*
   * <p>Since this field isn't {@code volatile}, if an instance of this class is shared across
   * threads before it is initialized, then each thread is likely to compute their own copy of the
   * value.
   */
  @SuppressWarnings("Immutable")
  @LazyInit
  private int publicSuffixIndexCache = SUFFIX_NOT_INITIALIZED;

  /**

@pytest.mark.parametrize(
    "path", ["/a/compute", "/a/compute/", "/b/compute", "/b/compute/"]
)
def test_post(path):
    data = {"a": 2, "b": "foo"}
    response = client.post(path, json=data)
    assert response.status_code == 200, response.text
    assert data == response.json()


@pytest.mark.parametrize(
    "path", ["/a/compute", "/a/compute/", "/b/compute", "/b/compute/"]
)
def test_post_invalid(path):

  @MapFeature.Require({SUPPORTS_PUT, SUPPORTS_REMOVE})
  public void testCompute_absentToPresent() {
    assertEquals(
        "Map.compute(absent, functionReturningValue) should return value",
        v3(),
        getMap()
            .compute(
                k3(),
                (k, v) -> {
                  assertEquals(k3(), k);
                  assertNull(v);
                  return v3();

  @MapFeature.Require({SUPPORTS_PUT, SUPPORTS_REMOVE})
  public void testCompute_absentToPresent() {
    assertEquals(
        "Map.compute(absent, functionReturningValue) should return value",
        v3(),
        getMap()
            .compute(
                k3(),
                (k, v) -> {
                  assertEquals(k3(), k);
                  assertNull(v);
                  return v3();

      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();
        // flip input bit for key2
        int key2 = key1 ^ (1 << i);
        // compute hash values
        int hash1 = function.hashInt(key1).asInt();
        int hash2 = function.hashInt(key2).asInt();

      int[] same = new int[hashBits];
      int[] diff = new int[hashBits];
      // go through trials to compute probability
      for (int j = 0; j < trials; j++) {
        int key1 = rand.nextInt();
        // flip input bit for key2
        int key2 = key1 ^ (1 << i);
        // compute hash values
        int hash1 = function.hashInt(key1).asInt();
        int hash2 = function.hashInt(key2).asInt();

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.

    // b/80241237

    Cache<String, String> c = CacheBuilder.newBuilder().maximumSize(1).build();

    assertThat(c.asMap().compute("hash-1", (k, v) -> "a")).isEqualTo("a");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "b")).isEqualTo("b");
    assertThat(c.asMap().compute("hash-1", (k, v) -> "c")).isEqualTo("c");
    assertThat(c.size()).isEqualTo(1);
    assertThat(c.asMap().computeIfAbsent("hash-2", k -> "")).isEqualTo("");

  //
  // 1) Optimal k = b * ln2
  // 2) p = (1 - e ^ (-kn/m))^k
  // 3) For optimal k: p = 2 ^ (-k) ~= 0.6185^b
  // 4) For optimal k: m = -nlnp / ((ln2) ^ 2)

  /**
   * Computes the optimal number of hash functions (k) for a given false positive probability (p).
   *
   * <p>See http://en.wikipedia.org/wiki/File:Bloom_filter_fp_probability.svg for the formula.
   *

      result of this License or out of the use or inability to use the
      Work (including but not limited to damages for loss of goodwill,
      work stoppage, computer failure or malfunction, or any and all
      other commercial damages or losses), even if such Contributor
      has been advised of the possibility of such damages.