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

app = FastAPI()


class Model(BaseModel):
    pass


class Model2(BaseModel):
    a: Model


class Model3(BaseModel):
    c: Model
    d: Model2


@app.get("/", response_model=Model3)
def f():
    return {"c": {}, "d": {"a": {}}}


client = TestClient(app)


def test_get_api_route():

    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",
        "model_b": {"username": "test-user"},
    }


@needs_pydanticv1
def test_validator_is_cloned(client: TestClient):

            return name

    async def get_model_c() -> ModelC:
        return ModelC(username="test-user", password="test-password")

    @app.get("/model/{name}", response_model=ModelA)
    async def get_model_a(name: str, model_c=Depends(get_model_c)):
        return {"name": name, "description": "model-a-desc", "foo": model_c}

    client = TestClient(app)
    return client


@needs_pydanticv2

# Extramodelle

Fahren wir beim letzten Beispiel fort. Es gibt normalerweise mehrere zusammengehörende Modelle.

Insbesondere Benutzermodelle, denn:

* Das **hereinkommende Modell** sollte ein Passwort haben können.
* Das **herausgehende Modell** sollte kein Passwort haben.

            modelsCreated(":", ":a")
        }

        when:
        executer.withArguments(ENABLE_CLI)
        def model2 = runBuildAction(new FetchCustomModelForEachProjectInTree())

        then:
        model2.size() == 2
        model2[0].message == "It works from project :"
        model2[1].message == "It works from project :a"

        and:
        fixture.assertStateLoaded()

        when:

## Modelos aninhados

Cada atributo de um modelo Pydantic tem um tipo.

Mas esse tipo pode ser outro modelo Pydantic.

Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos.

Tudo isso, aninhado arbitrariamente.

### Defina um sub-modelo

Por exemplo, nós podemos definir um modelo `Image`:

```Python hl_lines="9-11"

# OpenAPI-`models`

OpenAPI Pydantic-Modelle, werden zum Generieren und Validieren der generierten OpenAPI verwendet.

# Extra Models

Continuing with the previous example, it will be common to have more than one related model.

This is especially the case for user models, because:

* The **input model** needs to be able to have a password.
* The **output model** should not have a password.
* The **database model** would probably need to have a hashed password.

!!! danger
    Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.

## Nested Models

Each attribute of a Pydantic model has a type.

But that type can itself be another Pydantic model.

So, you can declare deeply nested JSON "objects" with specific attribute names, types and validations.

All that, arbitrarily nested.

### Define a submodel

For example, we can define an `Image` model:

=== "Python 3.10+"

        and:
        (model.left as GradleProject).name == "hello-world"
        (model.right as EclipseProject).gradleProject.name == "hello-world"

        and:
        def streamedModels = listener1.models as List<CustomModel>
        streamedModels.size() == 2
        streamedModels[0].value == 1
        streamedModels[1].value == 2


        when:
        withIsolatedProjects()