import warnings

import pytest
from fastapi import FastAPI
from fastapi.exceptions import FastAPIDeprecationWarning
from fastapi.responses import ORJSONResponse, UJSONResponse
from fastapi.testclient import TestClient
from pydantic import BaseModel


class Item(BaseModel):
    name: str
    price: float


# ORJSON


def _make_orjson_app() -> FastAPI:
    with warnings.catch_warnings():

from fastapi import FastAPI
from pydantic import BaseModel, HttpUrl

app = FastAPI()


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


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


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

from typing import Any

from fastapi import FastAPI
from fastapi.testclient import TestClient
from pydantic import BaseModel, ConfigDict


def test_read_with_orm_mode() -> None:
    class PersonBase(BaseModel):
        name: str
        lastname: str

    class Person(PersonBase):
        @property
        def full_name(self) -> str:
            return f"{self.name} {self.lastname}"

from datetime import datetime

from pydantic import BaseModel


class User(BaseModel):
    id: int
    name: str = "John Doe"
    signup_ts: datetime | None = None
    friends: list[int] = []


external_data = {
    "id": "123",
    "signup_ts": "2017-06-01 12:22",
    "friends": [1, "2", b"3"],
}
user = User(**external_data)
print(user)

{* ../../docs_src/body_multiple_params/tutorial002_py310.py hl[20] *}


In this case, **FastAPI** will notice that there is more than one body parameter in the function (there are two parameters that are Pydantic models).

So, it will then use the parameter names as keys (field names) in the body, and expect a body like:

```JSON
{
    "item": {
        "name": "Foo",
        "description": "The pretender",

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


@app.put("/items/{item_id}")
async def update_item(
    *,
    item_id: int,
    item: Item = Body(
        openapi_examples={
            "normal": {
                "summary": "A normal example",

from fastapi import Body, FastAPI
from pydantic import BaseModel, Field

app = FastAPI()


class Item(BaseModel):
    name: str
    description: str | None = Field(
        default=None, title="The description of the item", max_length=300
    )
    price: float = Field(gt=0, description="The price must be greater than zero")
    tax: float | None = None


@app.put("/items/{item_id}")

from typing import Annotated

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(

from fastapi import Depends, FastAPI, Security
from fastapi.security import APIKeyHeader
from fastapi.testclient import TestClient
from inline_snapshot import snapshot
from pydantic import BaseModel

app = FastAPI()

api_key = APIKeyHeader(name="key", auto_error=False)


class User(BaseModel):
    username: str


def get_current_user(oauth_header: str | None = Security(api_key)):
    if oauth_header is None:
        return None

The `Hero` class is very similar to a Pydantic model (in fact, underneath, it actually *is a Pydantic model*).

There are a few differences:

* `table=True` tells SQLModel that this is a *table model*, it should represent a **table** in the SQL database, it's not just a *data model* (as would be any other regular Pydantic class).