if3%SrSSKJr SSKrSSKrSSKrSSKrSSKJ r SSK J r SSK J r SSKJrJrJrJrJr SSKJrJrJrJr SS KJrJr S S KJr S S KJr S S K J!r!J"r" S SKJ#r# SSK$J%r%J&r& SSK'J(r( SSK)J*r*J+r+ SSK,J-r- SSK.J/r/ SSK0J1r1 SSK2J3r3 SSK4J5r5 \(aSSK6J7r8 S SK9J:r: "SS\8\5r;S&S'SjjrS*S"jr?\@\A\R\4rCS#\DS$'\ S+S%j5rEg),z0Private logic for creating pydantic dataclasses.) annotationsN) Generator)contextmanager)partial) TYPE_CHECKINGAnyClassVarProtocolcast) ArgsKwargsSchemaSerializerSchemaValidator core_schema) TypeAliasTypeIs)PydanticUndefinedAnnotation) FieldInfo)PluggableSchemaValidatorcreate_schema_validator)PydanticDeprecatedSince20)_config _decorators)collect_dataclass_fields)GenerateSchemaInvalidSchemaError)get_standard_typevars_map)set_dataclass_mocks) NsResolver)generate_pydantic_signature)LazyClassAttribute)DataclassInstance) ConfigDictct\rSrSr%SrS\S'S\S'S\S'S \S 'S \S 'S \S'S\S'\SSj5rSrg)PydanticDataclass(a!A protocol containing attributes only available once a class has been decorated as a Pydantic dataclass. Attributes: __pydantic_config__: Pydantic-specific configuration settings for the dataclass. __pydantic_complete__: Whether dataclass building is completed, or if there are still undefined fields. __pydantic_core_schema__: The pydantic-core schema used to build the SchemaValidator and SchemaSerializer. __pydantic_decorators__: Metadata containing the decorators defined on the dataclass. __pydantic_fields__: Metadata about the fields defined on the dataclass. __pydantic_serializer__: The pydantic-core SchemaSerializer used to dump instances of the dataclass. __pydantic_validator__: The pydantic-core SchemaValidator used to validate instances of the dataclass. zClassVar[ConfigDict]__pydantic_config__zClassVar[bool]__pydantic_complete__z ClassVar[core_schema.CoreSchema]__pydantic_core_schema__z$ClassVar[_decorators.DecoratorInfos]__pydantic_decorators__zClassVar[dict[str, FieldInfo]]__pydantic_fields__zClassVar[SchemaSerializer]__pydantic_serializer__z4ClassVar[SchemaValidator | PluggableSchemaValidator]__pydantic_validator__cgNclss g/var/www/html/qgis-automation/mouza_env/lib/python3.13/site-packages/pydantic/_internal/_dataclasses.py__pydantic_fields_complete__.PydanticDataclass.__pydantic_fields_complete__=s7:r1N)returnbool) __name__ __module__ __qualname____firstlineno____doc____annotations__ classmethodr5__static_attributes__r1r7r4r&r&(sA 21--"BB!EE;;!;; TT : :r7r&c:[U5n[XX1S9nX@lg)zCollect and set `cls.__pydantic_fields__`. Args: cls: The class. config_wrapper: The config wrapper instance. ns_resolver: Namespace resolver to use when getting dataclass annotations. ) ns_resolver typevars_mapconfig_wrapperN)rrr,)r3rErCrDfieldss r4set_dataclass_fieldsrGAs%-S1L % <F%r7TF) raise_errorsrC _force_buildc URnSSjnURS3UlX`lURUl[ XUS9 U(dUR (a [ U5 g[US5(a[R"S[5 [U5n[UUUS9n[S[[UUR UR"UR$S S 95UlUR)U5n UR/UR0S 9n UR3U 5n [7SU5nXl[;XUR<URSXR>5Ul [CX5Ul"S Ul#g ![*a,n U(ae[ US U R,S 35 S n A gS n A ff=f![4a [ U5 gf=f)aJFinish building a pydantic dataclass. This logic is called on a class which has already been wrapped in `dataclasses.dataclass()`. This is somewhat analogous to `pydantic._internal._model_construction.complete_model_class`. Args: cls: The class. config_wrapper: The config wrapper instance. raise_errors: Whether to raise errors, defaults to `True`. ns_resolver: The namespace resolver instance to use when collecting dataclass fields and during schema building. _force_build: Whether to force building the dataclass, no matter if [`defer_build`][pydantic.config.ConfigDict.defer_build] is set. Returns: `True` if building a pydantic dataclass is successfully completed, `False` otherwise. Raises: PydanticUndefinedAnnotation: If `raise_error` is `True` and there is an undefined annotations. cRSnUnURR[X5US9 g)NT) self_instance)r.validate_pythonr )__dataclass_self__argskwargs__tracebackhide__ss r4__init__$complete_dataclass..