o 3h_|@sjUdZddlmZddlZddlZddlZddlmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZddlmZmZddlmZmZd d lmZmZmZd d lm Z d d l!m"Z"d d l#m$Z$d dlm%Z%m&Z&ej'dkrwddlm(Z(nddl m(Z(ej)Z*ej+dfddiej,GdddZ-ej+dfddiej,GdddZ.ej+dfddiej,GdddZ/ej+dfddiej,GdddZ0e r)Gddde(Z1Gddde(Z2Gdd d e(Z3Gd!d"d"e(Z4ee2ej5e1ej6fZ7ee4ej8e3ej9fZ:ee;eeefed$<ed%ee7e=fd&Z?ed'ee:e=fd&Z@ed(ZAd#e>d)<ed*d*d+dgd7d8ZBed*d*d+dhd;d8ZBed*d*d<did>d8ZBd?ded@djdBd8ZBedCZCedDddEZDGdFdGdGejEe(eDZFGdHdIdIe(eCZGGdJdKdKe(eCZHGdLdMdMe(ZIGdNdOdOe(ZJGdPdQdQe(ZKGdRdSdSe(ZLeeCgeCfZM eeCejNegeCfZO eeHeCeGeCfZPeeKeLeIeJfZQeeOeCeMeCfZRedkdUdVZSedldYdVZSedmd[dVZSdnd]dVZSed^ZTe re eTd*fZUnej+dfiej,Gd_d`d`ZUe re eTd*fZVnej+dfiej,GdadbdbZVedcZWGdddedeZXdS)ozBThis module contains related classes and functions for validation.) annotationsN) partialmethod) FunctionType) TYPE_CHECKING AnnotatedAnyCallableLiteralTypeVarUnioncastoverload)PydanticUndefined core_schema)Self TypeAlias) _decorators _generics_internal_dataclass)GetCoreSchemaHandler)PydanticUserError) version_short)ArbitraryTypeWarningPydanticDeprecatedSince212) )ProtocolfrozenTc@s2eZdZUdZded<dd d ZedddZdS)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.WithInfoValidatorFunctionfunc source_typerhandlerrreturncore_schema.CoreSchemacCsT||}t|jddd}|rttj|j}tj||dSttj|j}tj||dS)Nafterfieldmodetypeschema)_inspect_validatorr!r rWithInfoValidatorFunction"with_info_after_validator_functionNoInfoValidatorFunction no_info_after_validator_function)selfr"r#r,info_argr!r4Y/var/www/html/chatgem/venv/lib/python3.10/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__Ksz+AfterValidator.__get_pydantic_core_schema__ decorator>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]rcCs ||jdS)Nr!r9clsr7r4r4r5_from_decoratorUs zAfterValidator._from_decoratorNr"rr#rr$r%r7r8r$r)__name__ __module__ __qualname____doc____annotations__r6 classmethodr<r4r4r4r5rs *  rc@>eZdZUdZded<eZded<dd d ZedddZ dS)BeforeValidatora!!! abstract "Usage Documentation" [field *before* validators](../concepts/validators.md#field-before-validator) A metadata class that indicates that a validation should be applied **before** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. Example: ```python from typing import Annotated from pydantic import BaseModel, BeforeValidator MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)] class Model(BaseModel): a: MyInt print(Model(a=1).a) #> 2 try: Model(a='a') except TypeError as e: print(e) #> can only concatenate str (not "int") to str ``` r r!rjson_schema_input_typer"r#rr$r%cCr||}|jtur dn||j}t|jddd}|r*ttj|j}tj|||dSttj |j}tj |||dS)Nbeforer'r(r,json_schema_input_schema) rGrgenerate_schemar-r!r rr.#with_info_before_validator_functionr0!no_info_before_validator_functionr2r"r#r, input_schemar3r!r4r4r5r6s"  z,BeforeValidator.__get_pydantic_core_schema__r7r8rcC||j|jjdSN)r!rGr!inforGr:r4r4r5r<zBeforeValidator._from_decoratorNr=r> r?r@rArBrCrrGr6rDr<r4r4r4r5rFZs   rFc@rE)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 used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. Example: ```python from typing import Annotated, Union from pydantic import BaseModel, PlainValidator def validate(v: object) -> int: if not isinstance(v, (int, str)): raise ValueError(f'Expected int or str, go {type(v)}') return int(v) + 1 MyInt = Annotated[ int, PlainValidator(validate, 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`][]. r r!rrGr"r#rr$r%c Csddlm}z||}|dtjdd|||d}Wn |y(d}Ynw||j}t|jddd }|rHt tj |j}tj |||d St tj |j}tj |||d S) NrPydanticSchemaGenerationError serializationcS||SNr4vhr4r4r5z=PlainValidator.