a Of"@sdZddlmZddlZddlZddlZddlmZm Z ddlm Z m Z m Z ddl mZmZmZmZddlmZdd lmZdd lmZdd lmZmZdd lmZd dlmZmZmZd dl m!Z!d dl"m#Z#d dl$m%Z%d dl&m'Z'd dl(m)Z)d dl*m+Z+ej,r.__init__z .__init__Z__get_pydantic_core_schema__F)Zfrom_dunder_get_core_schemaunpack)Zref_mode`zall referenced typesztype[PydanticDataclass] dataclassstr)instancefieldvaluer&cs|||dSr')validate_assignment)rGrHrI validatorr(r*validated_setattrsz-complete_dataclass..validated_setattr)(hasattrwarningswarnDeprecationWarningrZget_cls_types_namespacer<rrrr+r3r.Z config_dictr1 __signature__getattrrrZgenerate_schemarrr,name core_configZ clean_schemaZCollectedInvalidtypingcastr2rr-Zplugin_settingsr5r r4rJr __setattr____get__) r7r9r=r8r:Z gen_schemasigr+Zget_core_schemaZschemaerUrMr(rKr*complete_dataclassWst         r\z"TypeGuard[type[StandardDataclass]])_clsr&cCs2t|o0t|d o0t|jtt|diS)a>Returns 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. ```py 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. r5r/) dataclassesr@rNsetr issupersetrS)r]r(r(r*is_builtin_dataclasss   ra)NN)6r6 __future__rZ _annotationsr^rVrO functoolsrrrrrZ pydantic_corerr r r typing_extensionsr errorsrr;rZplugin._schema_validatorrrrrrr_fieldsrZ_generate_schemarZ _genericsrZ _mock_val_serrZ_schema_generation_sharedr _signaturer TYPE_CHECKINGconfigrProtocolrr0rQr<r\rar(r(r(r*s<            n