c2', + '\\upharpoonleft' : '\U000021bf', + '\\upharpoonright' : '\U000021be', + '\\restriction' : '\U000021be', + '\\uparrow' : '\U00002191', + '\\Uparrow' : '\U000021d1', + '\\downarrow' : '\U00002193', + '\\Downarrow' : '\U000021d3', + '\\updownarrow' : '\U00002195', + '\\Updownarrow' : '\U000021d5', + '\\langle' : '\U000027e8', + '\\rangle' : '\U000027e9', + '\\lceil' : '\U00002308', + '\\rceil' : '\U00002309', + '\\lfloor' : '\U0000230a', + '\\rfloor' : '\U0000230b', + '\\flqq' : '\U000000ab', + '\\frqq' : '\U000000bb', + '\\bot' : '\U000022a5', + '\\top' : '\U000022a4', + '\\wedge' : '\U00002227', + '\\bigwedge' : '\U000022c0', + '\\vee' : '\U00002228', + '\\bigvee' : '\U000022c1', + '\\forall' : '\U00002200', + '\\exists' : '\U00002203', + '\\nexists' : '\U00002204', + '\\neg' : '\U000000ac', + '\\Box' : '\U000025a1', + '\\Diamond' : '\U000025c7', + '\\vdash' : '\U000022a2', + '\\models' : '\U000022a8', + '\\dashv' : '\U000022a3', + '\\surd' : '\U0000221a', + '\\le' : '\U00002264', + '\\ge' : '\U00002265', + '\\ll' : '\U0000226a', + '\\gg' : '\U0000226b', + '\\lesssim' : '\U00002272', + '\\gtrsim' : '\U00002273', + '\\lessapprox' : '\U00002a85', + '\\gtrapprox' : '\U00002a86', + '\\in' : '\U00002208', + '\\notin' : '\U00002209', + '\\subset' : '\U00002282', + '\\supset' : '\U00002283', + '\\subseteq' : '\U00002286', + '\\supseteq' : '\U00002287', + '\\sqsubset' : '\U0000228f', + '\\sqsupset' : '\U00002290', + '\\sqsubseteq' : '\U00002291', + '\\sqsupseteq' : '\U00002292', + '\\cap' : '\U00002229', + '\\bigcap' : '\U000022c2', + '\\cup' : '\U0000222a', + '\\bigcup' : '\U000022c3', + '\\sqcup' : '\U00002294', + '\\bigsqcup' : '\U00002a06', + '\\sqcap' : '\U00002293', + '\\Bigsqcap' : '\U00002a05', + '\\setminus' : '\U00002216', + '\\propto' : '\U0000221d', + '\\uplus' : '\U0000228e', + '\\bigplus' : '\U00002a04', + '\\sim' : '\U0000223c', + '\\doteq' : '\U00002250', + '\\simeq' : '\U00002243', + '\\approx' : '\U00002248', + '\\asymp' : '\U0000224d', + '\\cong' : '\U00002245', + '\\equiv' : '\U00002261', + '\\Join' : '\U000022c8', + '\\bowtie' : '\U00002a1d', + '\\prec' : '\U0000227a', + '\\succ' : '\U0000227b', + '\\preceq' : '\U0000227c', + '\\succeq' : '\U0000227d', + '\\parallel' : '\U00002225', + '\\mid' : '\U000000a6', + '\\pm' : '\U000000b1', + '\\mp' : '\U00002213', + '\\times' : '\U000000d7', + '\\div' : '\U000000f7', + '\\cdot' : '\U000022c5', + '\\star' : '\U000022c6', + '\\circ' : '\U00002218', + '\\dagger' : '\U00002020', + '\\ddagger' : '\U00002021', + '\\lhd' : '\U000022b2', + '\\rhd' : '\U000022b3', + '\\unlhd' : '\U000022b4', + '\\unrhd' : '\U000022b5', + '\\triangleleft' : '\U000025c3', + '\\triangleright' : '\U000025b9', + '\\triangle' : '\U000025b3', + '\\triangleq' : '\U0000225c', + '\\oplus' : '\U00002295', + '\\bigoplus' : '\U00002a01', + '\\otimes' : '\U00002297', + '\\bigotimes' : '\U00002a02', + '\\odot' : '\U00002299', + '\\bigodot' : '\U00002a00', + '\\ominus' : '\U00002296', + '\\oslash' : '\U00002298', + '\\dots' : '\U00002026', + '\\cdots' : '\U000022ef', + '\\sum' : '\U00002211', + '\\prod' : '\U0000220f', + '\\coprod' : '\U00002210', + '\\infty' : '\U0000221e', + '\\int' : '\U0000222b', + '\\oint' : '\U0000222e', + '\\clubsuit' : '\U00002663', + '\\diamondsuit' : '\U00002662', + '\\heartsuit' : '\U00002661', + '\\spadesuit' : '\U00002660', + '\\aleph' : '\U00002135', + '\\emptyset' : '\U00002205', + '\\nabla' : '\U00002207', + '\\partial' : '\U00002202', + '\\flat' : '\U0000266d', + '\\natural' : '\U0000266e', + '\\sharp' : '\U0000266f', + '\\angle' : '\U00002220', + '\\copyright' : '\U000000a9', + '\\textregistered' : '\U000000ae', + '\\textonequarter' : '\U000000bc', + '\\textonehalf' : '\U000000bd', + '\\textthreequarters' : '\U000000be', + '\\textordfeminine' : '\U000000aa', + '\\textordmasculine' : '\U000000ba', + '\\euro' : '\U000020ac', + '\\pounds' : '\U000000a3', + '\\yen' : '\U000000a5', + '\\textcent' : '\U000000a2', + '\\textcurrency' : '\U000000a4', + '\\textdegree' : '\U000000b0', + } + + isabelle_symbols = { + '\\' : '\U0001d7ec', + '\\' : '\U0001d7ed', + '\\' : '\U0001d7ee', + '\\' : '\U0001d7ef', + '\\' : '\U0001d7f0', + '\\' : '\U0001d7f1', + '\\' : '\U0001d7f2', + '\\' : '\U0001d7f3', + '\\' : '\U0001d7f4', + '\\' : '\U0001d7f5', + '\\' : '\U0001d49c', + '\\' : '\U0000212c', + '\\' : '\U0001d49e', + '\\' : '\U0001d49f', + '\\' : '\U00002130', + '\\' : '\U00002131', + '\\' : '\U0001d4a2', + '\\' : '\U0000210b', + '\\' : '\U00002110', + '\\' : '\U0001d4a5', + '\\' : '\U0001d4a6', + '\\' : '\U00002112', + '\\' : '\U00002133', + '\\' : '\U0001d4a9', + '\\' : '\U0001d4aa', + '\\

' : '\U0001d5c9', + '\\' : '\U0001d5ca', + '\\' : '\U0001d5cb', + '\\' : '\U0001d5cc', + '\\' : '\U0001d5cd', + '\\' : '\U0001d5ce', + '\\' : '\U0001d5cf', + '\\' : '\U0001d5d0', + '\\' : '\U0001d5d1', + '\\' : '\U0001d5d2', + '\\' : '\U0001d5d3', + '\\' : '\U0001d504', + '\\' : '\U0001d505', + '\\' : '\U0000212d', + '\\

' : '\U0001d507', + '\\' : '\U0001d508', + '\\' : '\U0001d509', + '\\' : '\U0001d50a', + '\\' : '\U0000210c', + '\\' : '\U00002111', + '\\' : '\U0001d50d', + '\\' : '\U0001d50e', + '\\' : '\U0001d50f', + '\\' : '\U0001d510', + '\\' : '\U0001d511', + '\\' : '\U0001d512', + '\\' : '\U0001d513', + '\\' : '\U0001d514', + '\\' : '\U0000211c', + '\\' : '\U0001d516', + '\\' : '\U0001d517', + '\\' : '\U0001d518', + '\\' : '\U0001d519', + '\\' : '\U0001d51a', + '\\' : '\U0001d51b', + '\\' : '\U0001d51c', + '\\' : '\U00002128', + '\\' : '\U0001d51e', + '\\' : '\U0001d51f', + '\\' : '\U0001d520', + '\\
