Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.11/site-packages/pydantic/dataclasses.py: 74%
Shortcuts on this page
r m x toggle line displays
j k next/prev highlighted chunk
0 (zero) top of page
1 (one) first highlighted chunk
1"""Provide an enhanced dataclass that performs validation."""
3from __future__ import annotations as _annotations
5import dataclasses
6import sys
7import types
8from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, overload
9from warnings import warn
11from typing_extensions import TypeGuard, dataclass_transform
13from ._internal import _config, _decorators, _namespace_utils, _typing_extra
14from ._internal import _dataclasses as _pydantic_dataclasses
15from ._migration import getattr_migration
16from .config import ConfigDict
17from .errors import PydanticUserError
18from .fields import Field, FieldInfo, PrivateAttr
20if TYPE_CHECKING:
21 from ._internal._dataclasses import PydanticDataclass
22 from ._internal._namespace_utils import MappingNamespace
24__all__ = 'dataclass', 'rebuild_dataclass'
26_T = TypeVar('_T')
28if sys.version_info >= (3, 10):
30 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
31 @overload
32 def dataclass(
33 *,
34 init: Literal[False] = False,
35 repr: bool = True,
36 eq: bool = True,
37 order: bool = False,
38 unsafe_hash: bool = False,
39 frozen: bool = False,
40 config: ConfigDict | type[object] | None = None,
41 validate_on_init: bool | None = None,
42 kw_only: bool = ...,
43 slots: bool = ...,
44 ) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore
45 ...
47 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
48 @overload
49 def dataclass(
50 _cls: type[_T], # type: ignore
51 *,
52 init: Literal[False] = False,
53 repr: bool = True,
54 eq: bool = True,
55 order: bool = False,
56 unsafe_hash: bool = False,
57 frozen: bool | None = None,
58 config: ConfigDict | type[object] | None = None,
59 validate_on_init: bool | None = None,
60 kw_only: bool = ...,
61 slots: bool = ...,
62 ) -> type[PydanticDataclass]: ...
64else:
66 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
67 @overload
68 def dataclass(
69 *,
70 init: Literal[False] = False,
71 repr: bool = True,
72 eq: bool = True,
73 order: bool = False,
74 unsafe_hash: bool = False,
75 frozen: bool | None = None,
76 config: ConfigDict | type[object] | None = None,
77 validate_on_init: bool | None = None,
78 ) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore
79 ...
81 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
82 @overload
83 def dataclass(
84 _cls: type[_T], # type: ignore
85 *,
86 init: Literal[False] = False,
87 repr: bool = True,
88 eq: bool = True,
89 order: bool = False,
90 unsafe_hash: bool = False,
91 frozen: bool | None = None,
92 config: ConfigDict | type[object] | None = None,
93 validate_on_init: bool | None = None,
94 ) -> type[PydanticDataclass]: ...
97@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr))
98def dataclass(
99 _cls: type[_T] | None = None,
100 *,
101 init: Literal[False] = False,
102 repr: bool = True,
103 eq: bool = True,
104 order: bool = False,
105 unsafe_hash: bool = False,
106 frozen: bool | None = None,
107 config: ConfigDict | type[object] | None = None,
108 validate_on_init: bool | None = None,
109 kw_only: bool = False,
110 slots: bool = False,
111) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
112 """!!! abstract "Usage Documentation"
113 [`dataclasses`](../concepts/dataclasses.md)
115 A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
116 but with added validation.
118 This function should be used similarly to `dataclasses.dataclass`.
120 Args:
121 _cls: The target `dataclass`.
122 init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
123 `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
124 own `__init__` function.
125 repr: A boolean indicating whether to include the field in the `__repr__` output.
126 eq: Determines if a `__eq__` method should be generated for the class.
127 order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
128 unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
129 frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
130 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).
131 config: The Pydantic config to use for the `dataclass`.
132 validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
133 are validated on init.
134 kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
135 slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
136 new attributes after instantiation.
