LqjxsUdZddlmZddlZddlZddlmZddlm Z ddl m Z m Z m Z mZmZmZmZmZmZddlmZmZddlmZdd lmZmZd d lmZmZmZd d lm Z d d l!m"Z"ejFdkrddlm$Z$nddl m$Z$ejJZ&ejNdLddiejPGddZ)ejNdLddiejPGddZ*ejNdLddiejPGddZ+ejNdLddiejPGddZ,e rGdde$Z-Gdde$Z.Gdde$Z/Gd d!e$Z0ee.ejbe-ejdfZ3ee0ejhe/ejjfZ6ee7e e e fe8e e fee fZ9d"e:d#<ed$ee3e9f%Z;ed&ee6e9f%Zed)d)d* dNd,Z>ed)d)d- dOd.Z>d/ded0 dPd1Z>ed2Z?ed3d4Z@Gd5d6eje$e@ZBGd7d8e$e?ZCGd9d:e$e?ZDGd;de$ZFGd?d@e$ZGGdAdBe$ZHee?ge?fZI ee?ejge?fZK eeDe?eCe?fZLeeGeHeEeFfZMeeKe?eIe?fZNe dQdCZOe dRdDZOe dSdEZO dTdFZOedGZPe re ePd)fZQn)ejNdLiejPGdHdIZQe re ePd)fZRyejNdLiejPGdJdKZRy)UzBThis module contains related classes and functions for validation.) annotationsN) partialmethod) FunctionType) TYPE_CHECKING AnnotatedAnyCallableLiteralTypeVarUnioncastoverload)PydanticUndefined core_schema)r)Self TypeAlias) _decorators _generics_internal_dataclass)GetCoreSchemaHandler)PydanticUserError) )ProtocolfrozenTc6eZdZUdZded<ddZeddZy) AfterValidatoraT!!! abstract "Usage Documentation" [field *after* validators](../concepts/validators.md#field-after-validator) A metadata class that indicates that a validation should be applied **after** the inner validation logic. Attributes: func: The validator function. Example: ```python from typing import Annotated from pydantic import AfterValidator, BaseModel, ValidationError MyInt = Annotated[int, AfterValidator(lambda v: v + 1)] class Model(BaseModel): a: MyInt print(Model(a=1).a) #> 2 try: Model(a='a') except ValidationError as e: print(e.json(indent=2)) ''' [ { "type": "int_parsing", "loc": [ "a" ], "msg": "Input should be a valid integer, unable to parse string as an integer", "input": "a", "url": "https://errors.pydantic.dev/2/v/int_parsing" } ] ''' ``` Kcore_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunctionfunccD||}t|jd}|rFttj|j}tj |||j Sttj|j}tj||S)Nafter)schema field_name)r#) _inspect_validatorr r rWithInfoValidatorFunction"with_info_after_validator_functionr$NoInfoValidatorFunction no_info_after_validator_function)self source_typehandlerr#info_argr s j/var/www/html/ai-menu-system/python-ai/venv/lib/python3.12/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__z+AfterValidator.__get_pydantic_core_schema__Isz%%dii9  ==tyyIDAA$vbibtbtu u ;;TYYGD??VT Tc(||jS)Nr r2cls decorators r._from_decoratorzAfterValidator._from_decoratorSs ''r0Nr+rr,rreturncore_schema.CoreSchemar5z>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]r8r)__name__ __module__ __qualname____doc____annotations__r/ classmethodr6r0r.rrs+(T VUU((r0rcDeZdZUdZded<eZded<d dZed dZ y) BeforeValidatora!!! abstract "Usage Documentation" [field *before* validators](../concepts/validators.md#field-before-validator) A metadata class that indicates that a validation should be applied **before** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode). Example: ```python from typing import Annotated from pydantic import BaseModel, BeforeValidator MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)] class Model(BaseModel): a: MyInt print(Model(a=1).a) #> 2 try: Model(a='a') except TypeError as e: print(e) #> can only concatenate str (not "int") to str ``` rr rjson_schema_input_typec||}|jturdn|j|j}t|jd}|rGt t j|j}t j|||j|St t j|j}t j|||S)Nbeforer#r$json_schema_input_schemar#rH) rDrgenerate_schemar%r r rr&#with_info_before_validator_functionr$r(!no_info_before_validator_functionr*r+r,r# input_schemar-r s r.r/z,BeforeValidator.