' : '\U0001d521', + '\\' : '\U0001d522', + '\\' : '\U0001d523', + '\\' : '\U0001d524', + '\\' : '\U0001d525', + '\\' : '\U0001d526', + '\\' : '\U0001d527', + '\\' : '\U0001d528', + '\\' : '\U0001d529', + '\\' : '\U0001d52a', + '\\' : '\U0001d52b', + '\\' : '\U0001d52c', + '\\' : '\U0001d52d', + '\\' : '\U0001d52e', + '\\' : '\U0001d52f', + '\\' : '\U0001d530', + '\\' : '\U0001d531', + '\\' : '\U0001d532', + '\\' : '\U0001d533', + '\\' : '\U0001d534', + '\\' : '\U0001d535', + '\\' : '\U0001d536', + '\\' : '\U0001d537', + '\\' : '\U000003b1', + '\\' : '\U000003b2', + '\\' : '\U000003b3', + '\\' : '\U000003b4', + '\\' : '\U000003b5', + '\\' : '\U000003b6', + '\\' : '\U000003b7', + '\\' : '\U000003b8', + '\\' : '\U000003b9', + '\\' : '\U000003ba', + '\\' : '\U000003bb', + '\\' : '\U000003bc', + '\\' : '\U000003bd', + '\\' : '\U000003be', + '\\' : '\U000003c0', + '\\' : '\U000003c1', + '\\' : '\U000003c3', + '\\' : '\U000003c4', + '\\' : '\U000003c5', + '\\' : '\U000003c6', + '\\' : '\U000003c7', + '\\' : '\U000003c8', + '\\' : '\U000003c9', + '\\' : '\U00000393', + '\\' : '\U00000394', + '\\' : '\U00000398', + '\\' : '\U0000039b', + '\\' : '\U0000039e', + '\\' : '\U000003a0', + '\\' : '\U000003a3', + '\\' : '\U000003a5', + '\\' : '\U000003a6', + '\\' : '\U000003a8', + '\\' : '\U000003a9', + '\\' : '\U0001d539', + '\\' : '\U00002102', + '\\' : '\U00002115', + '\\' : '\U0000211a', + '\\' : '\U0000211d', + '\\' : '\U00002124', + '\\' : '\U00002190', + '\\' : '\U000027f5', + '\\' : '\U00002192', + '\\' : '\U000027f6', + '\\' : '\U000021d0', + '\\' : '\U000027f8', + '\\' : '\U000021d2', + '\\' : '\U000027f9', + '\\' : '\U00002194', + '\\' : '\U000027f7', + '\\' : '\U000021d4', + '\\' : '\U000027fa', + '\\' : '\U000021a6', + '\\' : '\U000027fc', + '\\' : '\U00002500', + '\\' : '\U00002550', + '\\' : '\U000021a9', + '\\' : '\U000021aa', + '\\' : '\U000021bd', + '\\' : '\U000021c1', + '\\' : '\U000021bc', + '\\' : '\U000021c0', + '\\' : '\U000021cc', + '\\' : '\U0000219d', + '\\' : '\U000021c3', + '\\' : '\U000021c2', + '\\' : '\U000021bf', + '\\' : '\U000021be', + '\\' : '\U000021be', + '\\' : '\U00002237', + '\\' : '\U00002191', + '\\' : '\U000021d1', + '\\' : '\U00002193', + '\\' : '\U000021d3', + '\\' : '\U00002195', + '\\' : '\U000021d5', + '\\' : '\U000027e8', + '\\' : '\U000027e9', + '\\' : '\U00002308', + '\\' : '\U00002309', + '\\' : '\U0000230a', + '\\' : '\U0000230b', + '\\' : '\U00002987', + '\\' : '\U00002988', + '\\' : '\U000027e6', + '\\' : '\U000027e7', + '\\' : '\U00002983', + '\\' : '\U00002984', + '\\' : '\U000000ab', + '\\' : '\U000000bb', + '\\' : '\U000022a5', + '\\' : '\U000022a4', + '\\' : '\U00002227', + '\\' : '\U000022c0', + '\\' : '\U00002228', + '\\' : '\U000022c1', + '\\' : '\U00002200', + '\\' : '\U00002203', + '\\' : '\U00002204', + '\\' : '\U000000ac', + '\\' : '\U000025a1', + '\\' : '\U000025c7', + '\\' : '\U000022a2', + '\\' : '\U000022a8', + '\\' : '\U000022a9', + '\\' : '\U000022ab', + '\\' : '\U000022a3', + '\\' : '\U0000221a', + '\\' : '\U00002264', + '\\' : '\U00002265', + '\\' : '\U0000226a', + '\\' : '\U0000226b', + '\\' : '\U00002272', + '\\' : '\U00002273', + '\\' : '\U00002a85', + '\\' : '\U00002a86', + '\\' : '\U00002208', + '\\' : '\U00002209', + '\\' : '\U00002282', + '\\' : '\U00002283', + '\\' : '\U00002286', + '\\' : '\U00002287', + '\\' : '\U0000228f', + '\\' : '\U00002290', + '\\' : '\U00002291', + '\\' : '\U00002292', + '\\' : '\U00002229', + '\\' : '\U000022c2', + '\\' : '\U0000222a', + '\\' : '\U000022c3', + '\\' : '\U00002294', + '\\' : '\U00002a06', + '\\' : '\U00002293', + '\\' : '\U00002a05', + '\\' : '\U00002216', + '\\' : '\U0000221d', + '\\' : '\U0000228e', + '\\' : '\U00002a04', + '\\' : '\U00002260', + '\\' : '\U0000223c', + '\\' : '\U00002250', + '\\' : '\U00002243', + '\\' : '\U00002248', + '\\' : '\U0000224d', + '\\' : '\U00002245', + '\\' : '\U00002323', + '\\' : '\U00002261', + '\\' : '\U00002322', + '\\' : '\U000022c8', + '\\' : '\U00002a1d', + '\\' : '\U0000227a', + '\\' : '\U0000227b', + '\\' : '\U0000227c', + '\\' : '\U0000227d', + '\\' : '\U00002225', + '\\' : '\U000000a6', + '\\' : '\U000000b1', + '\\' : '\U00002213', + '\\' : '\U000000d7', + '\\
' : '\U000000f7', + '\\' : '\U000022c5', + '\\' : '\U000022c6', + '\\' : '\U00002219', + '\\' : '\U00002218', + '\\' : '\U00002020', + '\\' : '\U00002021', + '\\' : '\U000022b2', + '\\' : '\U000022b3', + '\\' : '\U000022b4', + '\\' : '\U000022b5', + '\\' : '\U000025c3', + '\\' : '\U000025b9', + '\\' : '\U000025b3', + '\\' : '\U0000225c', + '\\' : '\U00002295', + '\\' : '\U00002a01', + '\\' : '\U00002297', + '\\' : '\U00002a02', + '\\' : '\U00002299', + '\\' : '\U00002a00', + '\\' : '\U00002296', + '\\' : '\U00002298', + '\\' : '\U00002026', + '\\' : '\U000022ef', + '\\' : '\U00002211', + '\\' : '\U0000220f', + '\\' : '\U00002210', + '\\' : '\U0000221e', + '\\' : '\U0000222b', + '\\' : '\U0000222e', + '\\' : '\U00002663', + '\\' : '\U00002662', + '\\' : '\U00002661', + '\\' : '\U00002660', + '\\' : '\U00002135', + '\\' : '\U00002205', + '\\' : '\U00002207', + '\\' : '\U00002202', + '\\' : '\U0000266d', + '\\' : '\U0000266e', + '\\' : '\U0000266f', + '\\' : '\U00002220', + '\\' : '\U000000a9', + '\\' : '\U000000ae', + '\\' : '\U000000ad', + '\\' : '\U000000af', + '\\' : '\U000000bc', + '\\' : '\U000000bd', + '\\' : '\U000000be', + '\\' : '\U000000aa', + '\\' : '\U000000ba', + '\\
