## Change Summary

Ensure that a `SecretStr` type can be populated from json.

## Related issue number

Fix #7720

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [x] Unit tests for the changes exist
* [x] Tests pass on CI
* [ ] Documentation reflects the changes where applicable
* [x] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**


Selected Reviewer: @samuelcolvin
Fix failure to load `SecretStr` from JSON (regression in v2.4). ## Change Summary

Ensure that a `SecretStr` type can be populated from json.

## Related issue number

Fix #7720

## Checklist

* [x] The pull request title is a good summary of the changes - it will be used in the changelog
* [x] Unit tests for the changes exist
* [x] Tests pass on CI
* [ ] Documentation reflects the changes where applicable
* [x] My PR is ready to review, **please add a comment including the phrase "please review" to assign reviewers**


Selected Reviewer: @samuelcolvin
import sys
from typing import Any, Generic, Iterator, List, Set, TypeVar

import pytest
from annotated_types import BaseMetadata, GroupedMetadata, Gt, Lt, Predicate
from pydantic_core import PydanticUndefined, core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, Field, GetCoreSchemaHandler, TypeAdapter, ValidationError
from pydantic.errors import PydanticSchemaGenerationError
from pydantic.functional_validators import AfterValidator

NO_VALUE = object()


@pytest.mark.parametrize(
    'hint_fn,value,expected_repr',
    [
        (
            lambda: Annotated[int, Gt(0)],
            5,
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Field(gt=0)],
            5,
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: int,
            Field(5, gt=0),
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: int,
            Field(default_factory=lambda: 5, gt=0),
            'FieldInfo(annotation=int, required=False, default_factory=<lambda>, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Lt(2)],
            Field(5, gt=0),
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0), Lt(lt=2)])',
        ),
        (
            lambda: Annotated[int, Gt(0)],
            NO_VALUE,
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Gt(0)],
            Field(),
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: int,
            Field(gt=0),
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Gt(0)],
            PydanticUndefined,
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Field(gt=0), Lt(2)],
            5,
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0), Lt(lt=2)])',
        ),
        (
            lambda: Annotated[int, Field(alias='foobar')],
            PydanticUndefined,
            "FieldInfo(annotation=int, required=True, alias='foobar', alias_priority=2)",
        ),
    ],
)
def test_annotated(hint_fn, value, expected_repr):
    hint = hint_fn()

    if value is NO_VALUE:

        class M(BaseModel):
            x: hint

    else:

        class M(BaseModel):
            x: hint = value

    assert repr(M.model_fields['x']) == expected_repr


@pytest.mark.parametrize('metadata', [0, 'foo'])
def test_annotated_allows_unknown(metadata):
    class M(BaseModel):
        x: Annotated[int, metadata] = 5

    field_info = M.model_fields['x']
    assert len(field_info.metadata) == 1
    assert metadata in field_info.metadata, 'Records the unknown metadata'
    assert metadata in M.__annotations__['x'].__metadata__, 'Annotated type is recorded'


@pytest.mark.parametrize(
    ['hint_fn', 'value', 'empty_init_ctx'],
    [
        (
            lambda: int,
            PydanticUndefined,
            pytest.raises(ValueError, match=r'Field required \[type=missing,'),
        ),
        (
            lambda: Annotated[int, Field()],
            PydanticUndefined,
            pytest.raises(ValueError, match=r'Field required \[type=missing,'),
        ),
    ],
)
def test_annotated_instance_exceptions(hint_fn, value, empty_init_ctx):
    hint = hint_fn()

    class M(BaseModel):
        x: hint = value

    with empty_init_ctx:
        assert M().x == 5


def test_field_reuse():
    field = Field(description='Long description')

    class Model(BaseModel):
        one: int = field

    assert Model(one=1).model_dump() == {'one': 1}

    class AnnotatedModel(BaseModel):
        one: Annotated[int, field]

    assert AnnotatedModel(one=1).model_dump() == {'one': 1}


def test_config_field_info():
    class Foo(BaseModel):
        a: Annotated[int, Field(description='descr', json_schema_extra={'foobar': 'hello'})]

    assert Foo.model_json_schema(by_alias=True)['properties'] == {
        'a': {'title': 'A', 'description': 'descr', 'foobar': 'hello', 'type': 'integer'},
    }


