o ni{@s^UdZddlmZddlZddlZddlZddlmZddl m Z m Z m Z m Z mZmZmZmZmZddlmZmZddlmZmZdd lmZmZmZdd lmZdd lm Z dd l!m"Z"dd lm#Z#m$Z$ej%dkrqddlm&Z&nddl m&Z&ej'Z(ej)deddiej*GdddZ+ej)deddiej*GdddZ,ej)deddiej*GdddZ-ej)deddiej*GdddZ.e r#Gddde&Z/Gddde&Z0Gddde&Z1Gd d!d!e&Z2ee0ej3e/ej4fZ5ee2ej6e1ej7fZ8ee9e e e fe:e e fee fZ;d"eed'Z?d"eded?didAd7Z@edBZAedCddDZBGdEdFdFejCe&eBZDGdGdHdHe&eAZEGdIdJdJe&eAZFGdKdLdLe&ZGGdMdNdNe&ZHGdOdPdPe&ZIGdQdRdRe&ZJe eAgeAfZK e eAejLe geAfZM eeFeAeEeAfZNeeIeJeGeHfZOeeMeAeKeAfZPedjdTdUZQedkdXdUZQedldZdUZQdmd\dUZQed]ZRe re eRd)fZSnej)deiej*Gd^d_d_ZSe re eRd)fZTnej)deiej*Gd`dadaZTedbZUGdcddddZVdS)nzBThis module contains related classes and functions for validation.) annotationsN) partialmethod) 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 r3^/var/www/html/arapca_proje/venv/lib/python3.10/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__Jsz+AfterValidator.__get_pydantic_core_schema__ decorator>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]rcCs ||jdS)Nr r8clsr6r3r3r4_from_decoratorTs zAfterValidator._from_decoratorNr!rr"rr#r$r6r7r#r)__name__ __module__ __qualname____doc____annotations__r5 classmethodr;r3r3r3r4rs *  rc@>eZdZUdZded<eZded<dd d ZedddZ dS)BeforeValidatora!!! abstract "Usage Documentation" [field *before* validators](../concepts/validators.md#field-before-validator) A metadata class that indicates that a validation should be applied **before** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. Example: ```python from typing import Annotated from pydantic import BaseModel, BeforeValidator MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)] class Model(BaseModel): a: MyInt print(Model(a=1).a) #> 2 try: Model(a='a') except TypeError as e: print(e) #> can only concatenate str (not "int") to str ``` rr rjson_schema_input_typer!r"rr#r$cCr||}|jtur dn||j}t|jddd}|r*ttj|j}tj|||dSttj |j}tj |||dS)Nbeforer&r'r+json_schema_input_schema) rFr generate_schemar,r r rr-#with_info_before_validator_functionr/!no_info_before_validator_functionr1r!r"r+ input_schemar2r r3r3r4r5~s"  z,BeforeValidator.__get_pydantic_core_schema__r6r7rcC||j|jjdSN)r rFr inforFr9r3r3r4r;zBeforeValidator._from_decoratorNr<r= r>r?r@rArBr rFr5rCr;r3r3r3r4rEYs   rEc@rD)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, got {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`][]. rr rrFr!r"rr#r$c Csddlm}z||}|dtjdd|||d}Wn |y(d}Ynw||j}t|jddd }|rHt tj |j}tj |||d St tj |j}tj |||d S) Nr)PydanticSchemaGenerationError serializationcS||SNr3vhr3r3r4z=PlainValidator.__get_pydantic_core_schema__..)functionr+ return_schemaplainr&r')rXrJ)pydanticrWgetr#wrap_serializer_function_ser_schemarKrFr,r r r-"with_info_plain_validator_functionr/ no_info_plain_validator_function) r1r!r"rWr+rXrOr2r r3r3r4r5s:   z+PlainValidator.__get_pydantic_core_schema__r6r7rcCrPrQrRr9r3r3r4r;rTzPlainValidator._from_decoratorNr<r=) r>r?r@rArBrrFr5rCr;r3r3r3r4rVs .  )rVc@rD) WrapValidatora!!! abstract "Usage Documentation" [field *wrap* validators](../concepts/validators.md#field-wrap-validator) A metadata class that indicates that a validation should be applied **around** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. ```python from datetime import datetime from typing import Annotated from pydantic import BaseModel, ValidationError, WrapValidator def validate_timestamp(v, handler): if v == 'now': # we don't want to bother with further validation, just return the new value return datetime.now() try: return handler(v) except