from enum import Enum

from fastapi import FastAPI


class ModelName(str, Enum):
    alexnet = "alexnet"
    resnet = "resnet"
    lenet = "lenet"


app = FastAPI()


@app.get("/models/{model_name}")
async def get_model(model_name: ModelName):
    if model_name is ModelName.alexnet:
        return {"model_name": model_name, "message": "Deep Learning FTW!"}

    if model_name.value == "lenet":

    assert response.json() == {"model_name": "lenet", "message": "LeCNN all the images"}


def test_get_enums_resnet():
    response = client.get("/models/resnet")
    assert response.status_code == 200
    assert response.json() == {"model_name": "resnet", "message": "Have some residuals"}


def test_get_enums_invalid():
    response = client.get("/models/foo")
    assert response.status_code == 422

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}


/// tip | Tipp

Falls Sie sich fragen, was „AlexNet“, „ResNet“ und „LeNet“ ist, das sind Namen von <abbr title="Genau genommen, Deep-Learning-Modellarchitekturen">Modellen</abbr> für maschinelles Lernen.

///

### Einen *Pfad-Parameter* deklarieren { #declare-a-path-parameter }

Затем создайте атрибуты класса с фиксированными допустимыми значениями:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Подсказка

Если интересно, то "AlexNet", "ResNet" и "LeNet" - это названия <abbr title="Технически, архитектуры моделей глубокого обучения">моделей</abbr> Машинного обучения.

///

### Определение *параметра пути* { #declare-a-path-parameter }

Then create class attributes with fixed values, which will be the available valid values:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip

If you are wondering, "AlexNet", "ResNet", and "LeNet" are just names of Machine Learning <abbr title="Technically, Deep Learning model architectures">models</abbr>.

///

### Declare a *path parameter* { #declare-a-path-parameter }

Luego crea atributos de clase con valores fijos, que serán los valores válidos disponibles:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Consejo

Si te estás preguntando, "AlexNet", "ResNet" y "LeNet" son solo nombres de <abbr title="Técnicamente, arquitecturas de modelos de Deep Learning">modelos</abbr> de Machine Learning.

///

### Declarar un *path parameter* { #declare-a-path-parameter }

Em seguida, crie atributos de classe com valores fixos, que serão os valores válidos disponíveis:

{* ../../docs_src/path_params/tutorial005_py39.py hl[1,6:9] *}

/// tip | Dica
Se você está se perguntando, "AlexNet", "ResNet" e "LeNet" são apenas nomes de <abbr title="Tecnicamente, arquiteturas de modelos de Deep Learning">modelos</abbr> de Aprendizado de Máquina.
///

### Declare um parâmetro de path { #declare-a-path-parameter }

      }
    }
    out.close();

    // Check that source returns the right data
    assertThat(source.read()).isEqualTo(data);

    // Make sure that reset deleted the file
    out.reset();
    if (file != null) {
      assertFalse(file.exists());
    }
  }


  public void testThreshold_resetOnFinalize() throws Exception {
    testThreshold(0, 100, true, true);

            assertEquals('D', stream.read());

            // Reset will throw IOException because mark is not supported
            try {
                stream.reset();
                fail("reset() should throw IOException when mark is not supported");
            } catch (IOException e) {
                // Expected - FileInputStream does not support mark/reset
            }
        }
    }

     * This method delegates to {@link FileInputStream#reset()}.
     *
     * @throws IOException if an I/O error occurs.
     */
    @Override
    public synchronized void reset() throws IOException {
        fileInputStream.reset();
    }

    /**
     * Skips over and discards {@code n} bytes of data from this input stream.