module attributes {tf.versions = {producer = 888 : i32}} {
func.func @testReshapeShapeInference(%arg0: tensor<3x4xi32>) -> tensor<*xi32> {
  %cst = arith.constant dense<[1, 6, 2]> : tensor<3xi32>
  // CHECK: "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<1x6x2xi32>
  %0 = "tfl.reshape"(%arg0, %cst) : (tensor<3x4xi32>, tensor<3xi32>) -> tensor<*xi32>
  func.return %0 : tensor<*xi32>
}
}

// -----

// CHECK-NEXT:    %[[BMM_0:.*]] = "tfl.batch_matmul"(%[[RESHAPED_0]], %[[RESHAPED_1]]) <{adj_x = false, adj_y = false, asymmetric_quantize_inputs = false}> : (tensor<3x5x12xf32>, tensor<3x12x4xf32>) -> tensor<3x5x4xf32>
// CHECK-NEXT:    %[[RESHAPED_BMM:.*]] = mhlo.reshape %[[BMM_0]]
// CHECK-NEXT:    return %[[RESHAPED_BMM]] : tensor<3x5x1x4xf32>
}

func.func @valid_unranked_inputs_on_reshape(%arg0: tensor<3x4xi32>, %arg1: tensor<*xi32>) -> tensor<3x4xi32> {
  // CHECK: "tfl.reshape"(%arg0, %arg1)
  %0 = "tfl.reshape"(%arg0, %arg1) : (tensor<3x4xi32>, tensor<*xi32>) -> tensor<3x4xi32>
  func.return %0 : tensor<3x4xi32>
}

// -----

// CHECK-LABEL: valid_one_dynamic_dim_on_reshape
func.func @valid_one_dynamic_dim_on_reshape(%arg0: tensor<3x4xi32>) -> tensor<1x3x4xi32> {

  %0 = "tf.InplaceUpdate"(%arg0, %arg2, %arg1) : (tensor<8x8x4xf32>, tensor<3xi32>, tensor<3x8x4xf32>) -> tensor<8x8x4xf32>

  // CHECK:  return [[UPDATE3]] : tensor<8x8x4xf32>
  func.return %0 : tensor<8x8x4xf32>
}

// -----

// CHECK-LABEL: xla_dynamic_update_slice

// tensorflow/lite/kernels/transpose_test.cc
func.func @transpose_3d() -> tensor<4x2x3xi32> {
  %cst = arith.constant dense<[[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]]> : tensor<2x3x4xi32>
  %cst_perm = arith.constant dense<[2, 0, 1]> : tensor<3xi32>

  %0 = stablehlo.constant dense<1> : tensor<3x4x2xi32>
  %1 = stablehlo.constant dense<1> : tensor<2x3x2xi64>
  %2 = "stablehlo.gather"(%0, %1) {
  dimension_numbers = #stablehlo.gather<
    offset_dims = [2, 3],
    collapsed_slice_dims = [0],
    start_index_map = [1, 0],
    index_vector_dim = 2>,
  slice_sizes = array<i64: 1, 2, 2>,
  indices_are_sorted = false
} : (tensor<3x4x2xi32>, tensor<2x3x2xi64>) -> tensor<2x3x2x2xi32>

// CHECK:           %[[VAL_11:.*]] = arith.constant dense<[3, 5, 1, 4]> : tensor<4xi64>
// CHECK:           %[[VAL_12:.*]] = "tf.Reshape"(%[[VAL_10]], %[[VAL_11]]) : (tensor<3x5x4xf32>, tensor<4xi64>) -> tensor<3x5x1x4xf32>
// CHECK:           return %[[VAL_12]] : tensor<3x5x1x4xf32>
// CHECK:         }

  %arg0_shape = "tf.Const"() {value = dense<[1, 8, 4]> : tensor<3xi32>} : () -> tensor<3xi32>
  %arg0_reshaped = "tf.Reshape"(%arg0, %arg0_shape) : (tensor<8x4xf32>, tensor<3xi32>) -> tensor<1x8x4xf32>
  %zeroi2 = "tf.Const"() {value = dense<0> : tensor<2xi32>} : () -> tensor<2xi32>
  %axis = "tf.Const"() {value = dense<0> : tensor<i32>} : () -> tensor<i32>

}

// Returns a RankedTensorType which is similar to `input_type` but replaces the
// dimension size of `dim` with `dim_size`.  For example,
// `SubstituteRankedTensorTypeDimSize(tensor<3x4xi32>, 1, 2)` returns
// `tensor<3x2xi32>`.
static RankedTensorType SubstituteRankedTensorTypeDimSize(
    RankedTensorType input_type, int64_t dim, int64_t dim_size) {
  auto shape = input_type.getShape().vec();