ValidationError: # validation failed, in this case we want to return a default value return datetime(2000, 1, 1) MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)] class Model(BaseModel): a: MyTimestamp print(Model(a='now').a) #> 2032-01-02 03:04:05.000006 print(Model(a='invalid').a) #> 2000-01-01 00:00:00 ``` zScore_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunctionr rrFr!r"rr#r$cCrG)Nwrapr&r'rI) rFr rKr,r r rWithInfoWrapValidatorFunction!with_info_wrap_validator_functionNoInfoWrapValidatorFunctionno_info_wrap_validator_functionrNr3r3r4r5+s&  z*WrapValidator.__get_pydantic_core_schema__r6r7rcCrPrQrRr9r3r3r4r;CrTzWrapValidator._from_decoratorNr<r=rUr3r3r3r4rhs &  rhc@seZdZdddZdS) _OnlyValueValidatorClsMethodr:rvaluer#cCdSrZr3r1r:ror3r3r4__call__Nz%_OnlyValueValidatorClsMethod.__call__Nr:rrorr#rr>r?r@rrr3r3r3r4rnMrnc@eZdZd ddZd S) _V2ValidatorClsMethodr:rrorScore_schema.ValidationInfo[Any]r#cCrprZr3r1r:rorSr3r3r4rrQrsz_V2ValidatorClsMethod.__call__Nr:rrorrSryr#rrur3r3r3r4rxPrvrxc@rw) _OnlyValueWrapValidatorClsMethodr:rror"(core_schema.ValidatorFunctionWrapHandlerr#cCrprZr3r1r:ror"r3r3r4rrTrsz)_OnlyValueWrapValidatorClsMethod.__call__N)r:rrorr"r}r#rrur3r3r3r4r|Srvr|c@seZdZd d d Zd S) _V2WrapValidatorClsMethodr:rror"r}rSryr#cCrprZr3r1r:ror"rSr3r3r4rrWz"_V2WrapValidatorClsMethod.__call__N) r:rrorr"r}rSryr#rrur3r3r3r4rVrvrr_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)rHr%rirbFieldValidatorModes.) check_fieldsrFr&strfieldsr(Literal['wrap']r bool | NonerFrr#6Callable[[_V2WrapValidatorType], _V2WrapValidatorType]cGrprZr3r&r(rrFrr3r3r4field_validatoryrLiteral['before', 'plain']RCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]cGrprZr3rr3r3r4rr)r(rLiteral['after']cGrprZr3)r&r(rrr3r3r4rrr%)r(rrFCallable[[Any], Any]cst|s t|trtddddvr tur tdddtur*dkr*t|gRtdd Ds?td d ddfdd }|S)a!!! 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: *fields: The field names the validator should apply to. 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'`. Raises: PydanticUserError: - If the decorator is used without any arguments (at least one field name must be provided). - If the provided field names are not strings. - If `json_schema_input_type` is provided with an unsupported `mode`. - If the decorator is applied to an instance method. zThe `@field_validator` decorator cannot be used without arguments, at least one field must be provided. For example: `@field_validator('', ...)`.zdecorator-missing-argumentscode)rHrbriz;`json_schema_input_type` can't be used when mode is set to zvalidator-input-typerbcss|]}t|tVqdSrZ) isinstancer).0r&r3r3r4 sz"field_validator..zThe provided field names to the `@field_validator` decorator should be strings. For example: `@field_validator('', '', ...).`zdecorator-invalid-fieldsfHCallable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any]r#(_decorators.PydanticDescriptorProxy[Any]cs>t|r tdddt|}tjd}t||S)NzFThe `@field_validator` decorator cannot be applied to instance methodszvalidator-instance-methodr)rr(rrF)ris_instance_method_from_sigr%ensure_classmethod_based_on_signatureFieldValidatorDecoratorInfoPydanticDescriptorProxyrdec_inforrrFr(r3r4decs   zfield_validator..decN)rrr#r)callablerrCrr rall)r&r(rrFrrr3rr4rs(@  _ModelType _ModelTypeCo) covariantc@seZdZdZ d d d d ZdS) ModelWrapValidatorHandlerz]`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`.Nrorouter_locationstr | int | Noner#rcCrprZr3)r1rorr3r3r4rr z"ModelWrapValidatorHandler.