Загрузить файлы в «venv/Lib/site-packages/pydantic/_internal»

venv/Lib/site-packages/pydantic/_internal/_typing_extra.py

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+"""Logic for interacting with type annotations, mostly extensions, shims and hacks to wrap Python's typing module."""
+
+from __future__ import annotations
+
+import collections.abc
+import re
+import sys
+import types
+import typing
+from functools import partial
+from inspect import Signature, signature
+from typing import TYPE_CHECKING, Any, Callable, cast
+
+import typing_extensions
+from typing_extensions import deprecated, get_args, get_origin
+from typing_inspection import typing_objects
+from typing_inspection.introspection import is_union_origin
+
+from pydantic.version import version_short
+
+from ._namespace_utils import GlobalsNamespace, MappingNamespace, NsResolver, get_module_ns_of
+
+if sys.version_info < (3, 10):
+    NoneType = type(None)
+    EllipsisType = type(Ellipsis)
+else:
+    from types import EllipsisType as EllipsisType
+    from types import NoneType as NoneType
+
+if sys.version_info >= (3, 14):
+    import annotationlib
+
+if TYPE_CHECKING:
+    from pydantic import BaseModel
+    from pydantic.fields import FieldInfo
+
+# As per https://typing-extensions.readthedocs.io/en/latest/#runtime-use-of-types,
+# always check for both `typing` and `typing_extensions` variants of a typing construct.
+# (this is implemented differently than the suggested approach in the `typing_extensions`
+# docs for performance).
+
+
+_t_annotated = typing.Annotated
+_te_annotated = typing_extensions.Annotated
+
+
+def is_annotated(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Annotated` special form.
+
+    ```python {test="skip" lint="skip"}
+    is_annotated(Annotated[int, ...])
+    #> True
+    ```
+    """
+    origin = get_origin(tp)
+    return origin is _t_annotated or origin is _te_annotated
+
+
+def annotated_type(tp: Any, /) -> Any | None:
+    """Return the type of the `Annotated` special form, or `None`."""
+    return tp.__origin__ if typing_objects.is_annotated(get_origin(tp)) else None
+
+
+def unpack_type(tp: Any, /) -> Any | None:
+    """Return the type wrapped by the `Unpack` special form, or `None`."""
+    return get_args(tp)[0] if typing_objects.is_unpack(get_origin(tp)) else None
+
+
+def is_hashable(tp: Any, /) -> bool:
+    """Return whether the provided argument is the `Hashable` class.
+
+    ```python {test="skip" lint="skip"}
+    is_hashable(Hashable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Hashable or get_origin(tp) is collections.abc.Hashable
+
+
+def is_callable(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Callable`, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_callable(Callable[[int], str])
+    #> True
+    is_callable(typing.Callable)
+    #> True
+    is_callable(collections.abc.Callable)
+    #> True
+    ```
+    """
+    # `get_origin` is documented as normalizing any typing-module aliases to `collections` classes,
+    # hence the second check:
+    return tp is collections.abc.Callable or get_origin(tp) is collections.abc.Callable
+
+
+_classvar_re = re.compile(r'((\w+\.)?Annotated\[)?(\w+\.)?ClassVar\[')
+
+
+def is_classvar_annotation(tp: Any, /) -> bool:
+    """Return whether the provided argument represents a class variable annotation.
+
+    Although not explicitly stated by the typing specification, `ClassVar` can be used
+    inside `Annotated` and as such, this function checks for this specific scenario.
+
+    Because this function is used to detect class variables before evaluating forward references
+    (or because evaluation failed), we also implement a naive regex match implementation. This is
+    required because class variables are inspected before fields are collected, so we try to be
+    as accurate as possible.
+    """
+    if typing_objects.is_classvar(tp):
+        return True
+
+    origin = get_origin(tp)
+
+    if typing_objects.is_classvar(origin):
+        return True
+
+    if typing_objects.is_annotated(origin):
+        annotated_type = tp.__origin__
+        if typing_objects.is_classvar(annotated_type) or typing_objects.is_classvar(get_origin(annotated_type)):
+            return True
+
+    str_ann: str | None = None
+    if isinstance(tp, typing.ForwardRef):
+        str_ann = tp.__forward_arg__
+    if isinstance(tp, str):
+        str_ann = tp
+
+    if str_ann is not None and _classvar_re.match(str_ann):
+        # stdlib dataclasses do something similar, although a bit more advanced
+        # (see `dataclass._is_type`).
+        return True
+
+    return False
+
+
+_t_final = typing.Final
+_te_final = typing_extensions.Final
+
+
+# TODO implement `is_finalvar_annotation` as Final can be wrapped with other special forms:
+def is_finalvar(tp: Any, /) -> bool:
+    """Return whether the provided argument is a `Final` special form, parametrized or not.
+
+    ```python {test="skip" lint="skip"}
+    is_finalvar(Final[int])
+    #> True
+    is_finalvar(Final)
+    #> True
+    """
+    # Final is not necessarily parametrized:
+    if tp is _t_final or tp is _te_final:
+        return True
+    origin = get_origin(tp)
+    return origin is _t_final or origin is _te_final
+
+
+_NONE_TYPES: tuple[Any, ...] = (None, NoneType, typing.Literal[None], typing_extensions.Literal[None])
+
+
+def is_none_type(tp: Any, /) -> bool:
+    """Return whether the argument represents the `None` type as part of an annotation.
+
+    ```python {test="skip" lint="skip"}
+    is_none_type(None)
+    #> True
+    is_none_type(NoneType)
+    #> True
+    is_none_type(Literal[None])
+    #> True
+    is_none_type(type[None])
+    #> False
+    """
+    return tp in _NONE_TYPES
+
+
+def is_namedtuple(tp: Any, /) -> bool:
+    """Return whether the provided argument is a named tuple class.
+
+    The class can be created using `typing.NamedTuple` or `collections.namedtuple`.
+    Parametrized generic classes are *not* assumed to be named tuples.
+    """
+    from ._utils import lenient_issubclass  # circ. import
+
+    return lenient_issubclass(tp, tuple) and hasattr(tp, '_fields')
+
+
+# TODO In 2.12, delete this export. It is currently defined only to not break
+# pydantic-settings which relies on it:
+origin_is_union = is_union_origin
+
+
+def is_generic_alias(tp: Any, /) -> bool:
+    return isinstance(tp, (types.GenericAlias, typing._GenericAlias))  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# TODO: Ideally, we should avoid relying on the private `typing` constructs:
+
+if sys.version_info < (3, 10):
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias)  # pyright: ignore[reportAttributeAccessIssue]
+else:
+    WithArgsTypes: tuple[Any, ...] = (typing._GenericAlias, types.GenericAlias, types.UnionType)  # pyright: ignore[reportAttributeAccessIssue]
+
+
+# Similarly, we shouldn't rely on this `_Final` class, which is even more private than `_GenericAlias`:
+typing_base: Any = typing._Final  # pyright: ignore[reportAttributeAccessIssue]
+
+
+### Annotation evaluations functions:
+
+
+def parent_frame_namespace(*, parent_depth: int = 2, force: bool = False) -> dict[str, Any] | None:
+    """Fetch the local namespace of the parent frame where this function is called.
+
+    Using this function is mostly useful to resolve forward annotations pointing to members defined in a local namespace,
+    such as assignments inside a function. Using the standard library tools, it is currently not possible to resolve
+    such annotations:
+
+    ```python {lint="skip" test="skip"}
+    from typing import get_type_hints
+
+    def func() -> None:
+        Alias = int
+
+        class C:
+            a: 'Alias'
+
+        # Raises a `NameError: 'Alias' is not defined`
+        get_type_hints(C)
+    ```
+
+    Pydantic uses this function when a Pydantic model is being defined to fetch the parent frame locals. However,
+    this only allows us to fetch the parent frame namespace and not other parents (e.g. a model defined in a function,
+    itself defined in another function). Inspecting the next outer frames (using `f_back`) is not reliable enough
+    (see https://discuss.python.org/t/20659).
+
+    Because this function is mostly used to better resolve forward annotations, nothing is returned if the parent frame's
+    code object is defined at the module level. In this case, the locals of the frame will be the same as the module
+    globals where the class is defined (see `_namespace_utils.get_module_ns_of`). However, if you still want to fetch
+    the module globals (e.g. when rebuilding a model, where the frame where the rebuild call is performed might contain
+    members that you want to use for forward annotations evaluation), you can use the `force` parameter.
