}

TEST_F(ImageGradTest, TestBilinear) {
  TestResizedShape(RESIZE_BILINEAR);
  TestResize<float, float, float>(RESIZE_BILINEAR);
  // Note that Y_T is always float for this op. We choose
  // double for the jacobian to capture the higher precision
  // between X_T and Y_T.
  TestResize<double, float, double>(RESIZE_BILINEAR);
}

TEST_F(ImageGradTest, TestBicubic) {
  TestResizedShape(RESIZE_BICUBIC);

  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,

// CHECK-DAG:           %[[VAL_3:.*]] = arith.constant dense<32> : tensor<2xi32>
// CHECK-DAG:           %[[VAL_4:.*]] = "tfl.resize_bilinear"(%[[VAL_2]], %[[VAL_3]]) <{align_corners = false, half_pixel_centers = true}> : (tensor<1x16x16x64xf32>, tensor<2xi32>) -> tensor<1x32x32x64xf32>
// CHECK-DAG:           %[[VAL_5:.*]] = arith.constant dense<[0, 3, 1, 2]> : tensor<4xi32>

  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,

  // since different model developers use RELU1 in different ways. Never
  // create another op called RELU1.
  RELU_N1_TO_1 = 20,
  RELU6 = 21,
  RESHAPE = 22,
  RESIZE_BILINEAR = 23,
  RNN = 24,
  SOFTMAX = 25,
  SPACE_TO_DEPTH = 26,
  SVDF = 27,
  TANH = 28,
  CONCAT_EMBEDDINGS = 29,
  SKIP_GRAM = 30,
  CALL = 31,
  CUSTOM = 32,
  EMBEDDING_LOOKUP_SPARSE = 33,

// -----

// CHECK-LABEL: testResizeBilinear
func.func @testResizeBilinear(%arg0 : tensor<1x100x100x3xf32>, %arg1 : tensor<4xi32>) -> tensor<?xf32> {
  // CHECK: "tfl.resize_bilinear"(%arg0, %arg1) <{align_corners = false, half_pixel_centers = false}>
  %0 = "tfl.resize_bilinear"(%arg0, %arg1) {align_corners = false, half_pixel_centers = false} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
}

func.func @resize_with_bilinear(%arg0: tensor<1x100x100x3xf32>, %arg1: tensor<4xi32>) -> tensor<?xf32> {
  %0 = "tf.ResizeBilinear"(%arg0, %arg1) {align_corners = true} : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32>
  func.return %0 : tensor<?xf32>
  // CHECK-LABEL: resize_with_bilinear
  // CHECK: "tfl.resize_bilinear"(%arg0, %arg1) <{align_corners = true, half_pixel_centers = false}> : (tensor<1x100x100x3xf32>, tensor<4xi32>) -> tensor<?xf32>

      // CHECK: %{{.*}} = stablehlo.custom_call @tf.ResizeBilinear(%arg0, %{{.*}}) {align_corners = false, device = "", half_pixel_centers = true} : (tensor<1x32x32x128xf32>, tensor<2xi32>) -> tensor<1x64x64x128xf32>

namespace odml {

// Adds all the necessary passes to lower a TF module to StableHLO.
// `skip_resize` enables or disables skipping conversion of tf.ResizeBilinear
// and tf.ResizeNearestNeighbor ops.
// `smuggle_disallowed_ops` enables or disables converting disallowed ops
// like tf.ResizeBilinear or tf.ResizeNearestNeighbor to mhlo.custom_call ops.
void AddTFToStablehloPasses(OpPassManager& pm, bool skip_resize,

func.func @mhlo_custom_call_does_not_legalize_tf_function(%arg0: tensor<1x4xf32>) -> tensor<1x8xf32> {
  %0 = mhlo.custom_call @tf.ResizeBilinear(%arg0) {
    backend_config = "this_is_a_test_string"
  } : (tensor<1x4xf32>) -> (tensor<1x8xf32>)
  func.return %0 : tensor<1x8xf32>

  //       CHECK: %0 = mhlo.custom_call @tf.ResizeBilinear(%arg0) {
  //  CHECK-SAME:   backend_config = "this_is_a_test_string"