Initial public upload from Projekte/PromptBoost (part 3)

.gitattributes

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 personalPromptBoost/.venv/bin/python3.13 filter=lfs diff=lfs merge=lfs -text
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 personalPromptBoost/.venv/lib/python3.13/site-packages/httptools/parser/url_parser.cpython-313-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text
+personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic_core/_pydantic_core.cpython-313-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text

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personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/WHEEL

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+Tag: py3-none-any

personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/licenses/LICENSE

@@ -0,0 +1,3 @@
+This software is made available under the terms of *either* of the licenses
+found in LICENSE.APACHE or LICENSE.BSD. Contributions to this software is made
+under the terms of *both* these licenses.

personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/licenses/LICENSE.APACHE

@@ -0,0 +1,177 @@
+
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+                           Version 2.0, January 2004
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+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+   END OF TERMS AND CONDITIONS

personalPromptBoost/.venv/lib/python3.13/site-packages/packaging-26.1.dist-info/licenses/LICENSE.BSD

@@ -0,0 +1,23 @@
+Copyright (c) Donald Stufft and individual contributors.
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+    1. 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
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+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
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+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.

personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/INSTALLER

@@ -0,0 +1 @@
+uv

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 <holger@merlinux.eu>
+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

personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/RECORD

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personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/WHEEL

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+Generator: setuptools (80.7.1)
+Root-Is-Purelib: true
+Tag: py3-none-any
+

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.

personalPromptBoost/.venv/lib/python3.13/site-packages/pluggy-1.6.0.dist-info/top_level.txt

@@ -0,0 +1 @@
+pluggy

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

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

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>`.
+    firstresult: bool
+    #: Whether the hook is :ref:`historic <historic>`.
+    historic: bool
+    #: Whether the hook :ref:`warns when implemented <warn_on_impl>`.
+    warn_on_impl: Warning | None
+    #: Whether the hook warns when :ref:`certain arguments are requested
+    #: <warn_on_impl>`.
+    #:
+    #: .. 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 <hookwrapper>`.
+    wrapper: bool
+    #: Whether the hook implementation is an :ref:`old-style wrapper
+    #: <old_style_hookwrappers>`.
+    hookwrapper: bool
+    #: Whether validation against a hook specification is :ref:`optional
+    #: <optionalhook>`.
+    optionalhook: bool
+    #: Whether to try to order this hook implementation :ref:`first
+    #: <callorder>`.
+    tryfirst: bool
+    #: Whether to try to order this hook implementation :ref:`last
+    #: <callorder>`.
+    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 <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"<HookCaller {self.name!r}>"
+
+    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, "<temp>", 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 <hookwrapper>`.
+        self.wrapper: Final = hook_impl_opts["wrapper"]
+        #: Whether the hook implementation is an :ref:`old-style wrapper
+        #: <old_style_hookwrappers>`.
+        self.hookwrapper: Final = hook_impl_opts["hookwrapper"]
+        #: Whether validation against a hook specification is :ref:`optional
+        #: <optionalhook>`.
+        self.optionalhook: Final = hook_impl_opts["optionalhook"]
+        #: Whether to try to order this hook implementation :ref:`first
+        #: <callorder>`.
+        self.tryfirst: Final = hook_impl_opts["tryfirst"]
+        #: Whether to try to order this hook implementation :ref:`last
+        #: <callorder>`.
+        self.trylast: Final = hook_impl_opts["trylast"]
+
+    def __repr__(self) -> str:
+        return f"<HookImpl plugin_name={self.plugin_name!r}, plugin={self.plugin!r}>"
+
+
+@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")

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)
+    <PluginManager.add_hookspecs>`.
+
+    You can register plugin objects (which contain hook implementations) by
+    calling :meth:`register(plugin) <PluginManager.register>`.
+
+    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) <register>`.
+        To obtain the name of a registered plugin use :meth:`get_name(plugin)
+        <get_name>` 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)}"

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
+    <hookwrappers>`."""
+
+    __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

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,))

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)

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
+    <old_style_hookwrappers>` 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
+    <hookwrappers>`, or use :func:`result.force_exception()
+    <pluggy.Result.force_exception>` to set the exception instead of raising.
+    """
+
+    __module__ = "pluggy"

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"]

personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/INSTALLER

@@ -0,0 +1 @@
+uv

personalPromptBoost/.venv/lib/python3.13/site-packages/pydantic-2.13.3.dist-info/RECORD

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+pydantic-2.13.3.dist-info/WHEEL,sha256=QccIxa26bgl1E6uMy58deGWi-0aeIkkangHcxk2kWfw,87
+pydantic-2.13.3.dist-info/licenses/LICENSE,sha256=qeGG88oWte74QxjnpwFyE1GgDLe4rjpDlLZ7SeNSnvM,1129
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+pydantic/validators.py,sha256=pwbIJXVb1CV2mAE4w_EGfNj7DwzsKaWw_tTL6cviTus,146
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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

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.

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 {<member name>: (package, <module name>)} 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__)

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)

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

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__', '<No __module__>')
+    if typing_objects.is_typealiastype(origin):
+        type_ref = f'{module_name}.{origin.__name__}:{id(origin)}'
+    else:
+        try:
+            qualname = getattr(origin, '__qualname__', f'<No __qualname__: {origin}>')
+        except Exception:
+            qualname = getattr(origin, '__qualname__', '<No __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'] = '<stripped>'
+                    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 = {'<stripped>'}
+
+                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

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

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)

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

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[<T> | <U>, 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[<T> | <U>, 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

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

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[<type>] = <default>.`',
+                    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

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

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 <class '__main__.Model'>; actual 1, expected at least 2
+
+        map_generic_model_arguments(Model, (str, bytes, int, complex))
+        #> TypeError: Too many arguments for <class '__main__.Model'>; 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

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()

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

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 = {}

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])}'
+        )

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__),
+    )

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)