// count == 0
    segment.setTableEntryForTesting(index, entry);
    assertNull(segment.get(key, hash));
    assertFalse(segment.containsKey(key, hash));
    assertFalse(segment.containsValue(value));

    // count == 1
    segment.count++;
    assertSame(value, segment.get(key, hash));
    assertTrue(segment.containsKey(key, hash));
    assertTrue(segment.containsValue(value));

    // count == 0
    segment.setTableEntryForTesting(index, entry);
    assertNull(segment.get(key, hash));
    assertFalse(segment.containsKey(key, hash));
    assertFalse(segment.containsValue(value));

    // count == 1
    segment.count++;
    assertSame(value, segment.get(key, hash));
    assertTrue(segment.containsKey(key, hash));
    assertTrue(segment.containsValue(value));

    return (bits + 31) & ~31;
  }

  /**
   * Returns a hash function which computes its hash code by concatenating the hash codes of the
   * underlying hash functions together. This can be useful if you need to generate hash codes of a
   * specific length.
   *
   * <p>For example, if you need 1024-bit hash codes, you could join two {@link Hashing#sha512} hash

import com.google.common.annotations.VisibleForTesting;
import com.google.common.base.CharMatcher.NamedFastMatcher;
import java.util.BitSet;

/**
 * An immutable version of CharMatcher for smallish sets of characters that uses a hash table with
 * linear probing to check for matches.
 *
 * @author Christopher Swenson
 */
@GwtIncompatible // no precomputation is done in GWT
@ElementTypesAreNonnullByDefault

 * constant time operations. Expected in the hashtable sense (depends on the hash function doing a
 * good job of distributing the elements to the buckets to a distribution not far from uniform), and
 * amortized since some operations can trigger a hash table resize.
 *
 * <p>This implementation consumes significantly less memory than {@code java.util.LinkedHashSet} or

 * amortized) constant time operations. Expected in the hashtable sense (depends on the hash
 * function doing a good job of distributing the elements to the buckets to a distribution not far
 * from uniform), and amortized since some operations can trigger a hash table resize.
 *
 * <p>As compared with {@link java.util.LinkedHashMap}, this structure places significantly reduced

    assertNull(segment.get(key, hash));

    // count == 0
    table.set(index, entry);
    assertNull(segment.get(key, hash));
    assertFalse(segment.containsKey(key, hash));
    assertFalse(segment.containsValue(value));

    // count == 1
    segment.count++;
    assertSame(value, segment.get(key, hash));
    assertTrue(segment.containsKey(key, hash));
    assertTrue(segment.containsValue(value));

    assertNull(segment.get(key, hash));

    // count == 0
    table.set(index, entry);
    assertNull(segment.get(key, hash));
    assertFalse(segment.containsKey(key, hash));
    assertFalse(segment.containsValue(value));

    // count == 1
    segment.count++;
    assertSame(value, segment.get(key, hash));
    assertTrue(segment.containsKey(key, hash));
    assertTrue(segment.containsValue(value));

    if (key == null) {
      return null;
    }
    int hash = hash(key);
    return segmentFor(hash).get(key, hash);
  }

  @CanIgnoreReturnValue // TODO(b/27479612): consider removing this
  V get(K key, CacheLoader<? super K, V> loader) throws ExecutionException {
    int hash = hash(checkNotNull(key));
    return segmentFor(hash).get(key, hash, loader);
  }

  @CheckForNull

   * objects per entry.  Instead we use an open-addressed hash table.  This design is basically
   * equivalent to RegularImmutableSet, save that instead of having a hash table containing the
   * elements directly and null for empty positions, we store indices of the keys in the hash table,
   * and ABSENT for empty positions.  We then look up the keys in alternatingKeysAndValues.
   *