__get_pydantic_core_schema__..)functionr, return_schemaplainr'r()rZrK)pydanticrYgetr#wrap_serializer_function_ser_schemarLrGr-r!r r."with_info_plain_validator_functionr0 no_info_plain_validator_function) r2r"r#rYr,rZrPr3r!r4r4r5r6s:   z+PlainValidator.__get_pydantic_core_schema__r7r8rcCrQrRrSr:r4r4r5r<rUzPlainValidator._from_decoratorNr=r>) r?r@rArBrCrrGr6rDr<r4r4r4r5rWs .  )rWc@rE) WrapValidatora!!! abstract "Usage Documentation" [field *wrap* validators](../concepts/validators.md#field-wrap-validator) A metadata class that indicates that a validation should be applied **around** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. ```python from datetime import datetime from typing import Annotated from pydantic import BaseModel, ValidationError, WrapValidator def validate_timestamp(v, handler): if v == 'now': # we don't want to bother with further validation, just return the new value return datetime.now() try: return handler(v) except ValidationError: # validation failed, in this case we want to return a default value return datetime(2000, 1, 1) MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)] class Model(BaseModel): a: MyTimestamp print(Model(a='now').a) #> 2032-01-02 03:04:05.000006 print(Model(a='invalid').a) #> 2000-01-01 00:00:00 ``` zScore_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunctionr!rrGr"r#rr$r%cCrH)Nwrapr'r(rJ) rGrrLr-r!r rWithInfoWrapValidatorFunction!with_info_wrap_validator_functionNoInfoWrapValidatorFunctionno_info_wrap_validator_functionrOr4r4r5r6,s&  z*WrapValidator.__get_pydantic_core_schema__r7r8rcCrQrRrSr:r4r4r5r<DrUzWrapValidator._from_decoratorNr=r>rVr4r4r4r5rjs &  rjc@seZdZdddZdS) _OnlyValueValidatorClsMethodr;rvaluer$cCdSr\r4r2r;rqr4r4r5__call__Oz%_OnlyValueValidatorClsMethod.__call__Nr;rrqrr$rr?r@rArtr4r4r4r5rpNrpc@eZdZd ddZd S) _V2ValidatorClsMethodr;rrqrTcore_schema.ValidationInfo[Any]r$cCrrr\r4r2r;rqrTr4r4r5rtRruz_V2ValidatorClsMethod.__call__Nr;rrqrrTr{r$rrwr4r4r4r5rzQrxrzc@ry) _OnlyValueWrapValidatorClsMethodr;rrqr#(core_schema.ValidatorFunctionWrapHandlerr$cCrrr\r4r2r;rqr#r4r4r5rtUruz)_OnlyValueWrapValidatorClsMethod.__call__N)r;rrqrr#rr$rrwr4r4r4r5r~Trxr~c@seZdZd d d Zd S) _V2WrapValidatorClsMethodr;rrqr#rrTr{r$cCrrr\r4r2r;rqr#rTr4r4r5rtXz"_V2WrapValidatorClsMethod.__call__N) r;rrqrr#rrTr{r$rrwr4r4r4r5rWrxrr_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)rIr&rkrdFieldValidatorModes.) check_fieldsrGr'strfieldsr)Literal['wrap']r bool | NonerGrr$6Callable[[_V2WrapValidatorType], _V2WrapValidatorType]cGrrr\r4r'r)rrGrr4r4r5field_validatorzrLiteral['before', 'plain']RCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]cGrrr\r4rr4r4r5rr)r)rLiteral['after']cGrrr\r4)r'r)rrr4r4r5rrr&)r)rrGCallable[[Any], Any]cst|tr tddddvrturtdddtur&dkr&t|gRtdd Ds;td d ddfdd }|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)rIrdrkz;`json_schema_input_type` can't be used when mode is set to zvalidator-input-typerdcss|]}t|tVqdSr\) isinstancer).0r'r4r4r5 sz"field_validator..z`@field_validator` fields should be passed as separate string args. E.g. usage should be `@validator('', '', ...)