def TF_Int32Ref : TF_TensorFlowType<"Int32Ref", "i32ref">;
def TF_Int64Ref : TF_TensorFlowType<"Int64Ref", "i64ref">;

def TF_Uint4Ref : TF_TensorFlowType<"Uint4Ref", "ui8ref">;
def TF_Uint8Ref : TF_TensorFlowType<"Uint8Ref", "ui8ref">;
def TF_Uint16Ref : TF_TensorFlowType<"Uint16Ref", "ui16ref">;
def TF_Uint32Ref : TF_TensorFlowType<"Uint32Ref", "ui32ref">;
def TF_Uint64Ref : TF_TensorFlowType<"Uint64Ref", "ui64ref">;

	for i := 0; i < b.N; i++ {
		i32res = int32(i) / 7
		boolres = int32(i)%7 == 0
	}
}

var u32res uint32

func BenchmarkDivconstU32(b *testing.B) {
	for i := 0; i < b.N; i++ {
		u32res = uint32(i) / 7
	}
}

func BenchmarkModconstU32(b *testing.B) {
	for i := 0; i < b.N; i++ {
		u32res = uint32(i) % 7
	}
}

HANDLE_TF_REF_TYPE(Uint16Ref, UINT16_REF, "uint16ref")
HANDLE_TF_REF_TYPE(Int16Ref, INT16_REF, "int16ref")
HANDLE_TF_REF_TYPE(Uint32Ref, UINT32_REF, "uint32ref")
HANDLE_TF_REF_TYPE(Int32Ref, INT32_REF, "int32ref")
HANDLE_TF_REF_TYPE(Uint64Ref, UINT64_REF, "uint64ref")
HANDLE_TF_REF_TYPE(Int64Ref, INT64_REF, "int64ref")
HANDLE_TF_REF_TYPE(StringRef, STRING_REF, "stringref")
HANDLE_TF_REF_TYPE(BoolRef, BOOL_REF, "boolref")

  %val0 = "tf.VariableV2"() {_class = ["loc:@v"], container = "", device = "", shape = #tf_type.shape<96>, shared_name = ""} : () -> tensor<96x!tf_type.f32ref>
  %val1 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  %val2 = "tf.Identity"(%val0) : (tensor<96x!tf_type.f32ref>) -> tensor<96xf32>
  func.return
}

// -----

// Test case: No follow-up ReadVariable case.

func.func @f() {

// Check that if result is a ref type, all operands need to be ref too.
func.func @invalid_merge(%arg0: tensor<4x!tf_type.f32ref>, %arg1: tensor<4xf32>) -> tensor<4x!tf_type.f32ref> {
  %result = tf_executor.graph {
    %value, %idx, %ctlMerge = "tf_executor.Merge"(%arg0, %arg1) : (tensor<4x!tf_type.f32ref>, tensor<4xf32>) -> (tensor<4x!tf_type.f32ref>, tensor<i32>, !tf_executor.control)

      %outputs_0, %control_1 = tf_executor.island wraps "tf.Identity"(%outputs) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<!tf_type.f32ref>) -> tensor<*xf32>
      tf_executor.fetch %outputs_0 : tensor<*xf32>
    }
    func.return %0 : tensor<*xf32>
  }

func.func @merge_with_ref_type(%arg0: tensor<4x!tf_type.f32ref>, %arg1: tensor<4xf32>) -> tensor<4xf32> {
  %result = tf_executor.graph {

// CHECK: tf_executor.Merge{{.*}}(tensor<4x!tf_type.f32ref>, tensor<4xf32>) -> (tensor<4xf32>, tensor<i32>, !tf_executor.control)
    %value, %idx, %ctlMerge = "tf_executor.Merge"(%arg0, %arg1) : (tensor<4x!tf_type.f32ref>, tensor<4xf32>) -> (tensor<4xf32>, tensor<i32>, !tf_executor.control)

  func.func @dont_update_for_ref() -> () {
    // CHECK: () -> tensor<4x!tf_type.f32ref>
    %11 = "tf.VariableV2"() {container = "", device = "", shape = #tf_type.shape<4>, shared_name = ""} : () -> tensor<4x!tf_type.f32ref>
    // CHECK: (tensor<4x!tf_type.f32ref>) -> tensor<4xf32>
    %12 = "tf.Identity"(%11) {device = ""} : (tensor<4x!tf_type.f32ref>) -> tensor<4xf32>
    // CHECK: (tensor<4xf32>) -> tensor<4xf32>

// Test reference variable support for some ops (no errors expected)

// CHECK-LABEL: @testMaximumWithRef
func.func @testMaximumWithRef(%arg0: tensor<!tf_type.f32ref>, %arg1: tensor<f32>) -> tensor<f32> {
  // CHECK: tf.Maximum
  %0 = "tf.Maximum"(%arg0, %arg1) : (tensor<!tf_type.f32ref>, tensor<f32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

// CHECK-LABEL: @testAddV2WithRef