@pytest.mark.skipif(sys.version_info < (3, 10), reason='repr different on older versions')
def test_annotated_alias() -> None:
    # https://github.com/pydantic/pydantic/issues/2971

    StrAlias = Annotated[str, Field(max_length=3)]
    IntAlias = Annotated[int, Field(default_factory=lambda: 2)]

    Nested = Annotated[List[StrAlias], Field(description='foo')]

    class MyModel(BaseModel):
        a: StrAlias = 'abc'
        b: StrAlias
        c: IntAlias
        d: IntAlias
        e: Nested

    fields_repr = {k: repr(v) for k, v in MyModel.model_fields.items()}
    assert fields_repr == {
        'a': "FieldInfo(annotation=str, required=False, default='abc', metadata=[MaxLen(max_length=3)])",
        'b': 'FieldInfo(annotation=str, required=True, metadata=[MaxLen(max_length=3)])',
        'c': 'FieldInfo(annotation=int, required=False, default_factory=<lambda>)',
        'd': 'FieldInfo(annotation=int, required=False, default_factory=<lambda>)',
        'e': "FieldInfo(annotation=List[Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=3)])]], required=True, description='foo')",
    }
    assert MyModel(b='def', e=['xyz']).model_dump() == dict(a='abc', b='def', c=2, d=2, e=['xyz'])


def test_modify_get_schema_annotated() -> None:
    calls: List[str] = []

    class CustomType:
        @classmethod
        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
            calls.append('CustomType:before')
            with pytest.raises(PydanticSchemaGenerationError):
                handler(source)
            schema = core_schema.no_info_plain_validator_function(lambda _: CustomType())
            calls.append('CustomType:after')
            return schema

    class PydanticMetadata:
        def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
            calls.append('PydanticMetadata:before')
            schema = handler(source)
            calls.append('PydanticMetadata:after')
            return schema

    class GroupedMetadataMarker(GroupedMetadata):
        def __iter__(self) -> Iterator[BaseMetadata]:
            # no way to actually hook into schema building
            # so just register when our iter is called
            calls.append('GroupedMetadataMarker:iter')
            yield from []

    class _(BaseModel):
        x: Annotated[CustomType, GroupedMetadataMarker(), PydanticMetadata()]

    # insert_assert(calls)
    assert calls == [
        'GroupedMetadataMarker:iter',
        'PydanticMetadata:before',
        'CustomType:before',
        'CustomType:after',
        'PydanticMetadata:after',
    ]

    calls.clear()

    class _(BaseModel):
        x: Annotated[CustomType, PydanticMetadata(), GroupedMetadataMarker()]

    # insert_assert(calls)
    assert calls == [
        'GroupedMetadataMarker:iter',
        'PydanticMetadata:before',
        'CustomType:before',
        'CustomType:after',
        'PydanticMetadata:after',
    ]

    calls.clear()


def test_get_pydantic_core_schema_source_type() -> None:
    types: Set[Any] = set()

    class PydanticMarker:
        def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
            types.add(source)
            return handler(source)

    class _(BaseModel):
        x: Annotated[Annotated[int, 'foo'], PydanticMarker()]

    assert types == {int}
    types.clear()

    T = TypeVar('T')

    class GenericModel(Generic[T], BaseModel):
        y: T

    class _(BaseModel):
        x: Annotated[GenericModel[int], PydanticMarker()]

    assert types == {GenericModel[int]}
    types.clear()


def test_merge_field_infos_type_adapter() -> None:
    ta = TypeAdapter(
        Annotated[
            int, Field(gt=0), Field(lt=100), Field(gt=1), Field(description='abc'), Field(3), Field(description=None)
        ]
    )

    default = ta.get_default_value()
    assert default is not None
    assert default.value == 3

    # insert_assert(ta.validate_python(2))
    assert ta.validate_python(2) == 2

    with pytest.raises(ValidationError) as exc_info:
        ta.validate_python(1)

