---

Imaginez que vous êtes l'ordinateur / le programme 🤖 dans cette histoire.

Pendant que vous faites la queue, vous être simplement inactif 😴, attendant votre tour, ne faisant rien de "productif". Mais la queue est rapide car le serveur 💁 prend seulement les commandes (et ne les prépare pas), donc tout va bien.

Sie erfordern in der Regel komplexere Konfigurationen und einen Nachrichten-/Job-Queue-Manager wie RabbitMQ oder Redis, ermöglichen Ihnen jedoch die Ausführung von Hintergrundtasks in mehreren Prozessen und insbesondere auf mehreren Servern.

* ✨ Add support for `deque` objects and children in `jsonable_encoder`. PR [#9433](https://github.com/tiangolo/fastapi/pull/9433) by [@cranium](https://github.com/cranium).

### Docs

Ces outils nécessitent généralement des configurations plus complexes ainsi qu'un gestionnaire de queue de message, comme RabbitMQ ou Redis, mais ils permettent d'exécuter des tâches d'arrière-plan dans différents process, et potentiellement, sur plusieurs serveurs.

    author_link: https://medium.com/@mike.p.moritz
    link: https://medium.com/@mike.p.moritz/using-docker-compose-to-deploy-a-lightweight-python-rest-api-with-a-job-queue-37e6072a209b
    title: Using Docker Compose to deploy a lightweight Python REST API with a job queue
  - author: '@euri10'
    author_link: https://gitlab.com/euri10
    link: https://gitlab.com/euri10/fastapi_cheatsheet

@needs_pydanticv2
def test_decimal_encoder_int():
    data = {"value": Decimal(2)}
    assert jsonable_encoder(data) == {"value": 2}


def test_encode_deque_encodes_child_models():
    class Model(BaseModel):
        test: str

    dq = deque([Model(test="test")])

    assert jsonable_encoder(dq)[0]["test"] == "test"


@needs_pydanticv2
def test_encode_pydantic_undefined():

sequence_annotation_to_type = {
    Sequence: list,
    List: list,
    list: list,
    Tuple: tuple,
    tuple: tuple,
    Set: set,
    set: set,
    FrozenSet: frozenset,
    frozenset: frozenset,
    Deque: deque,
    deque: deque,
}

sequence_types = tuple(sequence_annotation_to_type.keys())

if PYDANTIC_V2:
    from pydantic import PydanticSchemaGenerationError as PydanticSchemaGenerationError

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

	* O frontend armazena aquele token temporariamente em algum lugar.
	* O usuário clica no frontend para ir à outra seção daquele frontend do aplicativo web.
	* O frontend precisa buscar mais dados daquela API.
		* Mas precisa de autenticação para aquele endpoint em específico.

They tend to require more complex configurations, a message/job queue manager, like RabbitMQ or Redis, but they allow you to run background tasks in multiple processes, and especially, in multiple servers.