o Ahz@sZUdZddlmZddlZddlZddlZddlmZddl m Z ddl m Z m Z mZmZmZmZmZmZmZddlmZmZddlmZmZd d lmZmZmZd d lm Z d d l!m"Z"d d lm#Z#ej$dkroddlm%Z%nddl m%Z%ej&Z'ej(deddiej)GdddZ*ej(deddiej)GdddZ+ej(deddiej)GdddZ,ej(deddiej)GdddZ-e r!Gddde%Z.Gddde%Z/Gddde%Z0Gd d!d!e%Z1ee/ej2e.ej3fZ4ee1ej5e0ej6fZ7ee8eeefe9eefeefZ:d"e;d#<ed$ee4e:fd%Zd"e;d(<ed)d)d*dfd6d7Z?ed)d)d*dgd:d7Z?ed)d)d;dhd=d7Z?d>ded?didAd7Z?edBZ@edCddDZAGdEdFdFejBe%eAZCGdGdHdHe%e@ZDGdIdJdJe%e@ZEGdKdLdLe%ZFGdMdNdNe%ZGGdOdPdPe%ZHGdQdRdRe%ZIee@ge@fZJ ee@ejKege@fZL eeEe@eDe@fZMeeHeIeFeGfZNeeLe@eJe@fZOedjdTdUZPedkdXdUZPedldZdUZPdmd\dUZPed]ZQe re eQd)fZRnej(deiej)Gd^d_d_ZRe re eQd)fZSnej(deiej)Gd`dadaZSedbZTGdcddddZUdS)nzBThis 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) )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.CoreSchemacCsP||}t|jd}|rttj|j}tj||dSttj|j}tj||dS)Nafterschema)_inspect_validatorrr rWithInfoValidatorFunction"with_info_after_validator_functionNoInfoValidatorFunction no_info_after_validator_function)selfr r!r&info_argrr._/var/www/html/openai_agents/venv/lib/python3.10/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__Js z+AfterValidator.__get_pydantic_core_schema__ decorator>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]rcCs ||jdS)Nrr3clsr1r.r.r/_from_decoratorTs zAfterValidator._from_decoratorNr rr!rr"r#r1r2r"r)__name__ __module__ __qualname____doc____annotations__r0 classmethodr6r.r.r.r/rs *  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 ``` rrrjson_schema_input_typer r!rr"r#cCn||}|jtur dn||j}t|jd}|r(ttj|j}tj|||dSttj |j}tj |||dS)Nbeforer&json_schema_input_schema) rArgenerate_schemar'rr rr(#with_info_before_validator_functionr*!no_info_before_validator_functionr,r r!r& input_schemar-rr.r.r/r0~s"   z,BeforeValidator.__get_pydantic_core_schema__r1r2rcC||j|jjdSN)rrArinforAr4r.r.r/r6zBeforeValidator._from_decoratorNr7r8 r9r:r;r<r=rrAr0r>r6r.r.r.r/r@Ys   r@c@r?)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`][]. rrrrAr r!rr"r#c Csddlm}z||}|dtjdd|||d}Wn |y(d}Ynw||j}t|jd}|rFt tj |j}tj |||dSt tj |j}tj |||dS) NrPydanticSchemaGenerationError serializationcS||SNr.vhr.r.r/z=PlainValidator.__get_pydantic_core_schema__..)functionr& return_schemaplain)rTrE)pydanticrSgetr#wrap_serializer_function_ser_schemarFrAr'rr r("with_info_plain_validator_functionr* no_info_plain_validator_function) r,r r!rSr&rTrJr-rr.r.r/r0s:    z+PlainValidator.__get_pydantic_core_schema__r1r2rcCrKrLrMr4r.r.r/r6rOzPlainValidator._from_decoratorNr7r8) r9r:r;r<r=rrAr0r>r6r.r.r.r/rQs .  )rQc@r?) 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.WithInfoWrapValidatorFunctionrrrAr r!rr"r#cCrB)NwraprD) rArrFr'rr rWithInfoWrapValidatorFunction!with_info_wrap_validator_functionNoInfoWrapValidatorFunctionno_info_wrap_validator_functionrIr.r.r/r0+s&   z*WrapValidator.