from collections import deque
from dataclasses import dataclass
from datetime import datetime, timezone
from decimal import Decimal
from enum import Enum
from pathlib import PurePath, PurePosixPath, PureWindowsPath
from typing import Optional

import pytest
from fastapi._compat import PYDANTIC_V2, Undefined
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel, Field, ValidationError

import dataclasses
import datetime
from collections import defaultdict, deque
from decimal import Decimal
from enum import Enum
from ipaddress import (
    IPv4Address,
    IPv4Interface,
    IPv4Network,
    IPv6Address,
    IPv6Interface,
    IPv6Network,
)
from pathlib import Path, PurePath
from re import Pattern
from types import GeneratorType
from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union

```

## `Bucket` を取得する関数の追加

**Couchbase**では、bucketはdocumentのセットで、様々な種類のものがあります。

Bucketは通常、同一のアプリケーション内で互いに関係を持っています。

リレーショナルデータベースの世界でいう"database"(データベースサーバではなく特定のdatabase)と類似しています。

**MongoDB** で例えると"collection"と似た概念です。

次のコードでは主に `Bucket` を利用してCouchbaseを操作します。

この関数では以下の処理を行います:

* **Couchbase** クラスタ(1台の場合もあるでしょう)に接続
    * タイムアウトのデフォルト値を設定
* クラスタで認証を取得
* `Bucket` インスタンスを取得
    * タイムアウトのデフォルト値を設定

For example an object `orion_cat` (an instance of `Pet`) could have an attribute `orion_cat.type`, for the column `type`. And the value of that attribute could be, e.g. `"cat"`.

These ORMs also have tools to make the connections or relations between tables or entities.

This way, you could also have an attribute `orion_cat.owner` and the owner would contain the data for this pet's owner, taken from the table *owners*.

from collections import deque
from copy import copy
from dataclasses import dataclass, is_dataclass
from enum import Enum
from typing import (
    Any,
    Callable,
    Deque,
    Dict,
    FrozenSet,
    List,
    Mapping,
    Sequence,
    Set,
    Tuple,
    Type,
    Union,
)

from fastapi.exceptions import RequestErrorModel
from fastapi.types import IncEx, ModelNameMap, UnionType

And `threading.local` is not compatible with the new async features of modern Python.

!!! note "Technical Details"

import logging
import subprocess
import sys
from collections import Counter, defaultdict
from datetime import datetime, timedelta, timezone
from pathlib import Path
from typing import Any, Container, DefaultDict, Dict, List, Set, Union

import httpx
import yaml
from github import Github
from pydantic import BaseModel, SecretStr
from pydantic_settings import BaseSettings

github_graphql_url = "https://api.github.com/graphql"

from starlette.background import BackgroundTasks as StarletteBackgroundTasks
from typing_extensions import Annotated, Doc, ParamSpec

P = ParamSpec("P")


class BackgroundTasks(StarletteBackgroundTasks):
    """
    A collection of background tasks that will be called after a response has been
    sent to the client.

    Read more about it in the
    [FastAPI docs for Background Tasks](https://fastapi.tiangolo.com/tutorial/background-tasks/).

Then, in that case, it could be simpler to have **one container** with **multiple processes**, and a local tool (e.g. a Prometheus exporter) on the same container collecting Prometheus metrics for all the internal processes and exposing those metrics on that single container.

---

</html>
"""


class ConnectionManager:
    def __init__(self):
        self.active_connections: list[WebSocket] = []

    async def connect(self, websocket: WebSocket):
        await websocket.accept()
        self.active_connections.append(websocket)

    def disconnect(self, websocket: WebSocket):
        self.active_connections.remove(websocket)

    async def send_personal_message(self, message: str, websocket: WebSocket):