import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_POPULATION_VARIANCE;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_STATS;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_POPULATION_VARIANCE;

   * <p>Note that this function is necessarily well-defined for any discrete type.
   *
   * @return the distance as described above, or {@link Long#MIN_VALUE} or {@link Long#MAX_VALUE} if
   *     the distance is too small or too large, respectively.
   */
  public abstract long distance(C start, C end);

  /**
   * Returns the minimum value of type {@code C}, if it has one. The minimum value is the unique

    final int len = string.length();
    final long longSize = (long) len * (long) count;
    final int size = (int) longSize;
    if (size != longSize) {
      throw new ArrayIndexOutOfBoundsException("Required array size too large: " + longSize);
    }

    final char[] array = new char[size];
    string.getChars(0, len, array, 0);
    int n;
    for (n = len; n < size - n; n <<= 1) {
      System.arraycopy(array, 0, array, n, n);

   * method is resilient to overflow.
   *
   * @since 14.0
   */
  public static long mean(long x, long y) {
    // Efficient method for computing the arithmetic mean.
    // The alternative (x + y) / 2 fails for large values.
    // The alternative (x + y) >>> 1 fails for negative values.
    return (x & y) + ((x ^ y) >> 1);
  }

  /*
   * This bitmask is used as an optimization for cheaply testing for divisibility by 2, 3, or 5.

 * <p>The time taken to compute multiple quantiles on the same dataset using {@link Scale#indexes
 * indexes} is generally less than the total time taken to compute each of them separately, and
 * sometimes much less. For example, on a large enough dataset, computing the 90th and 99th
 * percentiles together takes about 55% as long as computing them separately.
 *
 * <p>When calling {@link ScaleAndIndex#compute} (in {@linkplain ScaleAndIndexes#compute either

   * for {@code minimumBits}, will return identically-behaving {@link HashFunction} instances.
   *
   * @param minimumBits a positive integer. This can be arbitrarily large. The returned {@link
   *     HashFunction} instance may use memory proportional to this integer.
   * @return a hash function, described above, that produces hash codes of length {@code
   *     minimumBits} or greater
   */

      }
    }
    Arrays.fill(contents, uniques, n, null);
    if (uniques < contents.length / 2) {
      // Deduplication eliminated many of the elements.  We don't want to retain an arbitrarily
      // large array relative to the number of elements, so we cap the ratio.
      contents = Arrays.copyOf(contents, uniques);
    }
    return new RegularImmutableSortedSet<E>(

  static final int[] LARGE_INTEGER_VALUES = {Integer.MAX_VALUE, Integer.MAX_VALUE / 2};
  static final double LARGE_INTEGER_VALUES_MEAN =
      BigInteger.valueOf(Integer.MAX_VALUE)
          .multiply(BigInteger.valueOf(3L))
          .divide(BigInteger.valueOf(4L))
          .doubleValue();
  static final double LARGE_INTEGER_VALUES_POPULATION_VARIANCE =

import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_POPULATION_VARIANCE;
import static com.google.common.math.StatsTesting.LARGE_INTEGER_VALUES_STATS;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_MEAN;
import static com.google.common.math.StatsTesting.LARGE_LONG_VALUES_POPULATION_VARIANCE;

   * modeled by "storedPermits" variable. This variable is zero when there is no underutilization,
   * and it can grow up to maxStoredPermits, for sufficiently large underutilization. So, the
   * requested permits, by an invocation acquire(permits), are served from:
   *
   * - stored permits (if available)
   *
   * - fresh permits (for any remaining permits)
   *