+
+    Args:
+        parent_depth: The depth at which to get the frame. Defaults to 2, meaning the parent frame where this function
+            is called will be used.
+        force: Whether to always return the frame locals, even if the frame's code object is defined at the module level.
+
+    Returns:
+        The locals of the namespace, or `None` if it was skipped as per the described logic.
+    """
+    frame = sys._getframe(parent_depth)
+
+    if frame.f_code.co_name.startswith('<generic parameters of'):
+        # As `parent_frame_namespace` is mostly called in `ModelMetaclass.__new__`,
+        # the parent frame can be the annotation scope if the PEP 695 generic syntax is used.
+        # (see https://docs.python.org/3/reference/executionmodel.html#annotation-scopes,
+        # https://docs.python.org/3/reference/compound_stmts.html#generic-classes).
+        # In this case, the code name is set to `<generic parameters of MyClass>`,
+        # and we need to skip this frame as it is irrelevant.
+        frame = cast(types.FrameType, frame.f_back)  # guaranteed to not be `None`
+
+    # note, we don't copy frame.f_locals here (or during the last return call), because we don't expect the namespace to be
+    # modified down the line if this becomes a problem, we could implement some sort of frozen mapping structure to enforce this.
+    if force:
+        return frame.f_locals
+
+    # If either of the following conditions are true, the class is defined at the top module level.
+    # To better understand why we need both of these checks, see
+    # https://github.com/pydantic/pydantic/pull/10113#discussion_r1714981531.
+    if frame.f_back is None or frame.f_code.co_name == '<module>':
+        return None
+
+    return frame.f_locals
+
+
+def _type_convert(arg: Any) -> Any:
+    """Convert `None` to `NoneType` and strings to `ForwardRef` instances.
+
+    This is a backport of the private `typing._type_convert` function. When
+    evaluating a type, `ForwardRef._evaluate` ends up being called, and is
+    responsible for making this conversion. However, we still have to apply
+    it for the first argument passed to our type evaluation functions, similarly
+    to the `typing.get_type_hints` function.
+    """
+    if arg is None:
+        return NoneType
+    if isinstance(arg, str):
+        # Like `typing.get_type_hints`, assume the arg can be in any context,
+        # hence the proper `is_argument` and `is_class` args:
+        return _make_forward_ref(arg, is_argument=False, is_class=True)
+    return arg
+
+
+def safe_get_annotations(obj: Any) -> dict[str, Any]:
+    """Get the annotations for the provided object, accounting for potential deferred forward references.
+
+    Starting with Python 3.14, accessing the `__annotations__` attribute might raise a `NameError` if
+    a referenced symbol isn't defined yet. In this case, we return the annotation in the *forward ref*
+    format.
+    """
+    if sys.version_info >= (3, 14):
+        return annotationlib.get_annotations(obj, format=annotationlib.Format.FORWARDREF)
+    else:
+        # TODO just do getattr(obj, '__annotations__', {}) when dropping support for Python 3.9:
+        if isinstance(obj, type):
+            return obj.__dict__.get('__annotations__', {})
+        else:
+            return getattr(obj, '__annotations__', {})
+
+
+def get_model_type_hints(
+    model_class: type[BaseModel],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, tuple[Any, bool]]:
+    """Collect annotations from a Pydantic model class, including those from parent classes.
+
+    Args:
+        model_class: The Pydantic model class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+
+    Returns:
+        A dictionary mapping annotation names to a two-tuple: the first element is the evaluated
+        type or the original annotation if a `NameError` occurred, the second element is a boolean
+        indicating if whether the evaluation succeeded.
+    """
+    hints: dict[str, Any] | dict[str, tuple[Any, bool]] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(model_class.__mro__[:-1]):
+        # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals
+        # from the ns_resolver, but we want to be able to know which specific field failed
+        # to evaluate:
+        ann = safe_get_annotations(base)
+
+        if not ann:
+            continue
+
+        with ns_resolver.push(base):
+            base_model_fields: dict[str, FieldInfo] | None = base.__dict__.get('__pydantic_fields__')
+
+            for name, value in ann.items():
+                if name.startswith('_'):
+                    globalns, localns = ns_resolver.types_namespace
+
+                    # For private attributes, we only need the annotation to detect the `ClassVar` special form.
+                    # For this reason, we still try to evaluate it, but we also catch any possible exception (on
+                    # top of the `NameError`s caught in `try_eval_type`) that could happen so that users are free
+                    # to use any kind of forward annotation for private fields (e.g. circular imports, new typing
+                    # syntax, etc).
+                    try:
+                        hints[name] = try_eval_type(value, globalns, localns)
+                    except Exception:
+                        hints[name] = (value, False)
+                else:
+                    if base_model_fields is not None and name in base_model_fields:
+                        # Avoid unnecessarily evaluating annotations from parent models, as we'll end up
+                        # copying the `FieldInfo` instance from it anyway if we need to.
+                        # We use the `annotation` attribute here, but in reality could put anything here,
+                        # As we are guaranteed to not make use of it:
+                        hints[name] = (base_model_fields[name].annotation, True)
+                    else:
+                        globalns, localns = ns_resolver.types_namespace
+
+                        hints[name] = try_eval_type(value, globalns, localns)
+
+    return hints
+
+
+def get_cls_type_hints(
+    obj: type[Any],
+    *,
+    ns_resolver: NsResolver | None = None,
+) -> dict[str, Any]:
+    """Collect annotations from a class, including those from parent classes.
+
+    Args:
+        obj: The class to inspect.
+        ns_resolver: A namespace resolver instance to use. Defaults to an empty instance.
+    """
+    hints: dict[str, Any] = {}
+    ns_resolver = ns_resolver or NsResolver()
+
+    for base in reversed(obj.__mro__):
+        # For Python 3.14, we could also use `Format.VALUE` and pass the globals/locals
+        # from the ns_resolver, but we want to be able to know which specific field failed
+        # to evaluate:
+        ann = safe_get_annotations(base)
+
+        if not ann:
+            continue
+
+        with ns_resolver.push(base):
+            globalns, localns = ns_resolver.types_namespace
+            for name, value in ann.items():
+                hints[name] = eval_type(value, globalns, localns)
+    return hints
+
+
+def try_eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> tuple[Any, bool]:
+    """Try evaluating the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+
+    Returns:
+        A two-tuple containing the possibly evaluated type and a boolean indicating
+            whether the evaluation succeeded or not.
+    """
+    value = _type_convert(value)
+
+    try:
+        return eval_type_backport(value, globalns, localns), True
+    except NameError:
+        return value, False
+
+
+def eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    """Evaluate the annotation using the provided namespaces.
+
+    Args:
+        value: The value to evaluate. If `None`, it will be replaced by `type[None]`. If an instance
+            of `str`, it will be converted to a `ForwardRef`.
+        localns: The global namespace to use during annotation evaluation.
+        globalns: The local namespace to use during annotation evaluation.
+    """
+    value = _type_convert(value)
+    return eval_type_backport(value, globalns, localns)
+
+
+@deprecated(
+    '`eval_type_lenient` is deprecated, use `try_eval_type` instead.',
+    category=None,
+)
+def eval_type_lenient(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> Any:
+    ev, _ = try_eval_type(value, globalns, localns)
+    return ev
+
+
+def eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    """An enhanced version of `typing._eval_type` which will fall back to using the `eval_type_backport`
+    package if it's installed to let older Python versions use newer typing constructs.
+
+    Specifically, this transforms `X | Y` into `typing.Union[X, Y]` and `list[X]` into `typing.List[X]`
+    (as well as all the types made generic in PEP 585) if the original syntax is not supported in the
+    current Python version.
+
+    This function will also display a helpful error if the value passed fails to evaluate.
+    """
+    try:
+        return _eval_type_backport(value, globalns, localns, type_params)
+    except TypeError as e:
+        if 'Unable to evaluate type annotation' in str(e):
+            raise
+
+        # If it is a `TypeError` and value isn't a `ForwardRef`, it would have failed during annotation definition.
+        # Thus we assert here for type checking purposes:
+        assert isinstance(value, typing.ForwardRef)
+
+        message = f'Unable to evaluate type annotation {value.__forward_arg__!r}.'
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise TypeError(message) from e
+    except RecursionError as e:
+        # TODO ideally recursion errors should be checked in `eval_type` above, but `eval_type_backport`
+        # is used directly in some places.
