)i_||%SrSSKJr SSKrSSKrSSKrSSKJr SSK J r SSK J r J r JrJrJrJrJrJrJr SSKJrJr SSKJrJr S S KJrJrJr S S KJ r S S K!J"r" S S K#J$r$ S SKJ%r%J&r& \RNS:aSSKJ(r( OSSK J(r( \RRr*\RV"SPSS0\RXD6"SS55r-\RV"SPSS0\RXD6"SS55r.\RV"SPSS0\RXD6"SS55r/\RV"SPSS0\RXD6"SS55r0\ (a"SS\(5r1"SS\(5r2"SS \(5r3"S!S"\(5r4\\2\Rj\1\Rl4r7\\4\Rp\3\Rr4r:\\;\\\4\<\\4\\4r=S#\>S$'\"S%\\7\=4S&9r?\"S'\\:\=4S&9r@\S(rAS#\>S)'\S*S*S+.SQS,jj5rB\S*S*S+.SRS-jj5rB\S*S*S..SSS/jj5rBS0S\S1.STS2jjrB\"S35rC\"S4SS59rD"S6S7\R\(\D5rF"S8S9\(\C5rG"S:S;\(\C5rH"S<S=\(5rI"S>S?\(5rJ"S@SA\(5rK"SBSC\(5rL\\C/\C4rM\\C\R\/\C4rO\\H\C\G\C4rP\\K\L\I\J4rQ\\O\C\M\C4rR\SUSDj5rS\SVSEj5rS\SWSFj5rSSXSGjrS\"SH5rT\ (a\ \TS*4rUO)\RV"SP0\RXD6"SISJ55rU\ (a\ \TS*4rVO)\RV"SP0\RXD6"SKSL55rV\"SM5rW"SNSO5rXg)YzBThis 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) )ProtocolfrozenTcB\rSrSr%SrS\S'S Sjr\S Sj5rSr g) AfterValidatora!!! 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*U"U5n[URSSS9nU(a8[[RUR5n[R "XSS9$[[R UR5n[R"XSS9$)Nafterfieldmodetypeschema)_inspect_validatorr$r rWithInfoValidatorFunction"with_info_after_validator_functionNoInfoValidatorFunction no_info_after_validator_function)self source_typehandlerr,info_argr$s ^/var/www/html/land-doc-ocr/venv/lib/python3.13/site-packages/pydantic/functional_validators.py__get_pydantic_core_schema__+AfterValidator.__get_pydantic_core_schema__Kso%%diigGL  ==tyyIDAA$V V ;;TYYGD??T Tc"U"URS9$)Nr$r;cls decorators r6_from_decoratorAfterValidator._from_decoratorUs ''r9Nr3rr4rreturncore_schema.CoreSchemar>z>_decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]rCr) __name__ __module__ __qualname____firstlineno____doc____annotations__r7 classmethodr?__static_attributes__rAr9r6r!r!s+(T VUU((r9r!cP\rSrSr%SrS\S'\rS\S'S Sjr\ S Sj5r S r g ) BeforeValidatorZa,!!! abstract "Usage Documentation" [field *before* validators](../concepts/validators.md#field-before-validator) A metadata class that indicates that a validation should be applied **before** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. Example: ```python from typing import Annotated from pydantic import BaseModel, BeforeValidator MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)] class Model(BaseModel): a: MyInt print(Model(a=1).a) #> 2 try: Model(a='a') except TypeError as e: print(e) #> can only concatenate str (not "int") to str ``` r#r$rjson_schema_input_typecU"U5nUR[LaSOURUR5n[URSSS9nU(a:[ [ RUR5n[ R"UUUS9$[ [ RUR5n[ R"XcUS9$)Nbeforer'r(r,json_schema_input_schema) rQrgenerate_schemar-r$r rr.#with_info_before_validator_functionr0!no_info_before_validator_functionr2r3r4r, input_schemar5r$s r6r7,BeforeValidator.__get_pydantic_core_schema__s%**.?? (()D)DE  &diihWM  ==tyyIDBB)5   ;;TYYGD@@l r9cLU"URURRS9$N)r$rQr$inforQr<s r6r?BeforeValidator._from_decorator##,>>#H#H  r9rANrBrE rFrGrHrIrJrKrrQr7rLr?rMrAr9r6rOrOZs5@ VU"3C3,  r9rOcP\rSrSr%SrS\S'\rS\S'S Sjr\ S Sj5r S r g ) PlainValidatora!!! abstract "Usage Documentation" [field *plain* validators](../concepts/validators.md#field-plain-validator) A metadata class that indicates that a validation should be applied **instead** of the inner validation logic. !!! note Before v2.9, `PlainValidator` wasn't always compatible with JSON Schema generation for `mode='validation'`. You can now use the `json_schema_input_type` argument to specify the input type of the function to be used in the JSON schema when `mode='validation'` (the default). See the example below for more details. