from pydantic import BaseModel

DecoratedCallable = TypeVar("DecoratedCallable", bound=Callable[..., Any])
UnionType = getattr(types, "UnionType", Union)
ModelNameMap = dict[Union[type[BaseModel], type[Enum]], str]
IncEx = Union[set[int], set[str], dict[int, Any], dict[str, Any]]

    return values


async def request_body_to_args(
    body_fields: list[ModelField],
    received_body: Optional[Union[dict[str, Any], FormData]],
    embed_body_fields: bool,
) -> tuple[dict[str, Any], list[dict[str, Any]]]:
    values: dict[str, Any] = {}
    errors: list[dict[str, Any]] = []
    assert body_fields, "request_body_to_args() should be called with fields"

 * <p>
 * Example usage:
 * <pre>
 * public class CustomPackagingProvider implements PackagingProvider {
 *     public Collection&lt;Packaging&gt; provides() {
 *         return Arrays.asList(
 *             packaging("docker-image"),
 *             packaging("flatpak")
 *         );
 *     }
 * }
 * </pre>
 *
 * @see org.apache.maven.api.Packaging

    val request = server.takeRequest()
    assertThat(request.body.readUtf8()).isEqualTo("request")
  }

  @Test
  fun testH2PriorKnowledgeServerFallback() {
    try {
      server.protocols = Arrays.asList(Protocol.H2_PRIOR_KNOWLEDGE, Protocol.HTTP_1_1)
      fail<Any>()
    } catch (expected: IllegalArgumentException) {
      assertThat(expected.message).isEqualTo(

        String version = "aVersion";
        String type = "jar";
        String classifier = "aClassifier";
        String downloadUrl = "http://somewhere.com/download";
        List<String> path = Arrays.asList("dependency1", "dependency2");
        String expected = "Missing artifact" + LS + LS + "  Try downloading the file manually from: " + LS

### 使用 Pydantic 的 `exclude_unset` 参数

更新部分数据时，可以在 Pydantic 模型的 `.dict()` 中使用 `exclude_unset` 参数。

比如，`item.dict(exclude_unset=True)`。

这段代码生成的 `dict` 只包含创建 `item` 模型时显式设置的数据，而不包括默认值。

然后再用它生成一个只含已设置（在请求中所发送）数据，且省略了默认值的 `dict`：

{* ../../docs_src/body_updates/tutorial002.py hl[34] *}

### 使用 Pydantic 的 `update` 参数

接下来，用 `.copy()` 为已有模型创建调用 `update` 参数的副本，该参数为包含更新数据的 `dict`。

例如，`stored_item_model.copy(update=update_data)`：

            builder.getMainModule().set("jdk.httpserver");
            builder.setStandardOutput(System.out);
            builder.setErrorOutput(System.err);
            builder.setArgs(Arrays.asList("-p", getPort().get(), "-d", getDocsDirectory().get().getAsFile().getAbsolutePath()));
            registry.start(getPath(), DeploymentRegistry.ChangeBehavior.RESTART, JavaApplicationHandle.class, builder);
        }
    }

Точно так же эта база данных не может принять Pydantic-модель (объект с атрибутами), а только `dict`.

Для этого можно использовать функцию `jsonable_encoder`.

Она принимает объект, например, Pydantic-модель, и возвращает его версию, совместимую с JSON:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

В данном примере она преобразует Pydantic-модель в `dict`, а `datetime` - в `str`.

    InputStream getInputStream() throws IOException;

    /**
     * Provides a user-friendly hint about the location of the source. This could be a local file path, a URI or just an
     * empty string. The intention is to assist users during error reporting.
     *
     * @return A user-friendly hint about the location of the source, never {@code null}.
     */
    String getLocation();

The same way, this database wouldn't receive a Pydantic model (an object with attributes), only a `dict`.

You can use `jsonable_encoder` for that.

It receives an object, like a Pydantic model, and returns a JSON compatible version:

{* ../../docs_src/encoder/tutorial001_py310.py hl[4,21] *}

In this example, it would convert the Pydantic model to a `dict`, and the `datetime` to a `str`.