Coverage for pydantic/dataclasses.py: 98.08%
112 statements
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1"""Provide an enhanced dataclass that performs validation."""
3from __future__ import annotations as _annotations 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
5import dataclasses 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
6import functools 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
7import sys 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
8import types 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
9from typing import TYPE_CHECKING, Any, Callable, Generic, Literal, NoReturn, TypeVar, overload 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
10from warnings import warn 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
12from typing_extensions import TypeGuard, dataclass_transform 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
14from ._internal import _config, _decorators, _namespace_utils, _typing_extra 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
15from ._internal import _dataclasses as _pydantic_dataclasses 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
16from ._migration import getattr_migration 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
17from .config import ConfigDict 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
18from .errors import PydanticUserError 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
19from .fields import Field, FieldInfo, PrivateAttr 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
21if TYPE_CHECKING: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
22 from ._internal._dataclasses import PydanticDataclass
23 from ._internal._namespace_utils import MappingNamespace
25__all__ = 'dataclass', 'rebuild_dataclass' 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
27_T = TypeVar('_T') 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
29if sys.version_info >= (3, 10): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
31 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
32 @overload 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
33 def dataclass( 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
34 *,
35 init: Literal[False] = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
36 repr: bool = True, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
37 eq: bool = True, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
38 order: bool = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
39 unsafe_hash: bool = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
40 frozen: bool = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
41 config: ConfigDict | type[object] | None = None, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
42 validate_on_init: bool | None = None, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
43 kw_only: bool = ..., 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
44 slots: bool = ..., 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
45 ) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore 1defghaklmnoJKLMrstuv
46 ...
48 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
49 @overload 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
50 def dataclass( 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
51 _cls: type[_T], # type: ignore 1defghaklmnoJKLMrstuv
52 *,
53 init: Literal[False] = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
54 repr: bool = True, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
55 eq: bool = True, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
56 order: bool = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
57 unsafe_hash: bool = False, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
58 frozen: bool | None = None, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
59 config: ConfigDict | type[object] | None = None, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
60 validate_on_init: bool | None = None, 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
61 kw_only: bool = ..., 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
62 slots: bool = ..., 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
63 ) -> type[PydanticDataclass]: ... 1defghaklmnoJKLMrstuv
65else:
67 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) 1DECFGPHI
68 @overload 1DECFGPHI
69 def dataclass( 1DECFGPHI
70 *,
71 init: Literal[False] = False, 1DECFGPHI
72 repr: bool = True, 1DECFGPHI
73 eq: bool = True, 1DECFGPHI
74 order: bool = False, 1DECFGPHI
75 unsafe_hash: bool = False, 1DECFGPHI
76 frozen: bool | None = None, 1DECFGPHI
77 config: ConfigDict | type[object] | None = None, 1DECFGPHI
78 validate_on_init: bool | None = None, 1DECFGPHI
79 ) -> Callable[[type[_T]], type[PydanticDataclass]]: # type: ignore 1C
80 ...
82 @dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) 1DECFGPHI
83 @overload 1DECFGPHI
84 def dataclass( 1DECFGPHI
85 _cls: type[_T], # type: ignore 1C
86 *,
87 init: Literal[False] = False, 1DECFGPHI
88 repr: bool = True, 1DECFGPHI
89 eq: bool = True, 1DECFGPHI
90 order: bool = False, 1DECFGPHI
91 unsafe_hash: bool = False, 1DECFGPHI
92 frozen: bool | None = None, 1DECFGPHI
93 config: ConfigDict | type[object] | None = None, 1DECFGPHI
94 validate_on_init: bool | None = None, 1DECFGPHI
95 ) -> type[PydanticDataclass]: ... 1C
98@dataclass_transform(field_specifiers=(dataclasses.field, Field, PrivateAttr)) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
99def dataclass( 1DEbcwxdefghFGijyzklmnoPNOJKLMHIpqABrstuv
100 _cls: type[_T] | None = None,
101 *,
102 init: Literal[False] = False,
103 repr: bool = True,
104 eq: bool = True,
105 order: bool = False,
106 unsafe_hash: bool = False,
107 frozen: bool | None = None,
108 config: ConfigDict | type[object] | None = None,
109 validate_on_init: bool | None = None,
110 kw_only: bool = False,
111 slots: bool = False,
112) -> Callable[[type[_T]], type[PydanticDataclass]] | type[PydanticDataclass]:
113 """!!! abstract "Usage Documentation"
114 [`dataclasses`](../concepts/dataclasses.md)
116 A decorator used to create a Pydantic-enhanced dataclass, similar to the standard Python `dataclass`,
117 but with added validation.
