from fastapi import Body, FastAPI
from pydantic import BaseModel

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = None
    price: float
    tax: float | None = None


class User(BaseModel):
    username: str
    full_name: str | None = None


@app.put("/items/{item_id}")
async def update_item(
    item_id: int, item: Item, user: User, importance: Annotated[int, Body()]
):

app = FastAPI()


class Image(BaseModel):
    url: str
    name: str


class Item(BaseModel):
    name: str
    description: Union[str, None] = None
    price: float
    tax: Union[float, None] = None
    tags: set[str] = set()
    image: Union[Image, None] = None


@app.put("/items/{item_id}")
async def update_item(item_id: int, item: Item):
    results = {"item_id": item_id, "item": item}

 * from the {@code pathScope} and the injected field type. If the type
 * is {@link org.apache.maven.api.Node Node} and {@code pathScope == ""},
 * then the dependencies will be <i>collected</i>.
 * If the type is {@link org.apache.maven.api.Node Node} or
 * {@code List<}{@link org.apache.maven.api.Node Node}{@code >},
 * and {@code pathScope != ""}, the dependencies will be <i>flattened</i>.

 * transformation:
 *
 * <pre>{@code
 * someGraphAlgorithm(startNode, node -> ImmutableList.of(node.mother(), node.father()));
 * }</pre>
 *
 * <p>Graph algorithms that need additional capabilities (accessing both predecessors and
 * successors, iterating over the edges, etc.) should declare their input to be of a type that
 * provides those capabilities, such as {@link Graph}, {@link ValueGraph}, or {@link Network}.
 *

        "item": {
            "name": "Foo",
            "price": 50.5,
            "description": None,
            "tax": None,
        },
        "user": {"username": "Dave", "full_name": None},
    }


def test_post_body_no_data(client: TestClient):
    response = client.put("/items/5", json=None)
    assert response.status_code == 422
    assert response.json() == IsDict(
        {
            "detail": [

        "item": {
            "name": "Foo",
            "price": 50.5,
            "description": None,
            "tax": None,
        },
        "user": {"username": "Dave", "full_name": None},
    }


@needs_py310
def test_post_body_no_data(client: TestClient):
    response = client.put("/items/5", json=None)
    assert response.status_code == 422
    assert response.json() == IsDict(
        {

        "item": {
            "name": "Foo",
            "price": 50.5,
            "description": None,
            "tax": None,
        },
        "user": {"username": "Dave", "full_name": None},
    }


@needs_py39
def test_post_body_no_data(client: TestClient):
    response = client.put("/items/5", json=None)
    assert response.status_code == 422
    assert response.json() == IsDict(
        {

    username: str
    password: str
    email: EmailStr
    full_name: str | None = None


class UserOut(BaseModel):
    username: str
    email: EmailStr
    full_name: str | None = None


class UserInDB(BaseModel):
    username: str
    hashed_password: str
    email: EmailStr
    full_name: str | None = None


def fake_password_hasher(raw_password: str):
    return "supersecret" + raw_password

from pydantic import BaseModel

app = FastAPI()

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="token")


class User(BaseModel):
    username: str
    email: Union[str, None] = None
    full_name: Union[str, None] = None
    disabled: Union[bool, None] = None


def fake_decode_token(token):
    return User(
        username=token + "fakedecoded", email="******@****.***", full_name="John Doe"
    )

        "item": {
            "name": "Foo",
            "price": 50.5,
            "description": None,
            "tax": None,
        },
        "user": {"username": "Dave", "full_name": None},
    }


@needs_py310
def test_post_body_no_data(client: TestClient):
    response = client.put("/items/5", json=None)
    assert response.status_code == 422
    assert response.json() == IsDict(
        {