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Diffstat (limited to '.venv/lib/python3.12/site-packages/pydantic/functional_validators.py')
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diff --git a/.venv/lib/python3.12/site-packages/pydantic/functional_validators.py b/.venv/lib/python3.12/site-packages/pydantic/functional_validators.py new file mode 100644 index 00000000..aa3da8c2 --- /dev/null +++ b/.venv/lib/python3.12/site-packages/pydantic/functional_validators.py @@ -0,0 +1,825 @@ +"""This module contains related classes and functions for validation.""" + +from __future__ import annotations as _annotations + +import dataclasses +import sys +from functools import partialmethod +from types import FunctionType +from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union, cast, overload + +from pydantic_core import PydanticUndefined, core_schema +from pydantic_core import core_schema as _core_schema +from typing_extensions import Annotated, Literal, Self, TypeAlias + +from ._internal import _decorators, _generics, _internal_dataclass +from .annotated_handlers import GetCoreSchemaHandler +from .errors import PydanticUserError + +if sys.version_info < (3, 11): + from typing_extensions import Protocol +else: + from typing import Protocol + +_inspect_validator = _decorators.inspect_validator + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class AfterValidator: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators + + 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_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" + } + ] + ''' + ``` + """ + + func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source_type) + info_arg = _inspect_validator(self.func, 'after') + if info_arg: + func = cast(core_schema.WithInfoValidatorFunction, self.func) + return core_schema.with_info_after_validator_function(func, schema=schema, field_name=handler.field_name) + else: + func = cast(core_schema.NoInfoValidatorFunction, self.func) + return core_schema.no_info_after_validator_function(func, schema=schema) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls(func=decorator.func) + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class BeforeValidator: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-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: + ```python + 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 + ``` + """ + + func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction + json_schema_input_type: Any = PydanticUndefined + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source_type) + input_schema = ( + None + if self.json_schema_input_type is PydanticUndefined + else handler.generate_schema(self.json_schema_input_type) + ) + + info_arg = _inspect_validator(self.func, 'before') + if info_arg: + func = cast(core_schema.WithInfoValidatorFunction, self.func) + return core_schema.with_info_before_validator_function( + func, + schema=schema, + field_name=handler.field_name, + json_schema_input_schema=input_schema, + ) + else: + func = cast(core_schema.NoInfoValidatorFunction, self.func) + return core_schema.no_info_before_validator_function( + func, schema=schema, json_schema_input_schema=input_schema + ) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls( + func=decorator.func, + json_schema_input_type=decorator.info.json_schema_input_type, + ) + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class PlainValidator: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-validators + + 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 of the function. This is only used to generate the appropriate + JSON Schema (in validation mode). If not provided, will default to `Any`. + + Example: + ```python + from typing import Union + + from typing_extensions import Annotated + + from pydantic import BaseModel, PlainValidator + + MyInt = Annotated[ + int, + PlainValidator( + lambda v: int(v) + 1, 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`. + """ + + func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction + json_schema_input_type: Any = Any + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + # Note that for some valid uses of PlainValidator, it is not possible to generate a core schema for the + # source_type, so calling `handler(source_type)` will error, which prevents us from generating a proper + # serialization schema. To work around this for use cases that will not involve serialization, we simply + # catch any