414 lines
18 KiB
Python
414 lines
18 KiB
Python
"""Provide an enhanced dataclass that performs validation."""
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from __future__ import annotations as _annotations
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import dataclasses
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import functools
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import sys
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import types
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from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, overload
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from warnings import warn
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from typing_extensions import TypeGuard, dataclass_transform
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from ._internal import _config, _decorators, _mock_val_ser, _namespace_utils, _typing_extra
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from ._internal import _dataclasses as _pydantic_dataclasses
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from ._migration import getattr_migration
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from .config import ConfigDict
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from .errors import PydanticUserError
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from .fields import Field, FieldInfo, PrivateAttr
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if TYPE_CHECKING:
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from ._internal._dataclasses import PydanticDataclass
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from ._internal._namespace_utils import MappingNamespace
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__all__ = 'dataclass', 'rebuild_dataclass'
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_T = TypeVar('_T')
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if sys.version_info >= (3, 10):
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@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
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@overload
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def dataclass(
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*,
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init: Literal[False] = False,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool = False,
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config: ConfigDict | type[object] | None = None,
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validate_on_init: bool | None = None,
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kw_only: bool = ...,
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slots: bool = ...,
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) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore
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...
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@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
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@overload
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def dataclass(
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_cls: type[_T], # type: ignore
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*,
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init: Literal[False] = False,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool | None = None,
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config: ConfigDict | type[object] | None = None,
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validate_on_init: bool | None = None,
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kw_only: bool = ...,
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slots: bool = ...,
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) -> type[PydanticDataclass]: ...
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else:
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@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
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@overload
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def dataclass(
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*,
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init: Literal[False] = False,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool | None = None,
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config: ConfigDict | type[object] | None = None,
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validate_on_init: bool | None = None,
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) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore
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...
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@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
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@overload
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def dataclass(
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_cls: type[_T], # type: ignore
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*,
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init: Literal[False] = False,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool | None = None,
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config: ConfigDict | type[object] | None = None,
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validate_on_init: bool | None = None,
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) -> type[PydanticDataclass]: ...
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@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
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def dataclass(
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_cls: type[_T] | None = None,
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*,
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init: Literal[False] = False,
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repr: bool = True,
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eq: bool = True,
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order: bool = False,
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unsafe_hash: bool = False,
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frozen: bool | None = None,
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config: ConfigDict | type[object] | None = None,
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validate_on_init: bool | None = None,
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kw_only: bool = False,
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slots: bool = False,
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) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
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"""!!! abstract "Usage Documentation"
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[`dataclasses`](../concepts/dataclasses.md)
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A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
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but with added validation.
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This function should be used similarly to `dataclasses.dataclass`.
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Args:
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_cls: The target `dataclass`.
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init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
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`dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
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own `__init__` function.
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repr: A boolean indicating whether to include the field in the `__repr__` output.
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eq: Determines if a `__eq__` method should be generated for the class.
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order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
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unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
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frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
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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).
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config: The Pydantic config to use for the `dataclass`.
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validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
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are validated on init.
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kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
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slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
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new attributes after instantiation.
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Returns:
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A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.
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Raises:
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AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
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"""
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assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
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assert validate_on_init is not False, 'validate_on_init=False is no longer supported'
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if sys.version_info >= (3, 10):
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kwargs = {'kw_only': kw_only, 'slots': slots}
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else:
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kwargs = {}
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def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
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"""Create a Pydantic dataclass from a regular dataclass.
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Args:
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cls: The class to create the Pydantic dataclass from.
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Returns:
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A Pydantic dataclass.
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"""
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from ._internal._utils import is_model_class
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if is_model_class(cls):
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raise PydanticUserError(
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f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
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code='dataclass-on-model',
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)
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original_cls = cls
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# we warn on conflicting config specifications, but only if the class doesn't have a dataclass base
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# because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about
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has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__)
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if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'):
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warn(
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f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. '
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f'The `config` specification from `dataclass` decorator will take priority.',
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category=UserWarning,
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stacklevel=2,
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)
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# if config is not explicitly provided, try to read it from the type
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config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None)
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config_wrapper = _config.ConfigWrapper(config_dict)
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decorators = _decorators.DecoratorInfos.build(cls)
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decorators.update_from_config(config_wrapper)
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# Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
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# Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
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# since dataclasses.dataclass will set this as the __doc__
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original_doc = cls.__doc__
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if _pydantic_dataclasses.is_stdlib_dataclass(cls):
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# Vanilla dataclasses include a default docstring (representing the class signature),
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# which we don't want to preserve.
