shanxia2.0/backend/venv/lib/python3.9/site-packages/pydantic/dataclasses.py

"""Provide an enhanced dataclass that performs validation."""
from __future__ import annotations as _annotations

import dataclasses
import sys
import types
from typing import TYPE_CHECKING, Any, Callable, Generic, NoReturn, TypeVar, overload

from typing_extensions import Literal, TypeGuard, dataclass_transform

from ._internal import _config, _decorators, _typing_extra
from ._internal import _dataclasses as _pydantic_dataclasses
from ._migration import getattr_migration
from .config import ConfigDict
from .fields import Field, FieldInfo

if TYPE_CHECKING:
    from ._internal._dataclasses import PydanticDataclass

__all__ = 'dataclass', 'rebuild_dataclass'

_T = TypeVar('_T')

if sys.version_info >= (3, 10):

    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
    @overload
    def dataclass(
        *,
        init: Literal[False] = False,
        repr: bool = True,
        eq: bool = True,
        order: bool = False,
        unsafe_hash: bool = False,
        frozen: bool = False,
        config: ConfigDict | type[object] | None = None,
        validate_on_init: bool | None = None,
        kw_only: bool = ...,
        slots: bool = ...,
    ) -> Callable[[type[_T]], type[PydanticDataclass]]:  # type: ignore
        ...

    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
    @overload
    def dataclass(
        _cls: type[_T],  # type: ignore
        *,
        init: Literal[False] = False,
        repr: bool = True,
        eq: bool = True,
        order: bool = False,
        unsafe_hash: bool = False,
        frozen: bool = False,
        config: ConfigDict | type[object] | None = None,
        validate_on_init: bool | None = None,
        kw_only: bool = ...,
        slots: bool = ...,
    ) -> type[PydanticDataclass]:
        ...

else:

    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
    @overload
    def dataclass(
        *,
        init: Literal[False] = False,
        repr: bool = True,
        eq: bool = True,
        order: bool = False,
        unsafe_hash: bool = False,
        frozen: bool = False,
        config: ConfigDict | type[object] | None = None,
        validate_on_init: bool | None = None,
    ) -> Callable[[type[_T]], type[PydanticDataclass]]:  # type: ignore
        ...

    @dataclass_transform(field_specifiers=(dataclasses.field, Field))
    @overload
    def dataclass(
        _cls: type[_T],  # type: ignore
        *,
        init: Literal[False] = False,
        repr: bool = True,
        eq: bool = True,
        order: bool = False,
        unsafe_hash: bool = False,
        frozen: bool = False,
        config: ConfigDict | type[object] | None = None,
        validate_on_init: bool | None = None,
    ) -> type[PydanticDataclass]:
        ...


@dataclass_transform(field_specifiers=(dataclasses.field, Field))
def dataclass(
    _cls: type[_T] | None = None,
    *,
    init: Literal[False] = False,
    repr: bool = True,
    eq: bool = True,
    order: bool = False,
    unsafe_hash: bool = False,
    frozen: bool = False,
    config: ConfigDict | type[object] | None = None,
    validate_on_init: bool | None = None,
    kw_only: bool = False,
    slots: bool = False,
) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
    """Usage docs: https://docs.pydantic.dev/2.5/concepts/dataclasses/

    A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
    but with added validation.

    This function should be used similarly to `dataclasses.dataclass`.

    Args:
        _cls: The target `dataclass`.
        init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
            `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
            own  `__init__` function.
        repr: A boolean indicating whether or not to include the field in the `__repr__` output.
        eq: Determines if a `__eq__` should be generated for the class.
        order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
        unsafe_hash: Determines if an unsafe hashing function should be included in the class.
        frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
            attributes to be modified from its constructor.
        config: A configuration for the `dataclass` generation.
        validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
            are validated on init.
        kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
        slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
            new attributes after instantiation.

    Returns:
        A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.

    Raises:
        AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
    """
    assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
    assert validate_on_init is not False, 'validate_on_init=False is no longer supported'

    if sys.version_info >= (3, 10):
        kwargs = dict(kw_only=kw_only, slots=slots)

        def make_pydantic_fields_compatible(cls: type[Any]) -> None:
            """Make sure that stdlib `dataclasses` understands `Field` kwargs like `kw_only`
            To do that, we simply change
              `x: int = pydantic.Field(..., kw_only=True)`
            into
              `x: int = dataclasses.field(default=pydantic.Field(..., kw_only=True), kw_only=True)`
            """
            for field_name in cls.__annotations__:
                try:
                    field_value = getattr(cls, field_name)
                except AttributeError:
                    # no default value has been set for this field
                    continue
                if isinstance(field_value, FieldInfo) and field_value.kw_only:
                    setattr(cls, field_name, dataclasses.field(default=field_value, kw_only=True))

    else:
        kwargs = {}

        def make_pydantic_fields_compatible(_) -> None:
            return None

    def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
        """Create a Pydantic dataclass from a regular dataclass.