119 This function should be used similarly to `dataclasses.dataclass`.
121 Args:
122 _cls: The target `dataclass`.
123 init: Included for signature compatibility with `dataclasses.dataclass`, and is passed through to
124 `dataclasses.dataclass` when appropriate. If specified, must be set to `False`, as pydantic inserts its
125 own `__init__` function.
126 repr: A boolean indicating whether to include the field in the `__repr__` output.
127 eq: Determines if a `__eq__` method should be generated for the class.
128 order: Determines if comparison magic methods should be generated, such as `__lt__`, but not `__eq__`.
129 unsafe_hash: Determines if a `__hash__` method should be included in the class, as in `dataclasses.dataclass`.
130 frozen: Determines if the generated class should be a 'frozen' `dataclass`, which does not allow its
131 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).
132 config: The Pydantic config to use for the `dataclass`.
133 validate_on_init: A deprecated parameter included for backwards compatibility; in V2, all Pydantic dataclasses
134 are validated on init.
135 kw_only: Determines if `__init__` method parameters must be specified by keyword only. Defaults to `False`.
136 slots: Determines if the generated class should be a 'slots' `dataclass`, which does not allow the addition of
137 new attributes after instantiation.
139 Returns:
140 A decorator that accepts a class as its argument and returns a Pydantic `dataclass`.
142 Raises:
143 AssertionError: Raised if `init` is not `False` or `validate_on_init` is `False`.
144 """
145 assert init is False, 'pydantic.dataclasses.dataclass only supports init=False' 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
146 assert validate_on_init is not False, 'validate_on_init=False is no longer supported' 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
148 if sys.version_info >= (3, 10): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
149 kwargs = {'kw_only': kw_only, 'slots': slots} 1bcwxdefghaijyzklmnoNOJKLMpqABrstuv
150 else:
151 kwargs = {} 1DECFGPHI
153 def make_pydantic_fields_compatible(cls: type[Any]) -> None: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
154 """Make sure that stdlib `dataclasses` understands `Field` kwargs like `kw_only`
155 To do that, we simply change
156 `x: int = pydantic.Field(..., kw_only=True)`
157 into
158 `x: int = dataclasses.field(default=pydantic.Field(..., kw_only=True), kw_only=True)`
159 """
160 for annotation_cls in cls.__mro__: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
161 annotations: dict[str, Any] = getattr(annotation_cls, '__annotations__', {}) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
162 for field_name in annotations: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
163 field_value = getattr(cls, field_name, None) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
164 # Process only if this is an instance of `FieldInfo`.
165 if not isinstance(field_value, FieldInfo): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
166 continue 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
168 # Initialize arguments for the standard `dataclasses.field`.
169 field_args: dict = {'default': field_value} 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
171 # Handle `kw_only` for Python 3.10+
172 if sys.version_info >= (3, 10) and field_value.kw_only: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
173 field_args['kw_only'] = True 1bcwxdefghaijyzklmnopqABrstuv
175 # Set `repr` attribute if it's explicitly specified to be not `True`.
176 if field_value.repr is not True: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
177 field_args['repr'] = field_value.repr 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
179 setattr(cls, field_name, dataclasses.field(**field_args)) 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
180 # In Python 3.9, when subclassing, information is pulled from cls.__dict__['__annotations__']
181 # for annotations, so we must make sure it's initialized before we add to it.
182 if cls.__dict__.get('__annotations__') is None: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
183 cls.__annotations__ = {} 1DECFGHI
184 cls.__annotations__[field_name] = annotations[field_name] 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
186 def create_dataclass(cls: type[Any]) -> type[PydanticDataclass]: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
187 """Create a Pydantic dataclass from a regular dataclass.
189 Args:
190 cls: The class to create the Pydantic dataclass from.
