// -----

func.func @foldDivF64() -> tensor<2xf64> {
  %0 = stablehlo.constant dense<[2.0, 3.0]> : tensor<2xf64>
  %1 = stablehlo.constant dense<[4.0, 6.0]> : tensor<2xf64>
  %2 = stablehlo.divide %0, %1 : tensor<2xf64>
  return %2 : tensor<2xf64>
}

// CHECK-LABEL: foldDivF64
// CHECK: stablehlo.constant dense<5.000000e-01> : tensor<2xf64>

// -----

func.func @foldDivI32() -> tensor<2xi32> {

  EXPECT_THAT(num_bytes, Eq(16));
}

TEST_F(GetSizeInBytesTest, Float64ConstOpSizeInBytes) {
  constexpr absl::string_view kConstOpExpr =
      R"mlir(%cst = "tf.Const"() {value = dense<3.0> : tensor<2xf64>} : () -> tensor<2xf64>)mlir";

  Block block{};
  TF::ConstOp int_tensor_const_op = ParseConstOp(kConstOpExpr, block, ctx_);

  const int64_t num_bytes = GetSizeInBytes(int_tensor_const_op);
  EXPECT_THAT(num_bytes, Eq(16));

//CHECK-NEXT: return %0 : tensor<1x161x1xf32>
//CHECK-NEXT:}

func.func @convert(%arg0: tensor<2xf64>) -> tensor<2xf32> {
  %0 = "vhlo.convert_v1" (%arg0) : (tensor<2xf64>) -> tensor<2xf32>
  return %0 : tensor<2xf32>
}

//CHECK:func.func private @convert(%arg0: tensor<2xf64>) -> tensor<2xf32> {
//CHECK-NEXT: %0 = "vhlo.convert_v1"(%arg0) : (tensor<2xf64>) -> tensor<2xf32>
//CHECK-NEXT: return %0 : tensor<2xf32>
//CHECK-NEXT:}

    // CHECK-NOT: tf.BesselI0e
    %0 = "tf.BesselI0e"(%arg0) : (tensor<3xf16>) -> (tensor<3xf16>)
    %1 = "tf.BesselI0e"(%arg1) : (tensor<3xf32>) -> (tensor<3xf32>)
    %2 = "tf.BesselI0e"(%arg2) : (tensor<3xf64>) -> (tensor<3xf64>)
    func.return %0, %1, %2 : tensor<3xf16>, tensor<3xf32>, tensor<3xf64>
  }

  // CHECK-LABEL: bessel_i1e

  // CHECK: return %[[RESULT]] : tensor<1x4xi32>
  %starts = "tf.Const"() {value = dense<[1, 0]> : tensor<2xi64>} : () -> (tensor<2xi64>)
  %sizes = "tf.Const"() {value = dense<[1, 4]> : tensor<2xi64>} : () -> (tensor<2xi64>)
  %0 = "tf.Slice"(%arg0, %starts, %sizes) : (tensor<?x4xi32>, tensor<2xi64>, tensor<2xi64>) -> tensor<1x4xi32>
  func.return %0 : tensor<1x4xi32>
}

// -----

func.func @testBroadcastGradientArgI64() -> (tensor<2xi64>, tensor<0xi64>) {
  %s0 = "tf.Const"() {value = dense<> : tensor<0xi64>} : () -> tensor<0xi64>
  %s1 = "tf.Const"() {value = dense<[2, 4]> : tensor<2xi64>} : () -> tensor<2xi64>
  %r0, %r1 = "tf.BroadcastGradientArgs"(%s0, %s1) {} : (tensor<0xi64>, tensor<2xi64>) -> (tensor<2xi64>, tensor<0xi64>)

  // CHECK-DAG: %[[MATMUL_LHS_SHAPE:.*]] = "tf.Const"() <{value = dense<[4, 5]> : tensor<2xi64>}> : () -> tensor<2xi64>
  // CHECK-DAG: %[[MATMUL_RHS_SHAPE:.*]] = "tf.Const"() <{value = dense<[5, 6]> : tensor<2xi64>}> : () -> tensor<2xi64>

  // CHECK: %[[LHS_1:.*]] = "tf.Reshape"(%arg0, %[[MATMUL_LHS_SHAPE]]) : (tensor<1x4x5xf32>, tensor<2xi64>) -> tensor<4x5xf32>

}

func.func @real(%arg0: tensor<1 x complex<f64>>) -> tensor<1xf64> {
  %0 = "tf.Real"(%arg0) : (tensor<1 x complex<f64>>) -> tensor<1xf64>
  func.return %0: tensor<1xf64>

// CHECK-LABEL: real
// CHECK:  "tfl.real"(%arg0) : (tensor<1xcomplex<f64>>) -> tensor<1xf64>
// CHECK:  return
}

func.func @imag(%arg0: tensor<1 x complex<f64>>) -> tensor<1xf64> {

func.func @TfAdd(%arg0: tensor<4xf64>, %arg1: tensor<4xf64>) -> tensor<4xf64> {
  %0 = "tfl.custom"(%arg0, %arg1) {
    custom_code = "FlexAdd",
    custom_option = #tfl<const_bytes : "0x03416464001412034164641A001A002A070A015412023002320000021B171414042801">
  } : (tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64>

// CHECK: "tf.Add"(%arg0, %arg1) {T = f64}  : (tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64>
  func.return %0 : tensor<4xf64>
}

// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -emit-select-tf-ops -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<4xf64>, tensor<4xf64>) -> tensor<4xf64> {
^bb0(%arg0: tensor<4xf64>, %arg1: tensor<4xf64>):
// CHECK:  {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 32,
// CHECK-NEXT:    custom_code: "FlexAdd",
// CHECK-NEXT:    builtin_code: CUSTOM