final String converted = URLEncoder.encode(target, Constants.UTF_8);
                    if (target.equals(converted)) {
                        encodedBuf.append(Base64.getUrlEncoder().encodeToString(target.getBytes(Constants.CHARSET_UTF_8)));
                    } else {
                        encodedBuf.append(converted);
                    }

    this.min = min;
    this.max = max;
  }

  /**
   * Returns statistics over a dataset containing the given values.
   *
   * @param values a series of values, which will be converted to {@code double} values (this may
   *     cause loss of precision)
   */
  public static Stats of(Iterable<? extends Number> values) {
    StatsAccumulator accumulator = new StatsAccumulator();

#### Hashing de senha { #password-hashing }

"Hashing" significa: converter algum conteúdo (uma senha neste caso) em uma sequência de bytes (apenas uma string) que parece algo sem sentido.

Sempre que você passa exatamente o mesmo conteúdo (exatamente a mesma senha), você obtém exatamente a mesma sequência aleatória de caracteres.

Mas você não pode converter a sequência aleatória de caracteres de volta para a senha.

a `response_class` with a JSON media type (`application/json`), like is the case with the `JSONResponse`, the data you return will be automatically converted (and filtered) with any Pydantic `response_model` that you declared in the *path operation decorator*. But the data won't be serialized to JSON bytes with Pydantic, instead it will be converted with the `jsonable_encoder` and then passed to the `JSONResponse` class, which will serialize it to bytes using the standard JSON library in Python....

You couldn't get this kind of editor support if you were working directly with `dict` instead of Pydantic models.

But you don't have to worry about them either, incoming dicts are converted automatically and your output is converted automatically to JSON too.

## Bodies of arbitrary `dict`s { #bodies-of-arbitrary-dicts }

You can also declare a body as a `dict` with keys of some type and values of some other type.

 * similar to the way Maven configures plugins from the POM, i.e. some configuration like {@code <param>value</param>}
 * is mapped to an equally named property of the bean and converted. The properties of the bean are supposed to either
 * have a public setter or be backed by an equally named field (of any visibility).
 *
 * @since 3.0
 */
public interface BeanConfigurator {

    /**

///

#### Utiliser des `list` au lieu de `set` { #using-lists-instead-of-sets }

Si vous oubliez d'utiliser un `set` et utilisez une `list` ou un `tuple` à la place, FastAPI le convertira quand même en `set` et cela fonctionnera correctement :

{* ../../docs_src/response_model/tutorial006_py310.py hl[29,35] *}

## Récapitulatif { #recap }

 */
@Deprecated
public interface ReportingConverter {

    /**
     * Converts values from model's reporting section into the configuration for Maven Site Plugin 3.x.
     *
     * @param model The model whose reporting section should be converted, must not be <code>null</code>.
     * @param request The model building request that holds further settings, must not be {@code null}.

     * shared between many doc files.
     */
    @Input
    Map<String, String> teardowns = new HashMap()

    /**
     * A list of files that contain snippets that *probably* should be
     * converted to `// CONSOLE` but have yet to be converted. If a file is in
     * this list and doesn't contain unconverted snippets this task will fail.
     * If there are unconverted snippets not in this list then this task will

For example, you cannot put a Pydantic model in a `JSONResponse` without first converting it to a `dict` with all the data types (like `datetime`, `UUID`, etc) converted to JSON-compatible types.

For those cases, you can use the `jsonable_encoder` to convert your data before passing it to a response:

{* ../../docs_src/response_directly/tutorial001_py310.py hl[5:6,20:21] *}