int n;

  private final byte[][] encodingInputs = new byte[INPUTS_COUNT][];
  private final String[] decodingInputs = new String[INPUTS_COUNT];

  @BeforeExperiment
  public void setUp() {
    Random rng = new Random();
    for (int i = 0; i < encodingInputs.length; i++) {
      encodingInputs[i] = new byte[n];
      rng.nextBytes(encodingInputs[i]);
      decodingInputs[i] = encoding.encoding.encode(encodingInputs[i]);
    }

   * [acquire(5), acquire(1)] takes exactly the same time as [acquire(2), acquire(3), acquire(1)].
   */
  public void testTimeToWarmUpIsHonouredEvenWithWeights() {
    Random random = new Random();
    int warmupPermits = 10;
    double[] coldFactorsToTest = {2.0, 3.0, 10.0};
    double[] qpsToTest = {4.0, 2.0, 1.0, 0.5, 0.1};
    for (int trial = 0; trial < 100; trial++) {

   * [acquire(5), acquire(1)] takes exactly the same time as [acquire(2), acquire(3), acquire(1)].
   */
  public void testTimeToWarmUpIsHonouredEvenWithWeights() {
    Random random = new Random();
    int warmupPermits = 10;
    double[] coldFactorsToTest = {2.0, 3.0, 10.0};
    double[] qpsToTest = {4.0, 2.0, 1.0, 0.5, 0.1};
    for (int trial = 0; trial < 100; trial++) {

import java.util.Map;
import java.util.Random;
import java.util.Set;
import junit.framework.TestCase;

/**
 * Tests that the different algorithms benchmarked in {@link QuantilesBenchmark} are actually all
 * returning more-or-less the same answers.
 */
public class QuantilesAlgorithmTest extends TestCase {

  private static final Random RNG = new Random(82674067L);
  private static final int DATASET_SIZE = 1000;

```

////

Vamos focar nos parâmetros declarados:

* Mesmo que essa função seja uma dependência ("injetável") por si mesma, ela também declara uma outra dependência (ela "depende" de outra coisa).
    * Ela depende do `query_extractor`, e atribui o valor retornado pela função ao parâmetro `q`.
* Ela também declara um cookie opcional `last_query`, do tipo `str`.

    protected final Queue<Map<String, Object>> queue = new ConcurrentLinkedQueue<>();
    protected final AtomicBoolean isFinished = new AtomicBoolean(false);
    protected final Random random = new Random();

    protected final Client client;
    protected final SuggestSettings settings;
    protected final String indexName;
    protected final String[] supportedFields;

    protected int scrollSize = 1;

  private static final int RANDOM_SEED = new Random().nextInt();

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    new Random(RANDOM_SEED).nextBytes(testBytes);
  }

  // CRC32

  @Benchmark
  byte crc32HashFunction(int reps) {

  private static final int RANDOM_SEED = new Random().nextInt();

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {
    testBytes = new byte[size];
    new Random(RANDOM_SEED).nextBytes(testBytes);
  }

  // CRC32

  @Benchmark
  byte crc32HashFunction(int reps) {

   * class-unloading when ThreadLocals are not removed.
   */
  static final ThreadLocal<int @Nullable []> threadHashCode = new ThreadLocal<>();

  /** Generator of new random hash codes */
  static final Random rng = new Random();

  /** Number of CPUS, to place bound on table size */
  static final int NCPU = Runtime.getRuntime().availableProcessors();

  /** Table of cells. When non-null, size is a power of 2. */

    final int min;
    final int max;

    private ListSizeDistribution(int min, int max) {
      this.min = min;
      this.max = max;
    }

    public int chooseSize(Random random) {
      return random.nextInt(max - min + 1) + min;
    }
  }