192 Returns:
193 A Pydantic dataclass.
194 """
195 from ._internal._utils import is_model_class 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
197 if is_model_class(cls): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
198 raise PydanticUserError( 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
199 f'Cannot create a Pydantic dataclass from {cls.__name__} as it is already a Pydantic model',
200 code='dataclass-on-model',
201 )
203 original_cls = cls 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
205 # we warn on conflicting config specifications, but only if the class doesn't have a dataclass base
206 # because a dataclass base might provide a __pydantic_config__ attribute that we don't want to warn about
207 has_dataclass_base = any(dataclasses.is_dataclass(base) for base in cls.__bases__) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
208 if not has_dataclass_base and config is not None and hasattr(cls, '__pydantic_config__'): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
209 warn( 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
210 f'`config` is set via both the `dataclass` decorator and `__pydantic_config__` for dataclass {cls.__name__}. '
211 f'The `config` specification from `dataclass` decorator will take priority.',
212 category=UserWarning,
213 stacklevel=2,
214 )
216 # if config is not explicitly provided, try to read it from the type
217 config_dict = config if config is not None else getattr(cls, '__pydantic_config__', None) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
218 config_wrapper = _config.ConfigWrapper(config_dict) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
219 decorators = _decorators.DecoratorInfos.build(cls) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
221 # Keep track of the original __doc__ so that we can restore it after applying the dataclasses decorator
222 # Otherwise, classes with no __doc__ will have their signature added into the JSON schema description,
223 # since dataclasses.dataclass will set this as the __doc__
224 original_doc = cls.__doc__ 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
226 if _pydantic_dataclasses.is_builtin_dataclass(cls): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
227 # Don't preserve the docstring for vanilla dataclasses, as it may include the signature
228 # This matches v1 behavior, and there was an explicit test for it
229 original_doc = None 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
231 # We don't want to add validation to the existing std lib dataclass, so we will subclass it
232 # If the class is generic, we need to make sure the subclass also inherits from Generic
233 # with all the same parameters.
234 bases = (cls,) 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
235 if issubclass(cls, Generic): 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
236 generic_base = Generic[cls.__parameters__] # type: ignore 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
237 bases = bases + (generic_base,) 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
238 cls = types.new_class(cls.__name__, bases) 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
240 make_pydantic_fields_compatible(cls) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
242 # Respect frozen setting from dataclass constructor and fallback to config setting if not provided
243 if frozen is not None: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
244 frozen_ = frozen 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
245 if config_wrapper.frozen: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
246 # It's not recommended to define both, as the setting from the dataclass decorator will take priority.
247 warn( 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
248 f'`frozen` is set via both the `dataclass` decorator and `config` for dataclass {cls.__name__!r}.'
249 'This is not recommended. The `frozen` specification on `dataclass` will take priority.',
250 category=UserWarning,
251 stacklevel=2,
252 )
253 else:
254 frozen_ = config_wrapper.frozen or False 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
256 cls = dataclasses.dataclass( # type: ignore[call-overload] 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
257 cls,
258 # the value of init here doesn't affect anything except that it makes it easier to generate a signature
259 init=True,
260 repr=repr,
261 eq=eq,
262 order=order,
263 unsafe_hash=unsafe_hash,
264 frozen=frozen_,
265 **kwargs,
266 )
268 if config_wrapper.validate_assignment: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
270 @functools.wraps(cls.__setattr__) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
271 def validated_setattr(instance: Any, field: str, value: str, /) -> None: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
272 type(instance).__pydantic_validator__.validate_assignment(instance, field, value) 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
274 cls.__setattr__ = validated_setattr.__get__(None, cls) # type: ignore 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
276 if slots and not hasattr(cls, '__setstate__'): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
277 # If slots is set, `pickle` (relied on by `copy.copy()`) will use
278 # `__setattr__()` to reconstruct the dataclass. However, the custom
279 # `__setattr__()` set above relies on `validate_assignment()`, which
280 # in turn expects all the field values to be already present on the
281 # instance, resulting in attribute errors.
282 # As such, we make use of `object.__setattr__()` instead.
283 # Note that we do so only if `__setstate__()` isn't already set (this is the
284 # case if on top of `slots`, `frozen` is used).
286 # Taken from `dataclasses._dataclass_get/setstate()`:
287 def _dataclass_getstate(self: Any) -> list[Any]: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
288 return [getattr(self, f.name) for f in dataclasses.fields(self)] 1bcwxdefghaijyzklmnopqABrstuv
290 def _dataclass_setstate(self: Any, state: list[Any]) -> None: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
291 for field, value in zip(dataclasses.fields(self), state): 1bcwxdefghaijyzklmnopqABrstuv
292 object.__setattr__(self, field.name, value) 1bcwxdefghaijyzklmnopqABrstuv
294 cls.__getstate__ = _dataclass_getstate # pyright: ignore[reportAttributeAccessIssue] 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
295 cls.__setstate__ = _dataclass_setstate # pyright: ignore[reportAttributeAccessIssue] 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
297 # This is an undocumented attribute to distinguish stdlib/Pydantic dataclasses.
298 # It should be set as early as possible:
299 cls.__is_pydantic_dataclass__ = True 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
301 cls.__pydantic_decorators__ = decorators # type: ignore 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
302 cls.__doc__ = original_doc 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
303 cls.__module__ = original_cls.__module__ 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
304 cls.__qualname__ = original_cls.__qualname__ 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
305 cls.__pydantic_complete__ = False # `complete_dataclass` will set it to `True` if successful. 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
306 # TODO `parent_namespace` is currently None, but we could do the same thing as Pydantic models:
307 # fetch the parent ns using `parent_frame_namespace` (if the dataclass was defined in a function),
308 # and possibly cache it (see the `__pydantic_parent_namespace__` logic for models).