__call__rZ)rorrrr#rr>r?r@rArrr3r3r3r4r 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]rorr"%ModelWrapValidatorHandler[_ModelType]r#rcCrprZr3r~r3r3r4rr z&ModelWrapValidatorWithoutInfo.__call__N)r:rrorr"rr#rrr3r3r3r4rrc@seZdZdZdd d ZdS)ModelWrapValidatorzSA `@model_validator` decorated function signature. This is used when `mode='wrap'`.r:rrorr"rrScore_schema.ValidationInfor#rcCrprZr3rr3r3r4rr)s zModelWrapValidator.__call__N) r:rrorr"rrSrr#rrr3r3r3r4r&rc@seZdZdZdddZdS) #FreeModelBeforeValidatorWithoutInfoA `@model_validator` decorated function signature. This is used when `mode='before'` and the function does not have info argument. rorr#cCrprZr3)r1ror3r3r4rr;rz,FreeModelBeforeValidatorWithoutInfo.__call__N)rorr#rrr3r3r3r4r6rrc@seZdZdZd ddZdS) ModelBeforeValidatorWithoutInforr:rror#cCrprZr3rqr3r3r4rrJrz(ModelBeforeValidatorWithoutInfo.__call__Nrtrr3r3r3r4rErrc@seZdZdZd ddZd S) FreeModelBeforeValidatorUA `@model_validator` decorated function signature. This is used when `mode='before'`.rorrSryr#cCrprZr3)r1rorSr3r3r4rrXrz!FreeModelBeforeValidator.__call__N)rorrSryr#rrr3r3r3r4rUrrc@seZdZdZd dd Zd S) ModelBeforeValidatorrr:rrorSryr#cCrprZr3rzr3r3r4rrfrzModelBeforeValidator.__call__Nr{rr3r3r3r4rcrr|Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrprZr3r(r3r3r4model_validatorrrLiteral['before']rCallable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrprZr3rr3r3r4rr}Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrprZr3rr3r3r4rr"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) rrrrCwarningswarnrrModelValidatorDecoratorInforrrr3r4rs  zmodel_validator..decN)rrr#rr3)r(rr3rr4rs1AnyTypec@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||fSrZ)rr:rr3r3r4__class_getitem__ szInstanceOf.__class_getitem__sourcerr"rr$cCshddlm}tt|p|}z||}Wn |y!|YSwtjdd|d|d<tj||dS)Nr)GENERATE_SCHEMA_ERRORScSrYrZr3r[r3r3r4r^r_z9InstanceOf.__get_pydantic_core_schema__..r`r+rX) python_schema json_schema)#pydantic._internal._generate_schemarris_instance_schemar get_originrejson_or_python_schema)r:rr"rinstance_of_schemaoriginal_schemar3r3r4r5s    z'InstanceOf.__get_pydantic_core_schema__N)rrr#rrrr"rr#r$) r>r?r@rArCrr5object__hash__r3r3r3r4rs   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|tfSrZ)rrrr3r3r4r7sz SkipValidation.__class_getitem__rr"rr$csjttdt||Wdn1swYdfddgi}tj|tjddddS)Nignore pydantic_js_annotation_functionscs|SrZr3)_cr]rr3r4r^?r_z=SkipValidation.__get_pydantic_core_schema__..cSrYrZr3r[r3r3r4r^Cr_r)metadatarX)rcatch_warnings simplefilterrr any_schemare)r:rr"rr3rr4r5:s   z+SkipValidation.__get_pydantic_core_schema__N)rrr#rr) r>r?r@rArrCr5rrr3r3r3r4r*s    r _FromTypeTc@s$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||_||_dSrZ)rr)r1rrr3r3r4__init__ps zValidateAs.__init__rrr"rr$cCs||j}tj|j|dS)Nr*)rrr0r)r1rr"r+r3r3r4r5ts z'ValidateAs.__get_pydantic_core_schema__N)rrrrr#rr)r>r?r@rArr5r3r3r3r4rMs "rr3) r&rrrr(rrrrFrr#r) r&rrrr(rrrrFrr#r) r&rrrr(rrrr#r) r&rrrr(rrrrFrr#r)r(rr#r)r(rr#r)r(rr#r)r(rr#r)WrA __future__r _annotations dataclassessysr functoolsrtypingrrrrrr r r r pydantic_corer rtyping_extensionsrr _internalrrrannotated_handlersrerrorsrversionrrr version_inforinspect_validatorr, dataclass slots_truerrErVrhrnrxr|rr-r/ _V2Validatorrjrl_V2WrapValidatorrC staticmethodrrBrrrrrrValidatorFunctionWrapHandlerrrrrrrrModelAfterValidatorWithoutInfoValidationInfoModelAfterValidator_AnyModelWrapValidator_AnyModelBeforeValidator_AnyModelAfterValidatorrrrrrrr3r3r3r4s  ,     <BcJ ,    l       D<