' : '\U000000a7', + '\\' : '\U000000b6', + '\\' : '\U000000a1', + '\\' : '\U000000bf', + '\\' : '\U000020ac', + '\\' : '\U000000a3', + '\\' : '\U000000a5', + '\\' : '\U000000a2', + '\\' : '\U000000a4', + '\\' : '\U000000b0', + '\\' : '\U00002a3f', + '\\' : '\U00002127', + '\\' : '\U000025ca', + '\\' : '\U00002118', + '\\' : '\U00002240', + '\\' : '\U000022c4', + '\\' : '\U000000b4', + '\\' : '\U00000131', + '\\' : '\U000000a8', + '\\' : '\U000000b8', + '\\' : '\U000002dd', + '\\' : '\U000003f5', + '\\' : '\U000023ce', + '\\' : '\U00002039', + '\\' : '\U0000203a', + '\\' : '\U00002302', + '\\<^sub>' : '\U000021e9', + '\\<^sup>' : '\U000021e7', + '\\<^bold>' : '\U00002759', + '\\<^bsub>' : '\U000021d8', + '\\<^esub>' : '\U000021d9', + '\\<^bsup>' : '\U000021d7', + '\\<^esup>' : '\U000021d6', + } + + lang_map = {'isabelle' : isabelle_symbols, 'latex' : latex_symbols} + + def __init__(self, **options): + Filter.__init__(self, **options) + lang = get_choice_opt(options, 'lang', + ['isabelle', 'latex'], 'isabelle') + self.symbols = self.lang_map[lang] + + def filter(self, lexer, stream): + for ttype, value in stream: + if value in self.symbols: + yield ttype, self.symbols[value] + else: + yield ttype, value + + +class KeywordCaseFilter(Filter): + """Convert keywords to lowercase or uppercase or capitalize them. + + This means first letter uppercase, rest lowercase. + + This can be useful e.g. if you highlight Pascal code and want to adapt the + code to your styleguide. + + Options accepted: + + `case` : string + The casing to convert keywords to. Must be one of ``'lower'``, + ``'upper'`` or ``'capitalize'``. The default is ``'lower'``. + """ + + def __init__(self, **options): + Filter.__init__(self, **options) + case = get_choice_opt(options, 'case', + ['lower', 'upper', 'capitalize'], 'lower') + self.convert = getattr(str, case) + + def filter(self, lexer, stream): + for ttype, value in stream: + if ttype in Keyword: + yield ttype, self.convert(value) + else: + yield ttype, value + + +class NameHighlightFilter(Filter): + """Highlight a normal Name (and Name.*) token with a different token type. + + Example:: + + filter = NameHighlightFilter( + names=['foo', 'bar', 'baz'], + tokentype=Name.Function, + ) + + This would highlight the names "foo", "bar" and "baz" + as functions. `Name.Function` is the default token type. + + Options accepted: + + `names` : list of strings + A list of names that should be given the different token type. + There is no default. + `tokentype` : TokenType or string + A token type or a string containing a token type name that is + used for highlighting the strings in `names`. The default is + `Name.Function`. + """ + + def __init__(self, **options): + Filter.__init__(self, **options) + self.names = set(get_list_opt(options, 'names', [])) + tokentype = options.get('tokentype') + if tokentype: + self.tokentype = string_to_tokentype(tokentype) + else: + self.tokentype = Name.Function + + def filter(self, lexer, stream): + for ttype, value in stream: + if ttype in Name and value in self.names: + yield self.tokentype, value + else: + yield ttype, value + + +class ErrorToken(Exception): + pass + + +class RaiseOnErrorTokenFilter(Filter): + """Raise an exception when the lexer generates an error token. + + Options accepted: + + `excclass` : Exception class + The exception class to raise. + The default is `pygments.filters.ErrorToken`. + + .. versionadded:: 0.8 + """ + + def __init__(self, **options): + Filter.__init__(self, **options) + self.exception = options.get('excclass', ErrorToken) + try: + # issubclass() will raise TypeError if first argument is not a class + if not issubclass(self.exception, Exception): + raise TypeError + except TypeError: + raise OptionError('excclass option is not an exception class') + + def filter(self, lexer, stream): + for ttype, value in stream: + if ttype is Error: + raise self.exception(value) + yield ttype, value + + +class VisibleWhitespaceFilter(Filter): + """Convert tabs, newlines and/or spaces to visible characters. + + Options accepted: + + `spaces` : string or bool + If this is a one-character string, spaces will be replaces by this string. + If it is another true value, spaces will be replaced by ``·`` (unicode + MIDDLE DOT). If it is a false value, spaces will not be replaced. The + default is ``False``. + `tabs` : string or bool + The same as for `spaces`, but the default replacement character is ``»`` + (unicode RIGHT-POINTING DOUBLE ANGLE QUOTATION MARK). The default value + is ``False``. Note: this will not work if the `tabsize` option for the + lexer is nonzero, as tabs will already have been expanded then. + `tabsize` : int + If tabs are to be replaced by this filter (see the `tabs` option), this + is the total number of characters that a tab should be expanded to. + The default is ``8``. + `newlines` : string or bool + The same as for `spaces`, but the default replacement character is ``¶`` + (unicode PILCROW SIGN). The default value is ``False``. + `wstokentype` : bool + If true, give whitespace the special `Whitespace` token type. This allows + styling the visible whitespace differently (e.g. greyed out), but it can + disrupt background colors. The default is ``True``. + + .. versionadded:: 0.8 + """ + + def __init__(self, **options): + Filter.