a full-time Guava team member. [Feedback](https://stackoverflow.com/a/4543114) from our
        users indicates that they really appreciate Guava's high power-to-weight ratio. It's
        important to us to keep Guava as easy to use and understand as we can. That means boiling
        features down to compact but powerful abstractions, and controlling feature bloat carefully.

            "five", 5,
            "six", 6,
            "seven", 7,
            "eight", 8),
        "one",
        1,
        "two",
        2,
        "three",
        3,
        "four",
        4,
        "five",
        5,
        "six",
        6,
        "seven",
        7,
        "eight",
        8);
    assertMapEquals(
        ImmutableMap.of(
            "one", 1,

    final int weight;

    WeightedWeakValueReference(
        ReferenceQueue<V> queue, V referent, ReferenceEntry<K, V> entry, int weight) {
      super(queue, referent, entry);
      this.weight = weight;
    }

    @Override
    public int getWeight() {
      return weight;
    }

    @Override
    public ValueReference<K, V> copyFor(

   *   <li>When the RateLimiter is not used, this goes right (up to maxPermits)
   *   <li>When the RateLimiter is used, this goes left (down to zero), since if we have
   *       storedPermits, we serve from those first
   *   <li>When _unused_, we go right at a constant rate! The rate at which we move to the right is
   *       chosen as maxPermits / warmupPeriod. This ensures that the time it takes to go from 0 to

            "five", 5,
            "six", 6,
            "seven", 7,
            "eight", 8),
        "one",
        1,
        "two",
        2,
        "three",
        3,
        "four",
        4,
        "five",
        5,
        "six",
        6,
        "seven",
        7,
        "eight",
        8);
    assertMapEquals(
        ImmutableBiMap.of(
            "one", 1,

   * code would be responsible for populating a "real" {@code T} (which might still be the value
   * {@code null}!) before returning it to callers. Depending on how the code is structured, a
   * nullness analysis might not understand that the field has been populated. To avoid that problem
   * without having to add {@code @SuppressWarnings}, the code can call this method.
   *

 *
 * <pre>{@code
 * final String name = ...;
 * inFlight.add(name);
 * ListenableFuture<Result> future = service.query(name);
 * future.addListener(new Runnable() {
 *   public void run() {
 *     processedCount.incrementAndGet();
 *     inFlight.remove(name);
 *     lastProcessed.set(name);
 *     logger.info("Done with {0}", name);
 *   }
 * }, executor);

     * setException() will be a no-op because the Future is already done.
     *
     * Both scenarios are bad: The output Future might never complete, or, if it does complete, it
     * might not run some of its listeners. The likely result is that the app will hang. (And of
     * course stack overflows are bad news in general. For example, we may have overflowed in the

    if (supportsRemove) {
      // We use a copy of "entryToRemove" in the assertion because "entryToRemove" might be
      // invalidated and have undefined behavior after entrySet.removeAll(entriesToRemove),
      // for example entryToRemove.getValue() might be null.
      Entry<K, V> entryToRemoveCopy =
          Helpers.mapEntry(entryToRemove.getKey(), entryToRemove.getValue());

import com.google.caliper.api.SkipThisScenarioException;
import java.util.List;

/**
 * Quick and dirty benchmark of {@link Throwables#lazyStackTrace(Throwable)}. We benchmark a "caller
 * finder" implementation that might be used in a logging framework.
 */
public class LazyStackTraceBenchmark {
  @Param({"20", "200", "2000"})
  int stackDepth;

  @Param({"-1", "3", "15"})
  int breakAt;

  int recursionCount;