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(),

  // 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

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;

        }
      }
    },
    RANDOM {
      @Override
      void arrange(List<Integer> list) {}
    };

    abstract void arrange(List<Integer> list);
  }

  private ImmutableList<Integer> input;

  @BeforeExperiment
  void setUp() {
    checkArgument(size > 0, "empty collection not supported");
    Set<Integer> set = new LinkedHashSet<>(size);

    Random random = new Random();

      nonnegative[i] = randomNonNegativeBigInteger(Long.SIZE - 1).longValue();
      longs[i] = RANDOM_SOURCE.nextLong();
      factorialArguments[i] = RANDOM_SOURCE.nextInt(30);
      binomialArguments[i][1] = RANDOM_SOURCE.nextInt(MathBenchmarking.biggestBinomials.length);
      int k = binomialArguments[i][1];
      binomialArguments[i][0] = RANDOM_SOURCE.nextInt(MathBenchmarking.biggestBinomials[k] - k) + k;
    }
  }

  @Benchmark

  @BeforeExperiment
  void setUp() {
    Random random = new Random(randomSeed);
    xInts = new int[SAMPLE_SIZE];
    yInts = new int[SAMPLE_SIZE];
    xLongs = new long[SAMPLE_SIZE];
    yLongs = new long[SAMPLE_SIZE];
    constant = new int[SAMPLE_SIZE];
    for (int i = 0; i < SAMPLE_SIZE; i++) {
      xInts[i] = random.nextInt(Integer.MAX_VALUE);
      yInts[i] = random.nextInt(Integer.MAX_VALUE);

  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();
      }
    }
  }

  // Use a statically configured random instance for all of the benchmarks
  private static final Random random = new Random(42);

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

  @Param HashFunctionEnum hashFunctionEnum;

  private byte[] testBytes;

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

  @Benchmark

    treeMultiset = TreeMultiset.create();

    Random random = new Random();

    int sizeRemaining = size;

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

    val s = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
    for (i in s.indices) {
      assertRoundTrip(s.substring(0, i).encodeUtf8())
    }
    val random = Random(123456789L)
    val buf = ByteArray(4096)
    random.nextBytes(buf)
    assertRoundTrip(buf.toByteString())
  }

  private fun assertRoundTrip(data: ByteString) {
    val encodeBuffer = Buffer()
    encode(data, encodeBuffer)