* href="http://gee.cs.oswego.edu/dl/html/StreamParallelGuidance.html">efficiently splittable</a>
   * if and only if {@code stream} was efficiently splittable and its underlying spliterator
   * reported {@link Spliterator#SUBSIZED}. This is generally the case if the underlying stream
   * comes from a data structure supporting efficient indexed random access, typically an array or
   * list.
   *

 * scopes and their associated implied scopes, so that the user can filter apply a series of implication rules in a
 * single step. This should be a more efficient implementation of multiple standard {@link ScopeArtifactFilter}
 * instances ORed together.
 *
 */
public class CumulativeScopeArtifactFilter extends AbstractScopeArtifactFilter {

    private Set<String> scopes;

            : ImmutableSet.copyOf(ImmutableList.sortedCopyOf(columnComparator, columnSpaceBuilder));

    return forOrderedComponents(cellList, rowSpace, columnSpace);
  }

  /** A factory that chooses the most space-efficient representation of the table. */
  static <R, C, V> RegularImmutableTable<R, C, V> forOrderedComponents(
      ImmutableList<Cell<R, C, V>> cellList,
      ImmutableSet<R> rowSpace,
      ImmutableSet<C> columnSpace) {

   * href="http://gee.cs.oswego.edu/dl/html/StreamParallelGuidance.html">efficiently splittable</a>
   * if and only if {@code stream} was efficiently splittable and its underlying spliterator
   * reported {@link Spliterator#SUBSIZED}. This is generally the case if the underlying stream
   * comes from a data structure supporting efficient indexed random access, typically an array or
   * list.
   *

</a>

---

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     */
    long deadline = System.nanoTime() + unit.toNanos(timeout);
    int added = 0;
    while (added < numElements) {
      // we could rely solely on #poll, but #drainTo might be more efficient when there are multiple
      // elements already available (e.g. LinkedBlockingQueue#drainTo locks only once)
      added += q.drainTo(buffer, numElements - added);

  }

  /*
   * Not subclassing AbstractMultiset$MultisetIterator because next() needs to
   * retrieve the Map.Entry<E, Count> entry, which can then be used for
   * a more efficient remove() call.
   */
  private class MapBasedMultisetIterator implements Iterator<E> {
    final Iterator<Map.Entry<E, Count>> entryIterator;
    @CheckForNull Map.Entry<E, Count> currentEntry;
    int occurrencesLeft;

     */
    long deadline = System.nanoTime() + unit.toNanos(timeout);
    int added = 0;
    while (added < numElements) {
      // we could rely solely on #poll, but #drainTo might be more efficient when there are multiple
      // elements already available (e.g. LinkedBlockingQueue#drainTo locks only once)
      added += q.drainTo(buffer, numElements - added);

    reverse(array, fromIndex, toIndex);
  }

  /**
   * Reverses the elements of {@code array}. This is equivalent to {@code
   * Collections.reverse(Ints.asList(array))}, but is likely to be more efficient.
   *
   * @since 23.1
   */
  public static void reverse(int[] array) {
    checkNotNull(array);
    reverse(array, 0, array.length);
  }

  /**

 * Ordering#leastOf(Iterable, int)}, which provides the same implementation with an interface
 * tailored to that use case.
 *
 * <p>This uses the same efficient implementation as {@link Ordering#leastOf(Iterable, int)},
 * offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link