    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {'type': 'greater_than', 'loc': (), 'msg': 'Input should be greater than 1', 'input': 1, 'ctx': {'gt': 1}}
    ]

    # insert_assert(ta.json_schema())
    assert ta.json_schema() == {
        'default': 3,
        'description': 'abc',
        'exclusiveMaximum': 100,
        'exclusiveMinimum': 1,
        'type': 'integer',
    }


def test_merge_field_infos_model() -> None:
    class Model(BaseModel):
        x: Annotated[
            int, Field(gt=0), Field(lt=100), Field(gt=1), Field(description='abc'), Field(3), Field(description=None)
        ] = Field(5)

    # insert_assert(Model.model_json_schema())
    assert Model.model_json_schema() == {
        'properties': {
            'x': {'default': 5, 'exclusiveMaximum': 100, 'exclusiveMinimum': 1, 'title': 'X', 'type': 'integer'}
        },
        'title': 'Model',
        'type': 'object',
    }


def test_model_dump_doesnt_dump_annotated_dunder():
    class Model(BaseModel):
        one: int

    AnnotatedModel = Annotated[Model, ...]

    # In Pydantic v1, `AnnotatedModel.dict()` would have returned
    # `{'one': 1, '__orig_class__': typing.Annotated[...]}`
    assert AnnotatedModel(one=1).model_dump() == {'one': 1}


def test_merge_field_infos_ordering() -> None:
    TheType = Annotated[int, AfterValidator(lambda x: x), Field(le=2), AfterValidator(lambda x: x * 2), Field(lt=4)]

    class Model(BaseModel):
        x: TheType

    assert Model(x=1).x == 2

    with pytest.raises(ValidationError) as exc_info:
        Model(x=2)
    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {'type': 'less_than', 'loc': ('x',), 'msg': 'Input should be less than 4', 'input': 2, 'ctx': {'lt': 4}}
    ]

    with pytest.raises(ValidationError) as exc_info:
        Model(x=3)
    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {
            'type': 'less_than_equal',
            'loc': ('x',),
            'msg': 'Input should be less than or equal to 2',
            'input': 3,
            'ctx': {'le': 2},
        }
    ]


def test_validate_float_inf_nan_python() -> None:
    ta = TypeAdapter(Annotated[float, AfterValidator(lambda _: float('nan')), Field(allow_inf_nan=False)])

    with pytest.raises(ValidationError) as exc_info:
        ta.validate_python(1.0)

    # insert_assert(exc_info.value.errors(include_url=False))
    # TODO: input should be float('nan'), this seems like a subtle bug in pydantic-core
    assert exc_info.value.errors(include_url=False) == [
        {'type': 'finite_number', 'loc': (), 'msg': 'Input should be a finite number', 'input': 1.0}
    ]


def test_predicate_error_python() -> None:
    ta = TypeAdapter(Annotated[int, Predicate(lambda x: x > 0)])

    with pytest.raises(ValidationError) as exc_info:
        ta.validate_python(-1)

    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {
            'type': 'predicate_failed',
            'loc': (),
            'msg': 'Predicate test_predicate_error_python.<locals>.<lambda> failed',
            'input': -1,
        }
    ]


def test_annotated_field_info_not_lost_from_forwardref():
    from pydantic import BaseModel

    class ForwardRefAnnotatedFieldModel(BaseModel):
        foo: 'Annotated[Integer, Field(alias="bar", default=1)]' = 2
        foo2: 'Annotated[Integer, Field(alias="bar2", default=1)]' = Field(default=2, alias='baz')

    Integer = int

    ForwardRefAnnotatedFieldModel.model_rebuild()

    assert ForwardRefAnnotatedFieldModel(bar=3).foo == 3
    assert ForwardRefAnnotatedFieldModel(baz=3).foo2 == 3

    with pytest.raises(ValidationError) as exc_info:
        ForwardRefAnnotatedFieldModel(bar='bar')
    assert exc_info.value.errors() == [
        {
            'input': 'bar',
            'loc': ('bar',),
            'msg': 'Input should be a valid integer, unable to parse string as an integer',
            'type': 'int_parsing',
            'url': 'https://errors.pydantic.dev/2.4/v/int_parsing',
        }
    ]

import sys
from typing import Any, Generic, Iterator, List, Set, TypeVar

import pytest
from annotated_types import BaseMetadata, GroupedMetadata, Gt, Lt, Predicate
from pydantic_core import PydanticUndefined, core_schema
from typing_extensions import Annotated

from pydantic import BaseModel, Field, GetCoreSchemaHandler, TypeAdapter, ValidationError
from pydantic.errors import PydanticSchemaGenerationError
from pydantic.functional_validators import AfterValidator