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. Example: ```python from typing import Annotated, Union from pydantic import BaseModel, PlainValidator def validate(v: object) -> int: if not isinstance(v, (int, str)): raise ValueError(f'Expected int or str, go {type(v)}') return int(v) + 1 MyInt = Annotated[ int, PlainValidator(validate, json_schema_input_type=Union[str, int]), # (1)! ] class Model(BaseModel): a: MyInt print(Model(a='1').a) #> 2 print(Model(a=1).a) #> 2 ``` 1. In this example, we've specified the `json_schema_input_type` as `Union[str, int]` which indicates to the JSON schema generator that in validation mode, the input type for the `a` field can be either a [`str`][] or an [`int`][]. r#r$rrQc SSKJn U"U5nURS[R"SUUR U5S95nUR UR 5n[URSSS9nU(a:[[RUR5n[R"UUUS 9$[[RUR5n[R"UUUS 9$!Ua SnNf=f) NrPydanticSchemaGenerationError serializationcU"U5$NrAvhs r6=PlainValidator.__get_pydantic_core_schema__..!A$r9)functionr, return_schemaplainr'r()rirU)pydanticrhgetr#wrap_serializer_function_ser_schemarVrQr-r$r r."with_info_plain_validator_functionr0 no_info_plain_validator_function) r2r3r4rhr,rirZr5r$s r6r7+PlainValidator.__get_pydantic_core_schema__s ; ![)F#JJ??.!")"9"9+"FM..t/J/JK %diigGL  ==tyyIDAA+)5   ;;TYYGD??+)5 - ! M !s>C11C<;C<cLU"URURRS9$r]r^r<s r6r?PlainValidator._from_decoratorrar9rANrBrE) rFrGrHrIrJrKrrQr7rLr?rMrAr9r6rdrds6,\ VU"%C%'R  r9rdcP\rSrSr%SrS\S'\rS\S'S Sjr\ S Sj5r S r g ) WrapValidatoriab!!! abstract "Usage Documentation" [field *wrap* validators](../concepts/validators.md#field-wrap-validator) A metadata class that indicates that a validation should be applied **around** the inner validation logic. Attributes: func: The validator function. json_schema_input_type: The input type used to generate the appropriate JSON Schema (in validation mode). The actual input type is `Any`. ```python from datetime import datetime from typing import Annotated from pydantic import BaseModel, ValidationError, WrapValidator def validate_timestamp(v, handler): if v == 'now': # we don't want to bother with further validation, just return the new value return datetime.now() try: return handler(v) except ValidationError: # validation failed, in this case we want to return a default value return datetime(2000, 1, 1) MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)] class Model(BaseModel): a: MyTimestamp print(Model(a='now').a) #> 2032-01-02 03:04:05.000006 print(Model(a='invalid').a) #> 2000-01-01 00:00:00 ``` zScore_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunctionr$rrQcU"U5nUR[LaSOURUR5n[URSSS9nU(a:[ [ RUR5n[ R"UUUS9$[ [ RUR5n[ R"UUUS9$)Nwrapr'r(rT) rQrrVr-r$r rWithInfoWrapValidatorFunction!with_info_wrap_validator_functionNoInfoWrapValidatorFunctionno_info_wrap_validator_functionrYs r6r7*WrapValidator.