*
 * <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.
 *

     *            The Type-2 message which this represents a response to.
     * @param targetName
     *            SPN of the target system, optional
     * @param passwordHash
     *            The NT password hash, takes precedence over password (which is no longer required unless legacy LM
     *            authentication is needed)
     * @param password
     *            The password to use when constructing the response.

    }
  }


  @J2ktIncompatible // slow enough to cause flakiness
  @GwtIncompatible // far too slow
  public void testSqrtOfPerfectSquareAsDoubleIsPerfect() {
    // This takes just over a minute on my machine.
    for (long n = 0; n <= LongMath.FLOOR_SQRT_MAX_LONG; n++) {
      long actual = (long) Math.sqrt((double) (n * n));
      assertTrue(actual == n);
    }
  }

    thread.setPriority(Thread.MAX_PRIORITY);
    Thread.class.getMethod("resume").invoke(thread);
    thread.join();
    // It's possible to race and suspend the thread just before the park call actually takes effect,
    // causing the thread to be suspended for 3.5 seconds, and then park itself for 2 seconds after
    // being resumed. To avoid a flake in this scenario, calculate how long that thread actually

	if took > maxDurationSecondsForLog {
		// Log if we took a lot of time to load.
		logger.Info("IAM expired STS purge took %.2fs", took)
	}

	// Store the newly populated map in the iam cache. This takes care of
	// removing stale entries from the existing map.
	cache.iamSTSAccountsMap = stsAccountsFromStore

	stsAccPoliciesFromStore.Range(func(k string, v MappedPolicy) bool {
		cache.iamSTSPolicyMap.Store(k, v)

  }

  @GwtIncompatible // Shorts.fromByteArray, Shorts.toByteArray
  public void testByteArrayRoundTrips() {
    Random r = new Random(5);
    byte[] b = new byte[Shorts.BYTES];

    // total overkill, but, it takes 0.1 sec so why not...
    for (int i = 0; i < 10000; i++) {
      short num = (short) r.nextInt();
      assertThat(Shorts.fromByteArray(Shorts.toByteArray(num))).isEqualTo(num);

      r.nextBytes(b);

   * elements are in the first {@code k} positions of {@code contents}, and {@code contents[i] ==
   * null} for {@code k <= i < n}.
   *
   * <p>This method takes ownership of {@code contents}; do not modify {@code contents} after this
   * returns.
   *
   * @throws NullPointerException if any of the first {@code n} elements of {@code contents} is null
   */

        .isEqualTo(0xFFEEDDCC);
  }

  public void testByteArrayRoundTrips() {
    Random r = new Random(5);
    byte[] b = new byte[Ints.BYTES];

    // total overkill, but, it takes 0.1 sec so why not...
    for (int i = 0; i < 10000; i++) {
      int num = r.nextInt();
      assertThat(Ints.fromByteArray(Ints.toByteArray(num))).isEqualTo(num);

      r.nextBytes(b);

 *
 * <p>To implement a multimap, a subclass must define the method {@link #createCollection()}, which
 * creates an empty collection of values for a key.
 *
 * <p>The multimap constructor takes a map that has a single entry for each distinct key. When you
 * insert a key-value pair with a key that isn't already in the multimap, {@code

 * Benchmarks (as of December 2011) show that:
 *
 * <ul>
 *   <li>for an unnested {@code lock()} and {@code unlock()}, a cycle detecting lock takes 38ns as
 *       opposed to the 24ns taken by a plain lock.
 *   <li>for nested locking, the cost increases with the depth of the nesting:
 *       <ul>
 *         <li>2 levels: average of 64ns per lock()/unlock()