`zvalidator-invalid-fieldsfHCallable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any]r$(_decorators.PydanticDescriptorProxy[Any]cs>t|r tdddt|}tjd}t||S)Nz8`@field_validator` cannot be applied to instance methodszvalidator-instance-methodr)rr)rrG)ris_instance_method_from_sigr%ensure_classmethod_based_on_signatureFieldValidatorDecoratorInfoPydanticDescriptorProxyrdec_inforrrGr)r4r5decs   zfield_validator..decN)rrr$r)rrrrrall)r'r)rrGrrr4rr5rs( D  _ModelType _ModelTypeCo) covariantc@seZdZdZ d d d d ZdS) ModelWrapValidatorHandlerz]`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`.Nrqrouter_locationstr | int | Noner$rcCrrr\r4)r2rqrr4r4r5rtz"ModelWrapValidatorHandler.__call__r\)rqrrrr$rr?r@rArBrtr4r4r4r5r src@seZdZdZd d d Zd S)ModelWrapValidatorWithoutInfozA `@model_validator` decorated function signature. This is used when `mode='wrap'` and the function does not have info argument. r;type[_ModelType]rqrr#%ModelWrapValidatorHandler[_ModelType]r$rcCrrr\r4rr4r4r5rt z&ModelWrapValidatorWithoutInfo.__call__N)r;rrqrr#rr$rrr4r4r4r5rrc@seZdZdZdd d ZdS)ModelWrapValidatorzSA `@model_validator` decorated function signature. This is used when `mode='wrap'`.r;rrqrr#rrTcore_schema.ValidationInfor$rcCrrr\r4rr4r4r5rt-s zModelWrapValidator.__call__N) r;rrqrr#rrTrr$rrr4r4r4r5r*rc@seZdZdZdddZdS) #FreeModelBeforeValidatorWithoutInfoA `@model_validator` decorated function signature. This is used when `mode='before'` and the function does not have info argument. rqrr$cCrrr\r4)r2rqr4r4r5rt?rz,FreeModelBeforeValidatorWithoutInfo.__call__N)rqrr$rrr4r4r4r5r:rrc@seZdZdZd ddZdS) ModelBeforeValidatorWithoutInforr;rrqr$cCrrr\r4rsr4r4r5rtNrz(ModelBeforeValidatorWithoutInfo.__call__Nrvrr4r4r4r5rIrrc@seZdZdZd ddZd S) FreeModelBeforeValidatorUA `@model_validator` decorated function signature. This is used when `mode='before'`.rqrrTr{r$cCrrr\r4)r2rqrTr4r4r5rt\rz!FreeModelBeforeValidator.__call__N)rqrrTr{r$rrr4r4r4r5rYrrc@seZdZdZd dd Zd S) ModelBeforeValidatorrr;rrqrTr{r$cCrrr\r4r|r4r4r5rtjrzModelBeforeValidator.__call__Nr}rr4r4r4r5rgrr|Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrrr\r4r)r4r4r5model_validatorrrLiteral['before']rCallable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrrr\r4rr4r4r5rr}Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrrr\r4rr4r4r5rr"Literal['wrap', 'before', 'after']csdfdd }|S) a@!!! abstract "Usage Documentation" [Model Validators](../concepts/validators.md#model-validators) Decorate model methods for validation purposes. Example usage: ```python from typing_extensions import Self from pydantic import BaseModel, ValidationError, model_validator class Square(BaseModel): width: float height: float @model_validator(mode='after') def verify_square(self) -> Self: if self.width != self.height: raise ValueError('width and height do not match') return self s = Square(width=1, height=1) print(repr(s)) #> Square(width=1.0, height=1.0) try: Square(width=1, height=2) except ValidationError as e: print(e) ''' 1 validation error for Square Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict] ''' ``` For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators). Args: mode: A required string literal that specifies the validation mode. It can be one of the following: 'wrap', 'before', or 'after'. Returns: A decorator that can be used to decorate a function to be used as a model validator. rrr$rcsNt|}dkrt|trtjtdtdddtjd}t ||S)Nr&zUsing `@model_validator` with mode='after' on