*       <ul>
 *         <li>2 levels: average of 64ns per lock()/unlock()
 *         <li>3 levels: average of 77ns per lock()/unlock()
 *         <li>4 levels: average of 99ns per lock()/unlock()
 *         <li>5 levels: average of 103ns per lock()/unlock()
 *         <li>10 levels: average of 184ns per lock()/unlock()
 *         <li>20 levels: average of 393ns per lock()/unlock()
 *       </ul>
 * </ul>
 *

      assertEquivalent(stats.mean(), stream(array).average().getAsDouble());
      assertEquivalent(stats.sum(), stream(array).sum());
      assertEquivalent(stats.max(), stream(array).max().getAsDouble());
      assertEquivalent(stats.min(), stream(array).min().getAsDouble());
      // static method on Stats vs on instance method on DoubleStream
      assertEquivalent(Stats.meanOf(array), stream(array).average().getAsDouble());

   * numbers of keys and values without rehashing.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */
  public static <K extends @Nullable Object, V extends @Nullable Object>

   * MultimapBuilder.hashKeys(expectedKeys).arrayListValues(expectedValuesPerKey).build()}.
   *
   * @param expectedKeys the expected number of distinct keys
   * @param expectedValuesPerKey the expected average number of values per key
   * @throws IllegalArgumentException if {@code expectedKeys} or {@code expectedValuesPerKey} is
   *     negative
   */
  public static <K extends @Nullable Object, V extends @Nullable Object>

    static final int MAX_RUN_MULTIPLIER = 13;

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *
     * <p>The online hash flooding detecting in RegularSetBuilderImpl.add can detect e.g. many
     * exactly matching hash codes, which would cause construction to take O(n^2), but can't detect

     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This
     * works well for hash tables since the bin lists tend to be short. (The average length is less
     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to

 * add(x)} operations <i>do not</i> create objects for the garbage collector to deal with, and for
 * every element added, the garbage collector will have to traverse {@code 1.5} references on
 * average, in the marking phase, not {@code 5.0} as in {@code java.util.HashSet}.
 *
 * <p>If there are no removals, then {@link #iterator iteration} order is the same as insertion
 * order. Any removal invalidates any ordering guarantees.

     * traverse. When nodes would otherwise be changed, new nodes are created to replace them. This
     * works well for hash tables since the bin lists tend to be short. (The average length is less
     * than two.)
     *
     * Read operations can thus proceed without locking, but rely on selected uses of volatiles to

    // twice as many reads and writes. But benchmarking shows that they usually perform better than
    // Dolphin. Reversal is about as good as Successive on average, and it is much simpler,
    // especially since we already have a `reverse` method.
    checkNotNull(array);
    checkPositionIndexes(fromIndex, toIndex, array.length);
    if (array.length <= 1) {
      return;

import java.io.Serializable;
import java.nio.ByteBuffer;
import java.nio.ByteOrder;
import java.util.Iterator;
import javax.annotation.CheckForNull;

/**
 * A bundle of statistical summary values -- sum, count, mean/average, min and max, and several
 * forms of variance -- that were computed from a single set of zero or more floating-point values.
 *
 * <p>There are two ways to obtain a {@code Stats} instance:
 *
 * <ul>