import java.io.InputStream;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

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

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

  @Override

      String unused = interner.intern(Double.toHexString(Math.random()));
    }
    return reps;
  }

  @CanIgnoreReturnValue
  @Benchmark
  int strongInterner(int reps) {
    Interner<String> interner = Interners.newStrongInterner();
    for (int i = 0; i < reps; i++) {
      String unused = interner.intern(Double.toHexString(Math.random()));
    }
    return reps;
  }

  @CanIgnoreReturnValue

@NullUnmarked
public class AtomicLongMapTest extends TestCase {
  private static final int ITERATIONS = 100;
  private static final int MAX_ADDEND = 100;

  private final Random random = new Random(301);

  @J2ktIncompatible
  @GwtIncompatible // NullPointerTester
  public void testNulls() {
    NullPointerTester tester = new NullPointerTester();
    tester.testAllPublicConstructors(AtomicLongMap.class);

import java.math.BigInteger;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * 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
 */

    long seed = new Random().nextLong();
    Random random = new Random(seed);
    insertRandomly(elements, q, random);
    return seed;
  }

  private static void insertRandomly(
      ArrayList<Integer> elements, MinMaxPriorityQueue<Integer> q, Random random) {
    while (!elements.isEmpty()) {
      int selectedIndex = random.nextInt(elements.size());

import com.google.caliper.Benchmark;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * Benchmarks for certain methods of {@code UnsignedLongs}.
 *
 * @author Eamonn McManus
 */
@NullUnmarked
public class UnsignedLongsBenchmark {
  private static final int ARRAY_SIZE = 0x10000;
  private static final int ARRAY_MASK = 0x0ffff;
  private static final Random randomSource = new Random(314159265358979L);

  // 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

  @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);

import java.math.BigInteger;
import java.util.Random;
import org.jspecify.annotations.NullUnmarked;

/**
 * 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
 */

  }

  @Test
  public void undirectedGraph() {
    testGraphMutation(GraphBuilder.undirected());
  }

  private static void testGraphMutation(GraphBuilder<? super Integer> graphBuilder) {
    Random gen = new Random(42); // Fixed seed so test results are deterministic.

    for (int trial = 0; trial < NUM_TRIALS; ++trial) {
      MutableGraph<Integer> graph = graphBuilder.allowsSelfLoops(true).build();