Initial commit

venv/lib/python3.12/site-packages/fastapi/_compat/__init__.py

@@ -0,0 +1,40 @@
+from .shared import PYDANTIC_VERSION_MINOR_TUPLE as PYDANTIC_VERSION_MINOR_TUPLE
+from .shared import annotation_is_pydantic_v1 as annotation_is_pydantic_v1
+from .shared import field_annotation_is_scalar as field_annotation_is_scalar
+from .shared import (
+    field_annotation_is_scalar_sequence as field_annotation_is_scalar_sequence,
+)
+from .shared import field_annotation_is_sequence as field_annotation_is_sequence
+from .shared import (
+    is_bytes_or_nonable_bytes_annotation as is_bytes_or_nonable_bytes_annotation,
+)
+from .shared import is_bytes_sequence_annotation as is_bytes_sequence_annotation
+from .shared import is_pydantic_v1_model_instance as is_pydantic_v1_model_instance
+from .shared import (
+    is_uploadfile_or_nonable_uploadfile_annotation as is_uploadfile_or_nonable_uploadfile_annotation,
+)
+from .shared import (
+    is_uploadfile_sequence_annotation as is_uploadfile_sequence_annotation,
+)
+from .shared import lenient_issubclass as lenient_issubclass
+from .shared import sequence_types as sequence_types
+from .shared import value_is_sequence as value_is_sequence
+from .v2 import ModelField as ModelField
+from .v2 import PydanticSchemaGenerationError as PydanticSchemaGenerationError
+from .v2 import RequiredParam as RequiredParam
+from .v2 import Undefined as Undefined
+from .v2 import Url as Url
+from .v2 import copy_field_info as copy_field_info
+from .v2 import create_body_model as create_body_model
+from .v2 import evaluate_forwardref as evaluate_forwardref
+from .v2 import get_cached_model_fields as get_cached_model_fields
+from .v2 import get_definitions as get_definitions
+from .v2 import get_flat_models_from_fields as get_flat_models_from_fields
+from .v2 import get_missing_field_error as get_missing_field_error
+from .v2 import get_model_name_map as get_model_name_map
+from .v2 import get_schema_from_model_field as get_schema_from_model_field
+from .v2 import is_scalar_field as is_scalar_field
+from .v2 import serialize_sequence_value as serialize_sequence_value
+from .v2 import (
+    with_info_plain_validator_function as with_info_plain_validator_function,
+)

venv/lib/python3.12/site-packages/fastapi/_compat/shared.py

@@ -0,0 +1,214 @@
+import types
+import typing
+import warnings
+from collections import deque
+from collections.abc import Mapping, Sequence
+from dataclasses import is_dataclass
+from typing import (
+    Annotated,
+    Any,
+    TypeGuard,
+    TypeVar,
+    Union,
+    get_args,
+    get_origin,
+)
+
+from fastapi.types import UnionType
+from pydantic import BaseModel
+from pydantic.version import VERSION as PYDANTIC_VERSION
+from starlette.datastructures import UploadFile
+
+_T = TypeVar("_T")
+
+# Copy from Pydantic: pydantic/_internal/_typing_extra.py
+WithArgsTypes: tuple[Any, ...] = (
+    typing._GenericAlias,  # type: ignore[attr-defined]
+    types.GenericAlias,
+    types.UnionType,
+)  # pyright: ignore[reportAttributeAccessIssue]
+
+PYDANTIC_VERSION_MINOR_TUPLE = tuple(int(x) for x in PYDANTIC_VERSION.split(".")[:2])
+
+
+sequence_annotation_to_type = {
+    Sequence: list,
+    list: list,
+    tuple: tuple,
+    set: set,
+    frozenset: frozenset,
+    deque: deque,
+}
+
+sequence_types: tuple[type[Any], ...] = tuple(sequence_annotation_to_type.keys())
+
+
+# Copy of Pydantic: pydantic/_internal/_utils.py with added TypeGuard
+def lenient_issubclass(
+    cls: Any, class_or_tuple: type[_T] | tuple[type[_T], ...] | None
+) -> TypeGuard[type[_T]]:
+    try:
+        return isinstance(cls, type) and issubclass(cls, class_or_tuple)  # type: ignore[arg-type]
+    except TypeError:  # pragma: no cover
+        if isinstance(cls, WithArgsTypes):
+            return False
+        raise  # pragma: no cover
+
+
+def _annotation_is_sequence(annotation: type[Any] | None) -> bool:
+    if lenient_issubclass(annotation, (str, bytes)):
+        return False
+    return lenient_issubclass(annotation, sequence_types)
+
+
+def field_annotation_is_sequence(annotation: type[Any] | None) -> bool:
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        for arg in get_args(annotation):
+            if field_annotation_is_sequence(arg):
+                return True
