o h)@sdZddlmZddlZddlZddlZddlZddlm Z m Z ddlm Z m Z ddlm Z mZmZddlmZmZmZmZmZddlmZd d lmZd d lmZd d lmZd d lmZddlm Z m!Z!m"Z"ddl m#Z#ddl$m%Z%ddl&m'Z'm(Z(ddl)m*Z*ddl+m,Z,ddl-m.Z.ddl/m0Z0ej1rd dl2m3Z3Gdddej4Z5Gddde5ej4Z6neZ7d;dd/d0Z:d?d5d6Z;d@d9d:Z.__init__z .__init____get_pydantic_core_schema__F)from_dunder_get_core_schemaunpack)ref_mode`zall referenced typesztype[PydanticDataclass] dataclassinstance__fieldstr__valuecs|||dSr+)validate_assignment)rUrVrX validatorr,r.validated_setattrsz-complete_dataclass..validated_setattrT)rJr4r&rr(rr)r*)rUrrVrWrXrWr)r*)(hasattrwarningswarnDeprecationWarningrget_cls_types_namespacerCrrgenerate_dataclass_signaturer9r2r/ config_dictr5 __signature__getattrrrgenerate_schemarrr0name core_config clean_schemaCollectedInvalidtypingcastr7rr1plugin_settingsr;r r:rYr __setattr____get__) r=rFrDr?rA gen_schemasigr/get_core_schemaschemaerhr\r,rZr.complete_dataclassUsr          ruparamrcCs|j}t|trQ|j}|dkrt}|j}|j}|j}|dur*t|tr*t |r*|}n t|tr5t |r5|}|j}|t urI|j t urFt j j}ntj}|j|||dS|S)zCustom processing where the parameter default is of type FieldInfo Args: param (Parameter): The parameter Returns: Parameter: The custom processed parameter rN) annotationrgdefault)rx isinstancerrwrrgaliasvalidation_aliasrWr r default_factoryinspectrempty dataclasses_HAS_DEFAULT_FACTORYreplace)rv param_defaultrwrgrzr{rxr,r,r.process_param_defaultss&   rrBdict[str, FieldInfo]rrcCst|j||tdS)zGenerate signature for a pydantic dataclass. Args: cls: The dataclass. fields: The model fields. config_wrapper: The config wrapper instance. Returns: The dataclass signature. )initrBrFpost_process_parameter)rr/r)r=rBrFr,r,r.rbs rb_cls"TypeGuard[type[StandardDataclass]]cCs2t|ot|d ot|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. r;r3)r is_dataclassr]setr# issupersetre)rr,r,r.is_builtin_dataclasss  rr+)r=r>r?r@r)r*) r=rErFrGrDrHr?r@r)rH)rvrr)r)r=r>rBrrFrr)r)rrEr)r)=r< __future__r _annotationsrr}rkr^ functoolsrrrrrrr pydantic_corer r r r rtyping_extensionsrerrorsrrBrplugin._schema_validatorrrrrrr_fieldsr_generate_schemarr _genericsr _mock_val_serr_schema_generation_sharedr_utilsr TYPE_CHECKINGconfigr!Protocolr"r4r`rCrurrbrr,r,r,r.sD               i '