PydanticSchemaGenerationError that may be raised while attempting to build the serialization schema + # and abort any attempts to handle special serialization. + from pydantic import PydanticSchemaGenerationError + + try: + schema = handler(source_type) + # TODO if `schema['serialization']` is one of `'include-exclude-dict/sequence', + # schema validation will fail. That's why we use 'type ignore' comments below. + serialization = schema.get( + 'serialization', + core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), + schema=schema, + return_schema=handler.generate_schema(source_type), + ), + ) + except PydanticSchemaGenerationError: + serialization = None + + input_schema = handler.generate_schema(self.json_schema_input_type) + + info_arg = _inspect_validator(self.func, 'plain') + if info_arg: + func = cast(core_schema.WithInfoValidatorFunction, self.func) + return core_schema.with_info_plain_validator_function( + func, + field_name=handler.field_name, + serialization=serialization, # pyright: ignore[reportArgumentType] + json_schema_input_schema=input_schema, + ) + else: + func = cast(core_schema.NoInfoValidatorFunction, self.func) + return core_schema.no_info_plain_validator_function( + func, + serialization=serialization, # pyright: ignore[reportArgumentType] + json_schema_input_schema=input_schema, + ) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls( + func=decorator.func, + json_schema_input_type=decorator.info.json_schema_input_type, + ) + + +@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true) +class WrapValidator: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#field-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). + + ```python + 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 + ``` + """ + + func: core_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunction + json_schema_input_type: Any = PydanticUndefined + + def __get_pydantic_core_schema__(self, source_type: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + schema = handler(source_type) + input_schema = ( + None + if self.json_schema_input_type is PydanticUndefined + else handler.generate_schema(self.json_schema_input_type) + ) + + info_arg = _inspect_validator(self.func, 'wrap') + if info_arg: + func = cast(core_schema.WithInfoWrapValidatorFunction, self.func) + return core_schema.with_info_wrap_validator_function( + func, + schema=schema, + field_name=handler.field_name, + json_schema_input_schema=input_schema, + ) + else: + func = cast(core_schema.NoInfoWrapValidatorFunction, self.func) + return core_schema.no_info_wrap_validator_function( + func, + schema=schema, + json_schema_input_schema=input_schema, + ) + + @classmethod + def _from_decorator(cls, decorator: _decorators.Decorator[_decorators.FieldValidatorDecoratorInfo]) -> Self: + return cls( + func=decorator.func, + json_schema_input_type=decorator.info.json_schema_input_type, + ) + + +if TYPE_CHECKING: + + class _OnlyValueValidatorClsMethod(Protocol): + def __call__(self, cls: Any, value: Any, /) -> Any: ... + + class _V2ValidatorClsMethod(Protocol): + def __call__(self, cls: Any, value: Any, info: _core_schema.ValidationInfo, /) -> Any: ... + + class _OnlyValueWrapValidatorClsMethod(Protocol): + def __call__(self, cls: Any, value: Any, handler: _core_schema.ValidatorFunctionWrapHandler, /) -> Any: ... + + class _V2WrapValidatorClsMethod(Protocol): + def __call__( + self, + cls: Any, + value: Any, + handler: _core_schema.ValidatorFunctionWrapHandler, + info: _core_schema.ValidationInfo, + /, + ) -> Any: ... + + _V2Validator = Union[ + _V2ValidatorClsMethod, + _core_schema.WithInfoValidatorFunction, + _OnlyValueValidatorClsMethod, + _core_schema.NoInfoValidatorFunction, + ] + + _V2WrapValidator = Union[ + _V2WrapValidatorClsMethod, + _core_schema.WithInfoWrapValidatorFunction, + _OnlyValueWrapValidatorClsMethod, + _core_schema.NoInfoWrapValidatorFunction, + ] + + _PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]] + + _V2BeforeAfterOrPlainValidatorType = TypeVar( + '_V2BeforeAfterOrPlainValidatorType', + bound=Union[_V2Validator, _PartialClsOrStaticMethod], + ) + _V2WrapValidatorType = TypeVar('_V2WrapValidatorType', bound=Union[_V2WrapValidator, _PartialClsOrStaticMethod]) + +FieldValidatorModes: TypeAlias = Literal['before', 'after', 'wrap', 'plain'] + + +@overload +def field_validator( + field: str, + /, + *fields: str, + mode: Literal['wrap'], + check_fields: bool | None = ..., + json_schema_input_type: Any = ..., +) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]: ... + + +@overload +def field_validator( + field: str, + /, + *fields: str, + mode: Literal['before', 'plain'], + check_fields: bool | None = ..., + json_schema_input_type: Any = ..., +) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ... + + +@overload +def field_validator( + field: str, + /, + *fields: str, + mode: Literal['after'] = ..., + check_fields: bool | None = ..., +) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ... + + +def field_validator( + field: str, + /, + *fields: str, + mode: FieldValidatorModes = 'after', + check_fields: bool | None = None, + json_schema_input_type: Any = PydanticUndefined, +) -> Callable[[Any], Any]: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#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. + """ + if isinstance(field, FunctionType): + raise PydanticUserError( + '`@field_validator` should be used with fields and keyword arguments, not bare. ' + "E.g. usage should be `@validator('<field_name>', ...)`", + code='validator-no-fields', + ) + + if mode not in ('before', 'plain', 'wrap') and json_schema_input_type is not PydanticUndefined: + raise PydanticUserError( + f"`json_schema_input_type` can't be used when mode is set to {mode!r}", + code='validator-input-type', + ) + + if json_schema_input_type is PydanticUndefined and mode == 'plain': + json_schema_input_type = Any + + fields = field, *fields + if not all(isinstance(field, str) for field in fields): + raise PydanticUserError( + '`@field_validator` fields should be passed as separate string args. ' + "E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`", + code='validator-invalid-fields', + ) + + def dec( + f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any], + ) -> _decorators.PydanticDescriptorProxy[Any]: + if _decorators.is_instance_method_from_sig(f): + raise PydanticUserError( + '`@field_validator` cannot be applied to instance methods', code='validator-instance-method' + ) + + # auto apply the @classmethod decorator + f = _decorators.ensure_classmethod_based_on_signature(f) + + dec_info = _decorators.FieldValidatorDecoratorInfo( + fields=fields, mode=mode, check_fields=check_fields, json_schema_input_type=json_schema_input_type + ) + return _decorators.PydanticDescriptorProxy(f, dec_info) + + return dec + + +_ModelType = TypeVar('_ModelType') +_ModelTypeCo = TypeVar('_ModelTypeCo', covariant=True) + + +class ModelWrapValidatorHandler(_core_schema.ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]): + """`@model_validator` decorated function handler argument type. This is used when `mode='wrap'`.""" + + def __call__( # noqa: D102 + self, + value: Any, + outer_location: str | int | None = None, + /, + ) -> _ModelTypeCo: # pragma: no cover + ... + + +class ModelWrapValidatorWithoutInfo(Protocol[_ModelType]): + """A `@model_validator` decorated function signature. + This is used when `mode='wrap'` and the function does not have info argument. + """ + + def __call__( # noqa: D102 + self, + cls: type[_ModelType], + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + handler: ModelWrapValidatorHandler[_ModelType], + /, + ) -> _ModelType: ... + + +class ModelWrapValidator(Protocol[_ModelType]): + """A `@model_validator` decorated function signature. This is used when `mode='wrap'`.""" + + def __call__( # noqa: D102 + self, + cls: type[_ModelType], + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + handler: ModelWrapValidatorHandler[_ModelType], + info: _core_schema.ValidationInfo, + /, + ) -> _ModelType: ... + + +class FreeModelBeforeValidatorWithoutInfo(Protocol): + """A `@model_validator` decorated function signature. + This is used when `mode='before'` and the function does not have info argument. + """ + + def __call__( # noqa: D102 + self, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + /, + ) -> Any: ... + + +class ModelBeforeValidatorWithoutInfo(Protocol): + """A `@model_validator` decorated function signature. + This is used when `mode='before'` and the function does not have info argument. + """ + + def __call__( # noqa: D102 + self, + cls: Any, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + /, + ) -> Any: ... + + +class FreeModelBeforeValidator(Protocol): + """A `@model_validator` decorated function signature. This is used when `mode='before'`.""" + + def __call__( # noqa: D102 + self, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + info: _core_schema.ValidationInfo, + /, + ) -> Any: ... + + +class ModelBeforeValidator(Protocol): + """A `@model_validator` decorated function signature. This is used when `mode='before'`.""" + + def __call__( # noqa: D102 + self, + cls: Any, + # this can be a dict, a model instance + # or anything else that gets passed to validate_python + # thus validators _must_ handle all cases + value: Any, + info: _core_schema.ValidationInfo, + /, + ) -> Any: ... + + +ModelAfterValidatorWithoutInfo = Callable[[_ModelType], _ModelType] +"""A `@model_validator` decorated function signature. This is used when `mode='after'` and the function does not +have info argument. +""" + +ModelAfterValidator = Callable[[_ModelType, _core_schema.ValidationInfo], _ModelType] +"""A `@model_validator` decorated function signature. This is used when `mode='after'`.""" + +_AnyModelWrapValidator = Union[ModelWrapValidator[_ModelType], ModelWrapValidatorWithoutInfo[_ModelType]] +_AnyModelBeforeValidator = Union[ + FreeModelBeforeValidator, ModelBeforeValidator, FreeModelBeforeValidatorWithoutInfo, ModelBeforeValidatorWithoutInfo +] +_AnyModelAfterValidator = Union[ModelAfterValidator[_ModelType], ModelAfterValidatorWithoutInfo[_ModelType]] + + +@overload +def model_validator( + *, + mode: Literal['wrap'], +) -> Callable[ + [_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo] +]: ... + + +@overload +def model_validator( + *, + mode: Literal['before'], +) -> Callable[ + [_AnyModelBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo] +]: ... + + +@overload +def model_validator( + *, + mode: Literal['after'], +) -> Callable[ + [_AnyModelAfterValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo] +]: ... + + +def model_validator( + *, + mode: Literal['wrap', 'before', 'after'], +) -> Any: + """Usage docs: https://docs.pydantic.dev/2.10/concepts/validators/#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. + """ + + def dec(f: Any) -> _decorators.PydanticDescriptorProxy[Any]: + # auto apply the @classmethod decorator + f = _decorators.ensure_classmethod_based_on_signature(f) + dec_info = _decorators.ModelValidatorDecoratorInfo(mode=mode) + return _decorators.PydanticDescriptorProxy(f, dec_info) + + return dec + + +AnyType = TypeVar('AnyType') + + +if TYPE_CHECKING: + # If we add configurable attributes to IsInstance, we'd probably need to stop hiding it from type checkers like this + InstanceOf = Annotated[AnyType, ...] # `IsInstance[Sequence]` will be recognized by type checkers as `Sequence` + +else: + + @dataclasses.dataclass(**_internal_dataclass.slots_true) + class InstanceOf: + '''Generic 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' + │ } + ] + """ + ``` + ''' + + @classmethod + def __class_getitem__(cls, item: AnyType) -> AnyType: + return Annotated[item, cls()] + + @classmethod + def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + from pydantic import PydanticSchemaGenerationError + + # use the generic _origin_ as the second argument to isinstance when appropriate + instance_of_schema = core_schema.is_instance_schema(_generics.get_origin(source) or source) + + try: + # Try to generate the "standard" schema, which will be used when loading from JSON + original_schema = handler(source) + except PydanticSchemaGenerationError: + # If that fails, just produce a schema that can validate from python + return instance_of_schema + else: + # Use the "original" approach to serialization + instance_of_schema['serialization'] = core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), schema=original_schema + ) + return core_schema.json_or_python_schema(python_schema=instance_of_schema, json_schema=original_schema) + + __hash__ = object.__hash__ + + +if TYPE_CHECKING: + SkipValidation = Annotated[AnyType, ...] # SkipValidation[list[str]] will be treated by type checkers as list[str] +else: + + @dataclasses.dataclass(**_internal_dataclass.slots_true) + class SkipValidation: + """If 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. + """ + + def __class_getitem__(cls, item: Any) -> Any: + return Annotated[item, SkipValidation()] + + @classmethod + def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema: + original_schema = handler(source) + metadata = {'pydantic_js_annotation_functions': [lambda _c, h: h(original_schema)]} + return core_schema.any_schema( + metadata=metadata, + serialization=core_schema.wrap_serializer_function_ser_schema( + function=lambda v, h: h(v), schema=original_schema + ), + ) + + __hash__ = object.__hash__ |