__init__(self, **options) + for name, default in [('spaces', '·'), + ('tabs', '»'), + ('newlines', '¶')]: + opt = options.get(name, False) + if isinstance(opt, str) and len(opt) == 1: + setattr(self, name, opt) + else: + setattr(self, name, (opt and default or '')) + tabsize = get_int_opt(options, 'tabsize', 8) + if self.tabs: + self.tabs += ' ' * (tabsize - 1) + if self.newlines: + self.newlines += '\n' + self.wstt = get_bool_opt(options, 'wstokentype', True) + + def filter(self, lexer, stream): + if self.wstt: + spaces = self.spaces or ' ' + tabs = self.tabs or '\t' + newlines = self.newlines or '\n' + regex = re.compile(r'\s') + + def replacefunc(wschar): + if wschar == ' ': + return spaces + elif wschar == '\t': + return tabs + elif wschar == '\n': + return newlines + return wschar + + for ttype, value in stream: + yield from _replace_special(ttype, value, regex, Whitespace, + replacefunc) + else: + spaces, tabs, newlines = self.spaces, self.tabs, self.newlines + # simpler processing + for ttype, value in stream: + if spaces: + value = value.replace(' ', spaces) + if tabs: + value = value.replace('\t', tabs) + if newlines: + value = value.replace('\n', newlines) + yield ttype, value + + +class GobbleFilter(Filter): + """Gobble source code lines (eats initial characters). + + This filter drops the first ``n`` characters off every line of code. This + may be useful when the source code fed to the lexer is indented by a fixed + amount of space that isn't desired in the output. + + Options accepted: + + `n` : int + The number of characters to gobble. + + .. versionadded:: 1.2 + """ + def __init__(self, **options): + Filter.__init__(self, **options) + self.n = get_int_opt(options, 'n', 0) + + def gobble(self, value, left): + if left < len(value): + return value[left:], 0 + else: + return '', left - len(value) + + def filter(self, lexer, stream): + n = self.n + left = n # How many characters left to gobble. + for ttype, value in stream: + # Remove ``left`` tokens from first line, ``n`` from all others. + parts = value.split('\n') + (parts[0], left) = self.gobble(parts[0], left) + for i in range(1, len(parts)): + (parts[i], left) = self.gobble(parts[i], n) + value = '\n'.join(parts) + + if value != '': + yield ttype, value + + +class TokenMergeFilter(Filter): + """Merge consecutive tokens with the same token type in the output stream. + + .. versionadded:: 1.2 + """ + def __init__(self, **options): + Filter.__init__(self, **options) + + def filter(self, lexer, stream): + current_type = None + current_value = None + for ttype, value in stream: + if ttype is current_type: + current_value += value + else: + if current_type is not None: + yield current_type, current_value + current_type = ttype + current_value = value + if current_type is not None: + yield current_type, current_value + + +FILTERS = { + 'codetagify': CodeTagFilter, + 'keywordcase': KeywordCaseFilter, + 'highlight': NameHighlightFilter, + 'raiseonerror': RaiseOnErrorTokenFilter, + 'whitespace': VisibleWhitespaceFilter, + 'gobble': GobbleFilter, + 'tokenmerge': TokenMergeFilter, + 'symbols': SymbolFilter, +} diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatter.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatter.py new file mode 100644 index 0000000000000000000000000000000000000000..4fa603d551c99fab81f2aa29037c3b8b89d0fcd5 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatter.py @@ -0,0 +1,129 @@ +""" + pygments.formatter + ~~~~~~~~~~~~~~~~~~ + + Base formatter class. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import codecs + +from pygments.util import get_bool_opt +from pygments.styles import get_style_by_name + +__all__ = ['Formatter'] + + +def _lookup_style(style): + if isinstance(style, str): + return get_style_by_name(style) + return style + + +class Formatter: + """ + Converts a token stream to text. + + Formatters should have attributes to help selecting them. These + are similar to the corresponding :class:`~pygments.lexer.Lexer` + attributes. + + .. autoattribute:: name + :no-value: + + .. autoattribute:: aliases + :no-value: + + .. autoattribute:: filenames + :no-value: + + You can pass options as keyword arguments to the constructor. + All formatters accept these basic options: + + ``style`` + The style to use, can be a string or a Style subclass + (default: "default"). Not used by e.g. the + TerminalFormatter. + ``full`` + Tells the formatter to output a "full" document, i.e. + a complete self-contained document. This doesn't have + any effect for some formatters (default: false). + ``title`` + If ``full`` is true, the title that should be used to + caption the document (default: ''). + ``encoding`` + If given, must be an encoding name. This will be used to + convert the Unicode token strings to byte strings in the + output. If it is "" or None, Unicode strings will be written + to the output file, which most file-like objects do not + support (default: None). + ``outencoding`` + Overrides ``encoding`` if given. + + """ + + #: Full name for the formatter, in human-readable form. + name = None + + #: A list of short, unique identifiers that can be used to lookup + #: the formatter from a list, e.g. using :func:`.get_formatter_by_name()`. + aliases = [] + + #: A list of fnmatch patterns that match filenames for which this + #: formatter can produce output. The patterns in this list should be unique + #: among all formatters. + filenames = [] + + #: If True, this formatter outputs Unicode strings when no encoding + #: option is given. + unicodeoutput = True + + def __init__(self, **options): + """ + As with lexers, this constructor takes arbitrary optional arguments, + and if you override it, you should first process your own options, then + call the base class implementation. + """ + self.style = _lookup_style(options.get('style', 'default')) + self.full = get_bool_opt(options, 'full', False) + self.title = options.get('title', '') + self.encoding = options.get('encoding', None) or None + if self.encoding in ('guess', 'chardet'): + # can happen for e.g. pygmentize -O encoding=guess + self.encoding = 'utf-8' + self.encoding = options.get('outencoding') or self.encoding + self.options = options + + def get_style_defs(self, arg=''): + """ + This method must return statements or declarations suitable to define + the current style for subsequent highlighted text (e.g. CSS classes + in the `HTMLFormatter`). + + The optional argument `arg` can be used to modify the generation and + is formatter dependent (it is standardized because it can be given on + the command line). + + This method is called by the ``-S`` :doc:`command-line option `, + the `arg` is then given by the ``-a`` option. + """ + return '' + + def format(self, tokensource, outfile): + """ + This method must format the tokens from the `tokensource` iterable and + write the formatted version to the file object `outfile`. + + Formatter options can control how exactly the tokens are converted. + """ + if self.encoding: + # wrap the outfile in a StreamWriter + outfile = codecs.lookup(self.encoding)[3](outfile) + return self.format_unencoded(tokensource, outfile) + + # Allow writing Formatter[str] or Formatter[bytes]. That's equivalent to + # Formatter. This helps when using third-party type stubs from typeshed. + def __class_getitem__(cls, name): + return cls diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/__init__.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..829496dc97e147d159e106ddc12a49e8e416faf1 --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/__init__.py @@ -0,0 +1,157 @@ +""" + pygments.formatters + ~~~~~~~~~~~~~~~~~~~ + + Pygments formatters. + + :copyright: Copyright 2006-present by the Pygments team, see AUTHORS. + :license: BSD, see LICENSE for details. +""" + +import re +import sys +import types +import fnmatch +from os.path import basename + +from pygments.formatters._mapping import FORMATTERS +from pygments.plugin import find_plugin_formatters +from pygments.util import ClassNotFound + +__all__ = ['get_formatter_by_name', 'get_formatter_for_filename', + 'get_all_formatters', 'load_formatter_from_file'] + list(FORMATTERS) + +_formatter_cache = {} # classes by name +_pattern_cache = {} + + +def _fn_matches(fn, glob): + """Return whether the supplied file name fn matches pattern filename.""" + if glob not in _pattern_cache: + pattern = _pattern_cache[glob] = re.compile(fnmatch.translate(glob)) + return pattern.match(fn) + return _pattern_cache[glob].match(fn) + + +def _load_formatters(module_name): + """Load a formatter (and all others in the module too).""" + mod = __import__(module_name, None, None, ['__all__']) + for formatter_name in mod.__all__: + cls = getattr(mod, formatter_name) + _formatter_cache[cls.name] = cls + + +def get_all_formatters(): + """Return a generator for all formatter classes.""" + # NB: this returns formatter classes, not info like get_all_lexers(). + for info in FORMATTERS.values(): + if info[1] not in _formatter_cache: + _load_formatters(info[0]) + yield _formatter_cache[info[1]] + for _, formatter in find_plugin_formatters(): + yield formatter + + +def find_formatter_class(alias): + """Lookup a formatter by alias. + + Returns None if not found. + """ + for module_name, name, aliases, _, _ in FORMATTERS.values(): + if alias in aliases: + if name not in _formatter_cache: + _load_formatters(module_name) + return _formatter_cache[name] + for _, cls in find_plugin_formatters(): + if alias in cls.aliases: + return cls + + +def get_formatter_by_name(_alias, **options): + """ + Return an instance of a :class:`.Formatter` subclass that has `alias` in its + aliases list. The formatter is given the `options` at its instantiation. + + Will raise :exc:`pygments.util.ClassNotFound` if no formatter with that + alias is found. + """ + cls = find_formatter_class(_alias) + if cls is None: + raise ClassNotFound(f"no formatter found for name {_alias!r}") + return cls(**options) + + +def load_formatter_from_file(filename, formattername="CustomFormatter", **options): + """ + Return a `Formatter` subclass instance loaded from the provided file, relative + to the current directory. + + The file is expected to contain a Formatter class named ``formattername`` + (by default, CustomFormatter). Users should be very careful with the input, because + this method is equivalent to running ``eval()`` on the input file. The formatter is + given the `options` at its instantiation. + + :exc:`pygments.util.ClassNotFound` is raised if there are any errors loading + the formatter. + + .. versionadded:: 2.2 + """ + try: + # This empty dict will contain the namespace for the exec'd file + custom_namespace = {} + with open(filename, 'rb') as f: + exec(f.read(), custom_namespace) + # Retrieve the class `formattername` from that namespace + if formattername not in custom_namespace: + raise ClassNotFound(f'no valid {formattername} class found in {filename}') + formatter_class = custom_namespace[formattername] + # And finally instantiate it with the options + return formatter_class(**options) + except OSError as err: + raise ClassNotFound(f'cannot read {filename}: {err}') + except ClassNotFound: + raise + except Exception as err: + raise ClassNotFound(f'error when loading custom formatter: {err}') + + +def get_formatter_for_filename(fn, **options): + """ + Return a :class:`.Formatter` subclass instance that has a filename pattern + matching `fn`. The formatter is given the `options` at its instantiation. + + Will raise :exc:`pygments.util.ClassNotFound` if no formatter for that filename + is found. + """ + fn = basename(fn) + for modname, name, _, filenames, _ in FORMATTERS.values(): + for filename in filenames: + if _fn_matches(fn, filename): + if name not in _formatter_cache: + _load_formatters(modname) + return _formatter_cache[name](**options) + for _name, cls in find_plugin_formatters(): + for filename in cls.filenames: + if _fn_matches(fn, filename): + return cls(**options) + raise ClassNotFound(f"no formatter found for file name {fn!r}") + + +class _automodule(types.ModuleType): + """Automatically import formatters.""" + + def __getattr__(self, name): + info = FORMATTERS.get(name) + if info: + _load_formatters(info[0]) + cls = _formatter_cache[info[1]] + setattr(self, name, cls) + return cls + raise AttributeError(name) + + +oldmod = sys.modules[__name__] +newmod = _automodule(__name__) +newmod.