+        return False
+    return _annotation_is_sequence(annotation) or _annotation_is_sequence(
+        get_origin(annotation)
+    )
+
+
+def value_is_sequence(value: Any) -> bool:
+    return isinstance(value, sequence_types) and not isinstance(value, (str, bytes))
+
+
+def _annotation_is_complex(annotation: type[Any] | None) -> bool:
+    return (
+        lenient_issubclass(annotation, (BaseModel, Mapping, UploadFile))
+        or _annotation_is_sequence(annotation)
+        or is_dataclass(annotation)
+    )
+
+
+def field_annotation_is_complex(annotation: type[Any] | None) -> bool:
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        return any(field_annotation_is_complex(arg) for arg in get_args(annotation))
+
+    if origin is Annotated:
+        return field_annotation_is_complex(get_args(annotation)[0])
+
+    return (
+        _annotation_is_complex(annotation)
+        or _annotation_is_complex(origin)
+        or hasattr(origin, "__pydantic_core_schema__")
+        or hasattr(origin, "__get_pydantic_core_schema__")
+    )
+
+
+def field_annotation_is_scalar(annotation: Any) -> bool:
+    # handle Ellipsis here to make tuple[int, ...] work nicely
+    return annotation is Ellipsis or not field_annotation_is_complex(annotation)
+
+
+def field_annotation_is_scalar_sequence(annotation: type[Any] | None) -> bool:
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        at_least_one_scalar_sequence = False
+        for arg in get_args(annotation):
+            if field_annotation_is_scalar_sequence(arg):
+                at_least_one_scalar_sequence = True
+                continue
+            elif not field_annotation_is_scalar(arg):
+                return False
+        return at_least_one_scalar_sequence
+    return field_annotation_is_sequence(annotation) and all(
+        field_annotation_is_scalar(sub_annotation)
+        for sub_annotation in get_args(annotation)
+    )
+
+
+def is_bytes_or_nonable_bytes_annotation(annotation: Any) -> bool:
+    if lenient_issubclass(annotation, bytes):
+        return True
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        for arg in get_args(annotation):
+            if lenient_issubclass(arg, bytes):
+                return True
+    return False
+
+
+def is_uploadfile_or_nonable_uploadfile_annotation(annotation: Any) -> bool:
+    if lenient_issubclass(annotation, UploadFile):
+        return True
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        for arg in get_args(annotation):
+            if lenient_issubclass(arg, UploadFile):
+                return True
+    return False
+
+
+def is_bytes_sequence_annotation(annotation: Any) -> bool:
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        at_least_one = False
+        for arg in get_args(annotation):
+            if is_bytes_sequence_annotation(arg):
+                at_least_one = True
+                continue
+        return at_least_one
+    return field_annotation_is_sequence(annotation) and all(
+        is_bytes_or_nonable_bytes_annotation(sub_annotation)
+        for sub_annotation in get_args(annotation)
+    )
+
+
+def is_uploadfile_sequence_annotation(annotation: Any) -> bool:
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        at_least_one = False
+        for arg in get_args(annotation):
+            if is_uploadfile_sequence_annotation(arg):
+                at_least_one = True
+                continue
+        return at_least_one
+    return field_annotation_is_sequence(annotation) and all(
+        is_uploadfile_or_nonable_uploadfile_annotation(sub_annotation)
+        for sub_annotation in get_args(annotation)
+    )
+
+
+def is_pydantic_v1_model_instance(obj: Any) -> bool:
+    # TODO: remove this function once the required version of Pydantic fully
+    # removes pydantic.v1
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            from pydantic import v1
+    except ImportError:  # pragma: no cover
+        return False
+    return isinstance(obj, v1.BaseModel)
+
+
+def is_pydantic_v1_model_class(cls: Any) -> bool:
+    # TODO: remove this function once the required version of Pydantic fully
+    # removes pydantic.v1
+    try:
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", UserWarning)
+            from pydantic import v1