__get_pydantic_core_schema__,s%**.?? (()D)DE  &diif7K  AA499MD@@)5   ??KD>>)5 r9cLU"URURRS9$r]r^r<s r6r?WrapValidator._from_decoratorDrar9rANrBrErbrAr9r6r~r~s5$L ^]"3C30  r9r~c\rSrSrSSjrSrg)_OnlyValueValidatorClsMethodiNcgrkrAr2r=values r6__call__%_OnlyValueValidatorClsMethod.__call__OsCr9rANr=rrrrCrrFrGrHrIrrMrAr9r6rrNs?r9rc\rSrSrSSjrSrg)_V2ValidatorClsMethodiQcgrkrAr2r=rr_s r6r_V2ValidatorClsMethod.__call__Rscfr9rANr=rrrr_core_schema.ValidationInfo[Any]rCrrrAr9r6rrQsfr9rc\rSrSrSSjrSrg) _OnlyValueWrapValidatorClsMethodiTcgrkrAr2r=rr4s r6r)_OnlyValueWrapValidatorClsMethod.__call__Usorr9rAN)r=rrrr4(core_schema.ValidatorFunctionWrapHandlerrCrrrAr9r6rrTsrr9rc2\rSrSrSSjrSrg)_V2WrapValidatorClsMethodiWcgrkrAr2r=rr4r_s r6r"_V2WrapValidatorClsMethod.__call__Xsr9rAN) r=rrrr4rr_rrCrrrAr9r6rrWs7   >   2   r9rr_PartialClsOrStaticMethod"_V2BeforeAfterOrPlainValidatorType)bound_V2WrapValidatorType)rSr&rrtFieldValidatorModes.) check_fieldsrQcgrkrAr'r)rrQfieldss r6field_validatorrzs>Ar9cgrkrArs r6rrs Z]r9)r)rcgrkrA)r'r)rrs r6rrs Z]r9r&)r)rrQc^^^^[U[5(a [SSS9eTS;aT[La[ST<3SS9eT[La TS:Xa[mU/TQ7m[ ST55(d [S S S9eS UUUU4S jjnU$) 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: 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)rSrtrz;`json_schema_input_type` can't be used when mode is set to zvalidator-input-typertc3B# UHn[U[5v M g7frk) isinstancestr).0r's r6 "field_validator..s:6%z%%%6sz`@field_validator` fields should be passed as separate string args. E.g. usage should be `@validator('', '', ...)`zvalidator-invalid-fieldsc>[R"U5(a [SSS9e[R"U5n[R"TTTTS9n[R "X5$)Nz8`@field_validator` cannot be applied to instance methodszvalidator-instance-methodr)rr)rrQ)ris_instance_method_from_sigr%ensure_classmethod_based_on_signatureFieldValidatorDecoratorInfoPydanticDescriptorProxy)fdec_inforrrQr)s r6decfield_validator..decsh  2 21 5 5#JQl   = =a @:: Self: if self.width != self.height: raise ValueError('width and height do not match') return self s = Square(width=1, height=1) print(repr(s)) #> Square(width=1.0, height=1.0) try: Square(width=1, height=2) except ValidationError as e: print(e) ''' 1 validation error for Square Value error, width and height do not match [type=value_error, input_value={'width': 1, 'height': 2}, input_type=dict] ''' ``` For more in depth examples, see [Model Validators](../concepts/validators.md#model-validators). Args: mode: A required string literal that specifies the validation mode. It can be one of the following: 'wrap', 'before', or 'after'. Returns: A decorator that can be used to decorate a function to be used as a model validator. c>[R"U5nTS:Xa;[U[5(a&[R "[ S[5S3SS9 [R"TS9n[R"X5$)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) rrrrLwarningswarnrrModelValidatorDecoratorInfor)rrr)s r6rmodel_validator..decsx  = =a @ 7?z![99 MM3JJW/IZ[GH  ::E221??r9)rrrCrrA)r)rs` r6rrsb@ Jr9AnyTypecX\rSrSrSr\SSj5r\SSj5r\Rr Sr g) InstanceOfiuGeneric 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 [X"54$rk)rr=items r6__class_getitem__InstanceOf.__class_getitem__sT35[) )r9cSSKJn [R"[R "U5=(d U5nU"U5n[R "SUS9US'[R"XES9$!Ua Us$f=f)NrrgcU"U5$rkrArls r6ro9InstanceOf.__get_pydantic_core_schema__..