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original_doc = None
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# We don't want to add validation to the existing std lib dataclass, so we will subclass it
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# If the class is generic, we need to make sure the subclass also inherits from Generic
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# with all the same parameters.
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bases = (cls,)
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if issubclass(cls, Generic):
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generic_base = Generic[cls.__parameters__] # type: ignore
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bases = bases + (generic_base,)
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cls = types.new_class(cls.__name__, bases)
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# Respect frozen setting from dataclass constructor and fallback to config setting if not provided
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if frozen is not None:
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frozen_ = frozen
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if config_wrapper.frozen:
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# It's not recommended to define both, as the setting from the dataclass decorator will take priority.
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warn(
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f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.'
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'This is not recommended. The `frozen` specification on `dataclass` will take priority.',
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category=UserWarning,
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stacklevel=2,
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)
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else:
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frozen_ = config_wrapper.frozen or False
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# Make Pydantic's `Field()` function compatible with stdlib dataclasses. As we'll decorate
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# `cls` with the stdlib `@dataclass` decorator first, there are two attributes, `kw_only` and
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# `repr` that need to be understood *during* the stdlib creation. We do so in two steps:
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# 1. On the decorated class, wrap `Field()` assignment with `dataclass.field()`, with the
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# two attributes set (done in `as_dataclass_field()`)
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cls_anns = _typing_extra.safe_get_annotations(cls)
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for field_name in cls_anns:
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# We should look for assignments in `__dict__` instead, but for now we follow
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# the same behavior as stdlib dataclasses (see https://github.com/python/cpython/issues/88609)
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field_value = getattr(cls, field_name, None)
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if isinstance(field_value, FieldInfo):
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setattr(cls, field_name, _pydantic_dataclasses.as_dataclass_field(field_value))
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# 2. For bases of `cls` that are stdlib dataclasses, we temporarily patch their fields
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# (see the docstring of the context manager):
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with _pydantic_dataclasses.patch_base_fields(cls):
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cls = dataclasses.dataclass( # pyright: ignore[reportCallIssue]
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cls,
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# the value of init here doesn't affect anything except that it makes it easier to generate a signature
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init=True,
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repr=repr,
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eq=eq,
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order=order,
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unsafe_hash=unsafe_hash,
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frozen=frozen_,
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**kwargs,
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)
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if config_wrapper.validate_assignment:
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original_setattr = cls.__setattr__
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@functools.wraps(cls.__setattr__)
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def validated_setattr(instance: PydanticDataclass, name: str, value: Any, /) -> None:
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if frozen_:
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return original_setattr(instance, name, value) # pyright: ignore[reportCallIssue]
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inst_cls = type(instance)
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attr = getattr(inst_cls, name, None)
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if isinstance(attr, property):
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attr.__set__(instance, value)
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elif isinstance(attr, functools.cached_property):
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instance.__dict__.__setitem__(name, value)
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else:
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inst_cls.__pydantic_validator__.validate_assignment(instance, name, value)
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cls.__setattr__ = validated_setattr.__get__(None, cls) # type: ignore
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if slots and not hasattr(cls, '__setstate__'):
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# If slots is set, `pickle` (relied on by `copy.copy()`) will use
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# `__setattr__()` to reconstruct the dataclass. However, the custom
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# `__setattr__()` set above relies on `validate_assignment()`, which
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# in turn expects all the field values to be already present on the
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# instance, resulting in attribute errors.
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# As such, we make use of `object.__setattr__()` instead.
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# Note that we do so only if `__setstate__()` isn't already set (this is the
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# case if on top of `slots`, `frozen` is used).
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# Taken from `dataclasses._dataclass_get/setstate()`:
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def _dataclass_getstate(self: Any) -> list[Any]:
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return [getattr(self, f.name) for f in dataclasses.fields(self)]
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def _dataclass_setstate(self: Any, state: list[Any]) -> None:
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for field, value in zip(dataclasses.fields(self), state):
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object.__setattr__(self, field.name, value)
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cls.__getstate__ = _dataclass_getstate # pyright: ignore[reportAttributeAccessIssue]
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cls.__setstate__ = _dataclass_setstate # pyright: ignore[reportAttributeAccessIssue]
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# This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses.
