,h"dZddlmZddlZddlZddlZddlmZm Z ddlm Z m Z ddl m Z mZmZmZddlmZdd lmZdd lmZmZdd lmZd d lmZmZd dlmZd dlmZm Z d dl!m"Z"d dl#m$Z$d dl%m&Z&d dl'm(Z(d dl)m*Z*ejVr)ddl,m-Z.ddl/m0Z0ddl1m2Z2Gdde.ejfZ4neZ5 d d!dZ6dddd d"dZ7d#dZ8y)$z0Private logic for creating pydantic dataclasses.) annotationsN)partialwraps)AnyClassVar) ArgsKwargsSchemaSerializerSchemaValidator core_schema) TypeGuard)PydanticUndefinedAnnotation)PluggableSchemaValidatorcreate_schema_validator)PydanticDeprecatedSince20)_config _decorators)collect_dataclass_fields)GenerateSchemaInvalidSchemaError)get_standard_typevars_map)set_dataclass_mocks) NsResolver)generate_pydantic_signature)LazyClassAttribute)DataclassInstance) ConfigDict) FieldInfocXeZdZUdZded<ded<ded<ded <d ed <d ed <ded<y)PydanticDataclassaiA protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]__pydantic_config__zClassVar[bool]__pydantic_complete__z ClassVar[core_schema.CoreSchema]__pydantic_core_schema__z$ClassVar[_decorators.DecoratorInfos]__pydantic_decorators__zClassVar[dict[str, FieldInfo]]__pydantic_fields__zClassVar[SchemaSerializer]__pydantic_serializer__z4ClassVar[SchemaValidator | PluggableSchemaValidator]__pydantic_validator__N)__name__ __module__ __qualname____doc____annotations__a/var/www/html/Resume-Scraper/venv/lib/python3.12/site-packages/pydantic/_internal/_dataclasses.pyr!r!%s3 21--"BB!EE;;!;; TTr/r!cFt|}t||||}||_y)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&)clsr2r4r3fieldss r0set_dataclass_fieldsr7@s,-S1L % |jd| |j@x|_! tE| | |_#|jHr5tK|jLd fd } | jOd ||_&d |_(y #t*$r'} |rt |d | j,d Yd } ~ yd } ~ wwxYw#t4$rt |YywxYw)aFinish 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. cZd}|}|jjt|||y)NT) self_instance)r(validate_pythonr)__dataclass_self__argskwargs__tracebackhide__ss r0__init__z$complete_dataclass..__init__us.     00D&1IYZ0[r/z .__init__)r4F__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be called)r2r3 __signature__T)initr6validate_by_nameextra is_dataclass`N)titleztype[PydanticDataclass] dataclassc,j|||y)N)validate_assignment)instancefieldvalue validators r0validated_setattrz-complete_dataclass..validated_setattrs  ) )(E5 Ar/)r>r!r?rr@rreturnNone)rOrrPstrrQrVrTrU))rCr+ config_dictr"r7 defer_buildrhasattrwarningswarnDeprecationWarningrrrrrr&rGrHrEgenerate_schemarname core_configr) clean_schemartypingcastr$rr*plugin_settingsr(r r'rNr __setattr____get__r#)r5r4r8r2r9 original_initrCr3 gen_schemaschemaer_rSrRs @r0complete_dataclassrjTs:LLM\ #//0 :HCL,88Ck.I N66C s./ dfx -S1L!J+ '**+<< &&  C++C0!,,3<<,@K((0 ++/ 5C$*C -DS^^S%5%5{KQ_QoQo.C#36;"GC)) s  B  B,33D#> $C E '  C1QVVHA/  C s*(GH HG??HHHc tj|xrGt|d xr8t|jj tt |diS)aBReturns True if a class is a stdlib dataclass and *not* a pydantic dataclass. We check that - `_cls` is a dataclass - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`) - `_cls` does not have any annotations that are not dataclass fields e.g. ```python import dataclasses import pydantic.dataclasses @dataclasses.dataclass class A: x: int @pydantic.dataclasses.dataclass class B(A): y: int ``` In this case, when we first check `B`, we make an extra check and look at the annotations ('y'), which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x') Args: cls: The class. Returns: `True` if the class is a stdlib dataclass, `False` otherwise. r(r-) dataclassesrIrYset__dataclass_fields__ issupersetgetattr)_clss r0is_builtin_dataclassrrsZ>   & a67 7 a )) * 5 5c'$HY[]:^6_ `r/)NN)r5ztype[StandardDataclass]r2NsResolver | Noner4z_config.ConfigWrapper | NonerTrU) r5 type[Any]r4z_config.ConfigWrapperr8boolr2rsr9rurTru)rqrtrTz"TypeGuard[type[StandardDataclass]])9r, __future__r _annotationsrlrarZ functoolsrrrr pydantic_corerr r r typing_extensionsr errorsrplugin._schema_validatorrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_namespace_utilsr _signaturer_utilsr TYPE_CHECKING _typeshedrStandardDataclassconfigrr6rProtocolr!r\r7rjrrr.r/r0rs62 $  (0X0"-@0.(3& @#"U-vU03 &*37% %"%1% %0%) r r)r r # r  r rj"r/