__get_pydantic_core_schema__r1r2rcCrKrLrMr4r.r.r/r6CrOzWrapValidator._from_decoratorNr7r8rPr.r.r.r/rds &  rdc@seZdZdddZdS) _OnlyValueValidatorClsMethodr5rvaluer"cCdSrVr.r,r5rkr.r.r/__call__Nz%_OnlyValueValidatorClsMethod.__call__Nr5rrkrr"rr9r:r;rnr.r.r.r/rjMrjc@eZdZd ddZd S) _V2ValidatorClsMethodr5rrkrNcore_schema.ValidationInfo[Any]r"cCrlrVr.r,r5rkrNr.r.r/rnQroz_V2ValidatorClsMethod.__call__Nr5rrkrrNrur"rrqr.r.r.r/rtPrrrtc@rs) _OnlyValueWrapValidatorClsMethodr5rrkr!(core_schema.ValidatorFunctionWrapHandlerr"cCrlrVr.r,r5rkr!r.r.r/rnTroz)_OnlyValueWrapValidatorClsMethod.__call__N)r5rrkrr!ryr"rrqr.r.r.r/rxSrrrxc@seZdZd d d Zd S) _V2WrapValidatorClsMethodr5rrkr!ryrNrur"cCrlrVr.r,r5rkr!rNr.r.r/rnWz"_V2WrapValidatorClsMethod.__call__N) r5rrkrr!ryrNrur"rrqr.r.r.r/r{Vrrr{r_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)rCr$rer^FieldValidatorModes.) check_fieldsrAfieldstrfieldsmodeLiteral['wrap']r bool | NonerArr"6Callable[[_V2WrapValidatorType], _V2WrapValidatorType]cGrlrVr.rrrrArr.r.r/field_validatoryrLiteral['before', 'plain']RCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]cGrlrVr.rr.r.r/rr)rrLiteral['after']cGrlrVr.)rrrrr.r.r/rr}r$)rrrACallable[[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)rCr^rez;`json_schema_input_type` can't be used when mode is set to zvalidator-input-typer^css|]}t|tVqdSrV) isinstancer).0rr.r.r/ 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)rrrrA)ris_instance_method_from_sigr%ensure_classmethod_based_on_signatureFieldValidatorDecoratorInfoPydanticDescriptorProxyrdec_inforrrArr.r/decs   zfield_validator..decN)rrr"r)rrrrrall)rrrrArrr.rr/rs( D  _ModelType _ModelTypeCo) covariantc@seZdZdZ d d d d ZdS) ModelWrapValidatorHandlerz]`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`.Nrkrouter_locationstr | int | Noner"rcCrlrVr.)r,rkrr.r.r/rnz"ModelWrapValidatorHandler.__call__rV)rkrrrr"rr9r:r;r<rnr.r.r.r/r 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. r5type[_ModelType]rkrr!%ModelWrapValidatorHandler[_ModelType]r"rcCrlrVr.rzr.r.r/rn z&ModelWrapValidatorWithoutInfo.__call__N)r5rrkrr!rr"rrr.r.r.r/rrc@seZdZdZdd d ZdS)ModelWrapValidatorzSA `@model_validator` decorated function signature. This is used when `mode='wrap'`.r5rrkrr!rrNcore_schema.ValidationInfor"rcCrlrVr.r|r.r.r/rn,s zModelWrapValidator.__call__N) r5rrkrr!rrNrr"rrr.r.r.r/r)rc@seZdZdZdddZdS) #FreeModelBeforeValidatorWithoutInfoA `@model_validator` decorated function signature. This is used when `mode='before'` and the function does not have info argument. rkrr"cCrlrVr.)r,rkr.r.r/rn>r}z,FreeModelBeforeValidatorWithoutInfo.