NO_VALUE = object()


@pytest.mark.parametrize(
    'hint_fn,value,expected_repr',
    [
        (
            lambda: Annotated[int, Gt(0)],
            5,
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Field(gt=0)],
            5,
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: int,
            Field(5, gt=0),
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: int,
            Field(default_factory=lambda: 5, gt=0),
            'FieldInfo(annotation=int, required=False, default_factory=<lambda>, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Lt(2)],
            Field(5, gt=0),
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0), Lt(lt=2)])',
        ),
        (
            lambda: Annotated[int, Gt(0)],
            NO_VALUE,
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Gt(0)],
            Field(),
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: int,
            Field(gt=0),
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Gt(0)],
            PydanticUndefined,
            'FieldInfo(annotation=int, required=True, metadata=[Gt(gt=0)])',
        ),
        (
            lambda: Annotated[int, Field(gt=0), Lt(2)],
            5,
            'FieldInfo(annotation=int, required=False, default=5, metadata=[Gt(gt=0), Lt(lt=2)])',
        ),
        (
            lambda: Annotated[int, Field(alias='foobar')],
            PydanticUndefined,
            "FieldInfo(annotation=int, required=True, alias='foobar', alias_priority=2)",
        ),
    ],
)
def test_annotated(hint_fn, value, expected_repr):
    hint = hint_fn()

    if value is NO_VALUE:

        class M(BaseModel):
            x: hint

    else:

        class M(BaseModel):
            x: hint = value

    assert repr(M.model_fields['x']) == expected_repr


@pytest.mark.parametrize('metadata', [0, 'foo'])
def test_annotated_allows_unknown(metadata):
    class M(BaseModel):
        x: Annotated[int, metadata] = 5

    field_info = M.model_fields['x']
    assert len(field_info.metadata) == 1
    assert metadata in field_info.metadata, 'Records the unknown metadata'
    assert metadata in M.__annotations__['x'].__metadata__, 'Annotated type is recorded'


@pytest.mark.parametrize(
    ['hint_fn', 'value', 'empty_init_ctx'],
    [
        (
            lambda: int,
            PydanticUndefined,
            pytest.raises(ValueError, match=r'Field required \[type=missing,'),
        ),
        (
            lambda: Annotated[int, Field()],
            PydanticUndefined,
            pytest.raises(ValueError, match=r'Field required \[type=missing,'),
        ),
    ],
)
def test_annotated_instance_exceptions(hint_fn, value, empty_init_ctx):
    hint = hint_fn()

    class M(BaseModel):
        x: hint = value

    with empty_init_ctx:
        assert M().x == 5


def test_field_reuse():
    field = Field(description='Long description')

    class Model(BaseModel):
        one: int = field

    assert Model(one=1).model_dump() == {'one': 1}

    class AnnotatedModel(BaseModel):
        one: Annotated[int, field]

    assert AnnotatedModel(one=1).model_dump() == {'one': 1}


def test_config_field_info():
    class Foo(BaseModel):
        a: Annotated[int, Field(description='descr', json_schema_extra={'foobar': 'hello'})]

    assert Foo.model_json_schema(by_alias=True)['properties'] == {
        'a': {'title': 'A', 'description': 'descr', 'foobar': 'hello', 'type': 'integer'},
    }


@pytest.mark.skipif(sys.version_info < (3, 10), reason='repr different on older versions')
def test_annotated_alias() -> None:
    # https://github.com/pydantic/pydantic/issues/2971

    StrAlias = Annotated[str, Field(max_length=3)]
    IntAlias = Annotated[int, Field(default_factory=lambda: 2)]

