/**
 * {@code ObjectCountLinkedHashMap} is a subclass of {@code ObjectCountHashMap} with insertion
 * iteration order, and uses arrays to store key objects and count values. Comparing to using a
 * traditional {@code LinkedHashMap} implementation which stores keys and count values as map
 * entries, {@code ObjectCountLinkedHashMap} minimizes object allocation and reduces memory
 * footprint.
 */

 * <td>2.00
 * <td>N/A
 * <td>Traditional hexadecimal. Defaults to upper case.
 * <tr>
 * <td>{@link #base32()}
 * <td>A-Z 2-7
 * <td>1.60
 * <td>=
 * <td>Human-readable; no possibility of mixing up 0/O or 1/I. Defaults to upper case.
 * <tr>
 * <td>{@link #base32Hex()}
 * <td>0-9 A-V
 * <td>1.60
 * <td>=
 * <td>"Numerical" base 32; extended from the traditional hex alphabet. Defaults to upper case.
 * <tr>

   *   <li>{@code ImmutableList.of(2, "A")}
   *   <li>{@code ImmutableList.of(2, "B")}
   *   <li>{@code ImmutableList.of(2, "C")}
   * </ul>
   *
   * <p>The result is guaranteed to be in the "traditional", lexicographical order for Cartesian
   * products that you would get from nesting for loops:
   *
   * <pre>{@code
   * for (B b0 : lists.get(0)) {
   *   for (B b1 : lists.get(1)) {
   *     ...

 *
 * <p>While {@code ReadWriteLock} instances have different properties and can form cycles without
 * potential deadlock, this class treats {@code ReadWriteLock} instances as equivalent to
 * traditional exclusive locks. Although this increases the false positives that the locks detect
 * (i.e. cycles that will not actually result in deadlock), it simplifies the algorithm and

 * <p>The EventBus allows publish-subscribe-style communication between components without requiring
 * the components to explicitly register with one another (and thus be aware of each other). It is
 * designed exclusively to replace traditional Java in-process event distribution using explicit
 * registration. It is not a general-purpose publish-subscribe system, nor is it intended
 * for interprocess communication.
 *
 * <h2>Receiving Events</h2>
 *

import javax.annotation.CheckForNull;
import org.checkerframework.checker.nullness.qual.Nullable;

/**
 * {@code ObjectCountHashMap} uses arrays to store key objects and count values. Comparing to using
 * a traditional {@code HashMap} implementation which stores keys and count values as map entries,
 * {@code ObjectCountHashMap} minimizes object allocation and reduces memory footprint.
 *

   * interrupted.
   *
   * Imagine a monitor with multiple guards. A thread enters the monitor, satisfies all the guards,
   * and leaves, calling signalNextWaiter. With traditional locks and conditions, all the conditions
   * need to be signalled because it is not known which if any of them have waiters (and hasWaiters

 * href="http://portal.acm.org/citation.cfm?id=6621">min-max heap</a> developed by Atkinson, et al.
 * Unlike many other double-ended priority queues, it stores elements in a single array, as compact
 * as the traditional heap data structure used in {@link PriorityQueue}.
 *
 * <p>This class is not thread-safe, and does not accept null elements.
 *
 * <p><i>Performance notes:</i>
 *
 * <ul>

   *   <li>{@code ImmutableList.of(2, "A")}
   *   <li>{@code ImmutableList.of(2, "B")}
   *   <li>{@code ImmutableList.of(2, "C")}
   * </ul>
   *
   * <p>The result is guaranteed to be in the "traditional", lexicographical order for Cartesian
   * products that you would get from nesting for loops:
   *
   * <pre>{@code
   * for (B b0 : sets.get(0)) {
   *   for (B b1 : sets.get(1)) {
   *     ...

import java.util.concurrent.atomic.AtomicLong;
import javax.annotation.CheckForNull;

/**
 * A map containing {@code long} values that can be atomically updated. While writes to a
 * traditional {@code Map} rely on {@code put(K, V)}, the typical mechanism for writing to this map
 * is {@code addAndGet(K, long)}, which adds a {@code long} to the value currently associated with