def "discards duplicates values and retains iteration order of added elements"() {
        given:
        property.set(["123"] as Set)
        property.add("abc")
        property.addAll(Providers.of(["123", "abc", "123", "456"]))
        property.add("123")
        property.add("def")

        expect:
        property.get() as List == ["123", "abc", "456", "def"]
    }

	testPath, locker, _ := createLockTestServer(ctx, t)
	defer os.RemoveAll(testPath)

	lockRequesterInfo1 := lockRequesterInfo{
		Owner:           "owner",
		Writer:          true,
		UID:             "0123-4567",
		Timestamp:       UTCNow(),
		TimeLastRefresh: UTCNow(),
	}
	lockRequesterInfo2 := lockRequesterInfo{
		Owner:           "owner",
		Writer:          true,
		UID:             "89ab-cdef",

/**
 * Benchmark for {@code LocalCache.Segment.removeEntryFromChain}.
 *
 * @author Charles Fry
 */
@SuppressWarnings("CheckReturnValue")
public class ChainBenchmark {

  @Param({"1", "2", "3", "4", "5", "6"})
  int length;

  private Segment<Object, Object> segment;
  private ReferenceEntry<Object, Object> head;
  private @Nullable ReferenceEntry<Object, Object> chain;

  @SuppressWarnings("GuardedBy")

# CHECK: func private @"key/[[else]]"(
# CHECK-SAME: tf._original_func_name
# CHECK: func private @"key/[[then]]"(
# CHECK-SAME: tf._original_func_name


def Test():
  data = tf.constant([1, 2, 3, 4, 5, 6])
  # Create placeholders to prevent constant folding.
  x_op = tf.placeholder(dtype=tf.int32)
  y_op = tf.placeholder(dtype=tf.int32)
  less_op = tf.less(x_op, y_op)
  switch_op = control_flow_ops.switch(data, less_op)

  Tensor dest_cpu_tensor(cpu_allocator(), tensorflow::DT_INT32,
                         tensorflow::TensorShape(dimensions));
  TF_ASSERT_OK_AND_ASSIGN(auto pjrt_client, GetPjRtClient(DEVICE_GPU));
  std::vector<int32_t> data{1, 2, 3, 4, 5, 6};
  xla::Shape xla_shape = xla::ShapeUtil::MakeShape(xla::S32, dimensions);
  TF_ASSERT_OK_AND_ASSIGN(
      auto pjrt_buffer,
      pjrt_client->BufferFromHostBuffer(

	out[1] = a + 0xFFFFFFFE00000000
	// ppc64x/power10:"ADD\t[$]1234567,"
	// ppc64x/power9:"ADDIS\t[$]19,", "ADD\t[$]-10617,"
	// ppc64x/power8:"ADDIS\t[$]19,", "ADD\t[$]-10617,"
	out[2] = a + 1234567
	// ppc64x/power10:"ADD\t[$]-1234567,"
	// ppc64x/power9:"ADDIS\t[$]-19,", "ADD\t[$]10617,"
	// ppc64x/power8:"ADDIS\t[$]-19,", "ADD\t[$]10617,"
	out[3] = a - 1234567
	// ppc64x/power10:"ADD\t[$]2147450879,"

	{"01:02:03:04:05006", nil, "invalid MAC address"},
	{"01-02:03:04:05:06", nil, "invalid MAC address"},
	{"01:02-03-04-05-06", nil, "invalid MAC address"},
	{"0123:4567:89AF", nil, "invalid MAC address"},
	{"0123-4567-89AF", nil, "invalid MAC address"},
}

func TestParseMAC(t *testing.T) {
	match := func(err error, s string) bool {
		if s == "" {
			return err == nil
		}

	var values = []any{
		true,
		int(123),
		int8(123),
		int16(-12345),
		int32(123456),
		int64(-1234567),
		uint(123),
		uint8(123),
		uint16(12345),
		uint32(123456),
		uint64(1234567),
		uintptr(12345678),
		float32(1.2345),
		float64(1.2345678),
		complex64(1.2345 + 2.3456i),
		complex128(1.2345678 + 2.3456789i),
		[]byte("hello"),
		string("hello"),
	}

			for i := 0; i < b.N; i++ {
				l.LogAttrs(ctx, LevelInfo, "this is a typical log message",
					String("module", "github.com/google/go-cmp"),
					String("version", "v1.23.4"),
					Int("count", 23),
					Int("number", 123456),
				)
			}
		})
	}
}

func BenchmarkPreformatting(b *testing.B) {
	type req struct {
		Method  string
		URL     string
		TraceID string
		Addr    string
	}

  @parameterized.named_parameters(('ReflectMode', 'REFLECT'),
                                  ('SymmetricMode', 'SYMMETRIC'))
  def test_mirror_pad(self, mode):
    input_ = tf.constant([[1, 2, 3], [4, 5, 6]], dtype=tf.float32)
    paddings = tf.constant([[
        1,
        1,
    ], [2, 2]])
    kwargs = {
        'input': input_,
        'paddings': paddings,
        'mode': mode,
    }