698 lines
24 KiB
Python
698 lines
24 KiB
Python
"""This module contains related classes and functions for validation."""
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from __future__ import annotations as _annotations
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import dataclasses
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import sys
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from functools import partialmethod
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from types import FunctionType
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from typing import TYPE_CHECKING, Any, Callable, TypeVar, Union, cast, overload
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from pydantic_core import core_schema
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from pydantic_core import core_schema as _core_schema
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from typing_extensions import Annotated, Literal, TypeAlias
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from . import GetCoreSchemaHandler as _GetCoreSchemaHandler
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from ._internal import _core_metadata, _decorators, _generics, _internal_dataclass
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from .annotated_handlers import GetCoreSchemaHandler
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from .errors import PydanticUserError
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if sys.version_info < (3, 11):
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from typing_extensions import Protocol
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else:
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from typing import Protocol
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_inspect_validator = _decorators.inspect_validator
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@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
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class AfterValidator:
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"""Usage docs: https://docs.pydantic.dev/2.8/concepts/validators/#annotated-validators
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A metadata class that indicates that a validation should be applied **after** the inner validation logic.
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Attributes:
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func: The validator function.
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Example:
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```py
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from typing_extensions import Annotated
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from pydantic import AfterValidator, BaseModel, ValidationError
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MyInt = Annotated[int, AfterValidator(lambda v: v + 1)]
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class Model(BaseModel):
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a: MyInt
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print(Model(a=1).a)
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#> 2
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try:
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Model(a='a')
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except ValidationError as e:
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print(e.json(indent=2))
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'''
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[
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{
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"type": "int_parsing",
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"loc": [
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"a"
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],
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"msg": "Input should be a valid integer, unable to parse string as an integer",
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"input": "a",
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"url": "https://errors.pydantic.dev/2/v/int_parsing"
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}
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]
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'''
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```
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"""
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func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
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def __get_pydantic_core_schema__(self, source_type: Any, handler: _GetCoreSchemaHandler) -> core_schema.CoreSchema:
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schema = handler(source_type)
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info_arg = _inspect_validator(self.func, 'after')
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if info_arg:
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func = cast(core_schema.WithInfoValidatorFunction, self.func)
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return core_schema.with_info_after_validator_function(func, schema=schema, field_name=handler.field_name)
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else:
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func = cast(core_schema.NoInfoValidatorFunction, self.func)
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return core_schema.no_info_after_validator_function(func, schema=schema)
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@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
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class BeforeValidator:
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"""Usage docs: https://docs.pydantic.dev/2.8/concepts/validators/#annotated-validators
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A metadata class that indicates that a validation should be applied **before** the inner validation logic.
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Attributes:
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func: The validator function.
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Example:
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```py
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from typing_extensions import Annotated
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from pydantic import BaseModel, BeforeValidator
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MyInt = Annotated[int, BeforeValidator(lambda v: v + 1)]
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class Model(BaseModel):
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a: MyInt
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print(Model(a=1).a)
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#> 2
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try:
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Model(a='a')
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except TypeError as e:
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print(e)
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#> can only concatenate str (not "int") to str
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```
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"""
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func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
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def __get_pydantic_core_schema__(self, source_type: Any, handler: _GetCoreSchemaHandler) -> core_schema.CoreSchema:
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schema = handler(source_type)
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info_arg = _inspect_validator(self.func, 'before')
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if info_arg:
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func = cast(core_schema.WithInfoValidatorFunction, self.func)
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return core_schema.with_info_before_validator_function(func, schema=schema, field_name=handler.field_name)
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else:
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func = cast(core_schema.NoInfoValidatorFunction, self.func)
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return core_schema.no_info_before_validator_function(func, schema=schema)
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@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
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class PlainValidator:
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"""Usage docs: https://docs.pydantic.dev/2.8/concepts/validators/#annotated-validators
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A metadata class that indicates that a validation should be applied **instead** of the inner validation logic.
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Attributes:
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func: The validator function.