__get_pydantic_core_schema__}s%**.?? (()D)DE  &dii:  ==tyyIDBB"--)5   ;;TYYGD@@Vl r0cR||j|jjSN)r rDr inforDr3s r.r6zBeforeValidator._from_decorator##,>>#H#H  r0Nr7r: r;r<r=r>r?rrDr/r@r6rAr0r.rCrCXs5@ VU"3C3.  r0rCcDeZdZUdZded<eZded<d dZed dZ y) PlainValidatora!!! abstract "Usage Documentation" [field *plain* validators](../concepts/validators.md#field-plain-validator) A metadata class that indicates that a validation should be applied **instead** of the inner validation logic. !!! note Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`. You can now use the `json_schema_input_type` argument to specify the input type of the function to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details. Attributes: func: The validator function. json_schema_input_type: The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode). If not provided, will default to `Any`. Example: ```python from typing import Annotated, Union from pydantic import BaseModel, PlainValidator MyInt = Annotated[ int, PlainValidator( lambda v: int(v) + 1, json_schema_input_type=Union[str, int] # (1)! ), ] class Model(BaseModel): a: MyInt print(Model(a='1').a) #> 2 print(Model(a=1).a) #> 2 ``` 1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema generator that in validation mode, the input type for the `a` field can be either a `str` or an `int`. rr rrDc ddlm} ||}|jdtjd||j |}|j |j }t|jd}|rGttj|j}tj||j||Sttj|j}tj|||S#|$rd}YwxYw) NrPydanticSchemaGenerationError serializationc||SNrAvhs r.z=PlainValidator.__get_pydantic_core_schema__.. !A$r0)functionr# return_schemaplain)r$rZrH)rZrH)pydanticrYgetr#wrap_serializer_function_ser_schemarJrDr%r r r&"with_info_plain_validator_functionr$r( no_info_plain_validator_function) r*r+r,rYr#rZrNr-r s r.r/z+PlainValidator.__get_pydantic_core_schema__s ; ![)F#JJ??.!")"9"9+"FM..t/J/JK %dii9  ==tyyIDAA"--+)5   ;;TYYGD??+)5 !- ! M !sAC>>DDcR||j|jjSrPrQr3s r.r6zPlainValidator._from_decoratorrSr0Nr7r:) r;r<r=r>r?rrDr/r@r6rAr0r.rVrVs6(T VU"%C%(T  r0rVcDeZdZUdZded<eZded<d dZed dZ y) WrapValidatora!!! abstract "Usage Documentation" [field *wrap* validators](../concepts/validators.md#field-wrap-validator) A metadata class that indicates that a validation should be applied **around** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode). ```python from datetime import datetime from typing import Annotated from pydantic import BaseModel, ValidationError, WrapValidator def validate_timestamp(v, handler): if v == 'now': # we don't want to bother with further validation, just return the new value return datetime.now() try: return handler(v) except ValidationError: # validation failed, in this case we want to return a default value return datetime(2000, 1, 1) MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)] class Model(BaseModel): a: MyTimestamp print(Model(a='now').a) #> 2032-01-02 03:04:05.000006 print(Model(a='invalid').a) #> 2000-01-01 00:00:00 ``` zScore_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunctionr rrDc||}|jturdn|j|j}t|jd}|rGt t j|j}t j|||j|St t j|j}t j|||S)NwraprGrI) rDrrJr%r r rWithInfoWrapValidatorFunction!with_info_wrap_validator_functionr$NoInfoWrapValidatorFunctionno_info_wrap_validator_functionrMs r.r/z*WrapValidator.