sequence_annotation_to_type = {
    Sequence: list,
    list: list,
    tuple: tuple,
    set: set,
    frozenset: frozenset,
    deque: deque,
}

sequence_types = tuple(sequence_annotation_to_type.keys())

Url: type[Any]


# Copy of Pydantic v2, compatible with v1
def lenient_issubclass(
    cls: Any, class_or_tuple: Union[type[Any], tuple[type[Any], ...], None]
) -> bool:
    try:

{* ../../docs_src/query_params_str_validations/tutorial015_an_py310.py ln[16:19] hl[17] *}

#### Ein zufälliges Item { #a-random-item }

class ItemGroup(BaseModel):
    items: list[tuple[str, str]]


class Coordinate(BaseModel):
    x: float
    y: float


@app.post("/model-with-tuple/")
def post_model_with_tuple(item_group: ItemGroup):
    return item_group


@app.post("/tuple-of-models/")
def post_tuple_of_models(square: tuple[Coordinate, Coordinate]):
    return square


@app.post("/tuple-form/")

def _remap_definitions_and_field_mappings(
    *,
    model_name_map: ModelNameMap,
    definitions: dict[str, Any],
    field_mapping: dict[
        tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue
    ],
) -> tuple[
    dict[tuple[ModelField, Literal["validation", "serialization"]], JsonSchemaValue],
    dict[str, Any],
]:
    old_name_to_new_name_map = {}

def client() -> Iterator[TestClient]:
    with TestClient(app) as client:
        yield client


def _bench_get(benchmark, client: TestClient, path: str) -> tuple[int, bytes]:
    warmup = client.get(path)
    assert warmup.status_code == 200

    def do_request() -> tuple[int, bytes]:
        response = client.get(path)
        return response.status_code, response.content

    return benchmark(do_request)

        response=response,
        dependency_cache=dependency_cache,
    )


def _validate_value_with_model_field(
    *, field: ModelField, value: Any, values: dict[str, Any], loc: tuple[str, ...]
) -> tuple[Any, list[Any]]:
    if value is None:
        if field.required:
            return None, [get_missing_field_error(loc=loc)]
        else:
            return deepcopy(field.default), []

    AnyUrl: str,
}


def generate_encoders_by_class_tuples(
    type_encoder_map: dict[Any, Callable[[Any], Any]],
) -> dict[Callable[[Any], Any], tuple[Any, ...]]:
    encoders_by_class_tuples: dict[Callable[[Any], Any], tuple[Any, ...]] = defaultdict(
        tuple
    )
    for type_, encoder in type_encoder_map.items():
        encoders_by_class_tuples[encoder] += (type_,)
    return encoders_by_class_tuples

    *,
    route: routing.APIRoute,
    operation_ids: set[str],
    model_name_map: ModelNameMap,
    field_mapping: dict[
        tuple[ModelField, Literal["validation", "serialization"]], dict[str, Any]
    ],
    separate_input_output_schemas: bool = True,
) -> tuple[dict[str, Any], dict[str, Any], dict[str, Any]]:
    path = {}
    security_schemes: dict[str, Any] = {}
    definitions: dict[str, Any] = {}

    dependentSchemas: Optional[dict[str, "SchemaOrBool"]] = None
    prefixItems: Optional[list["SchemaOrBool"]] = None
    # TODO: uncomment and remove below when deprecating Pydantic v1
    # It generates a list of schemas for tuples, before prefixItems was available
    # items: Optional["SchemaOrBool"] = None
    items: Optional[Union["SchemaOrBool", list["SchemaOrBool"]]] = None
    contains: Optional["SchemaOrBool"] = None

Äquivalent zu `set(["name", "description"])`.

///

#### `list`en statt `set`s verwenden { #using-lists-instead-of-sets }

Wenn Sie vergessen, ein `set` zu verwenden, und stattdessen eine `list`e oder ein `tuple` übergeben, wird FastAPI die dennoch in ein `set` konvertieren, und es wird korrekt funktionieren:

{* ../../docs_src/response_model/tutorial006_py310.py hl[29,35] *}

## Zusammenfassung { #recap }