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Example:
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```py
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from typing_extensions import Annotated
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from pydantic import BaseModel, PlainValidator
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MyInt = Annotated[int, PlainValidator(lambda v: int(v) + 1)]
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class Model(BaseModel):
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a: MyInt
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print(Model(a='1').a)
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#> 2
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```
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"""
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func: core_schema.NoInfoValidatorFunction | core_schema.WithInfoValidatorFunction
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def __get_pydantic_core_schema__(self, source_type: Any, handler: _GetCoreSchemaHandler) -> core_schema.CoreSchema:
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# Note that for some valid uses of PlainValidator, it is not possible to generate a core schema for the
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# source_type, so calling `handler(source_type)` will error, which prevents us from generating a proper
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# serialization schema. To work around this for use cases that will not involve serialization, we simply
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# catch any PydanticSchemaGenerationError that may be raised while attempting to build the serialization schema
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# and abort any attempts to handle special serialization.
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from pydantic import PydanticSchemaGenerationError
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try:
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schema = handler(source_type)
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serialization = core_schema.wrap_serializer_function_ser_schema(function=lambda v, h: h(v), schema=schema)
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except PydanticSchemaGenerationError:
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serialization = None
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info_arg = _inspect_validator(self.func, 'plain')
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if info_arg:
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func = cast(core_schema.WithInfoValidatorFunction, self.func)
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return core_schema.with_info_plain_validator_function(
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func, field_name=handler.field_name, serialization=serialization
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)
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else:
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func = cast(core_schema.NoInfoValidatorFunction, self.func)
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return core_schema.no_info_plain_validator_function(func, serialization=serialization)
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@dataclasses.dataclass(frozen=True, **_internal_dataclass.slots_true)
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class WrapValidator:
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"""Usage docs: https://docs.pydantic.dev/2.8/concepts/validators/#annotated-validators
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A metadata class that indicates that a validation should be applied **around** the inner validation logic.
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Attributes:
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func: The validator function.
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```py
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from datetime import datetime
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from typing_extensions import Annotated
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from pydantic import BaseModel, ValidationError, WrapValidator
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def validate_timestamp(v, handler):
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if v == 'now':
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# we don't want to bother with further validation, just return the new value
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return datetime.now()
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try:
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return handler(v)
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except ValidationError:
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# validation failed, in this case we want to return a default value
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return datetime(2000, 1, 1)
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MyTimestamp = Annotated[datetime, WrapValidator(validate_timestamp)]
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class Model(BaseModel):
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a: MyTimestamp
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print(Model(a='now').a)
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#> 2032-01-02 03:04:05.000006
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print(Model(a='invalid').a)
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#> 2000-01-01 00:00:00
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```
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"""
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func: core_schema.NoInfoWrapValidatorFunction | core_schema.WithInfoWrapValidatorFunction
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def __get_pydantic_core_schema__(self, source_type: Any, handler: _GetCoreSchemaHandler) -> core_schema.CoreSchema:
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schema = handler(source_type)
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info_arg = _inspect_validator(self.func, 'wrap')
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if info_arg:
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func = cast(core_schema.WithInfoWrapValidatorFunction, self.func)
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return core_schema.with_info_wrap_validator_function(func, schema=schema, field_name=handler.field_name)
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else:
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func = cast(core_schema.NoInfoWrapValidatorFunction, self.func)
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return core_schema.no_info_wrap_validator_function(func, schema=schema)
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if TYPE_CHECKING:
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class _OnlyValueValidatorClsMethod(Protocol):
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def __call__(self, cls: Any, value: Any, /) -> Any: ...
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class _V2ValidatorClsMethod(Protocol):
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def __call__(self, cls: Any, value: Any, info: _core_schema.ValidationInfo, /) -> Any: ...
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class _V2WrapValidatorClsMethod(Protocol):
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def __call__(
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self,
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cls: Any,
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value: Any,
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handler: _core_schema.ValidatorFunctionWrapHandler,
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info: _core_schema.ValidationInfo,
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/,
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) -> Any: ...
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_V2Validator = Union[
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_V2ValidatorClsMethod,
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_core_schema.WithInfoValidatorFunction,
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_OnlyValueValidatorClsMethod,
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_core_schema.NoInfoValidatorFunction,
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]
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_V2WrapValidator = Union[
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_V2WrapValidatorClsMethod,
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_core_schema.WithInfoWrapValidatorFunction,
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]
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_PartialClsOrStaticMethod: TypeAlias = Union[classmethod[Any, Any, Any], staticmethod[Any, Any], partialmethod[Any]]
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_V2BeforeAfterOrPlainValidatorType = TypeVar(
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'_V2BeforeAfterOrPlainValidatorType',
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_V2Validator,
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_PartialClsOrStaticMethod,
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)
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_V2WrapValidatorType = TypeVar('_V2WrapValidatorType', _V2WrapValidator, _PartialClsOrStaticMethod)
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@overload
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def field_validator(
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field: str,
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/,
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*fields: str,
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mode: Literal['before', 'after', 'plain'] = ...,
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check_fields: bool | None = ...,
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) -> Callable[[_V2BeforeAfterOrPlainValidatorType], _V2BeforeAfterOrPlainValidatorType]: ...
