izFUdZddlmZddlZddlZddlZddlmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZddlmZmZddlmZmZd d lmZmZmZd d lm Z d d l!m"Z"d d lm#Z#ejHdkrddlm%Z%nddl m%Z%ejLZ'ejPdOddiejRGddZ*ejPdOddiejRGddZ+ejPdOddiejRGddZ,ejPdOddiejRGddZ-e rGdde%Z.Gdde%Z/Gdde%Z0Gd d!e%Z1ee/ejde.ejffZ4ee1ejje0ejlfZ7ee8eeefe9eefeefZ:d"e;d#<ed$ee4e:f%Z<ed&ee7e:f%Z=ed'Z>d"e;d(<ed)d)d* dPd+Z?ed)d)d* dQd,Z?ed)d)d- dRd.Z?d/ded0 dSd1Z?ed2Z@ed3d4ZAGd5d6eje%eAZCGd7d8e%e@ZDGd9d:e%e@ZEGd;de%ZGGd?d@e%ZHGdAdBe%ZIee@ge@fZJ ee@ejege@fZL eeEe@eDe@fZMeeHeIeFeGfZNeeLe@eJe@fZOe dTdCZPe dUdDZPe dVdEZP dWdFZPedGZQe re eQd)fZRn)ejPdOiejRGdHdIZRe re eQd)fZSn)ejPdOiejRGdJdKZSedLZTGdMdNZUy)XzBThis 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)ArbitraryTypeWarning) )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.WithInfoValidatorFunctionfuncc.||}t|jd}|r;ttj|j}tj ||Sttj |j}tj||S)Nafterschema)_inspect_validatorr!r rWithInfoValidatorFunction"with_info_after_validator_functionNoInfoValidatorFunction no_info_after_validator_function)self source_typehandlerr%info_argr!s ~/var/www/html/hwPaymentPortal-be-dev/hw-payment-portal-api/venv/lib/python3.12/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__z+AfterValidator.__get_pydantic_core_schema__Jsq%%dii9  ==tyyIDAA$vV V ;;TYYGD??VT Tc(||jS)Nr!r3cls decorators r/_from_decoratorzAfterValidator._from_decoratorTs ''r1Nr,rr-rreturncore_schema.CoreSchemar6z>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]r9r)__name__ __module__ __qualname____doc____annotations__r0 classmethodr7r1r/rrs+(T VUU((r1rcDeZdZUdZded<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 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_typec||}|jturdn|j|j}t|jd}|r>#H#H  r1Nr8r; r<r=r>r?r@rrEr0rAr7rBr1r/rDrDYs5@ VU"3C3,  r1rDcDeZdZUdZded<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 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!rrEc ddlm} ||}|jdtjd||j |}|j |j }t|jd}|rz=PlainValidator.__get_pydantic_core_schema__.. !A$r1)functionr% return_schemaplain)rZrI)pydanticrYgetr#wrap_serializer_function_ser_schemarJrEr&r!r r'"with_info_plain_validator_functionr) no_info_plain_validator_function) r+r,r-rYr%rZrNr.r!s r/r0z+PlainValidator.__get_pydantic_core_schema__s ; ![)F#JJ??.!")"9"9+"FM..t/J/JK %dii9  ==tyyIDAA+)5   ;;TYYGD??+)5 - ! M !sAC33C=<C=cR||j|jjSrPrQr4s r/r7zPlainValidator._from_decoratorrSr1Nr8r;) r<r=r>r?r@rrEr0rAr7rBr1r/rVrVs6,\ VU"%C%'R  r1rVcDeZdZUdZded<eZded<d dZed dZ y) WrapValidatora!!! abstract "Usage Documentation" [field *wrap* validators](../concepts/validators.md#field-wrap-validator) A metadata class that indicates that a validation should be applied **around** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. ```python from datetime import datetime from typing import Annotated from pydantic import BaseModel, ValidationError, WrapValidator def validate_timestamp(v, handler): if v == 'now': # we don't want to bother with further validation, just return the new value return datetime.now() try: return handler(v) except