        Args:
            cls: The class to create the Pydantic dataclass from.

        Returns:
            A Pydantic dataclass.
        """
        original_cls = cls

        config_dict = config
        if config_dict is None:
            # if not explicitly provided, read from the type
            cls_config = getattr(cls, '__pydantic_config__', None)
            if cls_config is not None:
                config_dict = cls_config
        config_wrapper = _config.ConfigWrapper(config_dict)
        decorators = _decorators.DecoratorInfos.build(cls)

        # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
        # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
        # since dataclasses.dataclass will set this as the __doc__
        original_doc = cls.__doc__

        if _pydantic_dataclasses.is_builtin_dataclass(cls):
            # Don't preserve the docstring for vanilla dataclasses, as it may include the signature
            # This matches v1 behavior, and there was an explicit test for it
            original_doc = None

            # We don't want to add validation to the existing std lib dataclass, so we will subclass it
            #   If the class is generic, we need to make sure the subclass also inherits from Generic
            #   with all the same parameters.
            bases = (cls,)
            if issubclass(cls, Generic):
                generic_base = Generic[cls.__parameters__]  # type: ignore
                bases = bases + (generic_base,)
            cls = types.new_class(cls.__name__, bases)

        make_pydantic_fields_compatible(cls)

        cls = dataclasses.dataclass(  # type: ignore[call-overload]
            cls,
            # the value of init here doesn't affect anything except that it makes it easier to generate a signature
            init=True,
            repr=repr,
            eq=eq,
            order=order,
            unsafe_hash=unsafe_hash,
            frozen=frozen,
            **kwargs,
        )

        cls.__pydantic_decorators__ = decorators  # type: ignore
        cls.__doc__ = original_doc
        cls.__module__ = original_cls.__module__
        cls.__qualname__ = original_cls.__qualname__
        pydantic_complete = _pydantic_dataclasses.complete_dataclass(
            cls, config_wrapper, raise_errors=False, types_namespace=None
        )
        cls.__pydantic_complete__ = pydantic_complete  # type: ignore
        return cls

    if _cls is None:
        return create_dataclass

    return create_dataclass(_cls)


__getattr__ = getattr_migration(__name__)

if (3, 8) <= sys.version_info < (3, 11):
    # Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
    # Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.

    def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn:
        """This function does nothing but raise an error that is as similar as possible to what you'd get
        if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
        to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
        """
        raise TypeError("'InitVar' object is not callable")

    dataclasses.InitVar.__call__ = _call_initvar


def rebuild_dataclass(
    cls: type[PydanticDataclass],
    *,
    force: bool = False,
    raise_errors: bool = True,
    _parent_namespace_depth: int = 2,
    _types_namespace: dict[str, Any] | None = None,
) -> bool | None:
    """Try to rebuild the pydantic-core schema for the dataclass.

    This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
    the initial attempt to build the schema, and automatic rebuilding fails.

    This is analogous to `BaseModel.model_rebuild`.

    Args:
        cls: The class to build the dataclass core schema for.
        force: Whether to force the rebuilding of the model schema, defaults to `False`.
        raise_errors: Whether to raise errors, defaults to `True`.
        _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
        _types_namespace: The types namespace, defaults to `None`.

    Returns:
        Returns `None` if the schema is already "complete" and rebuilding was not required.
        If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
    """
    if not force and cls.__pydantic_complete__:
        return None
    else:
        if _types_namespace is not None:
            types_namespace: dict[str, Any] | None = _types_namespace.copy()
        else:
            if _parent_namespace_depth > 0:
                frame_parent_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth) or {}
                # Note: we may need to add something similar to cls.__pydantic_parent_namespace__ from BaseModel
                #   here when implementing handling of recursive generics. See BaseModel.model_rebuild for reference.
                types_namespace = frame_parent_ns
            else:
                types_namespace = {}

            types_namespace = _typing_extra.get_cls_types_namespace(cls, types_namespace)
        return _pydantic_dataclasses.complete_dataclass(
            cls,
            _config.ConfigWrapper(cls.__pydantic_config__, check=False),
            raise_errors=raise_errors,
            types_namespace=types_namespace,
        )


def is_pydantic_dataclass(__cls: type[Any]) -> TypeGuard[type[PydanticDataclass]]:
    """Whether a class is a pydantic dataclass.

    Args:
        __cls: The class.

    Returns:
        `True` if the class is a pydantic dataclass, `False` otherwise.
    """
    return dataclasses.is_dataclass(__cls) and '__pydantic_validator__' in __cls.__dict__