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@overload
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def field_validator(
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field: str,
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/,
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*fields: str,
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mode: Literal['wrap'],
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check_fields: bool | None = ...,
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) -> Callable[[_V2WrapValidatorType], _V2WrapValidatorType]: ...
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FieldValidatorModes: TypeAlias = Literal['before', 'after', 'wrap', 'plain']
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def field_validator(
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field: str,
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/,
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*fields: str,
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mode: FieldValidatorModes = 'after',
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check_fields: bool | None = None,
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) -> Callable[[Any], Any]:
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"""Usage docs: https://docs.pydantic.dev/2.8/concepts/validators/#field-validators
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Decorate methods on the class indicating that they should be used to validate fields.
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Example usage:
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```py
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from typing import Any
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from pydantic import (
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BaseModel,
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ValidationError,
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field_validator,
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)
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class Model(BaseModel):
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a: str
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@field_validator('a')
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@classmethod
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def ensure_foobar(cls, v: Any):
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if 'foobar' not in v:
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raise ValueError('"foobar" not found in a')
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return v
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print(repr(Model(a='this is foobar good')))
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#> Model(a='this is foobar good')
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try:
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Model(a='snap')
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except ValidationError as exc_info:
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print(exc_info)
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'''
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1 validation error for Model
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a
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Value error, "foobar" not found in a [type=value_error, input_value='snap', input_type=str]
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'''
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```
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For more in depth examples, see [Field Validators](../concepts/validators.md#field-validators).
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Args:
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field: The first field the `field_validator` should be called on; this is separate
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from `fields` to ensure an error is raised if you don't pass at least one.
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*fields: Additional field(s) the `field_validator` should be called on.
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mode: Specifies whether to validate the fields before or after validation.
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check_fields: Whether to check that the fields actually exist on the model.
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Returns:
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A decorator that can be used to decorate a function to be used as a field_validator.
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Raises:
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PydanticUserError:
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- If `@field_validator` is used bare (with no fields).
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- If the args passed to `@field_validator` as fields are not strings.
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- If `@field_validator` applied to instance methods.
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"""
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if isinstance(field, FunctionType):
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raise PydanticUserError(
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'`@field_validator` should be used with fields and keyword arguments, not bare. '
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"E.g. usage should be `@validator('<field_name>', ...)`",
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code='validator-no-fields',
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)
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fields = field, *fields
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if not all(isinstance(field, str) for field in fields):
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raise PydanticUserError(
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'`@field_validator` fields should be passed as separate string args. '
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"E.g. usage should be `@validator('<field_name_1>', '<field_name_2>', ...)`",
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code='validator-invalid-fields',
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)
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def dec(
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f: Callable[..., Any] | staticmethod[Any, Any] | classmethod[Any, Any, Any],
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) -> _decorators.PydanticDescriptorProxy[Any]:
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if _decorators.is_instance_method_from_sig(f):
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raise PydanticUserError(
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'`@field_validator` cannot be applied to instance methods', code='validator-instance-method'
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)
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# auto apply the @classmethod decorator
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f = _decorators.ensure_classmethod_based_on_signature(f)
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dec_info = _decorators.FieldValidatorDecoratorInfo(fields=fields, mode=mode, check_fields=check_fields)
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return _decorators.PydanticDescriptorProxy(f, dec_info)
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return dec
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_ModelType = TypeVar('_ModelType')
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_ModelTypeCo = TypeVar('_ModelTypeCo', covariant=True)
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class ModelWrapValidatorHandler(_core_schema.ValidatorFunctionWrapHandler, Protocol[_ModelTypeCo]):
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"""@model_validator decorated function handler argument type. This is used when `mode='wrap'`."""
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def __call__( # noqa: D102
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self,
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value: Any,
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outer_location: str | int | None = None,
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/,
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) -> _ModelTypeCo: # pragma: no cover
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...