ValidationError: # validation failed, in this case we want to return a default value return datetime(2000, 1, 1) MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)] class Model(BaseModel): a: MyTimestamp print(Model(a='now').a) #> 2032-01-02 03:04:05.000006 print(Model(a='invalid').a) #> 2000-01-01 00:00:00 ``` zScore_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunctionr!rrEc||}|jturdn|j|j}t|jd}|r>)5 r1cR||j|jjSrPrQr4s r/r7zWrapValidator._from_decoratorCrSr1Nr8r;rTrBr1r/rlrls5$L ^]"3C30  r1rlceZdZddZy)_OnlyValueValidatorClsMethodcyr\rBr+r5values r/__call__z%_OnlyValueValidatorClsMethod.__call__Nr1Nr5rrxrr9rr<r=r>ryrBr1r/ruruMs?r1ruceZdZddZy)_V2ValidatorClsMethodcyr\rBr+r5rxrRs r/ryz_V2ValidatorClsMethod.__call__Qrzr1Nr5rrxrrRcore_schema.ValidationInfo[Any]r9rr|rBr1r/r~r~Psfr1r~ceZdZddZy) _OnlyValueWrapValidatorClsMethodcyr\rBr+r5rxr-s r/ryz)_OnlyValueWrapValidatorClsMethod.__call__Trzr1N)r5rrxrr-(core_schema.ValidatorFunctionWrapHandlerr9rr|rBr1r/rrSsrr1rc(eZdZ ddZy)_V2WrapValidatorClsMethodcyr\rBr+r5rxr-rRs r/ryz"_V2WrapValidatorClsMethod.__call__Wsr1N) r5rrxrr-rrRrr9rr|rBr1r/rrVs7   >   2   r1rr_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)rGr#rnrdFieldValidatorModes.) check_fieldsrEcyr\rBfieldmoderrEfieldss r/field_validatorrys>Ar1cyr\rBrs r/rrs Z]r1)rrcyr\rB)rrrrs r/rrs Z]r1r#)rrrEc 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)rGrdrnz;`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)rrrrE)ris_instance_method_from_sigr%ensure_classmethod_based_on_signatureFieldValidatorDecoratorInfoPydanticDescriptorProxy)fdec_inforrrErs r/deczfield_validator..decsg  2 21 5#JQl   = =a @::r?ryrBr1r/rr s+g ,0  )   r1rc(eZdZdZ ddZy)ModelWrapValidatorWithoutInfozA `@model_validator` decorated function signature. This is used when `mode='wrap'` and the function does not have info argument. cyr\rBrs r/ryz&ModelWrapValidatorWithoutInfo.__call__sr1N)r5type[_ModelType]rxrr-%ModelWrapValidatorHandler[_ModelType]r9rrrBr1r/rrs2     7   r1rc,eZdZdZ ddZy)ModelWrapValidatorzSA `@model_validator` decorated function signature. This is used when `mode='wrap'`.cyr\rBrs r/ryzModelWrapValidator.__call__,sr1N) r5rrxrr-rrRzcore_schema.ValidationInfor9rrrBr1r/rr)s:]     7 )   r1rc eZdZdZ ddZy)#FreeModelBeforeValidatorWithoutInfoA `@model_validator` decorated function signature. This is used when `mode='before'` and the function does not have info argument. cyr\rB)r+rxs r/ryz,FreeModelBeforeValidatorWithoutInfo.__call__>sr1N)rxrr9rrrBr1r/rr9s    r1rc$eZdZdZ ddZy)ModelBeforeValidatorWithoutInforcyr\rBrws r/ryz(ModelBeforeValidatorWithoutInfo.__call__Mr1Nr{rrBr1r/rrHs(    r1rc$eZdZdZ ddZy)FreeModelBeforeValidatorUA `@model_validator` decorated function signature. This is used when `mode='before'`.cyr\rB)r+rxrRs r/ryz!FreeModelBeforeValidator.__call__[rr1N)rxrrRrr9rrrBr1r/rrXs(_   .  r1rc(eZdZdZ ddZy)ModelBeforeValidatorrcyr\rBrs r/ryzModelBeforeValidator.__call__isr1NrrrBr1r/rrfs0_     .   