+    except ImportError:  # pragma: no cover
+        return False
+    return lenient_issubclass(cls, v1.BaseModel)
+
+
+def annotation_is_pydantic_v1(annotation: Any) -> bool:
+    if is_pydantic_v1_model_class(annotation):
+        return True
+    origin = get_origin(annotation)
+    if origin is Union or origin is UnionType:
+        for arg in get_args(annotation):
+            if is_pydantic_v1_model_class(arg):
+                return True
+    if field_annotation_is_sequence(annotation):
+        for sub_annotation in get_args(annotation):
+            if annotation_is_pydantic_v1(sub_annotation):
+                return True
+    return False

venv/lib/python3.12/site-packages/fastapi/_compat/v2.py

@@ -0,0 +1,480 @@
+import re
+import warnings
+from collections.abc import Sequence
+from copy import copy
+from dataclasses import dataclass, is_dataclass
+from enum import Enum
+from functools import lru_cache
+from typing import (
+    Annotated,
+    Any,
+    Literal,
+    Union,
+    cast,
+    get_args,
+    get_origin,
+)
+
+from fastapi._compat import lenient_issubclass, shared
+from fastapi.openapi.constants import REF_TEMPLATE
+from fastapi.types import IncEx, ModelNameMap, UnionType
+from pydantic import BaseModel, ConfigDict, Field, TypeAdapter, create_model
+from pydantic import PydanticSchemaGenerationError as PydanticSchemaGenerationError
+from pydantic import PydanticUndefinedAnnotation as PydanticUndefinedAnnotation
+from pydantic import ValidationError as ValidationError
+from pydantic._internal._schema_generation_shared import (  # type: ignore[attr-defined]
+    GetJsonSchemaHandler as GetJsonSchemaHandler,
+)
+from pydantic._internal._typing_extra import eval_type_lenient
+from pydantic.fields import FieldInfo as FieldInfo
+from pydantic.json_schema import GenerateJsonSchema as _GenerateJsonSchema
+from pydantic.json_schema import JsonSchemaValue as JsonSchemaValue
+from pydantic_core import CoreSchema as CoreSchema
+from pydantic_core import PydanticUndefined
+from pydantic_core import Url as Url
+from pydantic_core.core_schema import (
+    with_info_plain_validator_function as with_info_plain_validator_function,
+)
+
+RequiredParam = PydanticUndefined
+Undefined = PydanticUndefined
+evaluate_forwardref = eval_type_lenient
+
+
+class GenerateJsonSchema(_GenerateJsonSchema):
+    # TODO: remove when this is merged (or equivalent): https://github.com/pydantic/pydantic/pull/12841
+    # and dropping support for any version of Pydantic before that one (so, in a very long time)
+    def bytes_schema(self, schema: CoreSchema) -> JsonSchemaValue:
+        json_schema = {"type": "string", "contentMediaType": "application/octet-stream"}
+        bytes_mode = (
+            self._config.ser_json_bytes
+            if self.mode == "serialization"
+            else self._config.val_json_bytes
+        )
+        if bytes_mode == "base64":
+            json_schema["contentEncoding"] = "base64"
+        self.update_with_validations(json_schema, schema, self.ValidationsMapping.bytes)
+        return json_schema
+
+
+# TODO: remove when dropping support for Pydantic < v2.12.3
+_Attrs = {
+    "default": ...,
+    "default_factory": None,
+    "alias": None,
+    "alias_priority": None,
+    "validation_alias": None,
+    "serialization_alias": None,
+    "title": None,
+    "field_title_generator": None,
+    "description": None,
+    "examples": None,
+    "exclude": None,
+    "exclude_if": None,
+    "discriminator": None,
+    "deprecated": None,
+    "json_schema_extra": None,
+    "frozen": None,
+    "validate_default": None,
+    "repr": True,
+    "init": None,
+    "init_var": None,
+    "kw_only": None,
+}
+
+
+# TODO: remove when dropping support for Pydantic < v2.12.3
+def asdict(field_info: FieldInfo) -> dict[str, Any]:
+    attributes = {}
+    for attr in _Attrs:
+        value = getattr(field_info, attr, Undefined)
+        if value is not Undefined:
+            attributes[attr] = value
+    return {
+        "annotation": field_info.annotation,
+        "metadata": field_info.metadata,
+        "attributes": attributes,
+    }
+
+
+@dataclass
+class ModelField:
+    field_info: FieldInfo
+    name: str
+    mode: Literal["validation", "serialization"] = "validation"
+    config: ConfigDict | None = None
