Edit: _dataclasses.py
"""Private logic for creating pydantic dataclasses.""" from __future__ import annotations as _annotations import dataclasses import typing import warnings from functools import partial, wraps from typing import Any, Callable, ClassVar from pydantic_core import ( ArgsKwargs, SchemaSerializer, SchemaValidator, core_schema, ) from typing_extensions import TypeGuard from ..errors import PydanticUndefinedAnnotation from ..fields import FieldInfo from ..plugin._schema_validator import PluggableSchemaValidator, create_schema_validator from ..warnings import PydanticDeprecatedSince20 from . import _config, _decorators, _typing_extra from ._fields import collect_dataclass_fields from ._generate_schema import GenerateSchema from ._generics import get_standard_typevars_map from ._mock_val_ser import set_dataclass_mocks from ._schema_generation_shared import CallbackGetCoreSchemaHandler from ._signature import generate_pydantic_signature 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 | PluggableSchemaValidator] 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, config_wrapper: _config.ConfigWrapper | None = None, ) -> None: """Collect and set `cls.__pydantic_fields__`. Args: cls: The class. types_namespace: The types namespace, defaults to `None`. config_wrapper: The config wrapper instance, defaults to `None`. """ typevars_map = get_standard_typevars_map(cls) fields = collect_dataclass_fields(cls, types_namespace, typevars_map=typevars_map, config_wrapper=config_wrapper) cls.__pydantic_fields__ = fields # type: ignore def complete_dataclass( cls: type[Any], config_wrapper: _config.ConfigWrapper, *, raise_errors: bool = True, 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, config_wrapper=config_wrapper) 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_pydantic_signature( init=cls.__init__, fields=cls.__pydantic_fields__, # type: ignore config_wrapper=config_wrapper, is_dataclass=True, ) # 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 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__', {}))) )
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