h"SrSSKJr SSKrSSKrSSKrSSKJrJ r SSKJ r J r SSK J r JrJrJr SSKJr SS KJr SS KJrJr SS KJr S S KJrJr S SKJr S SKJrJ r S SK!J"r" S SK#J$r$ S SK%J&r& S SK'J(r( S SK)J*r* \RV(a)SSK,J-r. SSK/J0r0 SSK1J2r2 "SS\.\Rf5r4O\r5S S!Sjjr6SSSS.S"Sjjr7S#Sjr8g)$z0Private logic for creating pydantic dataclasses.) annotationsN)partialwraps)AnyClassVar) ArgsKwargsSchemaSerializerSchemaValidator core_schema) TypeGuard)PydanticUndefinedAnnotation)PluggableSchemaValidatorcreate_schema_validator)PydanticDeprecatedSince20)_config _decorators)collect_dataclass_fields)GenerateSchemaInvalidSchemaError)get_standard_typevars_map)set_dataclass_mocks) NsResolver)generate_pydantic_signature)LazyClassAttribute)DataclassInstance) ConfigDict) FieldInfoc`\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S \S'S\S'Srg)PydanticDataclass%a!A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]__pydantic_config__zClassVar[bool]__pydantic_complete__z ClassVar[core_schema.CoreSchema]__pydantic_core_schema__z$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*Q/var/www/html/env/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:@s%-S1L % <F%r2TF) raise_errorsr5 _force_buildc ^^ URnSSjnURS3UlX`lURUl[ XUS9 U(dUR (a [ U5 g[US5(a[R"S[5 [U5n[UUUS9n[S[[UUR UR"UR$S S 95UlUR)U5n UR/UR0S 9n UR3U 5n [6R8"SU5nXl[=XUR>URSXR@5=Ul!m [EX5Ul#URH(a7[KURL5SU 4Sjj5n U ROS U5Ul&S Ul(g ![*a,n U(ae[ US U R,S 35 S n A gS n A ff=f![4a [ U5 gf=f)aJFinish building a pydantic dataclass. This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`. This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`. Args: cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. ns_resolver: The namespace resolver instance to use when collecting dataclass fields and during schema building. _force_build: Whether to force building the dataclass, no matter if [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. Returns: `True` if building a pydantic dataclass is successfully completed, `False` otherwise. Raises: PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. cRSnUnURR[X5US9 g)NT) self_instance)r)validate_pythonr)__dataclass_self__argskwargs__tracebackhide__ss r3__init__$complete_dataclass..__init__us,     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)initr9validate_by_nameextra is_dataclass`N)titleztype[PydanticDataclass] dataclassc*>TRXU5 g)N)validate_assignment)instancefieldvalue validators r3validated_setattr-complete_dataclass..validated_setattrs  ) )(5 Ar2)rAr!rBrrCrreturnNone)rSrrTstrrUr[rYrZ))rFr- config_dictr#r: defer_buildrhasattrwarningswarnDeprecationWarningrrrrrr'rKrLrIgenerate_schemarname core_configr+ clean_schemartypingcastr%rr,plugin_settingsr)r r(rRr __setattr____get__r$)r8r7r;r5r< original_initrFr6 gen_schemaschemaerdrWrVs @r3complete_dataclassroTs:LLM\ #//0 :HL,88C.I N66C s.// dfx -S1L!J+ '**+<< &&  C++C0!,,3<<,@K((0 ++/ 5C$* -DS^^S%5%5{KQoQo.C#36"GC)) s  B  B,33D#> $C E '  C1QVVHA/  C s*.GH H%"H  HH,+H,c [R"U5=(aP [US5(+=(a8 [UR5R [[ US0555$)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. Returns: `True` if the class is a stdlib dataclass, `False` otherwise. r)r0) dataclassesrMr^set__dataclass_fields__ issupersetgetattr)_clss r3is_builtin_dataclassrwsZ>   & a67 7 a )) * 5 5c'$HY[]:^6_ `r2)NN)r8ztype[StandardDataclass]r5NsResolver | Noner7z_config.ConfigWrapper | NonerYrZ) r8 type[Any]r7z_config.ConfigWrapperr;boolr5rxr<rzrYrz)rvryrYz"TypeGuard[type[StandardDataclass]])9r/ __future__r _annotationsrqrfr_ functoolsrrrr pydantic_corerr r r typing_extensionsr errorsrplugin._schema_validatorrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_namespace_utilsr _signaturer_utilsr TYPE_CHECKING _typeshedrStandardDataclassconfigrr9rProtocolr!rar:rorwr*r2r3rs62 $  (0X0"-@0.(3& @#"U-vU03 &*37% %"%1% %0%) r r)r r # r  r rj"r2