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

"""Private logic for creating pydantic dataclasses."""
from __future__ import annotations as _annotations

import dataclasses
import inspect
import typing
import warnings
from functools import partial, wraps
from inspect import Parameter, Signature
from typing import Any, Callable, ClassVar

from pydantic_core import (
    ArgsKwargs,
    PydanticUndefined,
    SchemaSerializer,
    SchemaValidator,
    core_schema,
)
from typing_extensions import TypeGuard

from ..errors import PydanticUndefinedAnnotation
from ..fields import FieldInfo
from ..plugin._schema_validator import create_schema_validator
from ..warnings import PydanticDeprecatedSince20
from . import _config, _decorators, _typing_extra
from ._config import ConfigWrapper
from ._fields import collect_dataclass_fields
from ._generate_schema import GenerateSchema, generate_pydantic_signature
from ._generics import get_standard_typevars_map
from ._mock_val_ser import set_dataclass_mocks
from ._schema_generation_shared import CallbackGetCoreSchemaHandler
from ._utils import is_valid_identifier

if typing.TYPE_CHECKING:
    from ..config import ConfigDict

    class StandardDataclass(typing.Protocol):
        __dataclass_fields__: ClassVar[dict[str, Any]]
        __dataclass_params__: ClassVar[Any]  # in reality `dataclasses._DataclassParams`
        __post_init__: ClassVar[Callable[..., None]]

        def __init__(self, *args: object, **kwargs: object) -> None:
            pass

    class PydanticDataclass(StandardDataclass, typing.Protocol):
        """A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass.

        Attributes:
            __pydantic_config__: Pydantic-specific configuration settings for the dataclass.
            __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields.
            __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer.
            __pydantic_decorators__: Metadata containing the decorators defined on the dataclass.
            __pydantic_fields__: Metadata about the fields defined on the dataclass.
            __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass.
            __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass.
        """

        __pydantic_config__: ClassVar[ConfigDict]
        __pydantic_complete__: ClassVar[bool]
        __pydantic_core_schema__: ClassVar[core_schema.CoreSchema]
        __pydantic_decorators__: ClassVar[_decorators.DecoratorInfos]
        __pydantic_fields__: ClassVar[dict[str, FieldInfo]]
        __pydantic_serializer__: ClassVar[SchemaSerializer]
        __pydantic_validator__: ClassVar[SchemaValidator]

else:
    # See PyCharm issues https://youtrack.jetbrains.com/issue/PY-21915
    # and https://youtrack.jetbrains.com/issue/PY-51428
    DeprecationWarning = PydanticDeprecatedSince20


def set_dataclass_fields(cls: type[StandardDataclass], types_namespace: dict[str, Any] | None = None) -> None:
    """Collect and set `cls.__pydantic_fields__`.

    Args:
        cls: The class.
        types_namespace: The types namespace, defaults to `None`.
    """
    typevars_map = get_standard_typevars_map(cls)
    fields = collect_dataclass_fields(cls, types_namespace, typevars_map=typevars_map)

    cls.__pydantic_fields__ = fields  # type: ignore


def complete_dataclass(
    cls: type[Any],
    config_wrapper: _config.ConfigWrapper,
    *,
    raise_errors: bool = True,
    types_namespace: dict[str, Any] | None,
) -> bool:
    """Finish building a pydantic dataclass.

    This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`.

    This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`.

    Args:
        cls: The class.
        config_wrapper: The config wrapper instance.
        raise_errors: Whether to raise errors, defaults to `True`.
        types_namespace: The types namespace.

    Returns:
        `True` if building a pydantic dataclass is successfully completed, `False` otherwise.

    Raises:
        PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations.
    """
    if hasattr(cls, '__post_init_post_parse__'):
        warnings.warn(
            'Support for `__post_init_post_parse__` has been dropped, the method will not be called', DeprecationWarning
        )

    if types_namespace is None:
        types_namespace = _typing_extra.get_cls_types_namespace(cls)

    set_dataclass_fields(cls, types_namespace)

    typevars_map = get_standard_typevars_map(cls)
    gen_schema = GenerateSchema(
        config_wrapper,
        types_namespace,
        typevars_map,
    )

    # This needs to be called before we change the __init__
    sig = generate_dataclass_signature(cls, cls.__pydantic_fields__, config_wrapper)  # type: ignore

    # dataclass.__init__ must be defined here so its `__qualname__` can be changed since functions can't be copied.
    def __init__(__dataclass_self__: PydanticDataclass, *args: Any, **kwargs: Any) -> None:
        __tracebackhide__ = True
        s = __dataclass_self__
        s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)