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class ModelWrapValidatorWithoutInfo(Protocol[_ModelType]):
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"""A @model_validator decorated function signature.
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This is used when `mode='wrap'` and the function does not have info argument.
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"""
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def __call__( # noqa: D102
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self,
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cls: type[_ModelType],
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# this can be a dict, a model instance
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# or anything else that gets passed to validate_python
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# thus validators _must_ handle all cases
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value: Any,
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handler: ModelWrapValidatorHandler[_ModelType],
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/,
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) -> _ModelType: ...
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class ModelWrapValidator(Protocol[_ModelType]):
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"""A @model_validator decorated function signature. This is used when `mode='wrap'`."""
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def __call__( # noqa: D102
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self,
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cls: type[_ModelType],
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# this can be a dict, a model instance
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# or anything else that gets passed to validate_python
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# thus validators _must_ handle all cases
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value: Any,
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handler: ModelWrapValidatorHandler[_ModelType],
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info: _core_schema.ValidationInfo,
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/,
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) -> _ModelType: ...
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class FreeModelBeforeValidatorWithoutInfo(Protocol):
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"""A @model_validator decorated function signature.
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This is used when `mode='before'` and the function does not have info argument.
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"""
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def __call__( # noqa: D102
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self,
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# this can be a dict, a model instance
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# or anything else that gets passed to validate_python
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# thus validators _must_ handle all cases
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value: Any,
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/,
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) -> Any: ...
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class ModelBeforeValidatorWithoutInfo(Protocol):
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"""A @model_validator decorated function signature.
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This is used when `mode='before'` and the function does not have info argument.
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"""
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def __call__( # noqa: D102
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self,
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cls: Any,
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# this can be a dict, a model instance
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# or anything else that gets passed to validate_python
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# thus validators _must_ handle all cases
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value: Any,
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/,
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) -> Any: ...
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class FreeModelBeforeValidator(Protocol):
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"""A `@model_validator` decorated function signature. This is used when `mode='before'`."""
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def __call__( # noqa: D102
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self,
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# this can be a dict, a model instance
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# or anything else that gets passed to validate_python
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# thus validators _must_ handle all cases
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value: Any,
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info: _core_schema.ValidationInfo,
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/,
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) -> Any: ...
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class ModelBeforeValidator(Protocol):
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"""A `@model_validator` decorated function signature. This is used when `mode='before'`."""
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def __call__( # noqa: D102
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self,
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cls: Any,
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# this can be a dict, a model instance
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# or anything else that gets passed to validate_python
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# thus validators _must_ handle all cases
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value: Any,
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info: _core_schema.ValidationInfo,
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/,
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) -> Any: ...
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ModelAfterValidatorWithoutInfo = Callable[[_ModelType], _ModelType]
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"""A `@model_validator` decorated function signature. This is used when `mode='after'` and the function does not
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have info argument.
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"""
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ModelAfterValidator = Callable[[_ModelType, _core_schema.ValidationInfo], _ModelType]
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"""A `@model_validator` decorated function signature. This is used when `mode='after'`."""
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_AnyModelWrapValidator = Union[ModelWrapValidator[_ModelType], ModelWrapValidatorWithoutInfo[_ModelType]]
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_AnyModeBeforeValidator = Union[
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FreeModelBeforeValidator, ModelBeforeValidator, FreeModelBeforeValidatorWithoutInfo, ModelBeforeValidatorWithoutInfo
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]
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_AnyModelAfterValidator = Union[ModelAfterValidator[_ModelType], ModelAfterValidatorWithoutInfo[_ModelType]]
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@overload
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def model_validator(
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*,
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mode: Literal['wrap'],
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) -> Callable[
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[_AnyModelWrapValidator[_ModelType]], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
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]: ...
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@overload
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def model_validator(
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*,
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mode: Literal['before'],
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) -> Callable[
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[_AnyModeBeforeValidator], _decorators.PydanticDescriptorProxy[_decorators.ModelValidatorDecoratorInfo]
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]: ...
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@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/2.8/concepts/validators/#model-validators
|
|
|
|
Decorate model methods for validation purposes.
|
|
|
|
Example usage:
|
|
```py
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|
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:
|
|
```py
|
|
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 = _core_metadata.build_metadata_dict(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__
|