+        message = (
+            "If you made use of an implicit recursive type alias (e.g. `MyType = list['MyType']), "
+            'consider using PEP 695 type aliases instead. For more details, refer to the documentation: '
+            f'https://docs.pydantic.dev/{version_short()}/concepts/types/#named-recursive-types'
+        )
+        if sys.version_info >= (3, 11):
+            e.add_note(message)
+            raise
+        else:
+            raise RecursionError(f'{e.args[0]}\n{message}')
+
+
+def _eval_type_backport(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    try:
+        return _eval_type(value, globalns, localns, type_params)
+    except TypeError as e:
+        if not (isinstance(value, typing.ForwardRef) and is_backport_fixable_error(e)):
+            raise
+
+        try:
+            from eval_type_backport import eval_type_backport
+        except ImportError:
+            raise TypeError(
+                f'Unable to evaluate type annotation {value.__forward_arg__!r}. If you are making use '
+                'of the new typing syntax (unions using `|` since Python 3.10 or builtins subscripting '
+                'since Python 3.9), you should either replace the use of new syntax with the existing '
+                '`typing` constructs or install the `eval_type_backport` package.'
+            ) from e
+
+        return eval_type_backport(
+            value,
+            globalns,
+            localns,  # pyright: ignore[reportArgumentType], waiting on a new `eval_type_backport` release.
+            try_default=False,
+        )
+
+
+def _eval_type(
+    value: Any,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+    type_params: tuple[Any, ...] | None = None,
+) -> Any:
+    if sys.version_info >= (3, 14):
+        # Starting in 3.14, `_eval_type()` does *not* apply `_type_convert()`
+        # anymore. This means the `None` -> `type(None)` conversion does not apply:
+        evaluated = typing._eval_type(  # type: ignore
+            value,
+            globalns,
+            localns,
+            type_params=type_params,
+            # This is relevant when evaluating types from `TypedDict` classes, where string annotations
+            # are automatically converted to `ForwardRef` instances with a module set. In this case,
+            # Our `globalns` is irrelevant and we need to indicate `typing._eval_type()` that it should
+            # infer it from the `ForwardRef.__forward_module__` attribute instead (`typing.get_type_hints()`
+            # does the same). Note that this would probably be unnecessary if we properly iterated over the
+            # `__orig_bases__` for TypedDicts in `get_cls_type_hints()`:
+            prefer_fwd_module=True,
+        )
+        if evaluated is None:
+            evaluated = type(None)
+        return evaluated
+    elif sys.version_info >= (3, 13):
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns, type_params=type_params
+        )
+    else:
+        return typing._eval_type(  # type: ignore
+            value, globalns, localns
+        )
+
+
+def is_backport_fixable_error(e: TypeError) -> bool:
+    msg = str(e)
+
+    return sys.version_info < (3, 10) and msg.startswith('unsupported operand type(s) for |: ')
+
+
+def signature_no_eval(f: Callable[..., Any]) -> Signature:
+    """Get the signature of a callable without evaluating any annotations."""
+    if sys.version_info >= (3, 14):
+        from annotationlib import Format
+
+        return signature(f, annotation_format=Format.FORWARDREF)
+    else:
+        return signature(f)
+
+
+def get_function_type_hints(
+    function: Callable[..., Any],
+    *,
+    include_keys: set[str] | None = None,
+    globalns: GlobalsNamespace | None = None,
+    localns: MappingNamespace | None = None,
+) -> dict[str, Any]:
+    """Return type hints for a function.
+
+    This is similar to the `typing.get_type_hints` function, with a few differences:
+    - Support `functools.partial` by using the underlying `func` attribute.
+    - Do not wrap type annotation of a parameter with `Optional` if it has a default value of `None`
+      (related bug: https://github.com/python/cpython/issues/90353, only fixed in 3.11+).
+    """
+    if isinstance(function, partial):
+        annotations = safe_get_annotations(function.func)
+    else:
+        annotations = safe_get_annotations(function)
+
+    if globalns is None:
+        globalns = get_module_ns_of(function)
+    type_params: tuple[Any, ...] | None = None
+    if localns is None:
+        # If localns was specified, it is assumed to already contain type params. This is because
+        # Pydantic has more advanced logic to do so (see `_namespace_utils.ns_for_function`).
+        type_params = getattr(function, '__type_params__', ())
+
+    type_hints = {}
+    for name, value in annotations.items():
+        if include_keys is not None and name not in include_keys:
+            continue
+        if value is None:
+            value = NoneType
+        elif isinstance(value, str):
+            value = _make_forward_ref(value)
+
+        type_hints[name] = eval_type_backport(value, globalns, localns, type_params)
+
+    return type_hints
+
+
+# TODO use typing.ForwardRef directly when we stop supporting 3.9:
+if sys.version_info < (3, 9, 8) or (3, 10) <= sys.version_info < (3, 10, 1):
+
+    def _make_forward_ref(
+        arg: Any,
+        is_argument: bool = True,
+        *,
+        is_class: bool = False,
+    ) -> typing.ForwardRef:
+        """Wrapper for ForwardRef that accounts for the `is_class` argument missing in older versions.
+        The `module` argument is omitted as it breaks <3.9.8, =3.10.0 and isn't used in the calls below.
+
+        See https://github.com/python/cpython/pull/28560 for some background.
+        The backport happened on 3.9.8, see:
+        https://github.com/pydantic/pydantic/discussions/6244#discussioncomment-6275458,
+        and on 3.10.1 for the 3.10 branch, see:
+        https://github.com/pydantic/pydantic/issues/6912
+
+        Implemented as EAFP with memory.
+        """
+        return typing.ForwardRef(arg, is_argument)  # pyright: ignore[reportCallIssue]
+
+else:
+    _make_forward_ref = typing.ForwardRef  # pyright: ignore[reportAssignmentType]
+
+
+if sys.version_info >= (3, 10):
+    get_type_hints = typing.get_type_hints
+
+else:
+    """
+    For older versions of python, we have a custom implementation of `get_type_hints` which is a close as possible to
+    the implementation in CPython 3.10.8.
+    """
+
+    @typing.no_type_check
+    def get_type_hints(  # noqa: C901
+        obj: Any,
+        globalns: dict[str, Any] | None = None,
+        localns: dict[str, Any] | None = None,
+        include_extras: bool = False,
+    ) -> dict[str, Any]:  # pragma: no cover
+        """Taken verbatim from python 3.10.8 unchanged, except:
+        * type annotations of the function definition above.
+        * prefixing `typing.` where appropriate
+        * Use `_make_forward_ref` instead of `typing.ForwardRef` to handle the `is_class` argument.
+
+        https://github.com/python/cpython/blob/aaaf5174241496afca7ce4d4584570190ff972fe/Lib/typing.py#L1773-L1875
+
+        DO NOT CHANGE THIS METHOD UNLESS ABSOLUTELY NECESSARY.
+        ======================================================
+
+        Return type hints for an object.
+
+        This is often the same as obj.__annotations__, but it handles
+        forward references encoded as string literals, adds Optional[t] if a
+        default value equal to None is set and recursively replaces all
+        'Annotated[T, ...]' with 'T' (unless 'include_extras=True').
+
+        The argument may be a module, class, method, or function. The annotations
+        are returned as a dictionary. For classes, annotations include also
+        inherited members.
+
+        TypeError is raised if the argument is not of a type that can contain
+        annotations, and an empty dictionary is returned if no annotations are
+        present.
+
+        BEWARE -- the behavior of globalns and localns is counterintuitive
+        (unless you are familiar with how eval() and exec() work).  The
+        search order is locals first, then globals.
+
+        - If no dict arguments are passed, an attempt is made to use the
+          globals from obj (or the respective module's globals for classes),
+          and these are also used as the locals.  If the object does not appear
+          to have globals, an empty dictionary is used.  For classes, the search
+          order is globals first then locals.
+
+        - If one dict argument is passed, it is used for both globals and
+          locals.
+
+        - If two dict arguments are passed, they specify globals and
+          locals, respectively.
+        """
+        if getattr(obj, '__no_type_check__', None):
+            return {}
+        # Classes require a special treatment.
+        if isinstance(obj, type):
+            hints = {}
+            for base in reversed(obj.__mro__):
+                if globalns is None:
+                    base_globals = getattr(sys.modules.get(base.__module__, None), '__dict__', {})
+                else:
+                    base_globals = globalns
+                ann = base.__dict__.get('__annotations__', {})
+                if isinstance(ann, types.GetSetDescriptorType):
+                    ann = {}
+                base_locals = dict(vars(base)) if localns is None else localns
+                if localns is None and globalns is None:
+                    # This is surprising, but required.  Before Python 3.10,
+                    # get_type_hints only evaluated the globalns of
+                    # a class.  To maintain backwards compatibility, we reverse
+                    # the globalns and localns order so that eval() looks into
+                    # *base_globals* first rather than *base_locals*.
