g%SrSSKJr SSKrSSKrSSKrSSKJrJ r SSKJ r J r SSK J r JrJrJr SSKJr SS KJr SS KJrJr SS KJr S S KJrJr S SKJr S SKJr S SK J!r! S SK"J#r# S SK$J%r% S SK&J'r' S SK(J)r) S SK*J+r+ \RX(a)SSK-J.r/ SSK0J1r1 SSK2J3r3 "SS\/\Rh5r5O\r6S!S"Sjjr7SSSS.S#Sjjr8S$S jr9g)%z0Private logic for creating pydantic dataclasses.) annotationsN)partialwraps)AnyClassVar) ArgsKwargsSchemaSerializerSchemaValidator core_schema) TypeGuard)PydanticUndefinedAnnotation)PluggableSchemaValidatorcreate_schema_validator)PydanticDeprecatedSince20)_config _decorators)collect_dataclass_fields)GenerateSchema)get_standard_typevars_map)set_dataclass_mocks) NsResolver)CallbackGetCoreSchemaHandler)generate_pydantic_signature)LazyClassAttribute)DataclassInstance) ConfigDict) FieldInfoc`\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S \S'S\S'Srg)PydanticDataclass&a!A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]__pydantic_config__zClassVar[bool]__pydantic_complete__z ClassVar[core_schema.CoreSchema]__pydantic_core_schema__z$ClassVar[_decorators.DecoratorInfos]__pydantic_decorators__zClassVar[dict[str, FieldInfo]]__pydantic_fields__zClassVar[SchemaSerializer]__pydantic_serializer__z4ClassVar[SchemaValidator | PluggableSchemaValidator]__pydantic_validator__N)__name__ __module__ __qualname____firstlineno____doc____annotations____static_attributes__r*X/home/matz/Project1/venv/lib/python3.13/site-packages/pydantic/_internal/_dataclasses.pyr!r!&s3 21--"BB!EE;;!;; TTr2r!c:[U5n[XX2S9nX@lg)zCollect and set `cls.__pydantic_fields__`. Args: cls: The class. ns_resolver: Namespace resolver to use when getting dataclass annotations. config_wrapper: The config wrapper instance, defaults to `None`. ) ns_resolver typevars_mapconfig_wrapperN)rrr')clsr5r7r6fieldss r3set_dataclass_fieldsr:As%-S1L % <F%r2TF) raise_errorsr5 _force_buildc ^URnSSjnURS3UlX`lURUl[ XUS9 U(d'UR (a[ XR5 g[US5(a[R"S[5 [U5n[UUUS9n[S[[ UUR"UR$UR&S S 95Ul[+US S 5n U (a%U "U[-[UR.SS 9USS95n OUR/USS 9n UR5URS9n UR7U 5n [:R<"SU5nXl[AXURBURSXRD5=Ul#m[IX5Ul%URL(a7[OURP5SU4Sjj5n U RSS U5Ul(S Ul*g ![0a6n U(ae[ XRSU R2S35 S n A gS n A ff=f!UR8a [ XRS5 gf=f)aJFinish building a pydantic dataclass. This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`. This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`. Args: cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. ns_resolver: The namespace resolver instance to use when collecting dataclass fields and during schema building. _force_build: Whether to force building the dataclass, no matter if [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. Returns: `True` if building a pydantic dataclass is successfully completed, `False` otherwise. Raises: PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. cRSnUnURR[X5US9 g)NT) self_instance)r)validate_pythonr)__dataclass_self__argskwargs__tracebackhide__ss r3__init__$complete_dataclass..__init__vs,     00D1IYZ0[r2z .__init__)r7F__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be called)r5r6 __signature__T)initr9populate_by_nameextra is_dataclass__get_pydantic_core_schema__N)from_dunder_get_core_schemaunpack)ref_mode`)titlezall referenced typesztype[PydanticDataclass] dataclassc*>TRXU5 g)N)validate_assignment)instancefieldvalue validators r3validated_setattr-complete_dataclass..validated_setattrs  ) )(5 Ar2)rAr!rBrrCrreturnNone)rWrrXstrrYr_r]r^)+rFr- config_dictr#r: defer_buildrr+hasattrwarningswarnDeprecationWarningrrrrrr'rKrLrIgetattrrgenerate_schemarname core_config clean_schemaCollectedInvalidtypingcastr%rr,plugin_settingsr)r r(rVr __setattr____get__r$)r8r7r;r5r< original_initrFr6 gen_schemaget_core_schemaschemaerir[rZs @r3complete_dataclassrvUsk:LLM\ #//0 :HL,88C.I N66C.s.// dfx -S1L!J+ '**+<< &&  Cc#A4HO $,J66TYZ%F //QV/WF!,,3<<,@K((0 ++/ 5C$* -DS^^S%5%5{KQoQo.C#36"GC)) s  B  B,33D#> $C E '  C166(!}=   & &C/EFs0+H1HI! I(,II!&J  J c [R"U5=(aP [US5(+=(a8 [UR5R [[ US0555$)aReturns True if a class is a stdlib dataclass and *not* a pydantic dataclass. We check that - `_cls` is a dataclass - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`) - `_cls` does not have any annotations that are not dataclass fields e.g. ```python import dataclasses import pydantic.dataclasses @dataclasses.dataclass class A: x: int @pydantic.dataclasses.dataclass class B(A): y: int ``` In this case, when we first check `B`, we make an extra check and look at the annotations ('y'), which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x') Args: cls: The class. 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