/**
 * A decoded [mapping table] that can perform the [mapping step] of IDNA processing.
 *
 * This implementation is optimized for readability over efficiency.
 *
 * This implements non-transitional processing by preserving deviation characters.
 *
 * This implementation's STD3 rules are configured to `UseSTD3ASCIIRules=false`. This is
 * permissive and permits the `_` character.
 *

		for _, ent := range b.entries {
			delete(b.entries, ent.Key)
		}
	}
	c.evictList.Init()
}

// Add adds a value to the cache. Returns true if an eviction occurred.
// Returns false if there was no eviction: the item was already in the cache,
// or the size was not exceeded.
func (c *LRU[K, V]) Add(key K, value V) (evicted bool) {
	c.mu.Lock()
	defer c.mu.Unlock()
	now := time.Now()

    assertWithMessage("Eviction notifications must be received")
        .that(removalListener.getCount())
        .isEqualTo(10);

    CacheTesting.expireEntries((LoadingCache<?, ?>) cache, EXPIRING_TIME, ticker);
    // ensure that no new notifications are sent
    assertWithMessage("Eviction notifications must be received")
        .that(removalListener.getCount())

   * granularity. For example, access queues and write queues are kept per segment when they are
   * required by the selected eviction algorithm. As such, when writing unit tests it is not
   * uncommon to specify {@code concurrencyLevel(1)} in order to achieve more deterministic eviction
   * behavior.
   *
   * <p>Note that future implementations may abandon segment locking in favor of more advanced
   * concurrency controls.

    // maximumSize/Weight is specified in which case ensure that each segment gets at least 10
    // entries. The special casing for size-based eviction is only necessary because that eviction
    // happens per segment instead of globally, so too many segments compared to the maximum size
    // will result in random eviction behavior.
    int segmentShift = 0;
    int segmentCount = 1;
    while (segmentCount < concurrencyLevel

 * </pre>
 * <p>
 * Alternatively, for high-concurrency scenarios, consider implementing a custom
 * LRU cache using {@link java.util.concurrent.ConcurrentHashMap} with an eviction strategy.
 * </p>
 *
 * @author koichik
 * @param <K> the key type
 * @param <V> the value type
 */
public class LruHashMap<K, V> extends LinkedHashMap<K, V> {

     */
    @SuppressWarnings("GoodTime") // should accept a java.time.Duration
    void recordLoadException(long loadTime);

    /**
     * Records the eviction of an entry from the cache. This should only been called when an entry
     * is evicted due to the cache's eviction strategy, and not as a result of manual {@linkplain
     * Cache#invalidate invalidations}.
     */
    void recordEviction();

    /**

        assertEquals("Alice", groovyEngine.evaluate("return name", params1));
    }

    /**
     * Test that configurable script cache size works with eviction
     */
    @Test
    public void test_configurableScriptCacheSize() {
        final GroovyEngine engine = new GroovyEngine();
        engine.scriptCacheSize = 5;
        engine.init();

   * granularity. For example, access queues and write queues are kept per segment when they are
   * required by the selected eviction algorithm. As such, when writing unit tests it is not
   * uncommon to specify {@code concurrencyLevel(1)} in order to achieve more deterministic eviction
   * behavior.
   *
   * <p>Note that future implementations may abandon segment locking in favor of more advanced
   * concurrency controls.

    // maximumSize/Weight is specified in which case ensure that each segment gets at least 10
    // entries. The special casing for size-based eviction is only necessary because that eviction
    // happens per segment instead of globally, so too many segments compared to the maximum size
    // will result in random eviction behavior.
    int segmentShift = 0;
    int segmentCount = 1;
    while (segmentCount < concurrencyLevel