return [pet1, pet2]


client = TestClient(app)


def test_filter_top_level_model():
    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {

    return [pet1, pet2]


client = TestClient(app)


def test_filter_top_level_model():
    response = client.post(
        "/users", json={"email": "******@****.***", "password": "secret"}
    )
    assert response.json() == {"email": "******@****.***"}


def test_filter_second_level_model():
    response = client.get("/pets/1")
    assert response.json() == {

from fastapi import Depends, FastAPI, Security
from fastapi.testclient import TestClient

app = FastAPI()

counter_holder = {"counter": 0}


async def dep_counter():
    counter_holder["counter"] += 1
    return counter_holder["counter"]


async def super_dep(count: int = Depends(dep_counter)):
    return count


@app.get("/counter/")
async def get_counter(count: int = Depends(dep_counter)):
    return {"counter": count}

        return {"name": name, "description": "model-a-desc", "foo": model_c}

    client = TestClient(app)
    return client


@needs_pydanticv2
def test_filter_sub_model(client: TestClient):
    response = client.get("/model/modelA")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "name": "modelA",
        "description": "model-a-desc",

from ..utils import needs_pydanticv1


@pytest.fixture(name="client")
def get_client():
    from .app_pv1 import app

    client = TestClient(app)
    return client


@needs_pydanticv1
def test_filter_sub_model(client: TestClient):
    response = client.get("/model/modelA")
    assert response.status_code == 200, response.text
    assert response.json() == {
        "name": "modelA",
        "description": "model-a-desc",

            all_valid = False
          if ver < 35:
            print('WARNING: XLA does not support CUDA compute capabilities '
                  'lower than sm_35. Disable XLA when running on older GPUs.')
      else:
        ver = float(m.group(0))
        if ver < 3.0:
          print('ERROR: TensorFlow only supports CUDA compute capabilities 3.0 '