U #iqp @s~UdZddlmZddlZddlZddlmZddlm Z ddl m Z m Z m Z mZmZmZmZddlmZmZddlmZdd lmZmZmZmZd d lmZmZmZmZd d l m!Z!d d l"m#Z#ej$dkrddlm%Z%n ddl m%Z%ej&Z'ej(fddiej)GdddZ*ej(fddiej)GdddZ+ej(fddiej)GdddZ,ej(fddiej)GdddZ-e r>Gddde%Z.Gddde%Z/Gddde%Z0Gd d!d!e%Z1ee/ej2e.ej3fZ4ee1ej5e0ej6fZ7ee8e e e fe9e e fee fZ:d"e;d#<ed$ee4e:fd%Zd"e;d(<ed)d)d*d+d+d,d-d.d/d0d1d2Z?ed)d)d*d+d+d3d-d.d4d0d5d2Z?ed)d)d6d+d+d7d-d4d8d9d2Z?d:ded;d+d+d(d-d.dZ@ed?dd@ZAGdAdBdBejBe%eAZCGdCdDdDe%e@ZDGdEdFdFe%e@ZEGdGdHdHe%ZFGdIdJdJe%ZGGdKdLdLe%ZHGdMdNdNe%ZIe e@ge@fZJe e@ejKge@fZLeeEe@eDe@fZMeeHeIeFeGfZNeeLe@eJe@fZOed,dOdPdQdRZPedSdTdPdUdRZPed7dVdPdWdRZPdXd.dPdYdRZPedZZQe r.eeQd)fZRnej(fej)Gd[d\d\ZRe r^eeQd)fZSnej(fej)Gd]d^d^ZSdS)_zBThis module contains related classes and functions for validation.) annotationsN) partialmethod) FunctionType) TYPE_CHECKINGAnyCallableTypeVarUnioncastoverload)PydanticUndefined core_schema)r ) AnnotatedLiteralSelf TypeAlias)_core_metadata _decorators _generics_internal_dataclass)GetCoreSchemaHandler)PydanticUserError) )ProtocolfrozenTc@s@eZdZUdZded<dddddd Zed d d d dZdS)AfterValidatora8Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators A metadata class that indicates that a validation should be applied **after** the inner validation logic. Attributes: func: The validator function. Example: ```py from typing_extensions 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.WithInfoValidatorFunctionfuncrrcore_schema.CoreSchema source_typehandlerreturncCsX||}t|jd}|r8ttj|j}tj|||jdSttj|j}tj||dSdS)Nafter)schema field_name)r&) _inspect_validatorrr r WithInfoValidatorFunctionZ"with_info_after_validator_functionr'NoInfoValidatorFunctionZ no_info_after_validator_function)selfr"r#r&info_argrr-f/var/www/html/me.goteku.com/backend/venv/lib/python3.8/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__Hs z+AfterValidator.__get_pydantic_core_schema__>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]r decoratorr$cCs ||jdS)Nrr3clsr2r-r-r._from_decoratorRszAfterValidator._from_decoratorN)__name__ __module__ __qualname____doc____annotations__r/ classmethodr6r-r-r-r.rs ) rc@sLeZdZUdZded<eZded<ddddd d Zed d d ddZ dS)BeforeValidatoradUsage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators 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: ```py from typing_extensions 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_typerr r!cCs||}|jtkrdn ||j}tj|d}t|jd}|r`ttj |j}tj |||j |dSttj |j}tj |||dSdS)NZjs_input_core_schemabeforer&r'metadatar&rB)r>r generate_schemarbuild_metadata_dictr(rr r r)Z#with_info_before_validator_functionr'r*Z!no_info_before_validator_functionr+r"r#r& input_schemarBr,rr-r-r.r/{s"   z,BeforeValidator.__get_pydantic_core_schema__r0rr1cCs||j|jjdSN)rr>rinfor>r4r-r-r.r6szBeforeValidator._from_decoratorN r7r8r9r:r;r r>r/r<r6r-r-r-r.r=Ws  r=c@sLeZdZUdZded<eZded<ddddd d Zed d d ddZ dS)PlainValidatoraUsage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators A metadata class that indicates that a validation should be applied **instead** of 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). If not provided, will default to `Any`. Example: ```py from typing_extensions import Annotated from pydantic import BaseModel, PlainValidator MyInt = Annotated[int, PlainValidator(lambda v: int(v) + 1)] class Model(BaseModel): a: MyInt print(Model(a='1').a) #> 2 ``` rrrr>rr r!c Csddlm}z.||}|dtjdd|||d}Wn|k rRd}YnX||j}tj|d}t |j d}|rt tj |j } tj | |j||d St tj|j } tj| ||d SdS) NrPydanticSchemaGenerationError serializationcSs||SNr-vhr-r-r.z=PlainValidator.