#rqr9rrr,ri) python_schema json_schema)rurhris_instance_schemar get_originrwjson_or_python_schema)r=sourcer4rhinstance_of_schemaoriginal_schemas r6r7'InstanceOf.__get_pydantic_core_schema__s >"-!?!? @T@TU[@\@f`f!g  x")&/ 7B6e6e.7"?3#88GYww1 *)) *sA00A;:A;rAN)rrrCrr rr4rrCrD) rFrGrHrIrJrLrr7object__hash__rMrAr9r6rrs= @  *  *  x  x&??r9rcN\rSrSrSrSSjr\SSj5r\Rr Sr g) SkipValidationi.atIf 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([U[54$rk)rrrs r6r SkipValidation.__class_getitem__;sT>#334 4r9c ^[R"5 [R"S[5 U"U5mSSS5 SU4Sj/0n[R "U[R "STS9S9$!,(df  N@=f)Nignore pydantic_js_annotation_functionsc>U"T5$rkrA)_crnr s r6ro=SkipValidation.__get_pydantic_core_schema__..Cs 1_K]r9cU"U5$rkrArls r6rorGrqr9r)metadatari)rcatch_warnings simplefilterrr any_schemarw)r=r r4rr s @r6r7+SkipValidation.__get_pydantic_core_schema__>sp((*%%h0DE")&/+;=]<^_H))!)MM.  +*s $A55 BrAN)rrrCrr) rFrGrHrIrJrrLr7rrrMrAr9r6rr.s+  5    ??r9r _FromTypeTc,\rSrSrSrSSjrSSjrSrg) ValidateAsiQa-A helper class to validate a custom type from a type that is natively supported by Pydantic. Args: from_type: The type natively supported by Pydantic to use to perform validation. instantiation_hook: A callable taking the validated type as an argument, and returning the populated custom type. Example: ```python {lint="skip"} from typing import Annotated from pydantic import BaseModel, TypeAdapter, ValidateAs class MyCls: def __init__(self, a: int) -> None: self.a = a def __repr__(self) -> str: return f"MyCls(a={self.a})" class Model(BaseModel): a: int ta = TypeAdapter( Annotated[MyCls, ValidateAs(Model, lambda v: MyCls(a=v.a))] ) print(ta.validate_python({'a': 1})) #> MyCls(a=1) ``` cXlX lgrk from_typeinstantiation_hook)r2r&r's r6__init__ValidateAs.__init__ts ""4r9cdU"UR5n[R"URUS9$)Nr+)r&rr1r')r2r r4r,s r6r7'ValidateAs.__get_pydantic_core_schema__xs/(;;  # #  r9r%N)r'zCallable[[_FromTypeT], Any]r&ztype[_FromTypeT]rCNoner)rFrGrHrIrJr(r7rMrAr9r6r#r#QsD5 r9r#rA) r'rrrr)Literal['wrap']r bool | NonerQrrCz6Callable[[_V2WrapValidatorType], _V2WrapValidatorType]) r'rrrr)zLiteral['before', 'plain']rr.rQrrCRCallable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]) r'rrrr)Literal['after']rr.rCr/) r'rrrr)rrr.rQrrCzCallable[[Any], Any])r)r-rCz|Callable[[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])r)zLiteral['before']rCzrCallable[[_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])r)r0rCz}Callable[[_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]])r)z"Literal['wrap', 'before', 'after']rCr)YrJ __future__r _annotations dataclassessysr functoolsrtypesrtypingrrrr r r r r r pydantic_corerrtyping_extensionsrr _internalrrrannotated_handlersrerrorsrversionrrr version_inforinspect_validatorr- dataclass slots_truer!rOrdr~rrrrr.r0 _V2Validatorrr_V2WrapValidatorrL staticmethodrrKrrrrrrValidatorFunctionWrapHandlerrrrrrrrModelAfterValidatorWithoutInfoValidationInfoModelAfterValidator_AnyModelWrapValidator_AnyModelBeforeValidator_AnyModelAfterValidatorrrrrr!r#rAr9r6rLsH2 #ccc8-BB4%"Fg* 22 EdE&9&D&DE9(9(F9(x EdE&9&D&DE? ? F? D EdE&9&D&DE` ` F` F EdE&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         A ,A AH ) 7C<(J<0;;<9#9#=9#xw|,N<0;;<##=#>\ " , , r9