##### Warum Passwort-Hashing verwenden? { #why-use-password-hashing }

Wenn Ihre Datenbank gestohlen wird, hat der Dieb nicht die Klartext-Passwörter Ihrer Benutzer, sondern nur die Hashes.

Der Dieb kann also nicht versuchen, die gleichen Passwörter in einem anderen System zu verwenden (da viele Benutzer überall das gleiche Passwort verwenden, wäre dies gefährlich).

But you cannot convert from the gibberish back to the password.

##### Why use password hashing { #why-use-password-hashing }

If your database is stolen, the thief won't have your users' plaintext passwords, only the hashes.

So, the thief won't be able to try to use those same passwords in another system (as many users use the same password everywhere, this would be dangerous).

{* ../../docs_src/security/tutorial003_an_py310.py hl[82:85] *}

Primero pongamos esos datos en el modelo `UserInDB` de Pydantic.

Nunca deberías guardar passwords en texto plano, así que, usaremos el sistema de hash de passwords (falso).

Si los passwords no coinciden, devolvemos el mismo error.

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

"Hacer hash" significa: convertir algún contenido (un password en este caso) en una secuencia de bytes (solo un string) que parece un galimatías.

 *  **Okio dependency added.** OkHttp now depends on
    [Okio](https://github.com/square/okio), an I/O library that makes it easier
    to access, store and process data. Using this library internally makes OkHttp
    faster while consuming less memory. You can write a `RequestBody` as an Okio
    `BufferedSink` and a `ResponseBody` as an Okio `BufferedSource`. Standard

   * {@code checkedPow(2, log2(x, CEILING))}.
   *
   * @throws IllegalArgumentException if {@code x <= 0}
   * @throws ArithmeticException of the next-higher power of two is not representable as a {@code
   *     long}, i.e. when {@code x > 2^62}
   * @since 20.0
   */
  public static long ceilingPowerOfTwo(long x) {
    checkPositive("x", x);

 * artifact publishing by reducing the artifacts and packaging reports that consist of multiple files.
 *
 * Reducing the number of reports also makes it easier to find the important ones when analysing a failed build in
 * Team City.
 */

val testFilesCleanup = extensions.create<TestFileCleanUpExtension>("testFilesCleanup").apply {
    reportOnly.convention(false)
}

/**
 * @author Jesse Wilson
 */
final class Platform {
  static CharMatcher precomputeCharMatcher(CharMatcher matcher) {
    // CharMatcher.precomputed() produces CharMatchers that are maybe a little
    // faster (and that's debatable), but definitely more memory-hungry. We're
    // choosing to turn .precomputed() into a no-op in GWT, because it doesn't
    // seem to be a worthwhile tradeoff in a browser.
    return matcher;
  }

    return delegate.toArray(other);
  }

  @Override
  public String toString() {
    return delegate.toString();
  }

  // TODO(cpovirk): equals(), as well, in case it's any faster than Sets.equalsImpl?

  @Override
  public int hashCode() {
    return delegate.hashCode();
  }

	}

	if sipHashElement := hashKey("SIPMOD", "This will fail", 0, testUUID); sipHashElement != -1 {
		t.Errorf("Test: Expected \"-1\" but got \"%v\"", sipHashElement)
	}

	if sipHashElement := hashKey("UNKNOWN", "This will fail", 0, testUUID); sipHashElement != -1 {
		t.Errorf("Test: Expected \"-1\" but got \"%v\"", sipHashElement)
	}
}

// TestCrcHashMod - test crc hash.
func TestCrcHashMod(t *testing.T) {

  abstract ImmutableCollection<E> delegateCollection();

  @Override
  public boolean contains(@Nullable Object target) {
    // The collection's contains() is at least as fast as ImmutableList's
    // and is often faster.
    return delegateCollection().contains(target);
  }

  @Override
  public int size() {
    return delegateCollection().size();
  }

  @Override
  public boolean isEmpty() {