__get_pydantic_core_schema__(s%**.?? (()D)DE  &dii8  AA499MD@@"--)5   ??KD>>)5 r0cR||j|jjSrPrQr3s r.r6zWrapValidator._from_decoratorArSr0Nr7r:rTrAr0r.rlrls5$L ^]"3C32  r0rlceZdZddZy)_OnlyValueValidatorClsMethodcyr\rAr*r4values r.__call__z%_OnlyValueValidatorClsMethod.__call__Lr0Nr4rrxrr8rr;r<r=ryrAr0r.ruruKs?r0ruceZdZddZy)_V2ValidatorClsMethodcyr\rAr*r4rxrRs r.ryz_V2ValidatorClsMethod.__call__Orzr0Nr4rrxrrR_core_schema.ValidationInfor8rr|rAr0r.r~r~Nsbr0r~ceZdZddZy) _OnlyValueWrapValidatorClsMethodcyr\rAr*r4rxr,s r.ryz)_OnlyValueWrapValidatorClsMethod.__call__Rrzr0N)r4rrxrr,)_core_schema.ValidatorFunctionWrapHandlerr8rr|rAr0r.rrQssr0rc(eZdZ ddZy)_V2WrapValidatorClsMethodcyr\rAr*r4rxr,rRs r.ryz"_V2WrapValidatorClsMethod.__call__Usr0N) r4rrxrr,rrRrr8rr|rAr0r.rrTs7   ?   .   r0rr_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)rFr"rnrdFieldValidatorModes.) check_fieldsrDcyr\rAfieldmoderrDfieldss r.field_validatorrws>Ar0cyr\rArs r.rrs Z]r0)rrcyr\rA)rrrrs r.rrs Z]r0r")rrrDc t|tr tdddvrturtddtur dk(rt|gt dDs td d  d fd }|S) aO!!! abstract "Usage Documentation" [field validators](../concepts/validators.md#field-validators) Decorate methods on the class indicating that they should be used to validate fields. Example usage: ```python from typing import Any from pydantic import ( BaseModel, ValidationError, field_validator, ) class Model(BaseModel): a: str @field_validator('a') @classmethod def ensure_foobar(cls, v: Any): if 'foobar' not in v: raise ValueError('"foobar" not found in a') return v print(repr(Model(a='this is foobar good'))) #> Model(a='this is foobar good') try: Model(a='snap') except ValidationError as exc_info: print(exc_info) ''' 1 validation error for Model a Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str] ''' ``` For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators). Args: field: The first field the `field_validator` should be called on; this is separate from `fields` to ensure an error is raised if you don't pass at least one. *fields: Additional field(s) the `field_validator` should be called on. mode: Specifies whether to validate the fields before or after validation. check_fields: Whether to check that the fields actually exist on the model. json_schema_input_type: The input type of the function. This is only used to generate the appropriate JSON Schema (in validation mode) and can only specified when `mode` is either `'before'`, `'plain'` or `'wrap'`. Returns: A decorator that can be used to decorate a function to be used as a field_validator. Raises: PydanticUserError: - If `@field_validator` is used bare (with no fields). - If the args passed to `@field_validator` as fields are not strings. - If `@field_validator` applied to instance methods. z`@field_validator` should be used with fields and keyword arguments, not bare. E.g. usage should be `@validator('', ...)`zvalidator-no-fieldscode)rFrdrnz;`json_schema_input_type` can't be used when mode is set to zvalidator-input-typerdc3<K|]}t|tywr\) isinstancestr).0rs r. z"field_validator..s:%z%%:sz`@field_validator` fields should be passed as separate string args. E.g. usage should be `@validator('', '', ...)