) : EventListener() {
  val eventSequence: Deque<CallEvent> = ConcurrentLinkedDeque()

  private val forbiddenLocks = mutableListOf<Any>()

  /** The timestamp of the last taken event, used to measure elapsed time between events. */
  private var lastTimestampNs: Long? = null

  /** Confirm that the thread does not hold a lock on `lock` during the callback. */
  fun forbidLock(lock: Any) {
    forbiddenLocks.add(lock)

          if ((hash1 & (1 << k)) == (hash2 & (1 << k))) {
            same[k] += 1;
          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        Assert.assertEquals(0.50d, prob, epsilon);
      }

   * directly. Doing this for {@link com.google.common.net.PercentEscaper} more than doubled the
   * performance for unescaped strings (as measured by {@code CharEscapersBenchmark}).
   *
   * @param string the literal string to be escaped
   * @return the escaped form of {@code string}
   * @throws NullPointerException if {@code string} is null

   * @param offset the offset into the array at which to start
   * @return the value found in the array in the form of a long
   */
  static int load32(byte[] source, int offset) {
    // TODO(user): Measure the benefit of delegating this to LittleEndianBytes also.
    return (source[offset] & 0xFF)
        | ((source[offset + 1] & 0xFF) << 8)
        | ((source[offset + 2] & 0xFF) << 16)

          if ((hash1 & (1 << k)) == (hash2 & (1 << k))) {
            same[k] += 1;
          } else {
            diff[k] += 1;
          }
        }
      }
      // measure probability and assert it's within margin of error
      for (int j = 0; j < hashBits; j++) {
        double prob = (double) diff[j] / (double) (diff[j] + same[j]);
        Assert.assertEquals(0.50d, prob, epsilon);
      }

 * <p>A high-quality hash function strives for some subset of the following virtues:
 *
 * <ul>
 *   <li><b>collision-resistant:</b> while the definition above requires making at least <i>some</i>
 *       token attempt, one measure of the quality of a hash function is <i>how well</i> it succeeds
 *       at this goal. Important note: it may be easy to achieve the theoretical minimum collision

 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@code WeakReference}
 *   <li>values automatically wrapped in {@code WeakReference} or {@code SoftReference}

 * may cause corruption or runtime errors if not. It may however be shared amongst multiple OkHttpClient
 * instances.
 *
 * ## Cache Optimization
 *
 * To measure cache effectiveness, this class tracks three statistics:
 *
 *  * **[Request Count:][requestCount]** the number of HTTP requests issued since this cache was
 *    created.

   * underlying model is a continuous function mapping storedPermits (from 0.0 to maxStoredPermits)
   * onto the 1/rate (i.e. intervals) that is effective at the given storedPermits. "storedPermits"
   * essentially measure unused time; we spend unused time buying/storing permits. Rate is
   * "permits / time", thus "1 / rate = time / permits". Thus, "1/rate" (time / permits) times

 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@code WeakReference}
 *   <li>values automatically wrapped in {@code WeakReference} or {@code SoftReference}