__dict__.update(oldmod.__dict__) +sys.modules[__name__] = newmod +del newmod.newmod, newmod.oldmod, newmod.sys, newmod.types diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/_mapping.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/_mapping.py new file mode 100644 index 0000000000000000000000000000000000000000..72ca84040b626183e3328679db600c13472021be --- /dev/null +++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/_mapping.py @@ -0,0 +1,23 @@ +# Automatically generated by scripts/gen_mapfiles.py. +# DO NOT EDIT BY HAND; run `tox -e mapfiles` instead. + +FORMATTERS = { + 'BBCodeFormatter': ('pygments.formatters.bbcode', 'BBCode', ('bbcode', 'bb'), (), 'Format tokens with BBcodes. These formatting codes are used by many bulletin boards, so you can highlight your sourcecode with pygments before posting it there.'), + 'BmpImageFormatter': ('pygments.formatters.img', 'img_bmp', ('bmp', 'bitmap'), ('*.bmp',), 'Create a bitmap image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'), + 'GifImageFormatter': ('pygments.formatters.img', 'img_gif', ('gif',), ('*.gif',), 'Create a GIF image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'), + 'GroffFormatter': ('pygments.formatters.groff', 'groff', ('groff', 'troff', 'roff'), (), 'Format tokens with groff escapes to change their color and font style.'), + 'HtmlFormatter': ('pygments.formatters.html', 'HTML', ('html',), ('*.html', '*.htm'), "Format tokens as HTML 4 ```` tags. By default, the content is enclosed in a ``
`` tag, itself wrapped in a ``
`` tag (but see the `nowrap` option). The ``
``'s CSS class can be set by the `cssclass` option."),
+    'IRCFormatter': ('pygments.formatters.irc', 'IRC', ('irc', 'IRC'), (), 'Format tokens with IRC color sequences'),
+    'ImageFormatter': ('pygments.formatters.img', 'img', ('img', 'IMG', 'png'), ('*.png',), 'Create a PNG image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'),
+    'JpgImageFormatter': ('pygments.formatters.img', 'img_jpg', ('jpg', 'jpeg'), ('*.jpg',), 'Create a JPEG image from source code. This uses the Python Imaging Library to generate a pixmap from the source code.'),
+    'LatexFormatter': ('pygments.formatters.latex', 'LaTeX', ('latex', 'tex'), ('*.tex',), 'Format tokens as LaTeX code. This needs the `fancyvrb` and `color` standard packages.'),
+    'NullFormatter': ('pygments.formatters.other', 'Text only', ('text', 'null'), ('*.txt',), 'Output the text unchanged without any formatting.'),
+    'PangoMarkupFormatter': ('pygments.formatters.pangomarkup', 'Pango Markup', ('pango', 'pangomarkup'), (), 'Format tokens as Pango Markup code. It can then be rendered to an SVG.'),
+    'RawTokenFormatter': ('pygments.formatters.other', 'Raw tokens', ('raw', 'tokens'), ('*.raw',), 'Format tokens as a raw representation for storing token streams.'),
+    'RtfFormatter': ('pygments.formatters.rtf', 'RTF', ('rtf',), ('*.rtf',), 'Format tokens as RTF markup. This formatter automatically outputs full RTF documents with color information and other useful stuff. Perfect for Copy and Paste into Microsoft(R) Word(R) documents.'),
+    'SvgFormatter': ('pygments.formatters.svg', 'SVG', ('svg',), ('*.svg',), 'Format tokens as an SVG graphics file.  This formatter is still experimental. Each line of code is a ```` element with explicit ``x`` and ``y`` coordinates containing ```` elements with the individual token styles.'),
+    'Terminal256Formatter': ('pygments.formatters.terminal256', 'Terminal256', ('terminal256', 'console256', '256'), (), 'Format tokens with ANSI color sequences, for output in a 256-color terminal or console.  Like in `TerminalFormatter` color sequences are terminated at newlines, so that paging the output works correctly.'),
+    'TerminalFormatter': ('pygments.formatters.terminal', 'Terminal', ('terminal', 'console'), (), 'Format tokens with ANSI color sequences, for output in a text console. Color sequences are terminated at newlines, so that paging the output works correctly.'),
+    'TerminalTrueColorFormatter': ('pygments.formatters.terminal256', 'TerminalTrueColor', ('terminal16m', 'console16m', '16m'), (), 'Format tokens with ANSI color sequences, for output in a true-color terminal or console.  Like in `TerminalFormatter` color sequences are terminated at newlines, so that paging the output works correctly.'),
+    'TestcaseFormatter': ('pygments.formatters.other', 'Testcase', ('testcase',), (), 'Format tokens as appropriate for a new testcase.'),
+}
diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/bbcode.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/bbcode.py
new file mode 100644
index 0000000000000000000000000000000000000000..90d8d806f202e9dff22e33c431f3e35cefdd6642
--- /dev/null
+++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/bbcode.py
@@ -0,0 +1,108 @@
+"""
+    pygments.formatters.bbcode
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+    BBcode formatter.