+
+    @property
+    def alias(self) -> str:
+        a = self.field_info.alias
+        return a if a is not None else self.name
+
+    @property
+    def validation_alias(self) -> str | None:
+        va = self.field_info.validation_alias
+        if isinstance(va, str) and va:
+            return va
+        return None
+
+    @property
+    def serialization_alias(self) -> str | None:
+        sa = self.field_info.serialization_alias
+        return sa or None
+
+    @property
+    def default(self) -> Any:
+        return self.get_default()
+
+    def __post_init__(self) -> None:
+        with warnings.catch_warnings():
+            # Pydantic >= 2.12.0 warns about field specific metadata that is unused
+            # (e.g. `TypeAdapter(Annotated[int, Field(alias='b')])`). In some cases, we
+            # end up building the type adapter from a model field annotation so we
+            # need to ignore the warning:
+            if shared.PYDANTIC_VERSION_MINOR_TUPLE >= (2, 12):
+                from pydantic.warnings import UnsupportedFieldAttributeWarning
+
+                warnings.simplefilter(
+                    "ignore", category=UnsupportedFieldAttributeWarning
+                )
+            # TODO: remove after setting the min Pydantic to v2.12.3
+            # that adds asdict(), and use self.field_info.asdict() instead
+            field_dict = asdict(self.field_info)
+            annotated_args = (
+                field_dict["annotation"],
+                *field_dict["metadata"],
+                # this FieldInfo needs to be created again so that it doesn't include
+                # the old field info metadata and only the rest of the attributes
+                Field(**field_dict["attributes"]),
+            )
+            self._type_adapter: TypeAdapter[Any] = TypeAdapter(
+                Annotated[annotated_args],
+                config=self.config,
+            )
+
+    def get_default(self) -> Any:
+        if self.field_info.is_required():
+            return Undefined
+        return self.field_info.get_default(call_default_factory=True)
+
+    def validate(
+        self,
+        value: Any,
+        values: dict[str, Any] = {},  # noqa: B006
+        *,
+        loc: tuple[int | str, ...] = (),
+    ) -> tuple[Any, list[dict[str, Any]]]:
+        try:
+            return (
+                self._type_adapter.validate_python(value, from_attributes=True),
+                [],
+            )
+        except ValidationError as exc:
+            return None, _regenerate_error_with_loc(
+                errors=exc.errors(include_url=False), loc_prefix=loc
+            )
+
+    def serialize(
+        self,
+        value: Any,
+        *,
+        mode: Literal["json", "python"] = "json",
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool = True,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> Any:
+        # What calls this code passes a value that already called
+        # self._type_adapter.validate_python(value)
+        return self._type_adapter.dump_python(
+            value,
+            mode=mode,
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+        )
+
+    def serialize_json(
+        self,
+        value: Any,
+        *,
+        include: IncEx | None = None,
+        exclude: IncEx | None = None,
+        by_alias: bool = True,
+        exclude_unset: bool = False,
+        exclude_defaults: bool = False,
+        exclude_none: bool = False,
+    ) -> bytes:
+        # What calls this code passes a value that already called
+        # self._type_adapter.validate_python(value)
+        # This uses Pydantic's dump_json() which serializes directly to JSON
+        # bytes in one pass (via Rust), avoiding the intermediate Python dict
+        # step of dump_python(mode="json") + json.dumps().
+        return self._type_adapter.dump_json(
+            value,
+            include=include,
+            exclude=exclude,
+            by_alias=by_alias,
+            exclude_unset=exclude_unset,
+            exclude_defaults=exclude_defaults,
+            exclude_none=exclude_none,
+        )
+
+    def __hash__(self) -> int:
+        # Each ModelField is unique for our purposes, to allow making a dict from
+        # ModelField to its JSON Schema.