__call__N)rkrr"rrr.r.r.r/r9rrc@seZdZdZd ddZdS) ModelBeforeValidatorWithoutInforr5rrkr"cCrlrVr.rmr.r.r/rnMrz(ModelBeforeValidatorWithoutInfo.__call__Nrprr.r.r.r/rHrrc@seZdZdZd ddZd S) FreeModelBeforeValidatorUA `@model_validator` decorated function signature. This is used when `mode='before'`.rkrrNrur"cCrlrVr.)r,rkrNr.r.r/rn[rz!FreeModelBeforeValidator.__call__N)rkrrNrur"rrr.r.r.r/rXrrc@seZdZdZd dd Zd S) ModelBeforeValidatorrr5rrkrNrur"cCrlrVr.rvr.r.r/rnirzModelBeforeValidator.__call__Nrwrr.r.r.r/rfrr|Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrlrVr.rr.r.r/model_validatorrrLiteral['before']rCallable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrlrVr.rr.r.r/rr}Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]]cCrlrVr.rr.r.r/rr"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"rcs*dkr t|}tjd}t||S)Nr$r)rrModelValidatorDecoratorInforrrr.r/rs   zmodel_validator..decN)rrr"rr.)rrr.rr/rs1AnyTypec@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||fSrV)rr5rr.r.r/__class_getitem__szInstanceOf.__class_getitem__sourcerr!rr#cCshddlm}tt|p|}z||}Wn |y!|YSwtjdd|d|d<tj||dS)NrrRcSrUrVr.rWr.r.r/rZr[z9InstanceOf.__get_pydantic_core_schema__..r\r&rT) python_schema json_schema)r_rSris_instance_schemar get_originrajson_or_python_schema)r5rr!rSinstance_of_schemaoriginal_schemar.r.r/r0 s    z'InstanceOf.__get_pydantic_core_schema__N)rrr"rrrr!rr"r#) r9r:r;r<r>rr0object__hash__r.r.r.r/rs   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|tfSrV)rrrr.r.r/r1sz SkipValidation.__class_getitem__rr!rr#csjttdt||Wdn1swYdfddgi}tj|tjddddS)Nignore pydantic_js_annotation_functionscs|SrVr.)_crYrr.r/rZ9r[z=SkipValidation.__get_pydantic_core_schema__..cSrUrVr.rWr.r.r/rZ=r[r)metadatarT)warningscatch_warnings simplefilterrr any_schemara)r5rr!rr.rr/r04s   z+SkipValidation.__get_pydantic_core_schema__N)rrr"rr) r9r:r;r<rr>r0rrr.r.r.r/r$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||_||_dSrV)rr)r,rrr.r.r/__init__js zValidateAs.__init__rrr!rr#cCs||j}tj|j|dS)Nr%)rrr+r)r,rr!r&r.r.r/r0ns z'ValidateAs.__get_pydantic_core_schema__N)rrrrr"rr)r9r:r;r<rr0r.r.r.r/rGs "rr.) rrrrrrrrrArr"r) rrrrrrrrrArr"r) rrrrrrrrr"r) rrrrrrrrrArr"r)rrr"r)rrr"r)rrr"r)rrr"r)Vr< __future__r _annotations dataclassessysr functoolsrtypesrtypingrrrrr r r r r pydantic_corerrtyping_extensionsrr _internalrrrannotated_handlersrerrorsrr version_inforinspect_validatorr' dataclass slots_truerr@rQrdrjrtrxr{r(r* _V2Validatorrfrh_V2WrapValidatorr> staticmethodr~r=rrrrrrValidatorFunctionWrapHandlerrrrrrrrModelAfterValidatorWithoutInfoValidationInfoModelAfterValidator_AnyModelWrapValidator_AnyModelBeforeValidator_AnyModelAfterValidatorrrrrrrr.r.r.r/s   ,     <BcJ ,    o       ;<