    Nested = Annotated[List[StrAlias], Field(description='foo')]

    class MyModel(BaseModel):
        a: StrAlias = 'abc'
        b: StrAlias
        c: IntAlias
        d: IntAlias
        e: Nested

    fields_repr = {k: repr(v) for k, v in MyModel.model_fields.items()}
    assert fields_repr == {
        'a': "FieldInfo(annotation=str, required=False, default='abc', metadata=[MaxLen(max_length=3)])",
        'b': 'FieldInfo(annotation=str, required=True, metadata=[MaxLen(max_length=3)])',
        'c': 'FieldInfo(annotation=int, required=False, default_factory=<lambda>)',
        'd': 'FieldInfo(annotation=int, required=False, default_factory=<lambda>)',
        'e': "FieldInfo(annotation=List[Annotated[str, FieldInfo(annotation=NoneType, required=True, metadata=[MaxLen(max_length=3)])]], required=True, description='foo')",
    }
    assert MyModel(b='def', e=['xyz']).model_dump() == dict(a='abc', b='def', c=2, d=2, e=['xyz'])


def test_modify_get_schema_annotated() -> None:
    calls: List[str] = []

    class CustomType:
        @classmethod
        def __get_pydantic_core_schema__(cls, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
            calls.append('CustomType:before')
            with pytest.raises(PydanticSchemaGenerationError):
                handler(source)
            schema = core_schema.no_info_plain_validator_function(lambda _: CustomType())
            calls.append('CustomType:after')
            return schema

    class PydanticMetadata:
        def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
            calls.append('PydanticMetadata:before')
            schema = handler(source)
            calls.append('PydanticMetadata:after')
            return schema

    class GroupedMetadataMarker(GroupedMetadata):
        def __iter__(self) -> Iterator[BaseMetadata]:
            # no way to actually hook into schema building
            # so just register when our iter is called
            calls.append('GroupedMetadataMarker:iter')
            yield from []

    class _(BaseModel):
        x: Annotated[CustomType, GroupedMetadataMarker(), PydanticMetadata()]

    # insert_assert(calls)
    assert calls == [
        'GroupedMetadataMarker:iter',
        'PydanticMetadata:before',
        'CustomType:before',
        'CustomType:after',
        'PydanticMetadata:after',
    ]

    calls.clear()

    class _(BaseModel):
        x: Annotated[CustomType, PydanticMetadata(), GroupedMetadataMarker()]

    # insert_assert(calls)
    assert calls == [
        'GroupedMetadataMarker:iter',
        'PydanticMetadata:before',
        'CustomType:before',
        'CustomType:after',
        'PydanticMetadata:after',
    ]

    calls.clear()


def test_get_pydantic_core_schema_source_type() -> None:
    types: Set[Any] = set()

    class PydanticMarker:
        def __get_pydantic_core_schema__(self, source: Any, handler: GetCoreSchemaHandler) -> core_schema.CoreSchema:
            types.add(source)
            return handler(source)

    class _(BaseModel):
        x: Annotated[Annotated[int, 'foo'], PydanticMarker()]

    assert types == {int}
    types.clear()

    T = TypeVar('T')

    class GenericModel(Generic[T], BaseModel):
        y: T

    class _(BaseModel):
        x: Annotated[GenericModel[int], PydanticMarker()]

    assert types == {GenericModel[int]}
    types.clear()


def test_merge_field_infos_type_adapter() -> None:
    ta = TypeAdapter(
        Annotated[
            int, Field(gt=0), Field(lt=100), Field(gt=1), Field(description='abc'), Field(3), Field(description=None)
        ]
    )

    default = ta.get_default_value()
    assert default is not None
    assert default.value == 3

    # insert_assert(ta.validate_python(2))
    assert ta.validate_python(2) == 2

    with pytest.raises(ValidationError) as exc_info:
        ta.validate_python(1)

    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {'type': 'greater_than', 'loc': (), 'msg': 'Input should be greater than 1', 'input': 1, 'ctx': {'gt': 1}}
    ]

