// CHECK-LABEL: func @not_modified(%arg0: tensor<f32>, %arg1: tensor<1x224x224x3xf32>) -> (tensor<1x401408xf32>, tensor<1x224x224x3xf32>)
// CHECK-NEXT: %[[shape:.*]] = arith.constant dense<[1, 401408]> : tensor<2xi32>

// MinElement-LABEL: QuantizeConv2D
// Float16-LABEL: QuantizeConv2D
func.func @QuantizeConv2D(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x64xf32> {
  %0 = "quantfork.stats"(%arg0) {layerStats = dense<[0.000000e+00, 1.000000e+01]> : tensor<2xf32>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3xf32>
  %w = arith.constant dense<1.270000e+02> : tensor<64x3x3x3xf32>
  %b = arith.constant dense<-1.23697901> : tensor<64xf32>

^bb0(%arg0: tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>):
  %cst = arith.constant dense<-1.23697901> : tensor<32xf32>
  %2 = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x224x224x3xf32>
  %w = arith.constant dense<-1.0> : tensor<32x3x3x3xf32>
  %3 = "tfl.quantize"(%w) {qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.1>>} : (tensor<32x3x3x3xf32>) -> tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.1>>

func.func @QuantizeConv2D(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x64xf32> {
  %w = arith.constant dense<1.270000e+02> : tensor<64x3x3x3xf32>
  %b = arith.constant dense<-1.23697901> : tensor<64xf32>

^bb0(%arg0: tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>):
  %cst = arith.constant dense<-1.23697901> : tensor<32xf32>
  %2 = "tfl.dequantize"(%arg0) : (tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>) -> tensor<1x224x224x3xf32>
  %w = arith.constant dense<-1.0> : tensor<32x3x3x3xf32>
  %3 = "tfl.quantize"(%w) {qtype = tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.1>>} : (tensor<32x3x3x3xf32>) -> tensor<32x3x3x3x!quant.uniform<u8<1:255>:f32, 0.1>>

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

func.func @main(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> {
// CHECK: {
// CHECK-NEXT:  version: 3,
// CHECK-NEXT:  operator_codes: [ {
// CHECK-NEXT:    deprecated_builtin_code: 114,
// CHECK-NEXT:    version: 1,
// CHECK-NEXT:    builtin_code: QUANTIZE
// CHECK-NEXT:  }, {
// CHECK-NEXT:    deprecated_builtin_code: 3,

func.func @QuantizeFullyConnected(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x4xf32> {
  %w = arith.constant dense<127.0> : tensor<4x12xf32>
  %b = arith.constant dense<0.0> : tensor<4xf32>
  %fc = "tfl.fully_connected"(%arg0, %w, %b) {fused_activation_function = "NONE", keep_num_dims = false, weights_format = "DEFAULT"} : (tensor<1x224x224x3xf32>, tensor<4x12xf32>, tensor<4xf32>) -> tensor<1x112x112x4xf32>

  func.return %3 : tensor<1x32x32x16xf32>
}

// -----

// CHECK-LABEL: @test_depthwise_conv2d_replace_float
func.func @test_depthwise_conv2d_replace_float(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x32xf32> {
  // CHECK-DAG: %[[VAL0:.+]] = "tfl.pseudo_qconst"() <{qtype = tensor<32x3x3x3x!quant.uniform<{{.+}}>>, value = dense<42> : tensor<32x3x3x3xi8>}>

// QDQ-NEXT: return %[[q]] : tensor<384x128x!quant.uniform<i8:f32, 2.000000e+00>>
}

func.func @main(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x401408xf32> {
  %cst = arith.constant dense<[1, 401408]> : tensor<2xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>} : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3x!quant.uniform<u8:f32, 7.812500e-03:128>>

    %1:2 = "tf.IteratorGetNext"(%arg4) {device = "/job:localhost/replica:0/task:0/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> (tensor<2x224x224x3xf32>, tensor<2x1xf32>)