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# It should be set as early as possible:
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cls.__is_pydantic_dataclass__ = True
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cls.__pydantic_decorators__ = decorators # type: ignore
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cls.__doc__ = original_doc
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# Can be non-existent for dynamically created classes:
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firstlineno = getattr(original_cls, '__firstlineno__', None)
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cls.__module__ = original_cls.__module__
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if sys.version_info >= (3, 13) and firstlineno is not None:
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# As per https://docs.python.org/3/reference/datamodel.html#type.__firstlineno__:
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# Setting the `__module__` attribute removes the `__firstlineno__` item from the type’s dictionary.
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original_cls.__firstlineno__ = firstlineno
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cls.__firstlineno__ = firstlineno
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cls.__qualname__ = original_cls.__qualname__
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cls.__pydantic_fields_complete__ = classmethod(_pydantic_fields_complete)
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cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful.
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# TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models:
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# fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function),
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# and possibly cache it (see the `__pydantic_parent_namespace__` logic for models).
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_pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False)
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return cls
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return create_dataclass if _cls is None else create_dataclass(_cls)
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def _pydantic_fields_complete(cls: type[PydanticDataclass]) -> bool:
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"""Return whether the fields where successfully collected (i.e. type hints were successfully resolves).
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This is a private property, not meant to be used outside Pydantic.
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"""
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return all(field_info._complete for field_info in cls.__pydantic_fields__.values())
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__getattr__ = getattr_migration(__name__)
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if sys.version_info < (3, 11):
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# Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
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# Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.
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def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn:
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"""This function does nothing but raise an error that is as similar as possible to what you'd get
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if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
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to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
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"""
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raise TypeError("'InitVar' object is not callable")
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dataclasses.InitVar.__call__ = _call_initvar
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def rebuild_dataclass(
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cls: type[PydanticDataclass],
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*,
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force: bool = False,
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raise_errors: bool = True,
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_parent_namespace_depth: int = 2,
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_types_namespace: MappingNamespace | None = None,
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) -> bool | None:
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"""Try to rebuild the pydantic-core schema for the dataclass.
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This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
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the initial attempt to build the schema, and automatic rebuilding fails.
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This is analogous to `BaseModel.model_rebuild`.
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Args:
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cls: The class to rebuild the pydantic-core schema for.
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force: Whether to force the rebuilding of the schema, defaults to `False`.
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raise_errors: Whether to raise errors, defaults to `True`.
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_parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
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_types_namespace: The types namespace, defaults to `None`.
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Returns:
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Returns `None` if the schema is already "complete" and rebuilding was not required.
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If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
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"""
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if not force and cls.__pydantic_complete__:
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return None
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for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
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if attr in cls.__dict__ and not isinstance(getattr(cls, attr), _mock_val_ser.MockValSer):
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# Deleting the validator/serializer is necessary as otherwise they can get reused in
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# pydantic-core. Same applies for the core schema that can be reused in schema generation.
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delattr(cls, attr)
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cls.__pydantic_complete__ = False
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if _types_namespace is not None:
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rebuild_ns = _types_namespace
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elif _parent_namespace_depth > 0:
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rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
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else:
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rebuild_ns = {}
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ns_resolver = _namespace_utils.NsResolver(
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parent_namespace=rebuild_ns,
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)
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return _pydantic_dataclasses.complete_dataclass(
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cls,
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_config.ConfigWrapper(cls.__pydantic_config__, check=False),
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raise_errors=raise_errors,
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ns_resolver=ns_resolver,
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# We could provide a different config instead (with `'defer_build'` set to `True`)
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# of this explicit `_force_build` argument, but because config can come from the
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# decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass`
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# will overwrite `__pydantic_config__` with the provided config above:
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_force_build=True,
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)
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def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]:
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"""Whether a class is a pydantic dataclass.
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Args:
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class_: The class.
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Returns:
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`True` if the class is a pydantic dataclass, `False` otherwise.
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"""
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try:
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return '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_)
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except AttributeError:
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return False
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