+        return id(self)
+
+
+def _has_computed_fields(field: ModelField) -> bool:
+    computed_fields = field._type_adapter.core_schema.get("schema", {}).get(
+        "computed_fields", []
+    )
+    return len(computed_fields) > 0
+
+
+def get_schema_from_model_field(
+    *,
+    field: ModelField,
+    model_name_map: ModelNameMap,
+    field_mapping: dict[
+        tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
+    ],
+    separate_input_output_schemas: bool = True,
+) -> dict[str, Any]:
+    override_mode: Literal["validation"] | None = (
+        None
+        if (separate_input_output_schemas or _has_computed_fields(field))
+        else "validation"
+    )
+    field_alias = (
+        (field.validation_alias or field.alias)
+        if field.mode == "validation"
+        else (field.serialization_alias or field.alias)
+    )
+
+    # This expects that GenerateJsonSchema was already used to generate the definitions
+    json_schema = field_mapping[(field, override_mode or field.mode)]
+    if "$ref" not in json_schema:
+        # TODO remove when deprecating Pydantic v1
+        # Ref: https://github.com/pydantic/pydantic/blob/d61792cc42c80b13b23e3ffa74bc37ec7c77f7d1/pydantic/schema.py#L207
+        json_schema["title"] = field.field_info.title or field_alias.title().replace(
+            "_", " "
+        )
+    return json_schema
+
+
+def get_definitions(
+    *,
+    fields: Sequence[ModelField],
+    model_name_map: ModelNameMap,
+    separate_input_output_schemas: bool = True,
+) -> tuple[
+    dict[tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue],
+    dict[str, dict[str, Any]],
+]:
+    schema_generator = GenerateJsonSchema(ref_template=REF_TEMPLATE)
+    validation_fields = [field for field in fields if field.mode == "validation"]
+    serialization_fields = [field for field in fields if field.mode == "serialization"]
+    flat_validation_models = get_flat_models_from_fields(
+        validation_fields, known_models=set()
+    )
+    flat_serialization_models = get_flat_models_from_fields(
+        serialization_fields, known_models=set()
+    )
+    flat_validation_model_fields = [
+        ModelField(
+            field_info=FieldInfo(annotation=model),
+            name=model.__name__,
+            mode="validation",
+        )
+        for model in flat_validation_models
+    ]
+    flat_serialization_model_fields = [
+        ModelField(
+            field_info=FieldInfo(annotation=model),
+            name=model.__name__,
+            mode="serialization",
+        )
+        for model in flat_serialization_models
+    ]
+    flat_model_fields = flat_validation_model_fields + flat_serialization_model_fields
+    input_types = {f.field_info.annotation for f in fields}
+    unique_flat_model_fields = {
+        f for f in flat_model_fields if f.field_info.annotation not in input_types
+    }
+    inputs = [
+        (
+            field,
+            (
+                field.mode
+                if (separate_input_output_schemas or _has_computed_fields(field))
+                else "validation"
+            ),
+            field._type_adapter.core_schema,
+        )
+        for field in list(fields) + list(unique_flat_model_fields)
+    ]
+    field_mapping, definitions = schema_generator.generate_definitions(inputs=inputs)
+    for item_def in cast(dict[str, dict[str, Any]], definitions).values():
+        if "description" in item_def:
+            item_description = cast(str, item_def["description"]).split("\f")[0]
+            item_def["description"] = item_description
+    # definitions: dict[DefsRef, dict[str, Any]]
+    # but mypy complains about general str in other places that are not declared as
+    # DefsRef, although DefsRef is just str:
+    # DefsRef = NewType('DefsRef', str)
+    # So, a cast to simplify the types here
+    return field_mapping, cast(dict[str, dict[str, Any]], definitions)
+
+
+def is_scalar_field(field: ModelField) -> bool:
+    from fastapi import params
+
+    return shared.field_annotation_is_scalar(
+        field.field_info.annotation
+    ) and not isinstance(field.field_info, params.Body)
+
+
+def copy_field_info(*, field_info: FieldInfo, annotation: Any) -> FieldInfo:
+    cls = type(field_info)
+    merged_field_info = cls.from_annotation(annotation)
+    new_field_info = copy(field_info)
+    new_field_info.metadata = merged_field_info.metadata
+    new_field_info.annotation = merged_field_info.annotation
+    return new_field_info
+
+
+def serialize_sequence_value(*, field: ModelField, value: Any) -> Sequence[Any]:
