import java.io.IOException;
import java.io.InputStream;
import java.util.Random;

/** Returns a random portion of the requested bytes on each call. */
class RandomAmountInputStream extends FilterInputStream {
  private final Random random;

  public RandomAmountInputStream(InputStream in, Random random) {
    super(checkNotNull(in));
    this.random = checkNotNull(random);
  }

  @Override

  // tweak this to control hit rate
  @Param("2.5")
  double concentration;

  Random random = new Random();

  LoadingCache<Integer, Integer> cache;

  int max;

  static AtomicLong requests = new AtomicLong(0);
  static AtomicLong misses = new AtomicLong(0);

  @BeforeExperiment
  void setUp() {
    // random integers will be generated in this range, then raised to the

    assertThat(request.tag(UUID::class.java)).isSameAs(uuidTag)
    assertThat(request.tag<UUID>()).isSameAs(uuidTag)
  }

  @Test
  fun replaceOnlyTag() {
    val uuidTag1 = UUID.randomUUID()
    val uuidTag2 = UUID.randomUUID()
    val request =
      Request.Builder()
        .url("https://square.com")
        .tag(UUID::class.java, uuidTag1)
        .tag(UUID::class.java, uuidTag2)
        .build()

  @Param({"20", "2000"})
  int size;

  @Param({"2", "20"})
  int nonAlphaRatio; // one non-alpha char per this many chars

  @Param boolean noWorkToDo;

  Random random;
  String testString;

  @BeforeExperiment
  void setUp() {
    random = new Random(0xdeadbeef); // fix the seed so results are comparable across runs

    int nonAlpha = size / nonAlphaRatio;
    int alpha = size - nonAlpha;

        }
        return root;
      }
    },
    RANDOM {
      /**
       * Generates a tree with topology selected uniformly at random from the topologies of binary
       * trees of the specified size.
       */
      @Override
      Optional<BinaryNode> createTree(int size, Random rng) {
        int[] keys = new int[size];
        for (int i = 0; i < size; i++) {

import java.math.BigInteger;
import java.util.Random;

/**
 * Utilities for benchmarks.
 *
 * <p>In many cases, we wish to vary the order of magnitude of the input as much as we want to vary
 * the input itself, so most methods which generate values use an exponential distribution varying
 * the order of magnitude of the generated values uniformly at random.
 *
 * @author Louis Wasserman
 */

    treeMultiset = TreeMultiset.create();

    Random random = new Random();

    int sizeRemaining = size;

    // TODO(kevinb): generate better test contents for multisets
    while (sizeRemaining > 0) {
      // The JVM will return interned values for small ints.
      Integer value = random.nextInt(1000) + 128;
      int count = min(random.nextInt(10) + 1, sizeRemaining);
      sizeRemaining -= count;

    treeMultiset = TreeMultiset.create();

    Random random = new Random();

    int sizeRemaining = size;

    // TODO(kevinb): generate better test contents for multisets
    while (sizeRemaining > 0) {
      // The JVM will return interned values for small ints.
      Integer value = random.nextInt(1000) + 128;
      int count = min(random.nextInt(10) + 1, sizeRemaining);
      sizeRemaining -= count;

  private Queue<Integer> queue;

  private final Random random = new Random();

  @BeforeExperiment
  void setUp() {
    queue = heap.create(comparator.get());
    for (int i = 0; i < size; i++) {
      queue.add(random.nextInt());
    }
  }

  @Benchmark
  void pollAndAdd(int reps) {
    for (int i = 0; i < reps; i++) {
      // TODO(kevinb): precompute random #s?
      queue.add(queue.poll() ^ random.nextInt());

  int datasetSize;

  @Param QuantilesAlgorithm algorithm;

  private double[][] datasets = new double[0x100][];

  @BeforeExperiment
  void setUp() {
    Random rng = new Random();
    for (int i = 0; i < 0x100; i++) {
      datasets[i] = new double[datasetSize];
      for (int j = 0; j < datasetSize; j++) {
        datasets[i][j] = rng.nextDouble();
      }
    }
  }