// 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>}>

  }
  func.func private @quantize_i8(%arg0: tensor<1x224x224x3xf32>, %arg1: tensor<f32>, %arg2: tensor<i32>) -> tensor<1x224x224x3xi8> {
    %0 = "tf.Div"(%arg0, %arg1) : (tensor<1x224x224x3xf32>, tensor<f32>) -> tensor<1x224x224x3xf32>
    %1 = "tf.Round"(%0) : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3xf32>
    %2 = "tf.Cast"(%1) : (tensor<1x224x224x3xf32>) -> tensor<1x224x224x3xi32>

// 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>>

  %outputs_22 = "tf.Squeeze"(%outputs_20) {device = "", squeeze_dims = [0]} : (tensor<1x224x224x3xf32>) -> tensor<224x224x3xf32>
  %outputs_24 = "tf.Cast"(%outputs_22) {Truncate = false, device = ""} : (tensor<224x224x3xf32>) -> tensor<224x224x3xui8>

  // CHECK-LABEL: RedundantShapeOp
  // CHECK-NOT: "tf.Shape"
}

// CHECK-LABEL: @MoveTransposeAcrossPerChannelQuant
func.func @MoveTransposeAcrossPerChannelQuant(%arg0 : tensor<1x224x224x3xf32>) -> tensor<1x112x112x6xf32> {
  %cst = "tf.Const"() <{value = dense<6.0> : tensor<6x3x7x7xf32>}> : () -> tensor<6x3x7x7xf32>
  %cst_14 = "tf.Const"() <{value = dense<[2, 3, 1, 0]> : tensor<4xi64>}> : () -> tensor<4xi64>

  func.return %2 : tensor<8x7x6x5x?x3x2x1xf32>
}

// -----

func.func @depthwise_conv_2D_invalid_bias(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x112x112x32xf32> {
  %w = arith.constant dense<127.0> : tensor<32x3x3x3xf32>
  %b = arith.constant dense<0.0> : tensor<32x1xf32>

  func.func @__forward_call_369(%arg0: tensor<16x224x224x3xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<*x!tf_type.resource>, %arg2: tensor<*x!tf_type.resource>, %arg3: tensor<*x!tf_type.resource>, %arg4: tensor<*x!tf_type.resource>) -> (tensor<?x?xf32>, tensor<*xf32>, tensor<?x16384xf32>, tensor<16x112x112x?xf32>, tensor<16x224x224x3xf32>, tensor<*xf32>) attributes {tf.entry_function = {control_outputs = "", inputs = "inputs_0,conv1...

  }
  func.func private @quantize_i8(%arg0: tensor<1x2240x2240x3xf32>, %arg1: tensor<f32>, %arg2: tensor<i32>) -> tensor<1x2240x2240x3xi8> {
    %0 = "tf.Div"(%arg0, %arg1) : (tensor<1x2240x2240x3xf32>, tensor<f32>) -> tensor<1x2240x2240x3xf32>
    %1 = "tf.Round"(%0) : (tensor<1x2240x2240x3xf32>) -> tensor<1x2240x2240x3xf32>
    %2 = "tf.Cast"(%1) : (tensor<1x2240x2240x3xf32>) -> tensor<1x2240x2240x3xi32>