from pydantic import BaseModel, Field

from .utils import get_body_model_name

app = FastAPI()

# =====================================================================================
# Without aliases


@app.post("/optional-str", operation_id="optional_str")
async def read_optional_str(p: Annotated[str | None, Form()] = None):
    return {"p": p}


class FormModelOptionalStr(BaseModel):

from inline_snapshot import snapshot
from pydantic import BaseModel, Field

app = FastAPI()

# =====================================================================================
# Without aliases


@app.get("/required-str")
async def read_required_str(p: str):
    return {"p": p}


class QueryModelRequiredStr(BaseModel):
    p: str


@app.get("/model-required-str")

from inline_snapshot import snapshot
from pydantic import BaseModel, Field

app = FastAPI()

# =====================================================================================
# Without aliases


@app.get("/optional-str")
async def read_optional_str(p: Annotated[str | None, Header()] = None):
    return {"p": p}


class HeaderModelOptionalStr(BaseModel):
    p: str | None = None

Um desses sistemas de gerenciamento de contêineres distribuídos como o Kubernetes normalmente tem alguma maneira integrada de lidar com a **replicação de contêineres** enquanto ainda oferece **balanceamento de carga** para as solicitações recebidas. Tudo no **nível do cluster**.

from pydantic import BaseModel, Field

from .utils import get_body_model_name

app = FastAPI()

# =====================================================================================
# Without aliases


@app.post("/optional-list-str", operation_id="optional_list_str")
async def read_optional_list_str(
    p: Annotated[list[str] | None, Form()] = None,
):
    return {"p": p}

from fastapi.testclient import TestClient

from .utils import get_body_model_name

app = FastAPI()

# =====================================================================================
# Without aliases


@app.post("/optional-bytes", operation_id="optional_bytes")
async def read_optional_bytes(p: Annotated[bytes | None, File()] = None):
    return {"file_size": len(p) if p else None}

from pydantic import BaseModel, Field

from .utils import get_body_model_name

app = FastAPI()

# =====================================================================================
# Without aliases


@app.post("/required-str", operation_id="required_str")
async def read_required_str(p: Annotated[str, Form()]):
    return {"p": p}


class FormModelRequiredStr(BaseModel):
    p: str

from inline_snapshot import snapshot
from pydantic import BaseModel, Field

app = FastAPI()

# =====================================================================================
# Without aliases


@app.get("/optional-list-str")
async def read_optional_list_str(
    p: Annotated[list[str] | None, Query()] = None,
):
    return {"p": p}


class QueryModelOptionalListStr(BaseModel):

Vous êtes libre de les utiliser comme vous le souhaitez, **FastAPI** n’impose aucune restriction.

Mais ce guide vous montre, plus ou moins, la façon dont ils sont censés être utilisés.

///

### Utiliser le paramètre `exclude_unset` de Pydantic { #using-pydantics-exclude-unset-parameter }

# with the GNU style, we need to set GNU utilities to be default on Mac.
if [[ -n "$(which grealpath)" ]] &&  [[ -n "$(which gstat)" ]]; then
  alias realpath=grealpath
  alias stat=gstat
  # By default, aliases are only expanded in interactive shells, which means
  # that they are not substituted for their corresponding commands in shell
  # scripts. By setting "expand_aliases", we enable alias expansion in