+                    # This only affects ForwardRefs.
+                    base_globals, base_locals = base_locals, base_globals
+                for name, value in ann.items():
+                    if value is None:
+                        value = type(None)
+                    if isinstance(value, str):
+                        value = _make_forward_ref(value, is_argument=False, is_class=True)
+
+                    value = eval_type_backport(value, base_globals, base_locals)
+                    hints[name] = value
+            if not include_extras and hasattr(typing, '_strip_annotations'):
+                return {
+                    k: typing._strip_annotations(t)  # type: ignore
+                    for k, t in hints.items()
+                }
+            else:
+                return hints
+
+        if globalns is None:
+            if isinstance(obj, types.ModuleType):
+                globalns = obj.__dict__
+            else:
+                nsobj = obj
+                # Find globalns for the unwrapped object.
+                while hasattr(nsobj, '__wrapped__'):
+                    nsobj = nsobj.__wrapped__
+                globalns = getattr(nsobj, '__globals__', {})
+            if localns is None:
+                localns = globalns
+        elif localns is None:
+            localns = globalns
+        hints = getattr(obj, '__annotations__', None)
+        if hints is None:
+            # Return empty annotations for something that _could_ have them.
+            if isinstance(obj, typing._allowed_types):  # type: ignore
+                return {}
+            else:
+                raise TypeError(f'{obj!r} is not a module, class, method, or function.')
+        defaults = typing._get_defaults(obj)  # type: ignore
+        hints = dict(hints)
+        for name, value in hints.items():
+            if value is None:
+                value = type(None)
+            if isinstance(value, str):
+                # class-level forward refs were handled above, this must be either
+                # a module-level annotation or a function argument annotation
+
+                value = _make_forward_ref(
+                    value,
+                    is_argument=not isinstance(obj, types.ModuleType),
+                    is_class=False,
+                )
+            value = eval_type_backport(value, globalns, localns)
+            if name in defaults and defaults[name] is None:
+                value = typing.Optional[value]
+            hints[name] = value
+        return hints if include_extras else {k: typing._strip_annotations(t) for k, t in hints.items()}  # type: ignore

venv/Lib/site-packages/pydantic/_internal/_utils.py

@@ -0,0 +1,446 @@
+"""Bucket of reusable internal utilities.
+
+This should be reduced as much as possible with functions only used in one place, moved to that place.
+"""
+
+from __future__ import annotations as _annotations
+
+import dataclasses
+import keyword
+import sys
+import warnings
+import weakref
+from collections import OrderedDict, defaultdict, deque
+from collections.abc import Callable, Iterable, Mapping
+from collections.abc import Set as AbstractSet
+from copy import deepcopy
+from functools import cached_property
+from inspect import Parameter
+from itertools import zip_longest
+from types import BuiltinFunctionType, CodeType, FunctionType, GeneratorType, LambdaType, ModuleType
+from typing import TYPE_CHECKING, Any, Generic, TypeVar, overload
+
+from pydantic_core import MISSING, PydanticUndefined
+from typing_extensions import TypeAlias, TypeGuard, deprecated
+
+from pydantic import PydanticDeprecatedSince211
+
+from . import _repr, _typing_extra
+from ._import_utils import import_cached_base_model
+
+if TYPE_CHECKING:
+    # TODO remove type error comments when we drop support for Python 3.9
+    MappingIntStrAny: TypeAlias = Mapping[int, Any] | Mapping[str, Any]  # pyright: ignore[reportGeneralTypeIssues]
+    AbstractSetIntStr: TypeAlias = AbstractSet[int] | AbstractSet[str]  # pyright: ignore[reportGeneralTypeIssues]
+    from ..main import BaseModel
+
+
+# these are types that are returned unchanged by deepcopy
+IMMUTABLE_NON_COLLECTIONS_TYPES: set[type[Any]] = {
+    int,
+    float,
+    complex,
+    str,
+    bool,
+    bytes,
+    type,
+    _typing_extra.NoneType,
+    FunctionType,
+    BuiltinFunctionType,
+    LambdaType,
+    weakref.ref,
+    CodeType,
+    # note: including ModuleType will differ from behaviour of deepcopy by not producing error.
+    # It might be not a good idea in general, but considering that this function used only internally
+    # against default values of fields, this will allow to actually have a field with module as default value
+    ModuleType,
+    NotImplemented.__class__,
+    Ellipsis.__class__,
+}
+
+# these are types that if empty, might be copied with simple copy() instead of deepcopy()
+BUILTIN_COLLECTIONS: set[type[Any]] = {
+    list,
+    set,
+    tuple,
+    frozenset,
+    dict,
+    OrderedDict,
+    defaultdict,
+    deque,
+}
+
+
+def can_be_positional(param: Parameter) -> bool:
+    """Return whether the parameter accepts a positional argument.
+
+    ```python {test="skip" lint="skip"}
+    def func(a, /, b, *, c):
+        pass
+
+    params = inspect.signature(func).parameters
+    can_be_positional(params['a'])
+    #> True
+    can_be_positional(params['b'])
+    #> True
+    can_be_positional(params['c'])
+    #> False
+    ```
+    """
+    return param.kind in (Parameter.POSITIONAL_ONLY, Parameter.POSITIONAL_OR_KEYWORD)
+
+
+def sequence_like(v: Any) -> bool:
+    return isinstance(v, (list, tuple, set, frozenset, GeneratorType, deque))
+
+
+def lenient_isinstance(o: Any, class_or_tuple: type[Any] | tuple[type[Any], ...] | None) -> bool:  # pragma: no cover
+    try:
+        return isinstance(o, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:
+        return False
+
+
+def lenient_issubclass(cls: Any, class_or_tuple: Any) -> bool:  # pragma: no cover
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)
+    except TypeError:
+        if isinstance(cls, _typing_extra.WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def is_model_class(cls: Any) -> TypeGuard[type[BaseModel]]:
+    """Returns true if cls is a _proper_ subclass of BaseModel, and provides proper type-checking,
+    unlike raw calls to lenient_issubclass.
+    """
+    BaseModel = import_cached_base_model()
+
+    return lenient_issubclass(cls, BaseModel) and cls is not BaseModel
+
+
+def is_valid_identifier(identifier: str) -> bool:
+    """Checks that a string is a valid identifier and not a Python keyword.
+    :param identifier: The identifier to test.
+    :return: True if the identifier is valid.
+    """
+    return identifier.isidentifier() and not keyword.iskeyword(identifier)
+
+
+KeyType = TypeVar('KeyType')
+
+
+def deep_update(mapping: dict[KeyType, Any], *updating_mappings: dict[KeyType, Any]) -> dict[KeyType, Any]:
+    updated_mapping = mapping.copy()
+    for updating_mapping in updating_mappings:
+        for k, v in updating_mapping.items():
+            if k in updated_mapping and isinstance(updated_mapping[k], dict) and isinstance(v, dict):
+                updated_mapping[k] = deep_update(updated_mapping[k], v)
+            else:
+                updated_mapping[k] = v
+    return updated_mapping
+
+
+def update_not_none(mapping: dict[Any, Any], **update: Any) -> None:
+    mapping.update({k: v for k, v in update.items() if v is not None})
+
+
+T = TypeVar('T')
+
+
+def unique_list(
+    input_list: list[T] | tuple[T, ...],
+    *,
+    name_factory: Callable[[T], str] = str,
+) -> list[T]:
+    """Make a list unique while maintaining order.
+    We update the list if another one with the same name is set
+    (e.g. model validator overridden in subclass).
+    """
+    result: list[T] = []
+    result_names: list[str] = []
+    for v in input_list:
+        v_name = name_factory(v)
+        if v_name not in result_names:
+            result_names.append(v_name)
+            result.append(v)
+        else:
+            result[result_names.index(v_name)] = v
+
+    return result
+
+
+class ValueItems(_repr.Representation):
+    """Class for more convenient calculation of excluded or included fields on values."""
+
+    __slots__ = ('_items', '_type')
+
+    def __init__(self, value: Any, items: AbstractSetIntStr | MappingIntStrAny) -> None:
+        items = self._coerce_items(items)
+
+        if isinstance(value, (list, tuple)):
+            items = self._normalize_indexes(items, len(value))  # type: ignore
+
+        self._items: MappingIntStrAny = items  # type: ignore
+
+    def is_excluded(self, item: Any) -> bool:
+        """Check if item is fully excluded.
