final BigInteger firstBigInt = BigInteger.valueOf(first);
            BigInteger completeBigInt = lastBigInt.add(firstBigInt.shiftLeft(32));
            completeBigInt = completeBigInt.divide(BigInteger.valueOf(10000L));
            completeBigInt = completeBigInt.add(BigInteger.valueOf(-SmbConstants.MILLISECONDS_BETWEEN_1970_AND_1601));
            date = new Date(completeBigInt.longValue());
        }

    }

    @Test
    void testUTimeReadWrite() {
        byte[] buffer = new byte[4];
        long time = System.currentTimeMillis();
        // UTime is seconds since epoch, so divide by 1000
        long unixTime = time / 1000L;

        // Mocking date for timezone consistency
        TimeZone original = TimeZone.getDefault();
        try {

      // non-negative int, we can do long-arithmetic on index * (dataset.length - 1) / scale to get
      // a rounded ratio and a remainder which can be expressed as ints, without risk of overflow:
      int quotient = (int) LongMath.divide(numerator, scale, RoundingMode.DOWN);
      int remainder = (int) (numerator - (long) quotient * scale);
      selectInPlace(quotient, dataset, 0, dataset.length - 1);
      if (remainder == 0) {

  public void testBitSize() {
    double fpp = 0.03;
    for (int i = 1; i < 10000; i++) {
      long numBits = BloomFilter.optimalNumOfBits(i, fpp);
      int arraySize = Ints.checkedCast(LongMath.divide(numBits, 64, RoundingMode.CEILING));
      assertEquals(
          arraySize * Long.SIZE,
          BloomFilter.create(Funnels.unencodedCharsFunnel(), i, fpp).bitSize());
    }
  }

 * to that of {@code ConcurrentHashMap} in a reusable form, and extends it for semaphores and
 * read-write locks. Conceptually, lock striping is the technique of dividing a lock into many
 * <i>stripes</i>, increasing the granularity of a single lock and allowing independent operations
 * to lock different stripes and proceed concurrently, instead of creating contention for a single
 * lock.
 *

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  public void testBitSize() {
    double fpp = 0.03;
    for (int i = 1; i < 10000; i++) {
      long numBits = BloomFilter.optimalNumOfBits(i, fpp);
      int arraySize = Ints.checkedCast(LongMath.divide(numBits, 64, RoundingMode.CEILING));
      assertEquals(
          arraySize * Long.SIZE,
          BloomFilter.create(Funnels.unencodedCharsFunnel(), i, fpp).bitSize());
    }
  }

 *
 * <ul>
 *   <li>automatic loading of entries into the cache
 *   <li>least-recently-used eviction when a maximum size is exceeded (note that the cache is
 *       divided into segments, each of which does LRU internally)
 *   <li>time-based expiration of entries, measured since last access or last write
 *   <li>keys automatically wrapped in {@code WeakReference}

      int start = index * size;
      int end = min(start + size, list.size());
      return list.subList(start, end);
    }

    @Override
    public int size() {
      return IntMath.divide(list.size(), size, RoundingMode.CEILING);
    }

    @Override
    public boolean isEmpty() {
      return list.isEmpty();
    }
  }

        input.equals(".%2e", ignoreCase = true) ||
        input.equals("%2e%2e", ignoreCase = true)

    /**
     * Cuts this string up into alternating parameter names and values. This divides a query string
     * like `subject=math&easy&problem=5-2=3` into the list `["subject", "math", "easy", null,
     * "problem", "5-2=3"]`. Note that values may be null and may contain '=' characters.
     */