Current File : //proc/thread-self/root/opt/cloudlinux/venv/lib/python3.11/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 .fields import Field
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))
@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))
@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))
@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))
@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))
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 = 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/2.4/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 or not to include the field in the `__repr__` output.
eq: Determines if a `__eq__` 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 an unsafe hashing function should be included in the class.
frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
attributes to be modified from its constructor.
config: A configuration for the `dataclass` generation.
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 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.
"""
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)
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 build the dataclass core schema for.
force: Whether to force the rebuilding of the model 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(__cls: type[Any]) -> TypeGuard[type[PydanticDataclass]]:
"""Whether a class is a pydantic dataclass.
Args:
__cls: The class.
Returns:
`True` if the class is a pydantic dataclass, `False` otherwise.
"""
return dataclasses.is_dataclass(__cls) and '__pydantic_validator__' in __cls.__dict__
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