+
+        :param item: key or index of a value
+        """
+        return self.is_true(self._items.get(item))
+
+    def is_included(self, item: Any) -> bool:
+        """Check if value is contained in self._items.
+
+        :param item: key or index of value
+        """
+        return item in self._items
+
+    def for_element(self, e: int | str) -> AbstractSetIntStr | MappingIntStrAny | None:
+        """:param e: key or index of element on value
+        :return: raw values for element if self._items is dict and contain needed element
+        """
+        item = self._items.get(e)  # type: ignore
+        return item if not self.is_true(item) else None
+
+    def _normalize_indexes(self, items: MappingIntStrAny, v_length: int) -> dict[int | str, Any]:
+        """:param items: dict or set of indexes which will be normalized
+        :param v_length: length of sequence indexes of which will be
+
+        >>> self._normalize_indexes({0: True, -2: True, -1: True}, 4)
+        {0: True, 2: True, 3: True}
+        >>> self._normalize_indexes({'__all__': True}, 4)
+        {0: True, 1: True, 2: True, 3: True}
+        """
+        normalized_items: dict[int | str, Any] = {}
+        all_items = None
+        for i, v in items.items():
+            if not (isinstance(v, Mapping) or isinstance(v, AbstractSet) or self.is_true(v)):
+                raise TypeError(f'Unexpected type of exclude value for index "{i}" {v.__class__}')
+            if i == '__all__':
+                all_items = self._coerce_value(v)
+                continue
+            if not isinstance(i, int):
+                raise TypeError(
+                    'Excluding fields from a sequence of sub-models or dicts must be performed index-wise: '
+                    'expected integer keys or keyword "__all__"'
+                )
+            normalized_i = v_length + i if i < 0 else i
+            normalized_items[normalized_i] = self.merge(v, normalized_items.get(normalized_i))
+
+        if not all_items:
+            return normalized_items
+        if self.is_true(all_items):
+            for i in range(v_length):
+                normalized_items.setdefault(i, ...)
+            return normalized_items
+        for i in range(v_length):
+            normalized_item = normalized_items.setdefault(i, {})
+            if not self.is_true(normalized_item):
+                normalized_items[i] = self.merge(all_items, normalized_item)
+        return normalized_items
+
+    @classmethod
+    def merge(cls, base: Any, override: Any, intersect: bool = False) -> Any:
+        """Merge a `base` item with an `override` item.
+
+        Both `base` and `override` are converted to dictionaries if possible.
+        Sets are converted to dictionaries with the sets entries as keys and
+        Ellipsis as values.
+
+        Each key-value pair existing in `base` is merged with `override`,
+        while the rest of the key-value pairs are updated recursively with this function.
+
+        Merging takes place based on the "union" of keys if `intersect` is
+        set to `False` (default) and on the intersection of keys if
+        `intersect` is set to `True`.
+        """
+        override = cls._coerce_value(override)
+        base = cls._coerce_value(base)
+        if override is None:
+            return base
+        if cls.is_true(base) or base is None:
+            return override
+        if cls.is_true(override):
+            return base if intersect else override
+
+        # intersection or union of keys while preserving ordering:
+        if intersect:
+            merge_keys = [k for k in base if k in override] + [k for k in override if k in base]
+        else:
+            merge_keys = list(base) + [k for k in override if k not in base]
+
+        merged: dict[int | str, Any] = {}
+        for k in merge_keys:
+            merged_item = cls.merge(base.get(k), override.get(k), intersect=intersect)
+            if merged_item is not None:
+                merged[k] = merged_item
+
+        return merged
+
+    @staticmethod
+    def _coerce_items(items: AbstractSetIntStr | MappingIntStrAny) -> MappingIntStrAny:
+        if isinstance(items, Mapping):
+            pass
+        elif isinstance(items, AbstractSet):
+            items = dict.fromkeys(items, ...)  # type: ignore
+        else:
+            class_name = getattr(items, '__class__', '???')
+            raise TypeError(f'Unexpected type of exclude value {class_name}')
+        return items  # type: ignore
+
+    @classmethod
+    def _coerce_value(cls, value: Any) -> Any:
+        if value is None or cls.is_true(value):
+            return value
+        return cls._coerce_items(value)
+
+    @staticmethod
+    def is_true(v: Any) -> bool:
+        return v is True or v is ...
+
+    def __repr_args__(self) -> _repr.ReprArgs:
+        return [(None, self._items)]
+
+
+if TYPE_CHECKING:
+
+    def LazyClassAttribute(name: str, get_value: Callable[[], T]) -> T: ...
+
+else:
+
+    class LazyClassAttribute:
+        """A descriptor exposing an attribute only accessible on a class (hidden from instances).
+
+        The attribute is lazily computed and cached during the first access.
+        """
+
+        def __init__(self, name: str, get_value: Callable[[], Any]) -> None:
+            self.name = name
+            self.get_value = get_value
+
+        @cached_property
+        def value(self) -> Any:
+            return self.get_value()
+
+        def __get__(self, instance: Any, owner: type[Any]) -> None:
+            if instance is None:
+                return self.value
+            raise AttributeError(f'{self.name!r} attribute of {owner.__name__!r} is class-only')
+
+
+Obj = TypeVar('Obj')
+
+
+def smart_deepcopy(obj: Obj) -> Obj:
+    """Return type as is for immutable built-in types
+    Use obj.copy() for built-in empty collections
+    Use copy.deepcopy() for non-empty collections and unknown objects.
+    """
+    if obj is MISSING or obj is PydanticUndefined:
+        return obj  # pyright: ignore[reportReturnType]
+    obj_type = obj.__class__
+    if obj_type in IMMUTABLE_NON_COLLECTIONS_TYPES:
+        return obj  # fastest case: obj is immutable and not collection therefore will not be copied anyway
+    try:
+        if not obj and obj_type in BUILTIN_COLLECTIONS:
+            # faster way for empty collections, no need to copy its members
+            return obj if obj_type is tuple else obj.copy()  # tuple doesn't have copy method  # type: ignore
+    except (TypeError, ValueError, RuntimeError):
+        # do we really dare to catch ALL errors? Seems a bit risky
+        pass
+
+    return deepcopy(obj)  # slowest way when we actually might need a deepcopy
+
+
+_SENTINEL = object()
+
+
+def all_identical(left: Iterable[Any], right: Iterable[Any]) -> bool:
+    """Check that the items of `left` are the same objects as those in `right`.
+
+    >>> a, b = object(), object()
+    >>> all_identical([a, b, a], [a, b, a])
+    True
+    >>> all_identical([a, b, [a]], [a, b, [a]])  # new list object, while "equal" is not "identical"
+    False
+    """
+    for left_item, right_item in zip_longest(left, right, fillvalue=_SENTINEL):
+        if left_item is not right_item:
+            return False
+    return True
+
+
+def get_first_not_none(a: Any, b: Any) -> Any:
+    """Return the first argument if it is not `None`, otherwise return the second argument."""
+    return a if a is not None else b
+
+
+@dataclasses.dataclass(frozen=True)
+class SafeGetItemProxy:
+    """Wrapper redirecting `__getitem__` to `get` with a sentinel value as default
+
+    This makes is safe to use in `operator.itemgetter` when some keys may be missing
+    """
+
+    # Define __slots__manually for performances
+    # @dataclasses.dataclass() only support slots=True in python>=3.10
+    __slots__ = ('wrapped',)
+
+    wrapped: Mapping[str, Any]
+
+    def __getitem__(self, key: str, /) -> Any:
+        return self.wrapped.get(key, _SENTINEL)
+
+    # required to pass the object to operator.itemgetter() instances due to a quirk of typeshed
+    # https://github.com/python/mypy/issues/13713
+    # https://github.com/python/typeshed/pull/8785
+    # Since this is typing-only, hide it in a typing.TYPE_CHECKING block
+    if TYPE_CHECKING:
+
+        def __contains__(self, key: str, /) -> bool:
+            return self.wrapped.__contains__(key)
+
+
+_ModelT = TypeVar('_ModelT', bound='BaseModel')
+_RT = TypeVar('_RT')
+
+
+class deprecated_instance_property(Generic[_ModelT, _RT]):
+    """A decorator exposing the decorated class method as a property, with a warning on instance access.
