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Direktori : /opt/hc_python/lib64/python3.8/site-packages/pydantic/ |
Current File : //opt/hc_python/lib64/python3.8/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 .errors import PydanticUserError from .fields import Field, FieldInfo, PrivateAttr 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, PrivateAttr)) @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, PrivateAttr)) @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, PrivateAttr)) @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, PrivateAttr)) @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, PrivateAttr)) def dataclass( # noqa: C901 _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/2.8/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 to include the field in the `__repr__` output. eq: Determines if a `__eq__` method 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 a `__hash__` method should be included in the class, as in `dataclasses.dataclass`. frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its attributes to be modified after it has been initialized. config: The Pydantic config to use for the `dataclass`. 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) else: kwargs = {} 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 annotation_cls in cls.__mro__: # In Python < 3.9, `__annotations__` might not be present if there are no fields. # we therefore need to use `getattr` to avoid an `AttributeError`. annotations = getattr(annotation_cls, '__annotations__', []) for field_name in annotations: field_value = getattr(cls, field_name, None) # Process only if this is an instance of `FieldInfo`. if not isinstance(field_value, FieldInfo): continue # Initialize arguments for the standard `dataclasses.field`. field_args: dict = {'default': field_value} # Handle `kw_only` for Python 3.10+ if sys.version_info >= (3, 10) and field_value.kw_only: field_args['kw_only'] = True # Set `repr` attribute if it's explicitly specified to be not `True`. if field_value.repr is not True: field_args['repr'] = field_value.repr setattr(cls, field_name, dataclasses.field(**field_args)) # In Python 3.8, dataclasses checks cls.__dict__['__annotations__'] for annotations, # so we must make sure it's initialized before we add to it. if cls.__dict__.get('__annotations__') is None: cls.__annotations__ = {} cls.__annotations__[field_name] = annotations[field_name] 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. """ from ._internal._utils import is_model_class if is_model_class(cls): raise PydanticUserError( f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model', code='dataclass-on-model', ) 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 rebuild the pydantic-core schema for. force: Whether to force the rebuilding of the 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(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]: """Whether a class is a pydantic dataclass. Args: class_: The class. Returns: `True` if the class is a pydantic dataclass, `False` otherwise. """ try: return '__pydantic_validator__' in class_.__dict__ and dataclasses.is_dataclass(class_) except AttributeError: return False