+    origin_type = get_origin(field.field_info.annotation) or field.field_info.annotation
+    if origin_type is Union or origin_type is UnionType:  # Handle optional sequences
+        union_args = get_args(field.field_info.annotation)
+        for union_arg in union_args:
+            if union_arg is type(None):
+                continue
+            origin_type = get_origin(union_arg) or union_arg
+            break
+    assert issubclass(origin_type, shared.sequence_types)  # type: ignore[arg-type]
+    return shared.sequence_annotation_to_type[origin_type](value)  # type: ignore[no-any-return,index]
+
+
+def get_missing_field_error(loc: tuple[int | str, ...]) -> dict[str, Any]:
+    error = ValidationError.from_exception_data(
+        "Field required", [{"type": "missing", "loc": loc, "input": {}}]
+    ).errors(include_url=False)[0]
+    error["input"] = None
+    return error  # type: ignore[return-value]
+
+
+def create_body_model(
+    *, fields: Sequence[ModelField], model_name: str
+) -> type[BaseModel]:
+    field_params = {f.name: (f.field_info.annotation, f.field_info) for f in fields}
+    BodyModel: type[BaseModel] = create_model(model_name, **field_params)  # type: ignore[call-overload]
+    return BodyModel
+
+
+def get_model_fields(model: type[BaseModel]) -> list[ModelField]:
+    model_fields: list[ModelField] = []
+    for name, field_info in model.model_fields.items():
+        type_ = field_info.annotation
+        if lenient_issubclass(type_, (BaseModel, dict)) or is_dataclass(type_):
+            model_config = None
+        else:
+            model_config = model.model_config
+        model_fields.append(
+            ModelField(
+                field_info=field_info,
+                name=name,
+                config=model_config,
+            )
+        )
+    return model_fields
+
+
+@lru_cache
+def get_cached_model_fields(model: type[BaseModel]) -> list[ModelField]:
+    return get_model_fields(model)
+
+
+# Duplicate of several schema functions from Pydantic v1 to make them compatible with
+# Pydantic v2 and allow mixing the models
+
+TypeModelOrEnum = type["BaseModel"] | type[Enum]
+TypeModelSet = set[TypeModelOrEnum]
+
+
+def normalize_name(name: str) -> str:
+    return re.sub(r"[^a-zA-Z0-9.\-_]", "_", name)
+
+
+def get_model_name_map(unique_models: TypeModelSet) -> dict[TypeModelOrEnum, str]:
+    name_model_map = {}
+    for model in unique_models:
+        model_name = normalize_name(model.__name__)
+        name_model_map[model_name] = model
+    return {v: k for k, v in name_model_map.items()}
+
+
+def get_flat_models_from_model(
+    model: type["BaseModel"], known_models: TypeModelSet | None = None
+) -> TypeModelSet:
+    known_models = known_models or set()
+    fields = get_model_fields(model)
+    get_flat_models_from_fields(fields, known_models=known_models)
+    return known_models
+
+
+def get_flat_models_from_annotation(
+    annotation: Any, known_models: TypeModelSet
+) -> TypeModelSet:
+    origin = get_origin(annotation)
+    if origin is not None:
+        for arg in get_args(annotation):
+            if lenient_issubclass(arg, (BaseModel, Enum)):
+                if arg not in known_models:
+                    known_models.add(arg)  # type: ignore[arg-type]
+                    if lenient_issubclass(arg, BaseModel):
+                        get_flat_models_from_model(arg, known_models=known_models)
+            else:
+                get_flat_models_from_annotation(arg, known_models=known_models)
+    return known_models
+
+
+def get_flat_models_from_field(
+    field: ModelField, known_models: TypeModelSet
+) -> TypeModelSet:
+    field_type = field.field_info.annotation
+    if lenient_issubclass(field_type, BaseModel):
+        if field_type in known_models:
+            return known_models
+        known_models.add(field_type)
+        get_flat_models_from_model(field_type, known_models=known_models)
+    elif lenient_issubclass(field_type, Enum):
+        known_models.add(field_type)
+    else:
+        get_flat_models_from_annotation(field_type, known_models=known_models)
+    return known_models
+
+
+def get_flat_models_from_fields(
+    fields: Sequence[ModelField], known_models: TypeModelSet
+) -> TypeModelSet:
+    for field in fields:
+        get_flat_models_from_field(field, known_models=known_models)
+    return known_models
+
+
+def _regenerate_error_with_loc(
+    *, errors: Sequence[Any], loc_prefix: tuple[str | int, ...]
+) -> list[dict[str, Any]]:
+    updated_loc_errors: list[Any] = [
+        {**err, "loc": loc_prefix + err.get("loc", ())} for err in errors
+    ]
+
+    return updated_loc_errors