Table<String, Integer, C> smaller = create("foo", 1, 'a', "bar", 1, 'b');
    Table<String, Integer, C> swapOuter = create("bar", 1, 'a', "foo", 1, 'b', "bar", 3, 'c');
    Table<String, Integer, C> swapValues = create("foo", 1, 'c', "bar", 1, 'b', "foo", 3, 'a');

    new EqualsTester()
        .addEqualityGroup(table, hashCopy, reordered)
        .addEqualityGroup(smaller)
        .addEqualityGroup(swapOuter)

    //     imagine a line separating the first block from the second, we can proceed by exchanging
    //     the smaller of these blocks with the far end of the other one. That leaves us with a
    //     smaller version of the same problem.
    //     Say we are rotating abcdefgh by 5. We start with abcde|fgh. The smaller block is [fgh]:
    //     [abc]de|[fgh] -> [fgh]de|[abc]. Now [fgh] is in the right place, but we need to swap [de]

  @SuppressWarnings("Immutable")
  private final long[] array;

  /*
   * TODO(kevinb): evaluate the trade-offs of going bimorphic to save these two fields from most
   * instances. Note that the instances that would get smaller are the right set to care about
   * optimizing, because the rest have the option of calling `trimmed`.
   */

  private final transient int start; // it happens that we only serialize instances where this is 0

  @SuppressWarnings("Immutable")
  private final int[] array;

  /*
   * TODO(kevinb): evaluate the trade-offs of going bimorphic to save these two fields from most
   * instances. Note that the instances that would get smaller are the right set to care about
   * optimizing, because the rest have the option of calling `trimmed`.
   */

  private final transient int start; // it happens that we only serialize instances where this is 0

   *
   * <p><i>Performance notes:</i> while the cartesian product of lists of size {@code m, n, p} is a
   * list of size {@code m x n x p}, its actual memory consumption is much smaller. When the
   * cartesian product is constructed, the input lists are merely copied. Only as the resulting list
   * is iterated are the individual lists created, and these are not retained after iteration.
   *

  }

  public void testPeekingIteratorBehavesLikeIteratorOnSingletonIterable() {
    actsLikeIteratorHelper(singletonList(new Object()));
  }

  // TODO(cpovirk): instead of skipping, use a smaller number of steps
  @GwtIncompatible // works but takes 5 minutes to run
  public void testPeekingIteratorBehavesLikeIteratorOnThreeElementIterable() {
    actsLikeIteratorHelper(Lists.newArrayList("A", "B", "C"));
  }

   * {@link TreeSet} or the {@link Map#keySet} of an {@code IdentityHashMap}.
   *
   * <p><b>Note:</b> The returned view performs slightly better when {@code set1} is the smaller of
   * the two sets. If you have reason to believe one of your sets will generally be smaller than the
   * other, pass it first. Unfortunately, since this method sets the generic type of the returned

      Iterable<? extends Iterable<? extends T>> inputs) {
    return FluentIterable.concat(inputs);
  }

  /**
   * Divides an iterable into unmodifiable sublists of the given size (the final iterable may be
   * smaller). For example, partitioning an iterable containing {@code [a, b, c, d, e]} with a
   * partition size of 3 yields {@code [[a, b, c], [d, e]]} -- an outer iterable containing two

   * href="http://mathworld.wolfram.com/PrimeNumber.html">prime number</a>: an integer <i>greater
   * than one</i> that cannot be factored into a product of <i>smaller</i> positive integers.
   * Returns {@code false} if {@code n} is zero, one, or a composite number (one which <i>can</i> be
   * factored into smaller positive integers).
   *
   * <p>To test larger numbers, use {@link BigInteger#isProbablePrime}.
   *

  // Timed Get
  // There are a few design constraints to consider
  // * We want to be responsive to small timeouts, unpark() has non trivial latency overheads (I
  //   have observed 12 micros on 64-bit linux systems to wake up a parked thread). So if the
  //   timeout is small we shouldn't park(). This needs to be traded off with the cpu overhead of