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"""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, Literal, NoReturn, TypeVar, overload
from warnings import warn
from typing_extensions import 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]:
"""!!! abstract "Usage Documentation"
[`dataclasses`](../concepts/dataclasses.md)
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__:
annotations: dict[str, Any] = 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 3.9, when subclassing, information is pulled from 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,
)
# This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses.
# It should be set as early as possible:
cls.__is_pydantic_dataclass__ = True
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 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
for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
if attr in cls.__dict__:
# Deleting the validator/serializer is necessary as otherwise they can get reused in
# pydantic-core. Same applies for the core schema that can be reused in schema generation.
delattr(cls, attr)
cls.__pydantic_complete__ = False
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 '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_)
except AttributeError:
return False