from fastapi import FastAPI
from fastapi.responses import JSONResponse
from fastapi.testclient import TestClient
from pydantic import BaseModel

app = FastAPI()


class JsonApiResponse(JSONResponse):
    media_type = "application/vnd.api+json"


class Error(BaseModel):
    status: str
    title: str


class JsonApiError(BaseModel):
    errors: list[Error]


@app.get(
    "/a/{id}",

from typing import Optional

from fastapi import Depends, FastAPI, Security
from fastapi.security import APIKeyHeader
from fastapi.testclient import TestClient
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: Optional[str] = Security(api_key)):
    if oauth_header is None:
        return None

from typing import Annotated, Union

from fastapi import Depends, FastAPI
from fastapi.security import OAuth2PasswordBearer
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):

import importlib
import sys

import pytest
from dirty_equals import IsAnyStr
from fastapi.testclient import TestClient
from pydantic import ValidationError
from pytest import MonkeyPatch


@pytest.fixture(
    name="mod_name",
    params=[
        pytest.param("app01_py39"),
    ],
)
def get_mod_name(request: pytest.FixtureRequest):
    return f"docs_src.settings.{request.param}.main"


@pytest.fixture(name="client")

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).

Die `Hero`-Klasse ist einem Pydantic-Modell sehr ähnlich (faktisch ist sie darunter tatsächlich *ein Pydantic-Modell*).

Es gibt ein paar Unterschiede:

* `table=True` sagt SQLModel, dass dies ein *Tabellenmodell* ist, es soll eine **Tabelle** in der SQL-Datenbank darstellen, es ist nicht nur ein *Datenmodell* (wie es jede andere reguläre Pydantic-Klasse wäre).

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


items = {
    "foo": {"name": "Foo", "price": 50.2},
    "bar": {"name": "Bar", "description": "The Bar fighters", "price": 62, "tax": 20.2},
    "baz": {
        "name": "Baz",
        "description": "There goes my baz",

from typing import Union

from fastapi import FastAPI
from pydantic import BaseModel

app = FastAPI()


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


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

from dataclasses import field  # (1)

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: str | None = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)

from dataclasses import field  # (1)
from typing import Union

from fastapi import FastAPI
from pydantic.dataclasses import dataclass  # (2)


@dataclass
class Item:
    name: str
    description: Union[str, None] = None


@dataclass
class Author:
    name: str
    items: list[Item] = field(default_factory=list)  # (3)


app = FastAPI()


@app.post("/authors/{author_id}/items/", response_model=Author)  # (4)