* offering expected O(n + k log k) performance (worst case O(n log k)) for n calls to {@link
 * #offer} and a call to {@link #topK}, with O(k) memory. In comparison, quickselect has the same
 * asymptotics but requires O(n) memory, and a {@code PriorityQueue} implementation takes O(n log
 * k). In benchmarks, this implementation performs at least as well as either implementation, and
 * degrades more gracefully for worst-case input.
 *

   *     .collect(least(2, comparingInt(String::length)))
   * // returns {"foo", "quux"}
   * }</pre>
   *
   * <p>This {@code Collector} uses O(k) memory and takes expected time O(n) (worst-case O(n log
   * k)), as opposed to e.g. {@code Stream.sorted(comparator).limit(k)}, which currently takes O(n
   * log n) time and O(n) space.
   *
   * @throws IllegalArgumentException if {@code k < 0}
   * @since 22.0

   *     .collect(least(2, comparingInt(String::length)))
   * // returns {"foo", "quux"}
   * }</pre>
   *
   * <p>This {@code Collector} uses O(k) memory and takes expected time O(n) (worst-case O(n log
   * k)), as opposed to e.g. {@code Stream.sorted(comparator).limit(k)}, which currently takes O(n
   * log n) time and O(n) space.
   *
   * @throws IllegalArgumentException if {@code k < 0}
   */

 * dataset contains no finite values).
 *
 * <h3>Performance</h3>
 *
 * <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

    // Raising this number slows the worst-case contains behavior, speeds up hashFloodingDetected,
    // and reduces the false-positive probability.
    static final int MAX_RUN_MULTIPLIER = 13;

    /**
     * Checks the whole hash table for poor hash distribution. Takes O(n) in the worst case, O(n /
     * log n) on average.
     *

   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is
   *       detected, sacrificing the compactness guarantee in very rare cases in exchange for much
   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  @CheckForNull private transient Object table;

  /**

   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is
   *       detected, sacrificing the compactness guarantee in very rare cases in exchange for much
   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  @CheckForNull private transient Object table;

  /**

    checkNotNull(seq);

    // length to truncate the sequence to, not including the truncation indicator
    int truncationLength = maxLength - truncationIndicator.length();

    // in this worst case, this allows a maxLength equal to the length of the truncationIndicator,
    // meaning that a string will be truncated to just the truncation indicator itself
    checkArgument(
        truncationLength >= 0,

    checkNotNull(seq);

    // length to truncate the sequence to, not including the truncation indicator
    int truncationLength = maxLength - truncationIndicator.length();

    // in this worst case, this allows a maxLength equal to the length of the truncationIndicator,
    // meaning that a string will be truncated to just the truncation indicator itself
    checkArgument(
        truncationLength >= 0,

   *       simply switch over to use the JDK implementation wholesale if probable hash flooding is
   *       detected, sacrificing the compactness guarantee in very rare cases in exchange for much
   *       more reliable worst-case behavior.
   *   <li>null, if no entries have yet been added to the map
   * </ul>
   */
  @CheckForNull private transient Object table;

  /**