a classmethod is deprecated. Instead, use an instance method. See the documentation at https://docs.pydantic.dev/z,/concepts/validators/#model-after-validator.)categorymessage stacklevelr) rrrrDwarningswarnrrModelValidatorDecoratorInforrrr4r5rs  zmodel_validator..decN)rrr$rr4)r)rr4rr5rs1AnyTypec@s2eZdZdZedddZedd d ZejZdS) InstanceOfuGeneric type for annotating a type that is an instance of a given class. Example: ```python from pydantic import BaseModel, InstanceOf class Foo: ... class Bar(BaseModel): foo: InstanceOf[Foo] Bar(foo=Foo()) try: Bar(foo=42) except ValidationError as e: print(e) """ [ │ { │ │ 'type': 'is_instance_of', │ │ 'loc': ('foo',), │ │ 'msg': 'Input should be an instance of Foo', │ │ 'input': 42, │ │ 'ctx': {'class': 'Foo'}, │ │ 'url': 'https://errors.pydantic.dev/0.38.0/v/is_instance_of' │ } ] """ ``` itemrr$cCst||fSr\)rr;rr4r4r5__class_getitem__szInstanceOf.__class_getitem__sourcerr#rr%cCshddlm}tt|p|}z||}Wn |y!|YSwtjdd|d|d<tj||dS)NrrXcSr[r\r4r]r4r4r5r`#raz9InstanceOf.__get_pydantic_core_schema__..rbr,rZ) python_schema json_schema)rerYris_instance_schemar get_originrgjson_or_python_schema)r;rr#rYinstance_of_schemaoriginal_schemar4r4r5r6s    z'InstanceOf.__get_pydantic_core_schema__N)rrr$rrrr#rr$r%) r?r@rArBrDrr6object__hash__r4r4r4r5rs   rc@s.eZdZdZdddZedd d ZejZd S)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. rrr$cCst|tfSr\)rrrr4r4r5r;sz SkipValidation.__class_getitem__rr#rr%csjttdt||Wdn1swYdfddgi}tj|tjddddS)Nignore pydantic_js_annotation_functionscs|Sr\r4)_cr_rr4r5r`Craz=SkipValidation.__get_pydantic_core_schema__..cSr[r\r4r]r4r4r5r`Grar)metadatarZ)rcatch_warnings simplefilterrr any_schemarg)r;rr#rr4rr5r6>s   z+SkipValidation.__get_pydantic_core_schema__N)rrr$rr) r?r@rArBrrDr6rrr4r4r4r5r.s    r _FromTypeTc@s$eZdZdZddd ZdddZdS) ValidateAsaA helper class to validate a custom type from a type that is natively supported by Pydantic. Args: from_type: The type natively supported by Pydantic to use to perform validation. instantiation_hook: A callable taking the validated type as an argument, and returning the populated custom type. Example: ```python {lint="skip"} from typing import Annotated from pydantic import BaseModel, TypeAdapter, ValidateAs class MyCls: def __init__(self, a: int) -> None: self.a = a def __repr__(self) -> str: return f"MyCls(a={self.a})" class Model(BaseModel): a: int ta = TypeAdapter( Annotated[MyCls, ValidateAs(Model, lambda v: MyCls(a=v.a))] ) print(ta.validate_python({'a': 1})) #> MyCls(a=1) ``` instantiation_hookCallable[[_FromTypeT], Any] from_typetype[_FromTypeT]r$NonecCs||_||_dSr\)rr)r2rrr4r4r5__init__ts zValidateAs.__init__rrr#rr%cCs||j}tj|j|dS)Nr+)rrr1r)r2rr#r,r4r4r5r6xs z'ValidateAs.__get_pydantic_core_schema__N)rrrrr$rr)r?r@rArBrr6r4r4r4r5rQs "rr4) r'rrrr)rrrrGrr$r) r'rrrr)rrrrGrr$r) r'rrrr)rrrr$r) r'rrrr)rrrrGrr$r)r)rr$r)r)rr$r)r)rr$r)r)rr$r)YrB __future__r _annotations dataclassessysr functoolsrtypesrtypingrrrrr r r r r pydantic_corerrtyping_extensionsrr _internalrrrannotated_handlersrerrorsrversionrrr version_inforinspect_validatorr- dataclass slots_truerrFrWrjrprzr~rr.r0 _V2Validatorrlrn_V2WrapValidatorrD staticmethodrrCrrrrrrValidatorFunctionWrapHandlerrrrrrrrModelAfterValidatorWithoutInfoValidationInfoModelAfterValidator_AnyModelWrapValidator_AnyModelBeforeValidator_AnyModelAfterValidatorrrrrrrr4r4r4r5s   ,     <BcJ ,    o       D<