func.return %0 : tensor<1x1x1x16xf32>
}

// -----

func.func @testAvgPoolNoStrides(tensor<1x7x7x16xf32>) -> tensor<1x1x1x16xf32> {
^bb0(%arg0: tensor<1x7x7x16xf32>):
  // expected-error @+1 {{requires attribute 'strides'}}
  %0 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", ksize = [1, 7, 7, 1], padding = "VALID"} : (tensor<1x7x7x16xf32>) -> tensor<1x1x1x16xf32>

// Confirm a wide array of attribute survives the round-trip
func.func @main(tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> {
^bb0(%arg0: tensor<1x6x6x16xf32>):
  // CHECK: "tfl.average_pool_2d"(%{{.*}}) <{filter_height = 3 : i32, filter_width = 6 : i32, fused_activation_function = "NONE", padding = "VALID", stride_h = 3 : i32, stride_w = 1 : i32}> : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>

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

func.func @main(tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32> {
^bb0(%arg0: tensor<1x6x6x16xf32>):
  // CHECK:      {
  // CHECK-NEXT:   version: 3,
  // CHECK-NEXT:   operator_codes: [ {
  // CHECK-NEXT:     deprecated_builtin_code: 1,
  // CHECK-NEXT:     version: 1,
  // CHECK-NEXT:     builtin_code: AVERAGE_POOL_2D
  // CHECK-NEXT:   } ],

  func.return %1 : tensor<1x1x1x16xf32>

// CHECK: %[[avgp:.*]] = "tfl.average_pool_2d"(%arg0)
// CHECK: %[[dq:.*]] = "tfl.dequantize"(%[[avgp]]) : (tensor<1x1x1x16x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x1x1x16xf32>
// CHECK: return %[[dq]] : tensor<1x1x1x16xf32>
}

// CHECK-LABEL: QuantizeReshape2D

  %0 = "tfl.dequantize"(%arg0) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>) -> tensor<1x6x6x16xf32>
  %1 = "tfl.dequantize"(%arg1) : (tensor<1x6x6x16x!quant.uniform<u8:f32, 0.1>>) -> tensor<1x6x6x16xf32>
  %2 = "tfl.minimum"(%0, %1) : (tensor<1x6x6x16xf32>, tensor<1x6x6x16xf32>) -> tensor<1x6x6x16xf32>
  func.return %2 : tensor<1x6x6x16xf32>

// CHECK: %0 = "tfl.dequantize"(%arg0)
// CHECK: %1 = "tfl.dequantize"(%arg1)

  // Unsupported strides
  %2 = "tf.AvgPool"(%arg0) {T = "tfdtype$DT_FLOAT", data_format = "NHWC", ksize = [1, 3, 6, 1], padding = "VALID", strides = [1, 3, 1, 3]} : (tensor<1x6x6x16xf32>) -> tensor<1x1x1x16xf32>

  %5 = arith.addf %0, %1 : tensor<1x1x1x16xf32>
  %6 = arith.addf %2, %5 : tensor<1x1x1x16xf32>
  func.return %6 : tensor<1x1x1x16xf32>

         explicit_paddings = [1, 2, 3, 4, 5, 6, 7, 8],
         padding = "EXPLICIT",
         strides = [5, 6, 7, 8]
       } : (tensor<1x3x32x32xf32>, tensor<1x1x3x8xf32>) -> tensor<1x8x7x6xf32>

  func.return %0 : tensor<1x8x7x6xf32>
}

// CHECK-LABEL: func @transposeFusedBatchNormV3
func.func @transposeFusedBatchNormV3(
  %arg0: tensor<1x64x28x28xf32>,
  %arg1: tensor<64xf32>
) -> tensor<1x64x28x28xf32> {

         explicit_paddings = [1, 2, 3, 4, 5, 6, 7, 8],
         padding = "EXPLICIT",
         strides = [5, 6, 7, 8]
       } : (tensor<1x32x32x3xf32>, tensor<1x1x3x8xf32>) -> tensor<1x7x7x8xf32>

  func.return %0 : tensor<1x7x7x8xf32>
}

// CHECK-LABEL: func @transposeConv2DWithDefaultAttr
func.func @transposeConv2DWithDefaultAttr(%input: tensor<1x32x32x3xf32>, %filter: tensor<1x1x3x8xf32>) -> tensor<?x?x?x?xf32>
{

// CHECK:           %[[VAL_2:.*]] = "tf.Conv2D"(%[[VAL_0]], %[[VAL_1]]) <{data_format = "NHWC", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> : (tensor<1x8x8x207xf32>, tensor<3x3x207x16xf32>) -> tensor<1x8x8x16xf32>
// CHECK:           return %[[VAL_2]] : tensor<1x8x8x16xf32>
// CHECK:         }

// -----

// test invalid MaxPool2D
func.func @testMaxPool2DWrongOperandResultType(tensor<1x7x7x16xi32>) -> tensor<1x7x7x16xi32> {
^bb0(%arg0: tensor<1x7x7x16xi32>):
  // expected-error @+1 {{'tfl.max_pool_2d' op operand #0 must be tensor of 32-bit float or QUI8 type or QI8 type or QI16 type or TFLite quint8 type values, but got 'tensor<1x7x7x16xi32>'}}