// RUN: flatbuffer_translate -mlir-to-tflite-flatbuffer %s -o - | flatbuffer_to_string - | FileCheck %s

func.func @main(tensor<3x2xi32>) -> tensor<6xi32> {
^bb0(%arg0: tensor<3x2xi32>):
// CHECK: {
// CHECK-NEXT:   version: 3,
// CHECK-NEXT:   operator_codes: [ {
// CHECK-NEXT:     deprecated_builtin_code: 22,
// CHECK-NEXT:     version: 1,
// CHECK-NEXT:     builtin_code: RESHAPE
// CHECK-NEXT:   } ],
// CHECK-NEXT:   subgraphs: [ {

  %perm = arith.constant dense<[1, 0]> : tensor<2xi32>
  %0 = "tfl.transpose"(%arg0, %perm) : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
  %cst = arith.constant dense<[[1, 2], [3, 4], [5, 6]]> : tensor<3x2xi32>
  %1 = tfl.add %0, %cst { fused_activation_function = "NONE" } : tensor<3x2xi32>
  func.return %1 : tensor<3x2xi32>
}

  %2 = "tf.Cast"(%1) {Truncate = false} : (tensor<1x2xbf16>) -> tensor<1x2xf32>
  %3 = "tf.IdentityN"(%2) {device = ""} : (tensor<1x2xf32>) -> tensor<1x2xf32>
  return %3 : tensor<1x2xf32>
}

// CHECK: func @cast_bf16_matmul_to_fp32
// CHECK-DAG: %[[cst:.*]] = "tf.Const"() <{value = dense<{{.*}}> : tensor<10x2xf32>}> : () -> tensor<10x2xf32>
// CHECK: %[[matmul:.*]] = "tf.MatMul"(%arg0, %[[cst]])

  %3 = "tf.AddV2"(%rhs_dilation, %rhs_dilation) {device = ""} : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>
  %4 = "tf.AddV2"(%padding, %padding) {device = ""} : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
  %5 = "tf.AddV2"(%strides, %strides) {device = ""} : (tensor<3xi32>, tensor<3xi32>) -> tensor<3xi32>

  return %0 : tensor<8x4x14x14x16xf32>
}

  constexpr char mlir_module_string[] = R"mlir(
  module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 268 : i32}} {
    func.func @main(%arg0 : tensor<2x2xf32>) -> tensor<2x2xf32> {
      %max = "tf.Const"() { value = dense<12.0> : tensor<f32> } : () -> tensor<f32>
      %min = "tf.Const"() { value = dense<-25.0> : tensor<f32> } : () -> tensor<f32>

}

# CHECK: func @main(%[[VAL_0:.*]]: tensor<2x5x3xf32>, %[[VAL_1:.*]]: tensor<3x7xf32>) -> tensor<2x5x7xf32> attributes {tf.entry_function = {control_outputs = "", inputs = "Placeholder,Placeholder_1", outputs = "MatMul"}} {
# CHECK:     %[[VAL_2:.*]] = "tfl.batch_matmul"(%[[VAL_0]], %[[VAL_1]]) <{adj_x = false, adj_y = false}> : (tensor<2x5x3xf32>, tensor<3x7xf32>) -> tensor<2x5x7xf32>
# CHECK:     return %[[VAL_2]] : tensor<2x5x7xf32>

    %7 = call @uniform_quantize_0(%arg0, %0, %1) : (tensor<1x2xf32>, tensor<1x1xf32>, tensor<1x1xi8>) -> tensor<1x2xi32>
    %8 = stablehlo.convert %7 : (tensor<1x2xi32>) -> tensor<1x2xf32>
    %9 = stablehlo.convert %2 : (tensor<2x3xi8>) -> tensor<2x3xf32>
    %10 = stablehlo.dot_general %8, %9, contracting_dims = [1] x [0] : (tensor<1x2xf32>, tensor<2x3xf32>) -> tensor<1x3xf32>
    %11 = stablehlo.convert %3 : (tensor<1x3xi32>) -> tensor<1x3xf32>

    func.func @main(%arg0: tensor<3x3xf32>, %arg1: tensor<3x3xf32>) -> tensor<6x3xf32> {
      %axis = "tf.Const"() { value = dense<0> : tensor<i64> } : () -> tensor<i64>
      %1 = "tf.ConcatV2"(%arg0, %arg1, %axis) : (tensor<3x3xf32>, tensor<3x3xf32>, tensor<i64>) -> tensor<6x3xf32>
      func.return %1 : tensor<6x3xf32>
    }
  })";
  CreateModule(kMlirModuleStr);

  static constexpr char kMlirLegalOpStr[] = R"(
   func.func @infeed_dequeue_tuple_dynamic_error() -> (tensor<3x3xf32>, tensor<4x?xf32>) {
     %0:2 = "tf.InfeedDequeueTuple"() : () -> (tensor<3x3xf32>, tensor<4x?xf32>) func.return %0#0, %0#1 : tensor<3x3xf32>, tensor<4x?xf32>
   })";
  CellReader<int64_t> legalize_failure_count(
      "/tensorflow/core/tf2xla/v1/mlir_failed_xla_legalize_tf_pass_count");

// CHECK: return

  func.func private @composite_dot_general_fn(%arg0: tensor<1x4xf32>, %arg1: tensor<4x3xf32>) -> tensor<1x3xf32> attributes {_from_xla_call_module} {
      %0 = stablehlo.dot_general %arg0, %arg1, contracting_dims = [1] x [0] : (tensor<1x4xf32>, tensor<4x3xf32>) -> tensor<1x3xf32>
      return %0 : tensor<1x3xf32>
  }
}

// -----