import os
import re
import shutil
import subprocess
from html.parser import HTMLParser
from http.server import HTTPServer, SimpleHTTPRequestHandler
from multiprocessing import Pool
from pathlib import Path
from typing import Any, Optional, Union

import mkdocs.utils
import typer
import yaml
from jinja2 import Template
from ruff.__main__ import find_ruff_bin
from slugify import slugify as py_slugify

import http.client
import inspect
import warnings
from collections.abc import Sequence
from typing import Any, Optional, Union, cast

from fastapi import routing
from fastapi._compat import (
    ModelField,
    Undefined,
    get_compat_model_name_map,
    get_definitions,
    get_schema_from_model_field,
    lenient_issubclass,
)
from fastapi.datastructures import DefaultPlaceholder
from fastapi.dependencies.models import Dependant

### Імпорт `Query` та `Annotated`

Щоб це зробити, спочатку імпортуємо:

* `Query` з `fastapi`
* `Annotated` з `typing`

{* ../../docs_src/query_params_str_validations/tutorial002_an_py310.py hl[1,3] *}

/// info | Інформація

FastAPI додав підтримку `Annotated` (і почав рекомендувати його) у версії 0.95.0.

Using Scala version 2.11.8 (Java HotSpot(TM) 64-Bit Server VM, Java 1.8.0_112)
Type in expressions to have them evaluated.
Type :help for more information.

scala>
```

- At the _scala>_ prompt, submit the job by typing the following commands, Replace node names, file name, and file location with your values:

```
scala> val file = sc.textFile("s3a://testbucket/testdata")

We annotate the function return type as `BaseUser`, but we are actually returning a `UserIn` instance.

The editor, mypy, and other tools won't complain about this because, in typing terms, `UserIn` is a subclass of `BaseUser`, which means it's a *valid* type when what is expected is anything that is a `BaseUser`.

### FastAPI Data Filtering { #fastapi-data-filtering }

* 和你 **<abbr title="集成开发环境，和代码编辑器类似">IDE</abbr>/<abbr title="一个检查代码错误的程序">linter</abbr>/brain** 适配:
    * 因为 pydantic 数据结构仅仅是你定义的类的实例；自动补全，linting，mypy 以及你的直觉应该可以和你验证的数据一起正常工作。
* 验证**复杂结构**:
    * 使用分层的 Pydantic 模型, Python `typing`的 `List` 和 `Dict` 等等。
    * 验证器使我们能够简单清楚的将复杂的数据模式定义、检查并记录为 JSON Schema。
    * 你可以拥有深度**嵌套的 JSON** 对象并对它们进行验证和注释。
* **可扩展**:
    * Pydantic 允许定义自定义数据类型或者你可以用验证器装饰器对被装饰的模型上的方法扩展验证。

    * Pydantic 데이터 구조는 단순 여러분이 정의한 클래스의 인스턴스이기 때문에, 자동 완성, 린팅, mypy 그리고 여러분의 직관까지 여러분의 검증된 데이터와 올바르게 작동합니다.
* **복잡한 구조**를 검증합니다:
    * 계층적인 Pydantic 모델, 파이썬 `typing`의 `List`와 `Dict`, 그 외를 사용합니다.
    * 그리고 검증자는 복잡한 데이터 스키마를 명확하고 쉽게 정의 및 확인하며 JSON 스키마로 문서화합니다.
    * 여러분은 깊게 **중첩된 JSON** 객체를 가질 수 있으며, 이 객체 모두 검증하고 설명을 붙일 수 있습니다.
* **확장 가능성**:

    --hash=sha256:3afb05607b89aed0ffe25202399ee0867ad4d3cb4180d98aaf8eefa6a5f7d475 \
    --hash=sha256:b5b08f68937f138fe92f6c089b99f1e2da0ae56c52b78bf7075fd95420fd9a5a
    # via -r ci/official/requirements_updater/requirements.in
typing-extensions==4.14.1 \
    --hash=sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36 \
    --hash=sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76
    # via

    --hash=sha256:3afb05607b89aed0ffe25202399ee0867ad4d3cb4180d98aaf8eefa6a5f7d475 \
    --hash=sha256:b5b08f68937f138fe92f6c089b99f1e2da0ae56c52b78bf7075fd95420fd9a5a
    # via -r ci/official/requirements_updater/requirements.in
typing-extensions==4.14.1 \
    --hash=sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36 \
    --hash=sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76
    # via

    --hash=sha256:3afb05607b89aed0ffe25202399ee0867ad4d3cb4180d98aaf8eefa6a5f7d475 \
    --hash=sha256:b5b08f68937f138fe92f6c089b99f1e2da0ae56c52b78bf7075fd95420fd9a5a
    # via -r ci/official/requirements_updater/requirements.in
typing-extensions==4.14.1 \
    --hash=sha256:38b39f4aeeab64884ce9f74c94263ef78f3c22467c8724005483154c26648d36 \
    --hash=sha256:d1e1e3b58374dc93031d6eda2420a48ea44a36c2b4766a4fdeb3710755731d76
    # via