`zvalidator-invalid-fieldsctj|r tddtj|}tj}tj ||S)Nz8`@field_validator` cannot be applied to instance methodszvalidator-instance-methodr)rrrrD)ris_instance_method_from_sigr%ensure_classmethod_based_on_signatureFieldValidatorDecoratorInfoPydanticDescriptorProxy)fdec_inforrrDrs r.deczfield_validator..decsg  2 21 5#JQl   = =a @::ryrAr0r.rr s+g ,0  )   r0rc(eZdZdZ ddZy)ModelWrapValidatorWithoutInfozA `@model_validator` decorated function signature. This is used when `mode='wrap'` and the function does not have info argument. cyr\rArs r.ryz&ModelWrapValidatorWithoutInfo.__call__sr0N)r4type[_ModelType]rxrr,%ModelWrapValidatorHandler[_ModelType]r8rrrAr0r.rrs2     7   r0rc,eZdZdZ ddZy)ModelWrapValidatorzSA `@model_validator` decorated function signature. This is used when `mode='wrap'`.cyr\rArs r.ryzModelWrapValidator.__call__*sr0N) r4rrxrr,rrRrr8rrrAr0r.rr's:]     7 *   r0rc eZdZdZ ddZy)#FreeModelBeforeValidatorWithoutInfoA `@model_validator` decorated function signature. This is used when `mode='before'` and the function does not have info argument. cyr\rA)r*rxs r.ryz,FreeModelBeforeValidatorWithoutInfo.__call__<sr0N)rxrr8rrrAr0r.rr7s    r0rc$eZdZdZ ddZy)ModelBeforeValidatorWithoutInforcyr\rArws r.ryz(ModelBeforeValidatorWithoutInfo.__call__Kr0Nr{rrAr0r.rrFs(    r0rc$eZdZdZ ddZy)FreeModelBeforeValidatorUA `@model_validator` decorated function signature. This is used when `mode='before'`.cyr\rA)r*rxrRs r.ryz!FreeModelBeforeValidator.__call__Yrr0N)rxrrRrr8rrrAr0r.rrVs(_   *  r0rc(eZdZdZ ddZy)ModelBeforeValidatorrcyr\rArs r.ryzModelBeforeValidator.__call__gsr0NrrrAr0r.rrds0_     *   r0rcyr\rArs r.model_validatorr r0cyr\rArs r.rrrr0cyr\rArs r.rrrr0cdfd }|S)a@!!! abstract "Usage Documentation" [Model Validators](../concepts/validators.md#model-validators) Decorate model methods for validation purposes. Example usage: ```python from typing_extensions import Self from pydantic import BaseModel, ValidationError, model_validator class Square(BaseModel): width: float height: float @model_validator(mode='after') def verify_square(self) -> Self: if self.width != self.height: raise ValueError('width and height do not match') return self s = Square(width=1, height=1) print(repr(s)) #> Square(width=1.0, height=1.0) try: Square(width=1, height=2) except ValidationError as e: print(e) ''' 1 validation error for Square Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict] ''' ``` For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators). Args: mode: A required string literal that specifies the validation mode. It can be one of the following: 'wrap', 'before', or 'after'. Returns: A decorator that can be used to decorate a function to be used as a model validator. ctj|}tj}tj||S)Nr)rrModelValidatorDecoratorInfor)rrrs r.rzmodel_validator..decs6  = =a @::E221h??r0)rrr8rrA)rrs` r.rrsb@ Jr0AnyTypecLeZdZdZeddZeddZejZy) InstanceOfuGeneric type for annotating a type that is an instance of a given class. Example: ```python from pydantic import BaseModel, InstanceOf class Foo: ... class Bar(BaseModel): foo: InstanceOf[Foo] Bar(foo=Foo()) try: Bar(foo=42) except ValidationError as e: print(e) """ [ │ { │ │ 'type': 'is_instance_of', │ │ 'loc': ('foo',), │ │ 'msg': 'Input should be an instance of Foo', │ │ 'input': 42, │ │ 'ctx': {'class': 'Foo'}, │ │ 'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of' │ } ] """ ``` c"t||fSr\)rr4items r.