*   <li>size: The length of the byte array to hash.
 * </ul>
 *
 * @author Colin Decker
 */
public class ChecksumBenchmark {

  // Use a constant seed for all of the benchmarks to ensure apples to apples comparisons.
  private static final int RANDOM_SEED = new Random().nextInt();

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {

 *   <li>size: The length of the byte array to hash.
 * </ul>
 *
 * @author Colin Decker
 */
public class ChecksumBenchmark {

  // Use a constant seed for all of the benchmarks to ensure apples to apples comparisons.
  private static final int RANDOM_SEED = new Random().nextInt();

  @Param({"10", "1000", "100000", "1000000"})
  private int size;

  private byte[] testBytes;

  @BeforeExperiment
  void setUp() {

 *
 * <p><b>Note:</b> by default, the returned cache uses equality comparisons (the {@link
 * Object#equals equals} method) to determine equality for keys or values. However, if {@link
 * #weakKeys} was specified, the cache uses identity ({@code ==}) comparisons instead for keys.
 * Likewise, if {@link #weakValues} or {@link #softValues} was specified, the cache uses identity
 * comparisons for values.
 *

            // Just returning an Integer with the index here is faster, but requires a lot of if/then/else to check for
            // -1
            //  or QUALIFIERS.size and then resort to lexical ordering. Most comparisons are decided by the first
            // character,
            // so this is still fast. If more characters are needed then it requires a lexical sort anyway.

      int permutations = 1;
      int n = 1;
      int r = 1;
      while (n < sortedInputList.size()) {
        int comparison = comparator.compare(sortedInputList.get(n - 1), sortedInputList.get(n));
        if (comparison < 0) {
          // We move to the next non-repeated element.
          permutations = IntMath.saturatedMultiply(permutations, IntMath.binomial(n, r));
          r = 0;

		// a less or equal b
	}
	if bytes.Compare(a, b) > 0 {
		// a greater b
	}
	if bytes.Compare(a, b) >= 0 {
		// a greater or equal b
	}

	// Prefer Equal to Compare for equality comparisons.
	if bytes.Equal(a, b) {
		// a equal b
	}
	if !bytes.Equal(a, b) {
		// a not equal b
	}
}

func ExampleCompare_search() {
	// Binary search to find a matching byte slice.

 *
 * <p><b>Note:</b> by default, the returned cache uses equality comparisons (the {@link
 * Object#equals equals} method) to determine equality for keys or values. However, if {@link
 * #weakKeys} was specified, the cache uses identity ({@code ==}) comparisons instead for keys.
 * Likewise, if {@link #weakValues} or {@link #softValues} was specified, the cache uses identity
 * comparisons for values.
 *

   * generated using this are byte-wise identical to those created using the C++ version, but note
   * that this uses unsigned integers (see {@link com.google.common.primitives.UnsignedInts}).
   * Comparisons between the two should take this into account.
   *
   * <p>Fingerprint2011() is a form of Murmur2 on strings up to 32 bytes and a form of CityHash for

      int permutations = 1;
      int n = 1;
      int r = 1;
      while (n < sortedInputList.size()) {
        int comparison = comparator.compare(sortedInputList.get(n - 1), sortedInputList.get(n));
        if (comparison < 0) {
          // We move to the next non-repeated element.
          permutations = IntMath.saturatedMultiply(permutations, IntMath.binomial(n, r));
          r = 0;

    // Since the last element of the heap is from the bottom level, we
    // optimistically fill index position with elements from lower levels,
    // moving the hole down. In most cases this reduces the number of
    // comparisons with toTrickle, but in some cases we will need to bubble it
    // all the way up again.
    int vacated = heap.fillHoleAt(index);
    // Try to see if toTrickle can be bubbled up min levels.