+
+    This decorator takes a class method defined on the `BaseModel` class and transforms it into
+    an attribute. The attribute can be accessed on both the class and instances of the class. If accessed
+    via an instance, a deprecation warning is emitted stating that instance access will be removed in V3.
+    """
+
+    def __init__(self, fget: Callable[[type[_ModelT]], _RT], /) -> None:
+        # Note: fget should be a classmethod:
+        self.fget = fget
+
+    @overload
+    def __get__(self, instance: None, objtype: type[_ModelT]) -> _RT: ...
+    @overload
+    @deprecated(
+        'Accessing this attribute on the instance is deprecated, and will be removed in Pydantic V3. '
+        'Instead, you should access this attribute from the model class.',
+        category=None,
+    )
+    def __get__(self, instance: _ModelT, objtype: type[_ModelT]) -> _RT: ...
+    def __get__(self, instance: _ModelT | None, objtype: type[_ModelT]) -> _RT:
+        if instance is not None:
+            # fmt: off
+            attr_name = (
+                self.fget.__name__
+                if sys.version_info >= (3, 10)
+                else self.fget.__func__.__name__  # pyright: ignore[reportFunctionMemberAccess]
+            )
+            # fmt: on
+            warnings.warn(
+                f'Accessing the {attr_name!r} attribute on the instance is deprecated. '
+                'Instead, you should access this attribute from the model class.',
+                category=PydanticDeprecatedSince211,
+                stacklevel=2,
+            )
+        return self.fget.__get__(instance, objtype)()

venv/Lib/site-packages/pydantic/_internal/_validate_call.py

@@ -0,0 +1,141 @@
+from __future__ import annotations as _annotations
+
+import functools
+import inspect
+from collections.abc import Awaitable
+from functools import partial
+from typing import Any, Callable
+
+import pydantic_core
+
+from ..config import ConfigDict
+from ..plugin._schema_validator import create_schema_validator
+from ._config import ConfigWrapper
+from ._generate_schema import GenerateSchema, ValidateCallSupportedTypes
+from ._namespace_utils import MappingNamespace, NsResolver, ns_for_function
+from ._typing_extra import signature_no_eval
+
+
+def extract_function_name(func: ValidateCallSupportedTypes) -> str:
+    """Extract the name of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__name__})' if isinstance(func, functools.partial) else func.__name__
+
+
+def extract_function_qualname(func: ValidateCallSupportedTypes) -> str:
+    """Extract the qualname of a `ValidateCallSupportedTypes` object."""
+    return f'partial({func.func.__qualname__})' if isinstance(func, functools.partial) else func.__qualname__
+
+
+def update_wrapper_attributes(wrapped: ValidateCallSupportedTypes, wrapper: Callable[..., Any]):
+    """Update the `wrapper` function with the attributes of the `wrapped` function. Return the updated function."""
+    if inspect.iscoroutinefunction(wrapped):
+
+        @functools.wraps(wrapped)
+        async def wrapper_function(*args, **kwargs):  # type: ignore
+            return await wrapper(*args, **kwargs)
+    else:
+
+        @functools.wraps(wrapped)
+        def wrapper_function(*args, **kwargs):
+            return wrapper(*args, **kwargs)
+
+    # We need to manually update this because `partial` object has no `__name__` and `__qualname__`.
+    wrapper_function.__name__ = extract_function_name(wrapped)
+    wrapper_function.__qualname__ = extract_function_qualname(wrapped)
+    wrapper_function.raw_function = wrapped  # type: ignore
+
+    return wrapper_function
+
+
+class ValidateCallWrapper:
+    """This is a wrapper around a function that validates the arguments passed to it, and optionally the return value."""
+
+    __slots__ = (
+        'function',
+        'validate_return',
+        'schema_type',
+        'module',
+        'qualname',
+        'ns_resolver',
+        'config_wrapper',
+        '__pydantic_complete__',
+        '__pydantic_validator__',
+        '__return_pydantic_validator__',
+    )
+
+    def __init__(
+        self,
+        function: ValidateCallSupportedTypes,
+        config: ConfigDict | None,
+        validate_return: bool,
+        parent_namespace: MappingNamespace | None,
+    ) -> None:
+        self.function = function
+        self.validate_return = validate_return
+        if isinstance(function, partial):
+            self.schema_type = function.func
+            self.module = function.func.__module__
+        else:
+            self.schema_type = function
+            self.module = function.__module__
+        self.qualname = extract_function_qualname(function)
+
+        self.ns_resolver = NsResolver(
+            namespaces_tuple=ns_for_function(self.schema_type, parent_namespace=parent_namespace)
+        )
+        self.config_wrapper = ConfigWrapper(config)
+        if not self.config_wrapper.defer_build:
+            self._create_validators()
+        else:
+            self.__pydantic_complete__ = False
+
+    def _create_validators(self) -> None:
+        gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver)
+        schema = gen_schema.clean_schema(gen_schema.generate_schema(self.function))
+        core_config = self.config_wrapper.core_config(title=self.qualname)
+
+        self.__pydantic_validator__ = create_schema_validator(
+            schema,
+            self.schema_type,
+            self.module,
+            self.qualname,
+            'validate_call',
+            core_config,
+            self.config_wrapper.plugin_settings,
+        )
+        if self.validate_return:
+            signature = signature_no_eval(self.function)
+            return_type = signature.return_annotation if signature.return_annotation is not signature.empty else Any
+            gen_schema = GenerateSchema(self.config_wrapper, self.ns_resolver)
+            schema = gen_schema.clean_schema(gen_schema.generate_schema(return_type))
+            validator = create_schema_validator(
+                schema,
+                self.schema_type,
+                self.module,
+                self.qualname,
+                'validate_call',
+                core_config,
+                self.config_wrapper.plugin_settings,
+            )
+            if inspect.iscoroutinefunction(self.function):
+
+                async def return_val_wrapper(aw: Awaitable[Any]) -> None:
+                    return validator.validate_python(await aw)
+
+                self.__return_pydantic_validator__ = return_val_wrapper
+            else:
+                self.__return_pydantic_validator__ = validator.validate_python
+        else:
+            self.__return_pydantic_validator__ = None
+
+        self.__pydantic_complete__ = True
+
+    def __call__(self, *args: Any, **kwargs: Any) -> Any:
+        if not self.__pydantic_complete__:
+            self._create_validators()
+
+        res = self.__pydantic_validator__.validate_python(pydantic_core.ArgsKwargs(args, kwargs))
+        if self.__return_pydantic_validator__:
+            return self.__return_pydantic_validator__(res)
+        else:
+            return res

venv/Lib/site-packages/pydantic/_internal/_validators.py

@@ -0,0 +1,534 @@
+"""Validator functions for standard library types.
+
+Import of this module is deferred since it contains imports of many standard library modules.
+"""
+
+from __future__ import annotations as _annotations
+
+import collections.abc
+import math
+import re
+import typing
+from collections.abc import Sequence
+from decimal import Decimal
+from fractions import Fraction
+from ipaddress import IPv4Address, IPv4Interface, IPv4Network, IPv6Address, IPv6Interface, IPv6Network
+from typing import Any, Callable, TypeVar, Union, cast
+from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
+
+import typing_extensions
+from pydantic_core import PydanticCustomError, PydanticKnownError, core_schema
+from typing_extensions import get_args, get_origin
+from typing_inspection import typing_objects
+
+from pydantic._internal._import_utils import import_cached_field_info
+from pydantic.errors import PydanticSchemaGenerationError
+
+
+def sequence_validator(
+    input_value: Sequence[Any],
+    /,
+    validator: core_schema.ValidatorFunctionWrapHandler,
+) -> Sequence[Any]:
+    """Validator for `Sequence` types, isinstance(v, Sequence) has already been called."""
+    value_type = type(input_value)
+
+    # We don't accept any plain string as a sequence
+    # Relevant issue: https://github.com/pydantic/pydantic/issues/5595
+    if issubclass(value_type, (str, bytes)):
+        raise PydanticCustomError(
+            'sequence_str',
+            "'{type_name}' instances are not allowed as a Sequence value",
+            {'type_name': value_type.__name__},
+        )
+
+    # TODO: refactor sequence validation to validate with either a list or a tuple
+    # schema, depending on the type of the value.
+    # Additionally, we should be able to remove one of either this validator or the
+    # SequenceValidator in _std_types_schema.py (preferably this one, while porting over some logic).
+    # Effectively, a refactor for sequence validation is needed.