    __init__.__qualname__ = f'{cls.__qualname__}.__init__'

    cls.__init__ = __init__  # type: ignore
    cls.__pydantic_config__ = config_wrapper.config_dict  # type: ignore
    cls.__signature__ = sig  # type: ignore
    get_core_schema = getattr(cls, '__get_pydantic_core_schema__', None)
    try:
        if get_core_schema:
            schema = get_core_schema(
                cls,
                CallbackGetCoreSchemaHandler(
                    partial(gen_schema.generate_schema, from_dunder_get_core_schema=False),
                    gen_schema,
                    ref_mode='unpack',
                ),
            )
        else:
            schema = gen_schema.generate_schema(cls, from_dunder_get_core_schema=False)
    except PydanticUndefinedAnnotation as e:
        if raise_errors:
            raise
        set_dataclass_mocks(cls, cls.__name__, f'`{e.name}`')
        return False

    core_config = config_wrapper.core_config(cls)

    try:
        schema = gen_schema.clean_schema(schema)
    except gen_schema.CollectedInvalid:
        set_dataclass_mocks(cls, cls.__name__, 'all referenced types')
        return False

    # We are about to set all the remaining required properties expected for this cast;
    # __pydantic_decorators__ and __pydantic_fields__ should already be set
    cls = typing.cast('type[PydanticDataclass]', cls)
    # debug(schema)

    cls.__pydantic_core_schema__ = schema
    cls.__pydantic_validator__ = validator = create_schema_validator(
        schema, cls, cls.__module__, cls.__qualname__, 'dataclass', core_config, config_wrapper.plugin_settings
    )
    cls.__pydantic_serializer__ = SchemaSerializer(schema, core_config)

    if config_wrapper.validate_assignment:

        @wraps(cls.__setattr__)
        def validated_setattr(instance: Any, __field: str, __value: str) -> None:
            validator.validate_assignment(instance, __field, __value)

        cls.__setattr__ = validated_setattr.__get__(None, cls)  # type: ignore

    return True


def process_param_defaults(param: Parameter) -> Parameter:
    """Custom processing where the parameter default is of type FieldInfo

    Args:
        param (Parameter): The parameter

    Returns:
        Parameter: The custom processed parameter
    """
    param_default = param.default
    if isinstance(param_default, FieldInfo):
        annotation = param.annotation
        # Replace the annotation if appropriate
        # inspect does "clever" things to show annotations as strings because we have
        # `from __future__ import annotations` in main, we don't want that
        if annotation == 'Any':
            annotation = Any

        # Replace the field name with the alias if present
        name = param.name
        alias = param_default.alias
        validation_alias = param_default.validation_alias
        if validation_alias is None and isinstance(alias, str) and is_valid_identifier(alias):
            name = alias
        elif isinstance(validation_alias, str) and is_valid_identifier(validation_alias):
            name = validation_alias

        # Replace the field default
        default = param_default.default
        if default is PydanticUndefined:
            if param_default.default_factory is PydanticUndefined:
                default = inspect.Signature.empty
            else:
                # this is used by dataclasses to indicate a factory exists:
                default = dataclasses._HAS_DEFAULT_FACTORY  # type: ignore
        return param.replace(annotation=annotation, name=name, default=default)
    return param


def generate_dataclass_signature(
    cls: type[StandardDataclass], fields: dict[str, FieldInfo], config_wrapper: ConfigWrapper
) -> Signature:
    """Generate signature for a pydantic dataclass.

    Args:
        cls: The dataclass.
        fields: The model fields.
        config_wrapper: The config wrapper instance.

    Returns:
        The dataclass signature.
    """
    return generate_pydantic_signature(
        init=cls.__init__, fields=fields, config_wrapper=config_wrapper, post_process_parameter=process_param_defaults
    )


def is_builtin_dataclass(_cls: type[Any]) -> TypeGuard[type[StandardDataclass]]:
    """Returns True if a class is a stdlib dataclass and *not* a pydantic dataclass.

    We check that
    - `_cls` is a dataclass
    - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`)
    - `_cls` does not have any annotations that are not dataclass fields
    e.g.
    ```py
    import dataclasses

    import pydantic.dataclasses

    @dataclasses.dataclass
    class A:
        x: int

    @pydantic.dataclasses.dataclass
    class B(A):
        y: int
    ```
    In this case, when we first check `B`, we make an extra check and look at the annotations ('y'),
    which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x')

    Args:
        cls: The class.

    Returns:
        `True` if the class is a stdlib dataclass, `False` otherwise.
    """
    return (
        dataclasses.is_dataclass(_cls)
        and not hasattr(_cls, '__pydantic_validator__')
        and set(_cls.__dataclass_fields__).issuperset(set(getattr(_cls, '__annotations__', {})))
    )