* href="http://llimllib.github.io/bloomfilter-tutorial/">tutorial</a> may help you understand how
 * they work.
 *
 * <p>The false positive probability ({@code FPP}) of a Bloom filter is defined as the probability
 * that {@linkplain #mightContain(Object)} will erroneously return {@code true} for an object that
 * has not actually been put in the {@code BloomFilter}.
 *

                  + count
                  + " trials.");
        }
      }
    }
  }

  /**
   * Test for avalanche. Avalanche means that output bits differ with roughly 1/2 probability on
   * different input keys. This test verifies that each possible 1-bit key delta achieves avalanche.
   *
   * <p>For more information: http://burtleburtle.net/bob/hash/avalanche.html
   */

   * that minimizes the need for remapping as {@code buckets} grows. That is, {@code
   * consistentHash(h, n)} equals:
   *
   * <ul>
   *   <li>{@code n - 1}, with approximate probability {@code 1/n}
   *   <li>{@code consistentHash(h, n - 1)}, otherwise (probability {@code 1 - 1/n})
   * </ul>
   *
   * <p>This method is suitable for the common use case of dividing work among buckets that meet the
   * following conditions:
   *

 * striped.get(key1) != striped.get(key2)}; the elements might nevertheless be mapped to the same
 * lock. The lower the number of stripes, the higher the probability of this happening.
 *
 * <p>There are three flavors of this class: {@code Striped<Lock>}, {@code Striped<Semaphore>}, and
 * {@code Striped<ReadWriteLock>}. For each type, two implementations are offered: {@linkplain

   * saving space.
   */
  @VisibleForTesting static final double MAX_LOAD_FACTOR = 1.2;

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random
   * input.
   */
  @VisibleForTesting static final double HASH_FLOODING_FPP = 0.001;

  /**

// in case of SSE-S3. However, storing the ETag of an encrypted
// object in plaintext may reveal some information about the object.
// For example, two objects with the same ETag are identical with
// a very high probability.
//
// Therefore, an S3 implementation may encrypt an ETag before storing
// it. In this case, the stored ETag may not be a well-formed S3 ETag.
// For example, it can be larger due to a checksum added by authenticated

  public static <E extends @Nullable Object> CompactHashSet<E> createWithExpectedSize(
      int expectedSize) {
    return new CompactHashSet<>(expectedSize);
  }

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random
   * input.
   */
  @VisibleForTesting(
      )
  static final double HASH_FLOODING_FPP = 0.001;

  /**

              // number of expected insertions or don't run the test for so long.
              // Don't forget, the bloom filter slowly saturates over time and the
              // expected false positive probability goes up!
              assertThat(bloomFilter.expectedFpp()).isLessThan(safetyFalsePositiveRate);
            } while (stopwatch.elapsed(SECONDS) < 1);
          }
        };

  public static <E extends @Nullable Object> CompactHashSet<E> createWithExpectedSize(
      int expectedSize) {
    return new CompactHashSet<>(expectedSize);
  }

  /**
   * Maximum allowed false positive probability of detecting a hash flooding attack given random
   * input.
   */
  @VisibleForTesting(
      )
  static final double HASH_FLOODING_FPP = 0.001;

  /**

 * <p>The average time complexity of the computation is O(N) in the size of the dataset. There is a
 * worst case time complexity of O(N^2). You are extremely unlikely to hit this quadratic case on
 * randomly ordered data (the probability decreases faster than exponentially in N), but if you are
 * passing in unsanitized user data then a malicious user could force it. A light shuffle of the