+    if value_type is tuple:
+        input_value = list(input_value)
+
+    v_list = validator(input_value)
+
+    # the rest of the logic is just re-creating the original type from `v_list`
+    if value_type is list:
+        return v_list
+    elif issubclass(value_type, range):
+        # return the list as we probably can't re-create the range
+        return v_list
+    elif value_type is tuple:
+        return tuple(v_list)
+    else:
+        # best guess at how to re-create the original type, more custom construction logic might be required
+        return value_type(v_list)  # type: ignore[call-arg]
+
+
+def import_string(value: Any) -> Any:
+    if isinstance(value, str):
+        try:
+            return _import_string_logic(value)
+        except ImportError as e:
+            raise PydanticCustomError('import_error', 'Invalid python path: {error}', {'error': str(e)}) from e
+    else:
+        # otherwise we just return the value and let the next validator do the rest of the work
+        return value
+
+
+def _import_string_logic(dotted_path: str) -> Any:
+    """Inspired by uvicorn — dotted paths should include a colon before the final item if that item is not a module.
+    (This is necessary to distinguish between a submodule and an attribute when there is a conflict.).
+
+    If the dotted path does not include a colon and the final item is not a valid module, importing as an attribute
+    rather than a submodule will be attempted automatically.
+
+    So, for example, the following values of `dotted_path` result in the following returned values:
+    * 'collections': <module 'collections'>
+    * 'collections.abc': <module 'collections.abc'>
+    * 'collections.abc:Mapping': <class 'collections.abc.Mapping'>
+    * `collections.abc.Mapping`: <class 'collections.abc.Mapping'> (though this is a bit slower than the previous line)
+
+    An error will be raised under any of the following scenarios:
+    * `dotted_path` contains more than one colon (e.g., 'collections:abc:Mapping')
+    * the substring of `dotted_path` before the colon is not a valid module in the environment (e.g., '123:Mapping')
+    * the substring of `dotted_path` after the colon is not an attribute of the module (e.g., 'collections:abc123')
+    """
+    from importlib import import_module
+
+    components = dotted_path.strip().split(':')
+    if len(components) > 2:
+        raise ImportError(f"Import strings should have at most one ':'; received {dotted_path!r}")
+    attribute = None
+    if len(components) == 2:
+        attribute = components[1]
+    module_path = components[0]
+    if not module_path:
+        raise ImportError(f'Import strings should have a nonempty module name; received {dotted_path!r}')
+
+    try:
+        module = import_module(module_path)
+    except ModuleNotFoundError:
+        if attribute is None and '.' in module_path:
+            # Try interpreting the final dotted segment as an attribute, not a submodule
+            maybe_module_path, maybe_attribute = module_path.rsplit('.', 1)
+
+            try:
+                return _import_string_logic(f'{maybe_module_path}:{maybe_attribute}')
+            except ImportError:
+                pass
+        raise
+
+    if attribute is not None:
+        try:
+            return getattr(module, attribute)
+        except AttributeError as e:
+            raise ImportError(f'cannot import name {attribute!r} from {module_path!r}') from e
+    else:
+        return module
+
+
+def pattern_either_validator(input_value: Any, /) -> re.Pattern[Any]:
+    if isinstance(input_value, re.Pattern):
+        return input_value
+    elif isinstance(input_value, (str, bytes)):
+        # todo strict mode
+        return compile_pattern(input_value)  # type: ignore
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_str_validator(input_value: Any, /) -> re.Pattern[str]:
+    if isinstance(input_value, re.Pattern):
+        if isinstance(input_value.pattern, str):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    elif isinstance(input_value, str):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, bytes):
+        raise PydanticCustomError('pattern_str_type', 'Input should be a string pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+def pattern_bytes_validator(input_value: Any, /) -> re.Pattern[bytes]:
+    if isinstance(input_value, re.Pattern):
+        if isinstance(input_value.pattern, bytes):
+            return input_value
+        else:
+            raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    elif isinstance(input_value, bytes):
+        return compile_pattern(input_value)
+    elif isinstance(input_value, str):
+        raise PydanticCustomError('pattern_bytes_type', 'Input should be a bytes pattern')
+    else:
+        raise PydanticCustomError('pattern_type', 'Input should be a valid pattern')
+
+
+PatternType = TypeVar('PatternType', str, bytes)
+
+
+def compile_pattern(pattern: PatternType) -> re.Pattern[PatternType]:
+    try:
+        return re.compile(pattern)
+    except re.error:
+        raise PydanticCustomError('pattern_regex', 'Input should be a valid regular expression')
+
+
+def ip_v4_address_validator(input_value: Any, /) -> IPv4Address:
+    if isinstance(input_value, IPv4Address):
+        return input_value
+
+    try:
+        return IPv4Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_address', 'Input is not a valid IPv4 address')
+
+
+def ip_v6_address_validator(input_value: Any, /) -> IPv6Address:
+    if isinstance(input_value, IPv6Address):
+        return input_value
+
+    try:
+        return IPv6Address(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_address', 'Input is not a valid IPv6 address')
+
+
+def ip_v4_network_validator(input_value: Any, /) -> IPv4Network:
+    """Assume IPv4Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv4Network
+    """
+    if isinstance(input_value, IPv4Network):
+        return input_value
+
+    try:
+        return IPv4Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_network', 'Input is not a valid IPv4 network')
+
+
+def ip_v6_network_validator(input_value: Any, /) -> IPv6Network:
+    """Assume IPv6Network initialised with a default `strict` argument.
+
+    See more:
+    https://docs.python.org/library/ipaddress.html#ipaddress.IPv6Network
+    """
+    if isinstance(input_value, IPv6Network):
+        return input_value
+
+    try:
+        return IPv6Network(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_network', 'Input is not a valid IPv6 network')
+
+
+def ip_v4_interface_validator(input_value: Any, /) -> IPv4Interface:
+    if isinstance(input_value, IPv4Interface):
+        return input_value
+
+    try:
+        return IPv4Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v4_interface', 'Input is not a valid IPv4 interface')
+
+
+def ip_v6_interface_validator(input_value: Any, /) -> IPv6Interface:
+    if isinstance(input_value, IPv6Interface):
+        return input_value
+
+    try:
+        return IPv6Interface(input_value)
+    except ValueError:
+        raise PydanticCustomError('ip_v6_interface', 'Input is not a valid IPv6 interface')
+
+
+def fraction_validator(input_value: Any, /) -> Fraction:
+    if isinstance(input_value, Fraction):
+        return input_value
+
+    try:
+        return Fraction(input_value)
+    except ValueError:
+        raise PydanticCustomError('fraction_parsing', 'Input is not a valid fraction')
+
+
+def forbid_inf_nan_check(x: Any) -> Any:
+    if not math.isfinite(x):
+        raise PydanticKnownError('finite_number')
+    return x
+
+
+def _safe_repr(v: Any) -> int | float | str:
+    """The context argument for `PydanticKnownError` requires a number or str type, so we do a simple repr() coercion for types like timedelta.
+
+    See tests/test_types.py::test_annotated_metadata_any_order for some context.
+    """
+    if isinstance(v, (int, float, str)):
+        return v
+    return repr(v)
+
+
+def greater_than_validator(x: Any, gt: Any) -> Any:
+    try:
+        if not (x > gt):
+            raise PydanticKnownError('greater_than', {'gt': _safe_repr(gt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'gt' to supplied value {x}")
+
+
+def greater_than_or_equal_validator(x: Any, ge: Any) -> Any:
+    try:
+        if not (x >= ge):
+            raise PydanticKnownError('greater_than_equal', {'ge': _safe_repr(ge)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'ge' to supplied value {x}")
+
+
+def less_than_validator(x: Any, lt: Any) -> Any:
+    try:
+        if not (x < lt):
+            raise PydanticKnownError('less_than', {'lt': _safe_repr(lt)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'lt' to supplied value {x}")
+
+
+def less_than_or_equal_validator(x: Any, le: Any) -> Any:
+    try:
+        if not (x <= le):
+            raise PydanticKnownError('less_than_equal', {'le': _safe_repr(le)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'le' to supplied value {x}")
+
+
+def multiple_of_validator(x: Any, multiple_of: Any) -> Any:
+    try:
+        if x % multiple_of:
+            raise PydanticKnownError('multiple_of', {'multiple_of': _safe_repr(multiple_of)})
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'multiple_of' to supplied value {x}")
+
+
+def min_length_validator(x: Any, min_length: Any) -> Any:
+    try:
+        if not (len(x) >= min_length):
+            raise PydanticKnownError(
+                'too_short', {'field_type': 'Value', 'min_length': min_length, 'actual_length': len(x)}
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'min_length' to supplied value {x}")
+
+
+def max_length_validator(x: Any, max_length: Any) -> Any:
+    try:
+        if len(x) > max_length:
+            raise PydanticKnownError(
+                'too_long',
+                {'field_type': 'Value', 'max_length': max_length, 'actual_length': len(x)},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_length' to supplied value {x}")
+
+
+def _extract_decimal_digits_info(decimal: Decimal) -> tuple[int, int]:
+    """Compute the total number of digits and decimal places for a given [`Decimal`][decimal.Decimal] instance.
