# Using Dataclasses

FastAPI is built on top of **Pydantic**, and I have been showing you how to use Pydantic models to declare requests and responses.

But FastAPI also supports using <a href="https://docs.python.org/3/library/dataclasses.html" class="external-link" target="_blank">`dataclasses`</a> the same way:

{* ../../docs_src/dataclasses/tutorial001.py hl[1,7:12,19:20] *}

Vous pouvez connecter le <abbr title="En anglais: debugger">débogueur</abbr> dans votre éditeur, par exemple avec Visual Studio Code ou PyCharm.

## Faites appel à `uvicorn`

Dans votre application FastAPI, importez et exécutez directement `uvicorn` :

{* ../../docs_src/debugging/tutorial001.py hl[1,15] *}

### À propos de `__name__ == "__main__"`

from datetime import datetime, timedelta, timezone
from typing import Annotated, Union

import jwt
from fastapi import Depends, FastAPI, HTTPException, status
from fastapi.security import OAuth2PasswordBearer, OAuth2PasswordRequestForm
from jwt.exceptions import InvalidTokenError
from passlib.context import CryptContext
from pydantic import BaseModel

# to get a string like this run:
# openssl rand -hex 32

////

Этот код состояния будет использован в ответе и будет добавлен в схему OpenAPI.

/// note | "Технические детали"

Вы также можете использовать `from starlette import status`.

**FastAPI** предоставляет тот же `starlette.status` под псевдонимом `fastapi.status` для удобства разработчика. Но его источник - это непосредственно Starlette.

///

## Теги

Você já viu como testar as suas aplicações **FastAPI** utilizando o `TestClient` que é fornecido. Até agora, você viu apenas como escrever testes síncronos, sem utilizar funções `async`.

////

Dieser Statuscode wird in der Response verwendet und zum OpenAPI-Schema hinzugefügt.

/// note | "Technische Details"

Sie können auch `from starlette import status` verwenden.

**FastAPI** bietet dieselben `starlette.status`-Codes auch via `fastapi.status` an, als Annehmlichkeit für Sie, den Entwickler. Sie kommen aber direkt von Starlette.

///

## Tags

from typing import List, Tuple

from dirty_equals import IsDict
from fastapi import FastAPI, Form
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


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

from typing import List, Union

from fastapi import FastAPI
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: Union[str, None] = None
    description: Union[str, None] = None
    price: Union[float, None] = None
    tax: float = 10.5
    tags: List[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},

from fastapi import FastAPI
from fastapi.encoders import jsonable_encoder
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str | None = None
    description: str | None = None
    price: float | None = None
    tax: float = 10.5
    tags: list[str] = []


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The bartenders", "price": 62, "tax": 20.2},

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import HTMLResponse

app = FastAPI()


@app.post("/files/")
async def create_files(
    files: list[bytes] = File(description="Multiple files as bytes"),
):
    return {"file_sizes": [len(file) for file in files]}


@app.post("/uploadfiles/")
async def create_upload_files(
    files: list[UploadFile] = File(description="Multiple files as UploadFile"),
):