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Diffstat (limited to '.venv/lib/python3.12/site-packages/pydantic/dataclasses.py')
| -rw-r--r-- | .venv/lib/python3.12/site-packages/pydantic/dataclasses.py | 366 |
1 files changed, 366 insertions, 0 deletions
diff --git a/.venv/lib/python3.12/site-packages/pydantic/dataclasses.py b/.venv/lib/python3.12/site-packages/pydantic/dataclasses.py new file mode 100644 index 00000000..fe7709f8 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic/dataclasses.py @@ -0,0 +1,366 @@ +"""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 warnings import warn + +from typing_extensions import Literal, TypeGuard, dataclass_transform + +from ._internal import _config, _decorators, _namespace_utils, _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 + from ._internal._namespace_utils import MappingNamespace + +__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 | None = None, + 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 | None = None, + 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 | None = None, + config: ConfigDict | type[object] | None = None, + validate_on_init: bool | None = None, + ) -> type[PydanticDataclass]: ... + + +@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) +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 | None = None, + 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/2.10/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. If not set, the value from the provided `config` argument will be used (and will default to `False` otherwise). + 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 = {'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 + + # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base + # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about + has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__) + if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'): + warn( + f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. ' + f'The `config` specification from `dataclass` decorator will take priority.', + category=UserWarning, + stacklevel=2, + ) + + # if config is not explicitly provided, try to read it from the type + config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None) + 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) + + # Respect frozen setting from dataclass constructor and fallback to config setting if not provided + if frozen is not None: + frozen_ = frozen + if config_wrapper.frozen: + # It's not recommended to define both, as the setting from the dataclass decorator will take priority. + warn( + f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.' + 'This is not recommended. The `frozen` specification on `dataclass` will take priority.', + category=UserWarning, + stacklevel=2, + ) + else: + frozen_ = config_wrapper.frozen or False + + 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__ + cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful. + # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models: + # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function), + # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models). + _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False) + return cls + + return create_dataclass if _cls is None else 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: MappingNamespace | 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 + + if '__pydantic_core_schema__' in cls.__dict__: + delattr(cls, '__pydantic_core_schema__') # delete cached value to ensure full rebuild happens + + if _types_namespace is not None: + rebuild_ns = _types_namespace + elif _parent_namespace_depth > 0: + rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} + else: + rebuild_ns = {} + + ns_resolver = _namespace_utils.NsResolver( + parent_namespace=rebuild_ns, + ) + + return _pydantic_dataclasses.complete_dataclass( + cls, + _config.ConfigWrapper(cls.__pydantic_config__, check=False), + raise_errors=raise_errors, + ns_resolver=ns_resolver, + # We could provide a different config instead (with `'defer_build'` set to `True`) + # of this explicit `_force_build` argument, but because config can come from the + # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass` + # will overwrite `__pydantic_config__` with the provided config above: + _force_build=True, + ) + + +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 |