    # insert_assert(ta.json_schema())
    assert ta.json_schema() == {
        'default': 3,
        'description': 'abc',
        'exclusiveMaximum': 100,
        'exclusiveMinimum': 1,
        'type': 'integer',
    }


def test_merge_field_infos_model() -> None:
    class Model(BaseModel):
        x: Annotated[
            int, Field(gt=0), Field(lt=100), Field(gt=1), Field(description='abc'), Field(3), Field(description=None)
        ] = Field(5)

    # insert_assert(Model.model_json_schema())
    assert Model.model_json_schema() == {
        'properties': {
            'x': {'default': 5, 'exclusiveMaximum': 100, 'exclusiveMinimum': 1, 'title': 'X', 'type': 'integer'}
        },
        'title': 'Model',
        'type': 'object',
    }


def test_model_dump_doesnt_dump_annotated_dunder():
    class Model(BaseModel):
        one: int

    AnnotatedModel = Annotated[Model, ...]

    # In Pydantic v1, `AnnotatedModel.dict()` would have returned
    # `{'one': 1, '__orig_class__': typing.Annotated[...]}`
    assert AnnotatedModel(one=1).model_dump() == {'one': 1}


def test_merge_field_infos_ordering() -> None:
    TheType = Annotated[int, AfterValidator(lambda x: x), Field(le=2), AfterValidator(lambda x: x * 2), Field(lt=4)]

    class Model(BaseModel):
        x: TheType

    assert Model(x=1).x == 2

    with pytest.raises(ValidationError) as exc_info:
        Model(x=2)
    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {'type': 'less_than', 'loc': ('x',), 'msg': 'Input should be less than 4', 'input': 2, 'ctx': {'lt': 4}}
    ]

    with pytest.raises(ValidationError) as exc_info:
        Model(x=3)
    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {
            'type': 'less_than_equal',
            'loc': ('x',),
            'msg': 'Input should be less than or equal to 2',
            'input': 3,
            'ctx': {'le': 2},
        }
    ]


def test_validate_float_inf_nan_python() -> None:
    ta = TypeAdapter(Annotated[float, AfterValidator(lambda _: float('nan')), Field(allow_inf_nan=False)])

    with pytest.raises(ValidationError) as exc_info:
        ta.validate_python(1.0)

    # insert_assert(exc_info.value.errors(include_url=False))
    # TODO: input should be float('nan'), this seems like a subtle bug in pydantic-core
    assert exc_info.value.errors(include_url=False) == [
        {'type': 'finite_number', 'loc': (), 'msg': 'Input should be a finite number', 'input': 1.0}
    ]


def test_predicate_error_python() -> None:
    ta = TypeAdapter(Annotated[int, Predicate(lambda x: x > 0)])

    with pytest.raises(ValidationError) as exc_info:
        ta.validate_python(-1)

    # insert_assert(exc_info.value.errors(include_url=False))
    assert exc_info.value.errors(include_url=False) == [
        {
            'type': 'predicate_failed',
            'loc': (),
            'msg': 'Predicate test_predicate_error_python.<locals>.<lambda> failed',
            'input': -1,
        }
    ]


def test_annotated_field_info_not_lost_from_forwardref():
    from pydantic import BaseModel

    class ForwardRefAnnotatedFieldModel(BaseModel):
        foo: 'Annotated[Integer, Field(alias="bar", default=1)]' = 2
        foo2: 'Annotated[Integer, Field(alias="bar2", default=1)]' = Field(default=2, alias='baz')

    Integer = int

    ForwardRefAnnotatedFieldModel.model_rebuild()

    assert ForwardRefAnnotatedFieldModel(bar=3).foo == 3
    assert ForwardRefAnnotatedFieldModel(baz=3).foo2 == 3

    with pytest.raises(ValidationError) as exc_info:
        ForwardRefAnnotatedFieldModel(bar='bar')
    assert exc_info.value.errors() == [
        {
            'input': 'bar',
            'loc': ('bar',),
            'msg': 'Input should be a valid integer, unable to parse string as an integer',
            'type': 'int_parsing',
            'url': 'https://errors.pydantic.dev/2.4/v/int_parsing',
        }
    ]
