*/
  public static int consistentHash(long input, int buckets) {
    checkArgument(buckets > 0, "buckets must be positive: %s", buckets);
    LinearCongruentialGenerator generator = new LinearCongruentialGenerator(input);
    int candidate = 0;
    int next;

    // Jump from bucket to bucket until we go out of range
    while (true) {

  transient int modCount;
  /** Maps a bucket to the "entry" of its first element. */
  private transient int[] hashTableKToV;
  /** Maps a bucket to the "entry" of its first element. */
  private transient int[] hashTableVToK;
  /** Maps an "entry" to the "entry" that follows it in its bucket. */
  private transient int[] nextInBucketKToV;
  /** Maps an "entry" to the "entry" that follows it in its bucket. */

          int bucket = valueEntry.smearedValueHash & mask;
          valueEntry.nextInValueBucket = hashTable[bucket];
          hashTable[bucket] = valueEntry;
        }
      }
    }

    @CanIgnoreReturnValue
    @Override
    public boolean remove(@CheckForNull Object o) {
      int smearedHash = Hashing.smearedHash(o);
      int bucket = smearedHash & mask();

 * amortized) constant time operations. Expected in the hashtable sense (depends on the hash
 * function doing a good job of distributing the elements to the buckets to a distribution not far
 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>As compared with {@link java.util.LinkedHashMap}, this structure places significantly reduced

 * constant time operations. Expected in the hashtable sense (depends on the hash function doing a
 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>This implementation consumes significantly less memory than {@code java.util.LinkedHashSet} or

    for (int i = 0; i < originalCount; i++) {
      Object key = new Object();
      Object value = new Object();
      int hash = map.hash(key);
      // chain all entries together as we only have a single bucket
      entry = segment.newEntryForTesting(key, hash, entry);
      segment.setValueForTesting(entry, value);
    }
    segment.setTableEntryForTesting(0, entry);
    segment.count = originalCount;

    for (int i = 0; i < originalCount; i++) {
      Object key = new Object();
      Object value = new Object();
      int hash = map.hash(key);
      // chain all entries together as we only have a single bucket
      entry = segment.newEntryForTesting(key, hash, entry);
      segment.setValueForTesting(entry, value);
    }
    segment.setTableEntryForTesting(0, entry);
    segment.count = originalCount;

    this.lastInKeyInsertionOrder = null;
    this.size = 0;
    this.mask = tableSize - 1;
    this.modCount = 0;
  }

  /**
   * Finds and removes {@code entry} from the bucket linked lists in both the key-to-value direction
   * and the value-to-key direction.
   */
  private void delete(BiEntry<K, V> entry) {
    int keyBucket = entry.keyHash & mask;
    BiEntry<K, V> prevBucketEntry = null;

 *       in the hash code should be as evenly "spread out" through the hash code's bits as possible.
 *       The result is that, for example, when choosing a bucket in a hash table of size 2^8,
 *       <i>any</i> eight bits could be consistently used.
 *   <li><b>cryptographic:</b> certain hash functions such as {@link Hashing#sha512} are designed to

    Map<ClassWithTerribleHashCode, Integer> expected = new LinkedHashMap<>();
    Builder<ClassWithTerribleHashCode, Integer> builder = new Builder<>();
    int size = RegularImmutableMap.MAX_HASH_BUCKET_LENGTH + 10;
    for (int i = 0; i < size; i++) {
      ClassWithTerribleHashCode key = new ClassWithTerribleHashCode(i);
      builder.put(key, i);
      builder.put(key, -i);
      expected.put(key, -i);
    }