+
+    :copyright: Copyright 2006-present by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+
+from pygments.formatter import Formatter
+from pygments.util import get_bool_opt
+
+__all__ = ['BBCodeFormatter']
+
+
+class BBCodeFormatter(Formatter):
+    """
+    Format tokens with BBcodes. These formatting codes are used by many
+    bulletin boards, so you can highlight your sourcecode with pygments before
+    posting it there.
+
+    This formatter has no support for background colors and borders, as there
+    are no common BBcode tags for that.
+
+    Some board systems (e.g. phpBB) don't support colors in their [code] tag,
+    so you can't use the highlighting together with that tag.
+    Text in a [code] tag usually is shown with a monospace font (which this
+    formatter can do with the ``monofont`` option) and no spaces (which you
+    need for indentation) are removed.
+
+    Additional options accepted:
+
+    `style`
+        The style to use, can be a string or a Style subclass (default:
+        ``'default'``).
+
+    `codetag`
+        If set to true, put the output into ``[code]`` tags (default:
+        ``false``)
+
+    `monofont`
+        If set to true, add a tag to show the code with a monospace font
+        (default: ``false``).
+    """
+    name = 'BBCode'
+    aliases = ['bbcode', 'bb']
+    filenames = []
+
+    def __init__(self, **options):
+        Formatter.__init__(self, **options)
+        self._code = get_bool_opt(options, 'codetag', False)
+        self._mono = get_bool_opt(options, 'monofont', False)
+
+        self.styles = {}
+        self._make_styles()
+
+    def _make_styles(self):
+        for ttype, ndef in self.style:
+            start = end = ''
+            if ndef['color']:
+                start += '[color=#{}]'.format(ndef['color'])
+                end = '[/color]' + end
+            if ndef['bold']:
+                start += '[b]'
+                end = '[/b]' + end
+            if ndef['italic']:
+                start += '[i]'
+                end = '[/i]' + end
+            if ndef['underline']:
+                start += '[u]'
+                end = '[/u]' + end
+            # there are no common BBcodes for background-color and border
+
+            self.styles[ttype] = start, end
+
+    def format_unencoded(self, tokensource, outfile):
+        if self._code:
+            outfile.write('[code]')
+        if self._mono:
+            outfile.write('[font=monospace]')
+
+        lastval = ''
+        lasttype = None
+
+        for ttype, value in tokensource:
+            while ttype not in self.styles:
+                ttype = ttype.parent
+            if ttype == lasttype:
+                lastval += value
+            else:
+                if lastval:
+                    start, end = self.styles[lasttype]
+                    outfile.write(''.join((start, lastval, end)))
+                lastval = value
+                lasttype = ttype
+
+        if lastval:
+            start, end = self.styles[lasttype]
+            outfile.write(''.join((start, lastval, end)))
+
+        if self._mono:
+            outfile.write('[/font]')
+        if self._code:
+            outfile.write('[/code]')
+        if self._code or self._mono:
+            outfile.write('\n')
diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/groff.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/groff.py
new file mode 100644
index 0000000000000000000000000000000000000000..f5d3146b45b0103dbad53aa2822757b8102d1c42
--- /dev/null
+++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/groff.py
@@ -0,0 +1,170 @@
+"""
+    pygments.formatters.groff
+    ~~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Formatter for groff output.
+
+    :copyright: Copyright 2006-present by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import math
+from pygments.formatter import Formatter
+from pygments.util import get_bool_opt, get_int_opt
+
+__all__ = ['GroffFormatter']
+
+
+class GroffFormatter(Formatter):
+    """
+    Format tokens with groff escapes to change their color and font style.
+
+    .. versionadded:: 2.11
+
+    Additional options accepted:
+
+    `style`
+        The style to use, can be a string or a Style subclass (default:
+        ``'default'``).
+
+    `monospaced`
+        If set to true, monospace font will be used (default: ``true``).
+
+    `linenos`
+        If set to true, print the line numbers (default: ``false``).
+
+    `wrap`
+        Wrap lines to the specified number of characters. Disabled if set to 0
+        (default: ``0``).
+    """
+
+    name = 'groff'
+    aliases = ['groff','troff','roff']
+    filenames = []
+
+    def __init__(self, **options):
+        Formatter.__init__(self, **options)
+
+        self.monospaced = get_bool_opt(options, 'monospaced', True)
+        self.linenos = get_bool_opt(options, 'linenos', False)
+        self._lineno = 0
+        self.wrap = get_int_opt(options, 'wrap', 0)
+        self._linelen = 0
+
+        self.styles = {}
+        self._make_styles()
+
+
+    def _make_styles(self):
+        regular = '\\f[CR]' if self.monospaced else '\\f[R]'
+        bold = '\\f[CB]' if self.monospaced else '\\f[B]'
+        italic = '\\f[CI]' if self.monospaced else '\\f[I]'
+
+        for ttype, ndef in self.style:
+            start = end = ''
+            if ndef['color']:
+                start += '\\m[{}]'.format(ndef['color'])
+                end = '\\m[]' + end
+            if ndef['bold']:
+                start += bold
+                end = regular + end
+            if ndef['italic']:
+                start += italic
+                end = regular + end
+            if ndef['bgcolor']:
+                start += '\\M[{}]'.format(ndef['bgcolor'])
+                end = '\\M[]' + end
+
+            self.styles[ttype] = start, end
+
+
+    def _define_colors(self, outfile):
+        colors = set()
+        for _, ndef in self.style:
+            if ndef['color'] is not None:
+                colors.add(ndef['color'])
+
+        for color in sorted(colors):
+            outfile.write('.defcolor ' + color + ' rgb #' + color + '\n')
+
+
+    def _write_lineno(self, outfile):
+        self._lineno += 1
+        outfile.write("%s% 4d " % (self._lineno != 1 and '\n' or '', self._lineno))
+
+
+    def _wrap_line(self, line):
+        length = len(line.rstrip('\n'))
+        space = '     ' if self.linenos else ''
