import types
from enum import Enum
from typing import Any, Callable, Optional, TypeVar, Union

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]]

Up to now, you have been using common data types, like:

* `int`
* `float`
* `str`
* `bool`

But you can also use more complex data types.

And you will still have the same features as seen up to now:

* Great editor support.
* Data conversion from incoming requests.
* Data conversion for response data.
* Data validation.
* Automatic annotation and documentation.

     * Artifact type name for a JAR file containing test sources.
     */
    String TEST_JAVA_SOURCE = "test-java-source";

    /**
     * Returns the dependency type id.
     * The id uniquely identifies this <i>dependency type</i>.
     *
     * @return the id of this type, never {@code null}.
     */
    @Nonnull
    @Override
    String id();

    /**
     * Returns the dependency type language.
     *

package dns

import (
	"encoding/json"
	"time"
)

const (
	defaultTTL            = 30
	defaultContextTimeout = 5 * time.Minute
)

// SrvRecord - represents a DNS service record
type SrvRecord struct {
	Host     string      `json:"host,omitempty"`
	Port     json.Number `json:"port,omitempty"`
	Priority int         `json:"priority,omitempty"`
	Weight   int         `json:"weight,omitempty"`

## **Typer**, das FastAPI der CLIs { #typer-the-fastapi-of-clis }

<a href="https://typer.tiangolo.com" target="_blank"><img src="https://typer.tiangolo.com/img/logo-margin/logo-margin-vector.svg" style="width: 20%;"></a>

Anis Eleuch <******@****.***> 1701941636 -0800

Taran Pelkey <******@****.***> 1744754791 -0400

* `Decimal`:
    * `Decimal` estándar de Python.
    * En requests y responses, manejado igual que un `float`.
* Puedes revisar todos los tipos de datos válidos de Pydantic aquí: <a href="https://docs.pydantic.dev/latest/usage/types/types/" class="external-link" target="_blank">Tipos de datos de Pydantic</a>.

## Ejemplo { #example }

Aquí tienes un ejemplo de una *path operation* con parámetros usando algunos de los tipos anteriores.

from docs_src.custom_response.tutorial006_py39 import app

client = TestClient(app)


def test_get():
    response = client.get("/typer", follow_redirects=False)
    assert response.status_code == 307, response.text
    assert response.headers["location"] == "https://typer.tiangolo.com"


def test_openapi_schema():
    response = client.get("/openapi.json")
    assert response.status_code == 200, response.text

    * 生成的模式将指定这个 `str` 是 `binary` "格式"。
* `Decimal`:
    * 标准的 Python `Decimal`。
    * 在请求和响应中被当做 `float` 一样处理。
* 您可以在这里检查所有有效的pydantic数据类型: <a href="https://docs.pydantic.dev/latest/concepts/types/" class="external-link" target="_blank">Pydantic data types</a>.

## 例子

下面是一个*路径操作*的示例，其中的参数使用了上面的一些类型。

{* ../../docs_src/extra_data_types/tutorial001_an_py310.py hl[1,3,12:16] *}

注意，函数内的参数有原生的数据类型，你可以，例如，执行正常的日期操作，如: