}
              },
              directExecutor());
      for (int i = 0; i < 50_000; i++) {
        if (i % DIRECT_EXECUTIONS_PER_THREAD == 0) {
          // after some number of iterations, switch threads
          unused =
              serializer.submit(
                  new Callable<@Nullable Void>() {
                    @Override
                    public @Nullable Void call() {

        }

        assertTrue(latch.await(10, TimeUnit.SECONDS), "Concurrent operations should complete");
        executor.shutdown();

        // Circuit breaker should have handled concurrent operations
        assertTrue(successCount.get() > 0, "Should have some successful operations");
        assertTrue(failureCount.get() > 0, "Should have some failed operations");
    }

    @Test

	}

	tries++ // tried already once

	// Wait on each try for an update.
	ticker := time.NewTicker(1 * time.Second)
	defer ticker.Stop()

	for {
		// Only log once every 10 iterations, then reset the tries count.
		verbose = tries >= 10
		if verbose {
			tries = 1
		}

    class PerformanceTests {

        @RepeatedTest(value = 10, name = "Repeated encoding/decoding stability test {currentRepetition}/{totalRepetitions}")
        @DisplayName("Encoding and decoding is stable across multiple iterations")
        void testEncodingDecodingStability() throws Exception {
            ASN1ObjectIdentifier[] mechs = new ASN1ObjectIdentifier[] { OID_KRB, OID_NTLM };

    runLeastOfComparison(10, 30, 2);
  }

  private static void runLeastOfComparison(int iterations, int elements, int seeds) {
    Random random = new Random(42);
    Ordering<Integer> ordering = Ordering.natural();

    for (int i = 0; i < iterations; i++) {
      List<Integer> list = new ArrayList<>();
      for (int j = 0; j < elements; j++) {

    runLeastOfComparison(10, 30, 2);
  }

  private static void runLeastOfComparison(int iterations, int elements, int seeds) {
    Random random = new Random(42);
    Ordering<Integer> ordering = Ordering.natural();

    for (int i = 0; i < iterations; i++) {
      List<Integer> list = new ArrayList<>();
      for (int j = 0; j < elements; j++) {

     * with each iteration, so this algorithm takes O(log(digits)) iterations.
     *
     * We start out with a double-precision approximation, which may be higher or lower than the
     * true value. Therefore, we perform at least one Newton iteration to get a guess that's
     * definitely >= floor(sqrt(x)), and then continue the iteration until we reach a fixed point.
     */
    BigInteger sqrt0;
    int log2 = log2(x, FLOOR);

        CacheBuilder.newBuilder().weakValues().build(countingLoader);
    ConcurrentMap<Object, Object> map = cache.asMap();

    int iterations = 10;
    WeakReference<Object> ref = new WeakReference<>(null);
    int expectedComputations = 0;
    for (int i = 0; i < iterations; i++) {
      // The entry should get garbage collected and recomputed.
      Object oldValue = ref.get();
      if (oldValue == null) {

    BloomFilter<Object> bf = BloomFilter.create(HashTestUtils.BAD_FUNNEL, 10, 0.03);
    double fpp = bf.expectedFpp();
    assertThat(fpp).isEqualTo(0.0);
    // usually completed in less than 200 iterations
    while (fpp != 1.0) {
      boolean changed = bf.put(new Object());
      double newFpp = bf.expectedFpp();
      // if changed, the new fpp is strictly higher, otherwise it is the same

 * limitations under the License.
 */
```

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