138 Returns:
139 A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.
141 Raises:
142 AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
143 """
144 assert init is False, 'pydantic.dataclasses.dataclass only supports init=False'
145 assert validate_on_init is not False, 'validate_on_init=False is no longer supported'
147 if sys.version_info >= (3, 10):
148 kwargs = {'kw_only': kw_only, 'slots': slots}
149 else:
150 kwargs = {}
152 def make_pydantic_fields_compatible(cls: type[Any]) -> None:
153 """Make sure that stdlib `dataclasses` understands `Field` kwargs like `kw_only`
154 To do that, we simply change
155 `x: int = pydantic.Field(..., kw_only=True)`
156 into
157 `x: int = dataclasses.field(default=pydantic.Field(..., kw_only=True), kw_only=True)`
158 """
159 for annotation_cls in cls.__mro__:
160 annotations: dict[str, Any] = getattr(annotation_cls, '__annotations__', {})
161 for field_name in annotations:
162 field_value = getattr(cls, field_name, None)
163 # Process only if this is an instance of `FieldInfo`.
164 if not isinstance(field_value, FieldInfo):
165 continue
167 # Initialize arguments for the standard `dataclasses.field`.
168 field_args: dict = {'default': field_value}
170 # Handle `kw_only` for Python 3.10+
171 if sys.version_info >= (3, 10) and field_value.kw_only:
172 field_args['kw_only'] = True
174 # Set `repr` attribute if it's explicitly specified to be not `True`.
175 if field_value.repr is not True:
176 field_args['repr'] = field_value.repr
178 setattr(cls, field_name, dataclasses.field(**field_args))
179 # In Python 3.9, when subclassing, information is pulled from cls.__dict__['__annotations__']
180 # for annotations, so we must make sure it's initialized before we add to it.
181 if cls.__dict__.get('__annotations__') is None:
182 cls.__annotations__ = {}
183 cls.__annotations__[field_name] = annotations[field_name]
185 def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]:
186 """Create a Pydantic dataclass from a regular dataclass.
188 Args:
189 cls: The class to create the Pydantic dataclass from.
191 Returns:
192 A Pydantic dataclass.
193 """
194 from ._internal._utils import is_model_class
196 if is_model_class(cls):
197 raise PydanticUserError(
198 f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
199 code='dataclass-on-model',
200 )
202 original_cls = cls
204 # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base
205 # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about
206 has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__)
207 if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'):
208 warn(
209 f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. '
210 f'The `config` specification from `dataclass` decorator will take priority.',
211 category=UserWarning,
212 stacklevel=2,
213 )
215 # if config is not explicitly provided, try to read it from the type
216 config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None)
217 config_wrapper = _config.ConfigWrapper(config_dict)
218 decorators = _decorators.DecoratorInfos.build(cls)
220 # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
221 # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
222 # since dataclasses.dataclass will set this as the __doc__
223 original_doc = cls.__doc__
225 if _pydantic_dataclasses.is_builtin_dataclass(cls):
226 # Don't preserve the docstring for vanilla dataclasses, as it may include the signature
227 # This matches v1 behavior, and there was an explicit test for it
228 original_doc = None
230 # We don't want to add validation to the existing std lib dataclass, so we will subclass it
231 # If the class is generic, we need to make sure the subclass also inherits from Generic
232 # with all the same parameters.
233 bases = (cls,)
234 if issubclass(cls, Generic):
235 generic_base = Generic[cls.__parameters__] # type: ignore
236 bases = bases + (generic_base,)
237 cls = types.new_class(cls.__name__, bases)
239 make_pydantic_fields_compatible(cls)
241 # Respect frozen setting from dataclass constructor and fallback to config setting if not provided
242 if frozen is not None:
243 frozen_ = frozen
244 if config_wrapper.frozen:
245 # It's not recommended to define both, as the setting from the dataclass decorator will take priority.
246 warn(
247 f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.'
