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
 */

  @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++) {

    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();
      }
    }
  }

  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();
      }
    }
  }

// initialization vector (IV) and the bucket/object path.
//
//  1. Encrypt:
//     Input: ClientKey, bucket, object, metadata, object_data
//     -              IV := Random({0,1}²⁵⁶)
//     -       ObjectKey := SHA256(ClientKey || Random({0,1}²⁵⁶))
//     -       KeyEncKey := HMAC-SHA256(ClientKey, IV || 'SSE-C' || 'DAREv2-HMAC-SHA256' || bucket || '/' || object)
//     -       SealedKey := DAREv2_Enc(KeyEncKey, ObjectKey)

    sink.assertBytes(new byte[] {1, 2, 0, 0}); // padded with zeros
  }

  public void testString() {
    Random random = new Random();
    for (int i = 0; i < 100; i++) {
      byte[] bytes = new byte[64];
      random.nextBytes(bytes);
      String s = new String(bytes, UTF_16LE); // so all random strings are valid
      assertEquals(
          new Sink(4).putUnencodedChars(s).hash(),