// cpu stats
	cpuUser       MetricName = "user"
	cpuSystem     MetricName = "system"
	cpuIOWait     MetricName = "iowait"
	cpuIdle       MetricName = "idle"
	cpuNice       MetricName = "nice"
	cpuSteal      MetricName = "steal"
	cpuLoad1      MetricName = "load1"
	cpuLoad5      MetricName = "load5"
	cpuLoad15     MetricName = "load15"
	cpuLoad1Perc  MetricName = "load1_perc"

/**
 * Internal use only
 *
 * @author mbechler
 *
 * <p>This interface is intended for internal use.</p>
 */
public enum RequestParam {

    /**
     * No special request parameters.
     */
    NONE,

    /**
     * Wait indefinitely for a response
     */
    NO_TIMEOUT,

    /**
     * Do not retry request on failure
     */
    NO_RETRY,

    /**

        try {
            return CommonsCliUpgradeOptions.parse(context.parserRequest.args().toArray(new String[0]));
        } catch (ParseException e) {
            throw new IllegalArgumentException("Failed to parse command line options: " + e.getMessage(), e);
        }
    }

app = FastAPI()

security = HTTPBearer(auto_error=False)


@app.get("/users/me")
def read_current_user(
    credentials: Optional[HTTPAuthorizationCredentials] = Security(security),
):
    if credentials is None:
        return {"msg": "Create an account first"}
    return {"scheme": credentials.scheme, "credentials": credentials.credentials}


client = TestClient(app)


def test_security_http_bearer():

app = FastAPI()

oauth2_scheme = OAuth2PasswordBearer(tokenUrl="/token", auto_error=False)


@app.get("/items/")
async def read_items(token: Optional[str] = Security(oauth2_scheme)):
    if token is None:
        return {"msg": "Create an account first"}
    return {"token": token}


client = TestClient(app)


def test_no_token():
    response = client.get("/items")

/// info

Keep in mind that dataclasses can't do everything Pydantic models can do.

So, you might still need to use Pydantic models.

But if you have a bunch of dataclasses laying around, this is a nice trick to use them to power a web API using FastAPI. 🤓

///

## Dataclasses in `response_model` { #dataclasses-in-response-model }

You can also use `dataclasses` in the `response_model` parameter:

### 1. Prerequisites

- Install MinIO - [MinIO Quickstart Guide](https://docs.min.io/community/minio-object-store/operations/deployments/baremetal-deploy-minio-on-redhat-linux.html)
- Install `awscli` - [Installing AWS Command Line Interface](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-install.html)

### 2. Set bucket WORM configuration

Isso significa que, se você quiser atualizar o item `bar` usando `PUT` com um corpo contendo:

```Python
{
    "name": "Barz",
    "price": 3,
    "description": None,
}
```

Como ele não inclui o atributo já armazenado `"tax": 20.2`, o modelo de entrada assumiria o valor padrão de `"tax": 10.5`.

E os dados seriam salvos com esse "novo" `tax` de `10.5`.

        new LineProcessor<List<String>>() {
          final List<String> collector = new ArrayList<>();

          @Override
          public boolean processLine(String line) {
            collector.add(whitespace().trimFrom(line));
            return true;
          }

          @Override
          public List<String> getResult() {
            return collector;
          }
        };

- Source location from where the objects must be copied from
- Target location from where the objects must be copied to
- Fine grained filtering is available to pick relevant objects from source to copy from

MinIO batch jobs framework also provides

- Retrying a failed job automatically driven by user input
- Monitoring job progress in real-time