__get_pydantic_core_schema__..)functionr&Z return_schemar?plain)r'rOrB)rOrB)pydanticrNgetr #wrap_serializer_function_ser_schemarDr>rrEr(rr r)Z"with_info_plain_validator_functionr'r*Z no_info_plain_validator_function) r+r"r#rNr&rOrGrBr,rr-r-r.r/s<     z+PlainValidator.__get_pydantic_core_schema__r0rr1cCs||j|jjdSrHrIr4r-r-r.r6szPlainValidator._from_decoratorN) r7r8r9r:r;rr>r/r<r6r-r-r-r.rLs  +rLc@sLeZdZUdZded<eZded<ddddd d Zed d d ddZ dS) WrapValidatoraUsage docs: https://docs.pydantic.dev/2.9/concepts/validators/#annotated-validators 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). ```py from datetime import datetime from typing_extensions 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.WithInfoWrapValidatorFunctionrrr>rr r!cCs||}|jtkrdn ||j}tj|d}t|jd}|r`ttj |j}tj |||j |dSttj |j}tj |||dSdS)Nr?wraprArC)r>r rDrrEr(rr r WithInfoWrapValidatorFunctionZ!with_info_wrap_validator_functionr'NoInfoWrapValidatorFunctionZno_info_wrap_validator_functionrFr-r-r.r/s*   z*WrapValidator.__get_pydantic_core_schema__r0rr1cCs||j|jjdSrHrIr4r-r-r.r6/szWrapValidator._from_decoratorNrKr-r-r-r.r[s & r[c@seZdZddddddZdS)_OnlyValueValidatorClsMethodrr5valuer$cCsdSrPr-r+r5rar-r-r.__call__:rUz%_OnlyValueValidatorClsMethod.__call__Nr7r8r9rcr-r-r-r.r_9sr_c@s eZdZdddddddZdS)_V2ValidatorClsMethodr_core_schema.ValidationInfor5rarJr$cCsdSrPr-r+r5rarJr-r-r.rc=rUz_V2ValidatorClsMethod.__call__Nrdr-r-r-r.re<srec@s eZdZdddddddZdS) _OnlyValueWrapValidatorClsMethodr)_core_schema.ValidatorFunctionWrapHandlerr5rar#r$cCsdSrPr-r+r5rar#r-r-r.rc@rUz)_OnlyValueWrapValidatorClsMethod.__call__Nrdr-r-r-r.ri?sric@s"eZdZddddddddZdS)_V2WrapValidatorClsMethodrrjrfr5rar#rJr$cCsdSrPr-r+r5rar#rJr-r-r.rcCsz"_V2WrapValidatorClsMethod.__call__Nrdr-r-r-r.rmBsrmr_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)r@r%r\rWFieldValidatorModes.) check_fieldsr>strzLiteral['wrap']z bool | Nonerz6Callable[[_V2WrapValidatorType], _V2WrapValidatorType])fieldfieldsmoderur>r$cGsdSrPr-rwryrur>rxr-r-r.field_validatoresr{zLiteral[('before', 'plain')]zRCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]cGsdSrPr-rzr-r-r.r{ps)ryruzLiteral['after'])rwrxryrur$cGsdSrPr-)rwryrurxr-r-r.r{{sr%)ryrur>zCallable[[Any], Any]cst|trtddddkr8tk r8tdddtkrLdkrLt|ftdd Dsttd d dd d dfdd }|S)a1Usage docs: https://docs.pydantic.dev/2.9/concepts/validators/#field-validators Decorate methods on the class indicating that they should be used to validate fields. Example usage: ```py 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. 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This is used when `mode='wrap'`.Nrzstr | int | Noner)raouter_locationr$cCsdSrPr-)r+rarr-r-r.rcsz"ModelWrapValidatorHandler.__call__)Nr7r8r9r:rcr-r-r-r.rsrc@s$eZdZdZdddddddZd S) ModelWrapValidatorWithoutInfozA `@model_validator` decorated function signature. This is used when `mode='wrap'` and the function does not have info argument. type[_ModelType]r%ModelWrapValidatorHandler[_ModelType]rrkcCsdSrPr-rlr-r-r.rcs z&ModelWrapValidatorWithoutInfo.__call__Nrr-r-r-r.rsrc@s&eZdZdZdddddddd Zd S) ModelWrapValidatorzSA `@model_validator` decorated function signature. This is used when `mode='wrap'`.rrrrfrrncCsdSrPr-ror-r-r.rcs zModelWrapValidator.__call__Nrr-r-r-r.rsrc@s eZdZdZdddddZdS)#FreeModelBeforeValidatorWithoutInfoA `@model_validator` decorated function signature. 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Example usage: ```py 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. rrrcs"t|}tjd}t||S)Nr)rrZModelValidatorDecoratorInforrrr-r.rs  zmodel_validator..decr-)ryrr-rr.rs0AnyTypec@s@eZdZdZedddddZedddd d d ZejZd S) InstanceOfuGeneric type for annotating a type that is an instance of a given class. 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