309 _pydantic_dataclasses.complete_dataclass(cls, config_wrapper, raise_errors=False) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
310 return cls 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
312 return create_dataclass if _cls is None else create_dataclass(_cls) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
315__getattr__ = getattr_migration(__name__) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
317if sys.version_info < (3, 11): 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
318 # Monkeypatch dataclasses.InitVar so that typing doesn't error if it occurs as a type when evaluating type hints
319 # Starting in 3.11, typing.get_type_hints will not raise an error if the retrieved type hints are not callable.
321 def _call_initvar(*args: Any, **kwargs: Any) -> NoReturn: 1DEbcCaFGijPNHIpq
322 """This function does nothing but raise an error that is as similar as possible to what you'd get
323 if you were to try calling `InitVar[int]()` without this monkeypatch. The whole purpose is just
324 to ensure typing._type_check does not error if the type hint evaluates to `InitVar[<parameter>]`.
325 """
326 raise TypeError("'InitVar' object is not callable") 1DEbcCaFGijHIpq
328 dataclasses.InitVar.__call__ = _call_initvar 1DEbcCaFGijPNHIpq
331def rebuild_dataclass( 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
332 cls: type[PydanticDataclass],
333 *,
334 force: bool = False,
335 raise_errors: bool = True,
336 _parent_namespace_depth: int = 2,
337 _types_namespace: MappingNamespace | None = None,
338) -> bool | None:
339 """Try to rebuild the pydantic-core schema for the dataclass.
341 This may be necessary when one of the annotations is a ForwardRef which could not be resolved during
342 the initial attempt to build the schema, and automatic rebuilding fails.
344 This is analogous to `BaseModel.model_rebuild`.
346 Args:
347 cls: The class to rebuild the pydantic-core schema for.
348 force: Whether to force the rebuilding of the schema, defaults to `False`.
349 raise_errors: Whether to raise errors, defaults to `True`.
350 _parent_namespace_depth: The depth level of the parent namespace, defaults to 2.
351 _types_namespace: The types namespace, defaults to `None`.
353 Returns:
354 Returns `None` if the schema is already "complete" and rebuilding was not required.
355 If rebuilding _was_ required, returns `True` if rebuilding was successful, otherwise `False`.
356 """
357 if not force and cls.__pydantic_complete__: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
358 return None 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
360 for attr in ('__pydantic_core_schema__', '__pydantic_validator__', '__pydantic_serializer__'): 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
361 if attr in cls.__dict__: 361 ↛ 360line 361 didn't jump to line 360 because the condition on line 361 was always true1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
362 # Deleting the validator/serializer is necessary as otherwise they can get reused in
363 # pydantic-core. Same applies for the core schema that can be reused in schema generation.
364 delattr(cls, attr) 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
366 cls.__pydantic_complete__ = False 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
368 if _types_namespace is not None: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
369 rebuild_ns = _types_namespace 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
370 elif _parent_namespace_depth > 0: 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
371 rebuild_ns = _typing_extra.parent_frame_namespace(parent_depth=_parent_namespace_depth, force=True) or {} 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
372 else:
373 rebuild_ns = {} 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
375 ns_resolver = _namespace_utils.NsResolver( 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
376 parent_namespace=rebuild_ns,
377 )
379 return _pydantic_dataclasses.complete_dataclass( 1DEbcwxdefghCaFGijyzklmnoHIpqABrstuv
380 cls,
381 _config.ConfigWrapper(cls.__pydantic_config__, check=False),
382 raise_errors=raise_errors,
383 ns_resolver=ns_resolver,
384 # We could provide a different config instead (with `'defer_build'` set to `True`)
385 # of this explicit `_force_build` argument, but because config can come from the
386 # decorator parameter or the `__pydantic_config__` attribute, `complete_dataclass`
387 # will overwrite `__pydantic_config__` with the provided config above:
388 _force_build=True,
389 )
392def is_pydantic_dataclass(class_: type[Any], /) -> TypeGuard[type[PydanticDataclass]]: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
393 """Whether a class is a pydantic dataclass.
395 Args:
396 class_: The class.
398 Returns:
399 `True` if the class is a pydantic dataclass, `False` otherwise.
400 """
401 try: 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
402 return '__is_pydantic_dataclass__' in class_.__dict__ and dataclasses.is_dataclass(class_) 1DEbcwxdefghCaFGijyzklmnoPNOJKLMHIpqABrstuv
403 except AttributeError:
404 return False