+
+    This function handles both normalized and non-normalized Decimal instances.
+    Example: Decimal('1.230') -> 4 digits, 3 decimal places
+
+    Args:
+        decimal (Decimal): The decimal number to analyze.
+
+    Returns:
+        tuple[int, int]: A tuple containing the number of decimal places and total digits.
+
+    Though this could be divided into two separate functions, the logic is easier to follow if we couple the computation
+    of the number of decimals and digits together.
+    """
+    try:
+        decimal_tuple = decimal.as_tuple()
+
+        assert isinstance(decimal_tuple.exponent, int)
+
+        exponent = decimal_tuple.exponent
+        num_digits = len(decimal_tuple.digits)
+
+        if exponent >= 0:
+            # A positive exponent adds that many trailing zeros
+            # Ex: digit_tuple=(1, 2, 3), exponent=2 -> 12300 -> 0 decimal places, 5 digits
+            num_digits += exponent
+            decimal_places = 0
+        else:
+            # If the absolute value of the negative exponent is larger than the
+            # number of digits, then it's the same as the number of digits,
+            # because it'll consume all the digits in digit_tuple and then
+            # add abs(exponent) - len(digit_tuple) leading zeros after the decimal point.
+            # Ex: digit_tuple=(1, 2, 3), exponent=-2 -> 1.23 -> 2 decimal places, 3 digits
+            # Ex: digit_tuple=(1, 2, 3), exponent=-4 -> 0.0123 -> 4 decimal places, 4 digits
+            decimal_places = abs(exponent)
+            num_digits = max(num_digits, decimal_places)
+
+        return decimal_places, num_digits
+    except (AssertionError, AttributeError):
+        raise TypeError(f'Unable to extract decimal digits info from supplied value {decimal}')
+
+
+def max_digits_validator(x: Any, max_digits: Any) -> Any:
+    try:
+        _, num_digits = _extract_decimal_digits_info(x)
+        _, normalized_num_digits = _extract_decimal_digits_info(x.normalize())
+        if (num_digits > max_digits) and (normalized_num_digits > max_digits):
+            raise PydanticKnownError(
+                'decimal_max_digits',
+                {'max_digits': max_digits},
+            )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'max_digits' to supplied value {x}")
+
+
+def decimal_places_validator(x: Any, decimal_places: Any) -> Any:
+    try:
+        decimal_places_, _ = _extract_decimal_digits_info(x)
+        if decimal_places_ > decimal_places:
+            normalized_decimal_places, _ = _extract_decimal_digits_info(x.normalize())
+            if normalized_decimal_places > decimal_places:
+                raise PydanticKnownError(
+                    'decimal_max_places',
+                    {'decimal_places': decimal_places},
+                )
+        return x
+    except TypeError:
+        raise TypeError(f"Unable to apply constraint 'decimal_places' to supplied value {x}")
+
+
+def deque_validator(input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler) -> collections.deque[Any]:
+    return collections.deque(handler(input_value), maxlen=getattr(input_value, 'maxlen', None))
+
+
+def defaultdict_validator(
+    input_value: Any, handler: core_schema.ValidatorFunctionWrapHandler, default_default_factory: Callable[[], Any]
+) -> collections.defaultdict[Any, Any]:
+    if isinstance(input_value, collections.defaultdict):
+        default_factory = input_value.default_factory
+        return collections.defaultdict(default_factory, handler(input_value))
+    else:
+        return collections.defaultdict(default_default_factory, handler(input_value))
+
+
+def get_defaultdict_default_default_factory(values_source_type: Any) -> Callable[[], Any]:
+    FieldInfo = import_cached_field_info()
+
+    values_type_origin = get_origin(values_source_type)
+
+    def infer_default() -> Callable[[], Any]:
+        allowed_default_types: dict[Any, Any] = {
+            tuple: tuple,
+            collections.abc.Sequence: tuple,
+            collections.abc.MutableSequence: list,
+            list: list,
+            typing.Sequence: list,
+            set: set,
+            typing.MutableSet: set,
+            collections.abc.MutableSet: set,
+            collections.abc.Set: frozenset,
+            typing.MutableMapping: dict,
+            typing.Mapping: dict,
+            collections.abc.Mapping: dict,
+            collections.abc.MutableMapping: dict,
+            float: float,
+            int: int,
+            str: str,
+            bool: bool,
+        }
+        values_type = values_type_origin or values_source_type
+        instructions = 'set using `DefaultDict[..., Annotated[..., Field(default_factory=...)]]`'
+        if typing_objects.is_typevar(values_type):
+
+            def type_var_default_factory() -> None:
+                raise RuntimeError(
+                    'Generic defaultdict cannot be used without a concrete value type or an'
+                    ' explicit default factory, ' + instructions
+                )
+
+            return type_var_default_factory
+        elif values_type not in allowed_default_types:
+            # a somewhat subjective set of types that have reasonable default values
+            allowed_msg = ', '.join([t.__name__ for t in set(allowed_default_types.values())])
+            raise PydanticSchemaGenerationError(
+                f'Unable to infer a default factory for keys of type {values_source_type}.'
+                f' Only {allowed_msg} are supported, other types require an explicit default factory'
+                ' ' + instructions
+            )
+        return allowed_default_types[values_type]
+
+    # Assume Annotated[..., Field(...)]
+    if typing_objects.is_annotated(values_type_origin):
+        field_info = next((v for v in get_args(values_source_type) if isinstance(v, FieldInfo)), None)
+    else:
+        field_info = None
+    if field_info and field_info.default_factory:
+        # Assume the default factory does not take any argument:
+        default_default_factory = cast(Callable[[], Any], field_info.default_factory)
+    else:
+        default_default_factory = infer_default()
+    return default_default_factory
+
+
+def validate_str_is_valid_iana_tz(value: Any, /) -> ZoneInfo:
+    if isinstance(value, ZoneInfo):
+        return value
+    try:
+        return ZoneInfo(value)
+    except (ZoneInfoNotFoundError, ValueError, TypeError):
+        raise PydanticCustomError('zoneinfo_str', 'invalid timezone: {value}', {'value': value})
+
+
+NUMERIC_VALIDATOR_LOOKUP: dict[str, Callable] = {
+    'gt': greater_than_validator,
+    'ge': greater_than_or_equal_validator,
+    'lt': less_than_validator,
+    'le': less_than_or_equal_validator,
+    'multiple_of': multiple_of_validator,
+    'min_length': min_length_validator,
+    'max_length': max_length_validator,
+    'max_digits': max_digits_validator,
+    'decimal_places': decimal_places_validator,
+}
+
+IpType = Union[IPv4Address, IPv6Address, IPv4Network, IPv6Network, IPv4Interface, IPv6Interface]
+
+IP_VALIDATOR_LOOKUP: dict[type[IpType], Callable] = {
+    IPv4Address: ip_v4_address_validator,
+    IPv6Address: ip_v6_address_validator,
+    IPv4Network: ip_v4_network_validator,
+    IPv6Network: ip_v6_network_validator,
+    IPv4Interface: ip_v4_interface_validator,
+    IPv6Interface: ip_v6_interface_validator,
+}
+
+MAPPING_ORIGIN_MAP: dict[Any, Any] = {
+    typing.DefaultDict: collections.defaultdict,  # noqa: UP006
+    collections.defaultdict: collections.defaultdict,
+    typing.OrderedDict: collections.OrderedDict,  # noqa: UP006
+    collections.OrderedDict: collections.OrderedDict,
+    typing_extensions.OrderedDict: collections.OrderedDict,
+    typing.Counter: collections.Counter,
+    collections.Counter: collections.Counter,
+    # this doesn't handle subclasses of these
+    typing.Mapping: dict,
+    typing.MutableMapping: dict,
+    # parametrized typing.{Mutable}Mapping creates one of these
+    collections.abc.Mapping: dict,
+    collections.abc.MutableMapping: dict,
+}