* 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
   */

    } else {
      return new BigInteger(digits, RANDOM_SOURCE).setBit(digits);
    }
  }

  /**
   * Equivalent to calling randomPositiveBigInteger(numBits) and then flipping the sign with 50%
   * probability.
   */
  static BigInteger randomNonZeroBigInteger(int numBits) {
    BigInteger result = randomPositiveBigInteger(numBits);
    return RANDOM_SOURCE.nextBoolean() ? result : result.negate();
  }

  /**

   * 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:
   *

    } else {
      return new BigInteger(digits, RANDOM_SOURCE).setBit(digits);
    }
  }

  /**
   * Equivalent to calling randomPositiveBigInteger(numBits) and then flipping the sign with 50%
   * probability.
   */
  static BigInteger randomNonZeroBigInteger(int numBits) {
    BigInteger result = randomPositiveBigInteger(numBits);
    return RANDOM_SOURCE.nextBoolean() ? result : result.negate();
  }

  /**

 * 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

	a.doinit()
	b.doinit()
	C.mpz_gcdext(&d.i[0], &x.i[0], &y.i[0], &a.i[0], &b.i[0])
}

// ProbablyPrime performs n Miller-Rabin tests to check whether z is prime.
// If it returns true, z is prime with probability 1 - 1/4^n.
// If it returns false, z is not prime.
func (z *Int) ProbablyPrime(n int) bool {
	z.doinit()
	return int(C.mpz_probab_prime_p(&z.i[0], C.int(n))) > 0

   * 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;

  /**