__class_getitem__zInstanceOf.__class_getitem__sT35[) )r0cddlm}tjt j |xs|} ||}tj d||d<tj||S#|$r|cYSwxYw)NrrXc||Sr\rAr]s r.r`z9InstanceOf.__get_pydantic_core_schema__..rar0rbr#rZ) python_schema json_schema)rerYris_instance_schemar get_originrgjson_or_python_schema)r4sourcer,rYinstance_of_schemaoriginal_schemas r.r/z'InstanceOf.__get_pydantic_core_schema__s >"-!?!? @T@TU[@\@f`f!g  x")&/ 7B6e6e.7"?3#88GYgvww1 *)) *sA..A87A8N)rrr8rrrr,rr8r9) r;r<r=r>r@rr/object__hash__rAr0r.rrs= @  *  *  x  x&??r0rcBeZdZdZddZeddZejZy)SkipValidationaIf this is applied as an annotation (e.g., via `x: Annotated[int, SkipValidation]`), validation will be skipped. You can also use `SkipValidation[int]` as a shorthand for `Annotated[int, SkipValidation]`. This can be useful if you want to use a type annotation for documentation/IDE/type-checking purposes, and know that it is safe to skip validation for one or more of the fields. Because this converts the validation schema to `any_schema`, subsequent annotation-applied transformations may not have the expected effects. Therefore, when used, this annotation should generally be the final annotation applied to a type. c(t|tfSr\)rrrs r.rz SkipValidation.__class_getitem__.sT>#334 4r0c~||dfdgi}tj|tjdS)N pydantic_js_annotation_functionsc|Sr\rA)_cr_rs r.r`z=SkipValidation.__get_pydantic_core_schema__..4s 1_K]r0c||Sr\rAr]s r.r`z=SkipValidation.__get_pydantic_core_schema__..8rar0r)metadatarZ)r any_schemarg)r4rr,rrs @r.r/z+SkipValidation.__get_pydantic_core_schema__1sC%foO:=]<^_H))!)MM. r0N)rrr8rr) r;r<r=r>rr@r/rrrAr0r.rr!s+  5    ??r0rrA) rrrrrLiteral['wrap']r bool | NonerDrr8z6Callable[[_V2WrapValidatorType], _V2WrapValidatorType]) rrrrrzLiteral['before', 'plain']rrrDrr8RCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]) rrrrrLiteral['after']rrr8r) rrrrrrrrrDrr8zCallable[[Any], Any])rrr8z|Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])rzLiteral['before']r8zrCallable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])rrr8z}Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])rz"Literal['wrap', 'before', 'after']r8r)Sr> __future__r _annotations dataclassessys functoolsrtypesrtypingrrrr r r r r r pydantic_corerr _core_schematyping_extensionsrr _internalrrrannotated_handlersrerrorsr version_inforinspect_validatorr% dataclass slots_truerrCrVrlrur~rrr&r( _V2Validatorrorq_V2WrapValidatorr@ staticmethodrr?rrrrrrValidatorFunctionWrapHandlerrrrrrrrModelAfterValidatorWithoutInfoValidationInfoModelAfterValidator_AnyModelWrapValidator_AnyModelBeforeValidator_AnyModelAfterValidatorrrrrrAr0r.rsH2 #ccc85-BB4%g* 22EdE&9&D&DE9(9(F9(xEdE&9&D&DE@ @ F@ FEdE&9&D&DE] ] F] @EdE&9&D&DEH H FH V@x@cct8tH..$,, .L!22(00 2,1S#s]1K\Z]_bZbMcersvew1w+xyx)0,L";;<*&##9GWYrGrAst!()K!LYL !$"% A AA  A  A  A<A A !$"% ] ]] % ]  ]  ]X] ] ! # ] ]]  ]  ] X ] ]!( $"3 l ll  l  l  ll^\ " ~6   I I8T`Ka  HZ$8" *-  (  h  x  8 "*:, *B!C L,G,GH*TUZ1*=?\]g?hhi 24WYxx 3J ?A_`jAk kl         7 ,7 7t ) 7C<(J[<0;;<9#9#=9#xw|,N[<0;;<##=#r0