<!-- Thank you for your contribution! -->
<!-- Unless your change is trivial, please create an issue to discuss the change before creating a PR -->

## Change Summary

<!-- Please give a short summary of the changes. -->

This fixes an issue where the JSON schema can end up using the drive letter (like `C`, `D` etc) instead of the model name on Windows if the module with the model is imported from an absolute path on the filesystem (for example using importlib).  This was caused by splitting at the first colon `:` character to remove it and the ID following it, thus on an absolute path on Windows of the form `C:\...` ended up becoming just the drive letter. This fix changes it to remove the LAST colon character and anything following it.

Modified description post-further investigation of the issue / source of the problem:

This makes the model name used in the JSON Schema be correct also if the model is loaded from a module that has a colon in the name. This should not really happen in any correct use case (python modules should not typically have colons in them), but it's possible to achieve by accident if you use the absolute file path as the module name on Windows when working with importlib. In that case you would have ended up getting just the drive letter as a model name in the JSON Schema, this fix ensures it will still work correctly in that case.



## Related issue number

<!-- WARNING: please use "fix #123" style references so the issue is closed when this PR is merged. -->

fix #7860

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [ ] Unit tests for the changes exist
* [x] Tests pass on CI
* [ ] Documentation reflects the changes where applicable
* [ ] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**

Make the model name generation more robust in JSON schema. <!-- Thank you for your contribution! -->
<!-- Unless your change is trivial, please create an issue to discuss the change before creating a PR -->

## Change Summary

<!-- Please give a short summary of the changes. -->

This fixes an issue where the JSON schema can end up using the drive letter (like `C`, `D` etc) instead of the model name on Windows if the module with the model is imported from an absolute path on the filesystem (for example using importlib).  This was caused by splitting at the first colon `:` character to remove it and the ID following it, thus on an absolute path on Windows of the form `C:\...` ended up becoming just the drive letter. This fix changes it to remove the LAST colon character and anything following it.

Modified description post-further investigation of the issue / source of the problem:

This makes the model name used in the JSON Schema be correct also if the model is loaded from a module that has a colon in the name. This should not really happen in any correct use case (python modules should not typically have colons in them), but it's possible to achieve by accident if you use the absolute file path as the module name on Windows when working with importlib. In that case you would have ended up getting just the drive letter as a model name in the JSON Schema, this fix ensures it will still work correctly in that case.



## Related issue number

<!-- WARNING: please use "fix #123" style references so the issue is closed when this PR is merged. -->

fix #7860

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [ ] Unit tests for the changes exist
* [x] Tests pass on CI
* [ ] Documentation reflects the changes where applicable
* [ ] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**

"""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/2.4/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__

"""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/2.4/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__