248 'This is not recommended. The `frozen` specification on `dataclass` will take priority.',
249 category=UserWarning,
250 stacklevel=2,
251 )
252 else:
253 frozen_ = config_wrapper.frozen or False
255 cls = dataclasses.dataclass( # type: ignore[call-overload]
256 cls,
257 # the value of init here doesn't affect anything except that it makes it easier to generate a signature
258 init=True,
259 repr=repr,
260 eq=eq,
261 order=order,
262 unsafe_hash=unsafe_hash,
263 frozen=frozen_,
264 **kwargs,
265 )
267 # This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses.
268 # It should be set as early as possible:
269 cls.__is_pydantic_dataclass__ = True
271 cls.__pydantic_decorators__ = decorators # type: ignore
272 cls.__doc__ = original_doc
273 cls.__module__ = original_cls.__module__
274 cls.__qualname__ = original_cls.__qualname__
275 cls.__pydantic_fields_complete__ = classmethod(_pydantic_fields_complete)
276 cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful.
277 # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models:
278 # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function),
279 # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models).
280 _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False)
281 return cls
283 return create_dataclass if _cls is None else create_dataclass(_cls)
286def _pydantic_fields_complete(cls: type[PydanticDataclass]) -> bool:
287 """Return whether the fields where successfully collected (i.e. type hints were successfully resolves).
289 This is a private property, not meant to be used outside Pydantic.
290 """
291 return all(field_info._complete for field_info in cls.__pydantic_fields__.values())
294__getattr__ = getattr_migration(__name__)
296if sys.version_info < (3, 11):
297 # Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
298 # Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.
300 def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn:
301 """This function does nothing but raise an error that is as similar as possible to what you'd get
302 if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
303 to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
304 """
305 raise TypeError("'InitVar' object is not callable")
307 dataclasses.InitVar.__call__ = _call_initvar
310def rebuild_dataclass(
311 cls: type[PydanticDataclass],
312 *,
313 force: bool = False,
314 raise_errors: bool = True,
315 _parent_namespace_depth: int = 2,
316 _types_namespace: MappingNamespace | None = None,
317) -> bool | None:
318 """Try to rebuild the pydantic-core schema for the dataclass.
320 This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
321 the initial attempt to build the schema, and automatic rebuilding fails.
323 This is analogous to `BaseModel.model_rebuild`.
325 Args:
326 cls: The class to rebuild the pydantic-core schema for.
327 force: Whether to force the rebuilding of the schema, defaults to `False`.
328 raise_errors: Whether to raise errors, defaults to `True`.
329 _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
330 _types_namespace: The types namespace, defaults to `None`.
332 Returns:
333 Returns `None` if the schema is already "complete" and rebuilding was not required.
334 If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
335 """
336 if not force and cls.__pydantic_complete__:
337 return None
339 for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'):
340 if attr in cls.__dict__:
341 # Deleting the validator/serializer is necessary as otherwise they can get reused in
342 # pydantic-core. Same applies for the core schema that can be reused in schema generation.
343 delattr(cls, attr)
345 cls.__pydantic_complete__ = False
347 if _types_namespace is not None:
348 rebuild_ns = _types_namespace
349 elif _parent_namespace_depth > 0:
350 rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {}
351 else:
352 rebuild_ns = {}
354 ns_resolver = _namespace_utils.NsResolver(
355 parent_namespace=rebuild_ns,
356 )
358 return _pydantic_dataclasses.complete_dataclass(
359 cls,
360 _config.ConfigWrapper(cls.__pydantic_config__, check=False),
361 raise_errors=raise_errors,
362 ns_resolver=ns_resolver,
363 # We could provide a different config instead (with `'defer_build'` set to `True`)
364 # of this explicit `_force_build` argument, but because config can come from the
365 # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass`
366 # will overwrite `__pydantic_config__` with the provided config above:
367 _force_build=True,
368 )
371def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]:
372 """Whether a class is a pydantic dataclass.
374 Args:
375 class_: The class.
377 Returns:
378 `True` if the class is a pydantic dataclass, `False` otherwise.
379 """
380 try:
381 return '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_)
382 except AttributeError:
383 return False