r1rcyr\rBrs r/model_validatorr r1cyr\rBrs r/rrrr1cyr\rBrs r/rrrr1cdfd }|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. cdk7rtj|}tj}tj||S)Nr#r)rrModelValidatorDecoratorInfor)rrrs r/rzmodel_validator..decs= 7?AA!DA::E221h??r1)rrr9rrB)rrs` r/rrsb@ Jr1AnyTypecLeZdZdZeddZeddZejZy) 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\)rr5items r/__class_getitem__zInstanceOf.__class_getitem__sT35[) )r1cddlm}tjt j |xs|} ||}tj d||d<tj||S#|$r|cYSwxYw)NrrXc||Sr\rBr]s r/r`z9InstanceOf.__get_pydantic_core_schema__..rar1rbr%rZ) python_schema json_schema)rerYris_instance_schemar get_originrgjson_or_python_schema)r5sourcer-rYinstance_of_schemaoriginal_schemas r/r0z'InstanceOf.__get_pydantic_core_schema__ s >"-!?!? @T@TU[@\@f`f!g  x")&/ 7B6e6e.7"?3#88GYgvww1 *)) *sA..A87A8N)rrr9rrrr-rr9r:) r<r=r>r?rArr0object__hash__rBr1r/rrs= @  *  *  x  x&??r1rcBeZdZdZddZeddZejZy)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__1sT>#334 4r1ctj5tjdt||ddddfdgi}t j |t j dS#1swY>xYw)Nignore pydantic_js_annotation_functionsc|Sr\rB)_cr_rs r/r`z=SkipValidation.__get_pydantic_core_schema__..9s 1_K]r1c||Sr\rBr]s r/r`z=SkipValidation.__get_pydantic_core_schema__..=rar1r)metadatarZ)warningscatch_warnings simplefilterrr any_schemarg)r5rr-rrs @r/r0z+SkipValidation.__get_pydantic_core_schema__4sw((* 2%%h0DE")&/ 2;=]<^_H))!)MM.   2 2s #A66A?N)rrr9rr) r<r=r>r?rrAr0rrrBr1r/rr$s+  5    ??r1r _FromTypeTc eZdZdZddZddZy) 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) ``` c ||_||_yr\) from_typeinstantiation_hook)r+rrs r/__init__zValidateAs.__init__js""4r1ch||j}tj|j|S)Nr$)rrr*r)r+rr-r%s r/r0z'ValidateAs.__get_pydantic_core_schema__ns/(;;  # #  r1N)rzCallable[[_FromTypeT], Any]rztype[_FromTypeT]r9Noner)r<r=r>r?rr0rBr1r/rrGsD5 r1rrB) rrrrrLiteral['wrap']r bool | NonerErr9z6Callable[[_V2WrapValidatorType], _V2WrapValidatorType]) rrrrrzLiteral['before', 'plain']rrrErr9RCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]) rrrrrLiteral['after']rrr9r) rrrrrrrrrErr9zCallable[[Any], Any])rrr9z|Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])rzLiteral['before']r9zrCallable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])rrr9z}Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])rz"Literal['wrap', 'before', 'after']r9r)Vr? __future__r _annotations dataclassessysr functoolsrtypesrtypingrrrr r r r r r pydantic_corerrtyping_extensionsrr _internalrrrannotated_handlersrerrorsrr version_inforinspect_validatorr& dataclass slots_truerrDrVrlrur~rrr'r) _V2Validatorrorq_V2WrapValidatorrA staticmethodrr@rrrrrrValidatorFunctionWrapHandlerrrrrrrrModelAfterValidatorWithoutInfoValidationInfoModelAfterValidator_AnyModelWrapValidator_AnyModelBeforeValidator_AnyModelAfterValidatorrrrrrrrBr1r/rsH2 #ccc8-BB4%*g* 22EdE&9&D&DE9(9(F9(xEdE&9&D&DE? ? F? DEdE&9&D&DE` ` F` FEdE&9&D&DEG G FG T@x@ggs8sH--$++ -L!11(// 1,1S#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   H H(S_J`  HZ$8" *-  (  h  x  8 "*:, *B!C K,F,Fs,KLjXYZ1*=?\]g?hhi 24WYxx 3J ?A_`jAk kl         8 ,8 8v ) 7C<(J[<0;;<9#9#=9#xw|,N[<0;;<##=#>\ " , , r1