__init__vs,     00D1IYZ0[r7z .__init__)rErCF__post_init_post_parse__zVSupport for `__post_init_post_parse__` has been dropped, the method will not be called)rCrD __signature__T)initrFvalidate_by_nameextra is_dataclass`N)titleztype[PydanticDataclass] dataclass)rNr&rOrrPrr8None)$rSr< config_dictr(rG defer_buildrhasattrwarningswarnrrrr"rr!r,rXrYrVgenerate_schemarname core_configr: clean_schemarr r*rr;plugin_settingsr.r r-r)) r3rErHrCrI original_initrSrD gen_schemaschemaerfs r4complete_dataclassrmUs:LLM\ #//0 :HL,88CU N66C s.// d % -S1L!J+ '**+<< &&  C++C0!,,3<<,@K((0 (# .C#) !8S^^S%5%5{KQoQo"C#36"GC $C 1 '  C1QVVHA/  C s*-FF> F;"F66F;>GGcPSUR;=(a [US5(+$)aJReturns `True` if the class is a stdlib dataclass and *not* a Pydantic dataclass. Unlike the stdlib `dataclasses.is_dataclass()` function, this does *not* include subclasses of a dataclass that are themselves not dataclasses. Args: cls: The class. Returns: `True` if the class is a stdlib dataclass, `False` otherwise. __dataclass_fields__r.)__dict__rar2s r4is_stdlib_dataclassrqs# "S\\ 1 `'#G_:`6``r7c&SU0n[RS:aURbURUS'[RS:aUR(aSUS'URSLaURUS'[ R "S0UD6$) Ndefault)docrt Tkw_onlyreprr1)sys version_info descriptionryrz dataclassesfield)pydantic_field field_argss r4as_dataclass_fieldrs"+^! dataclass.Field(default=FieldInfo(repr=False), repr=True, ...) @pydantic_dc.dataclass class B(A): b: int = pydantic.Field(repr=False) ``` When passing `B` to the stdlib `@dataclass` decorator, it will look for fields in the parent classes and reuse them directly. When this context manager is active, `A` will be temporarily patched to be equivalent to: ```python {test="skip" lint="skip"} @stdlib_dc.dataclass class A: a: int = stdlib_dc.field(default=Field(repr=False), repr=False) ``` !!! note This is only applied to the bases of `cls`, and not `cls` itself. The reason is that the Pydantic dataclass decorator "owns" `cls` (in the previous example, `B`). As such, we instead modify the fields directly (in the previous example, we simply do `setattr(B, 'b', as_dataclass_field(pydantic_field))`). !!! note This approach is far from ideal, and can probably be the source of unwanted side effects/race conditions. The previous implemented approach was mutating the `__annotations__` dict of `cls`, which is no longer a safe operation in Python 3.14+, and resulted in unexpected behavior with field ordering anyway. rNroTrw)__mro__rpgetitems isinstancersrr}ryrzappendr copyr{r|) r3original_fields_listbase dc_fields field_namer&dc_fields_with_pydantic_field_defaultsrs new_dc_fieldrForiginal_fieldsoriginal_fields r4patch_base_fieldsrsn=? AB7;}}7H7HI_ac7d &/__%62 %6! %--3 **6%--:O:OSXS`S`SeSemqSq J %6 /2  2 1 ' '([ \%K%Q%Q%S! y%--8 $yy/ ##w.7??+/L(<AG %F-0A F-=F-G B0G F3/G 3*GG r0)r3ztype[StandardDataclass]rE_config.ConfigWrapperrCNsResolver | Noner8r^) r3 type[Any]rErrHr9rCrrIr9r8r9)r3rr8zTypeIs[type[StandardDataclass]])rrr8zdataclasses.Field[Any])r3rr8zGenerator[None])Fr> __future__r _annotationsrr~r{rbcollections.abcr contextlibr functoolsrtypingrrr r r pydantic_corer r rrtyping_extensionsrrerrorsrrFrplugin._schema_validatorrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_namespace_utilsr _signaturer!_utilsr" _typeshedr#StandardDataclassconfigr$r&rGrmrqrdictstrFieldrr?rr1r7r4rs162  %%?? 00X0"-@0.(3&@#;-x;8&*% %)%#% %0%) i i)i i # i  i iX a+$3 1 1# 667)7U4U4r7