~lDh"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*ej+r'ddl,m-Z.ddl/m0Z0ddl1m2Z2Gdde.ej3Z4neZ5 d/d0d"Z6d#dd$d%d1d+Z7d2d.Z8dS)3z0Private logic for creating pydantic dataclasses.) annotationsN)partialwraps)AnyClassVar) ArgsKwargsSchemaSerializerSchemaValidator core_schema) TypeGuard)PydanticUndefinedAnnotation)PluggableSchemaValidatorcreate_schema_validator)PydanticDeprecatedSince20)_config _decorators)collect_dataclass_fields)GenerateSchemaInvalidSchemaError)get_standard_typevars_map)set_dataclass_mocks) NsResolver)generate_pydantic_signature)LazyClassAttribute)DataclassInstance) ConfigDict) FieldInfocZeZdZUdZded<ded<ded<ded <d ed <d ed <ded<dS)PydanticDataclassaiA protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]__pydantic_config__zClassVar[bool]__pydantic_complete__z ClassVar[core_schema.CoreSchema]__pydantic_core_schema__z$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__`/users/jtampere/www/backend/venv/lib/python3.11/site-packages/pydantic/_internal/_dataclasses.pyr!r!%sr  2111----BBBBEEEE;;;;;;;;TTTTTTr/r!clstype[StandardDataclass] ns_resolverNsResolver | Noneconfig_wrapper_config.ConfigWrapper | NonereturnNonecXt|}t||||}||_dS)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`. )r3 typevars_mapr5N)rrr&)r1r3r5r:fieldss r0set_dataclass_fieldsr<@s?-S11L % c L |j}dd}|jd |_||_|j|_t ||| |s|jrt |d St|d rtj d tt|}t|||}tdtt||j|j|jd|_ ||} n4#t*$r'} |rt |d| jdYd} ~ d Sd} ~ wwxYw||j} || } n #t4$rt |Yd SwxYwt7jd|}| |_t=| ||j|jd| |j x|_! tE| | |_#|j$r;tK|j&d fd } | 'd||_&d|_(dS)aFinish building a pydantic dataclass. This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`. This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`. Args: cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. ns_resolver: The namespace resolver instance to use when collecting dataclass fields and during schema building. _force_build: Whether to force building the dataclass, no matter if [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. Returns: `True` if building a pydantic dataclass is successfully completed, `False` otherwise. Raises: PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. __dataclass_self__r!argsrkwargsr7r8cbd}|}|jt|||dS)NT) self_instance)r(validate_pythonr)rCrDrE__tracebackhide__ss r0__init__z$complete_dataclass..__init__us:    00D&1I1IYZ0[[[[[r/z .__init__)r5F__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be called)r3r: __signature__T)initr;validate_by_nameextra is_dataclass`N)titleztype[PydanticDataclass] dataclassinstancefieldstrvaluec6|||dS)N)validate_assignment)rUrVrX validators r0validated_setattrz-complete_dataclass..validated_setattrs!  ) )(E5 A A A A Ar/)rCr!rDrrErr7r8)rUrrVrWrXrWr7r8))rKr+ config_dictr"r< defer_buildrhasattrwarningswarnDeprecationWarningrrrrrr&rOrPrMgenerate_schemarname core_configr) clean_schemartypingcastr$rr*plugin_settingsr(r r'rZr __setattr____get__r#)r1r5r=r3r> original_initrKr: gen_schemaschemaerer\r[s @r0complete_dataclassrpTs:LM\\\\ #/:::HCL,8Ck.IIII N6C   us.//  dfx   -S11L!J+ '*+< &   C++C00 &   CQV///uuuuu  !,,3<,@@K((00 C   uu +/ 5 5C$*C -DS^S%5{KQ_Qo..C#36;"G"GC)? s   B B B B B  B,33D#>> $C 4s*#C99 D*D%%D* EE<;E<_cls"TypeGuard[type[StandardDataclass]]c tj|oSt|d oBt|jtt |diS)aBReturns True if a class is a stdlib dataclass and *not* a pydantic dataclass. We check that - `_cls` is a dataclass - `_cls` does not inherit from a processed pydantic dataclass (and thus have a `__pydantic_validator__`) - `_cls` does not have any annotations that are not dataclass fields e.g. ```python import dataclasses import pydantic.dataclasses @dataclasses.dataclass class A: x: int @pydantic.dataclasses.dataclass class B(A): y: int ``` In this case, when we first check `B`, we make an extra check and look at the annotations ('y'), which won't be a superset of all the dataclass fields (only the stdlib fields i.e. 'x') Args: cls: The class. Returns: `True` if the class is a stdlib dataclass, `False` otherwise. r(r-) dataclassesrQr_set__dataclass_fields__ issupersetgetattr)rqs r0is_builtin_dataclassrysf>  && a677 7 a ) * * 5 5c'$HY[]:^:^6_6_ ` `r/)NN)r1r2r3r4r5r6r7r8) r1r?r5r@r=rAr3r4r>rAr7rA)rqr?r7rr)9r, __future__r _annotationsrtrgr` functoolsrrrr pydantic_corerr r r typing_extensionsr errorsrplugin._schema_validatorrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_namespace_utilsr _signaturer_utilsr TYPE_CHECKING _typeshedrStandardDataclassconfigrr;rProtocolr!rbr<rpryr.r/r0rs66222222 $$$$$$$$  ('''''000000XXXXXXXX000000""""""""------@@@@@@@@000000......((((((333333&&&&&& 3@@@@@@######""""""UUUUU-vUUUU03 &*37%%%%%0%) rrrrrrj""""""r/