+        newline = ''
+
+        if length > self.wrap:
+            for i in range(0, math.floor(length / self.wrap)):
+                chunk = line[i*self.wrap:i*self.wrap+self.wrap]
+                newline += (chunk + '\n' + space)
+            remainder = length % self.wrap
+            if remainder > 0:
+                newline += line[-remainder-1:]
+                self._linelen = remainder
+        elif self._linelen + length > self.wrap:
+            newline = ('\n' + space) + line
+            self._linelen = length
+        else:
+            newline = line
+            self._linelen += length
+
+        return newline
+
+
+    def _escape_chars(self, text):
+        text = text.replace('\\', '\\[u005C]'). \
+                    replace('.', '\\[char46]'). \
+                    replace('\'', '\\[u0027]'). \
+                    replace('`', '\\[u0060]'). \
+                    replace('~', '\\[u007E]')
+        copy = text
+
+        for char in copy:
+            if len(char) != len(char.encode()):
+                uni = char.encode('unicode_escape') \
+                    .decode()[1:] \
+                    .replace('x', 'u00') \
+                    .upper()
+                text = text.replace(char, '\\[u' + uni[1:] + ']')
+
+        return text
+
+
+    def format_unencoded(self, tokensource, outfile):
+        self._define_colors(outfile)
+
+        outfile.write('.nf\n\\f[CR]\n')
+
+        if self.linenos:
+            self._write_lineno(outfile)
+
+        for ttype, value in tokensource:
+            while ttype not in self.styles:
+                ttype = ttype.parent
+            start, end = self.styles[ttype]
+
+            for line in value.splitlines(True):
+                if self.wrap > 0:
+                    line = self._wrap_line(line)
+
+                if start and end:
+                    text = self._escape_chars(line.rstrip('\n'))
+                    if text != '':
+                        outfile.write(''.join((start, text, end)))
+                else:
+                    outfile.write(self._escape_chars(line.rstrip('\n')))
+
+                if line.endswith('\n'):
+                    if self.linenos:
+                        self._write_lineno(outfile)
+                        self._linelen = 0
+                    else:
+                        outfile.write('\n')
+                        self._linelen = 0
+
+        outfile.write('\n.fi')
diff --git a/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/html.py b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/html.py
new file mode 100644
index 0000000000000000000000000000000000000000..17e67414520e016127fba1541207f23c19a0d187
--- /dev/null
+++ b/personalPromptBoost/.venv/lib/python3.13/site-packages/pygments/formatters/html.py
@@ -0,0 +1,997 @@
+"""
+    pygments.formatters.html
+    ~~~~~~~~~~~~~~~~~~~~~~~~
+
+    Formatter for HTML output.
+
+    :copyright: Copyright 2006-present by the Pygments team, see AUTHORS.
+    :license: BSD, see LICENSE for details.
+"""
+
+import functools
+import os
+import sys
+import os.path
+from io import StringIO
+
+from pygments.formatter import Formatter
+from pygments.token import Token, Text, STANDARD_TYPES
+from pygments.util import get_bool_opt, get_int_opt, get_list_opt
+
+import html
+
+try:
+    import ctags
+except ImportError:
+    ctags = None
+
+__all__ = ['HtmlFormatter']
+
+
+_escape_html_table = {
+    ord('&'): '&',
+    ord('<'): '<',
+    ord('>'): '>',
+    ord('"'): '"',
+    ord("'"): ''',
+}
+
+
+def escape_html(text, table=_escape_html_table):
+    """Escape &, <, > as well as single and double quotes for HTML."""
+    return text.translate(table)
+
+
+def webify(color):
+    if color.startswith('calc') or color.startswith('var'):
+        return color
+    else:
+        # Check if the color can be shortened from 6 to 3 characters
+        color = color.upper()
+        if (len(color) == 6 and
+            (    color[0] == color[1]
+             and color[2] == color[3]
+             and color[4] == color[5])):
+            return f'#{color[0]}{color[2]}{color[4]}'
+        else:
+            return f'#{color}'
+
+
+def _get_ttype_class(ttype):
+    fname = STANDARD_TYPES.get(ttype)
+    if fname:
+        return fname
+    aname = ''
+    while fname is None:
+        aname = '-' + ttype[-1] + aname
+        ttype = ttype.parent
+        fname = STANDARD_TYPES.get(ttype)
+    return fname + aname
+
+
+CSSFILE_TEMPLATE = '''\
+/*
+generated by Pygments 
+Copyright 2006-present by the Pygments team.
+Licensed under the BSD license, see LICENSE for details.
+*/
+%(styledefs)s
+'''
+
+DOC_HEADER = '''\
+
+
+
+
+  %(title)s
+  
+  
+
+
+
%(title)s

+
+'''
+
+DOC_HEADER_EXTERNALCSS = '''\
+
+
+
+
+  %(title)s
+  
+  
+
+
+
%(title)s

+
+'''
+
+DOC_FOOTER = '''\
+
+
+'''
+
+
+class HtmlFormatter(Formatter):
+    r"""
+    Format tokens as HTML 4 ```` tags. By default, the content is enclosed
+    in a ``
`` tag, itself wrapped in a ``
`` tag (but see the `nowrap` option).
+    The ``
``'s CSS class can be set by the `cssclass` option.
+
+    If the `linenos` option is set to ``"table"``, the ``
`` is
+    additionally wrapped inside a ```` which has one row and two
+    cells: one containing the line numbers and one containing the code.
+    Example:
+
+    .. sourcecode:: html
+
+        

+        

+          
+          
+        

+            
1
+            2

+          		
+            
def foo(bar):
+              pass
+            

+          

+
+    (whitespace added to improve clarity).
+
+    A list of lines can be specified using the `hl_lines` option to make these
+    lines highlighted (as of Pygments 0.11).
+
+    With the `full` option, a complete HTML 4 document is output, including
+    the style definitions inside a ``

' : '\U0001d4ab', + '\\' : '\U0001d4ac', + '\\' : '\U0000211b', + '\\' : '\U0001d4ae', + '\\' : '\U0001d4af', + '\\' : '\U0001d4b0', + '\\' : '\U0001d4b1', + '\\' : '\U0001d4b2', + '\\' : '\U0001d4b3', + '\\' : '\U0001d4b4', + '\\' : '\U0001d4b5', + '\\' : '\U0001d5ba', + '\\' : '\U0001d5bb', + '\\' : '\U0001d5bc', + '\\' : '\U0001d5bd', + '\\' : '\U0001d5be', + '\\' : '\U0001d5bf', + '\\' : '\U0001d5c0', + '\\' : '\U0001d5c1', + '\\' : '\U0001d5c2', + '\\' : '\U0001d5c3', + '\\' : '\U0001d5c4', + '\\' : '\U0001d5c5', + '\\' : '\U0001d5c6', + '\\' : '\U0001d5c7', + '\\' : '\U0001d5c8', + '\\