// RUN: tf-quant-opt %s -quant-convert-fake-quant-to-qdq -quant-lift-quantizable-spots-as-functions -quant-insert-quantized-functions -quant-quantize-composite-functions -symbol-dce | FileCheck %s

func.func @fake_quant_conv(%arg0: tensor<1x3x4x3xf32>, %arg1: tensor<2x3x3x2xf32>) -> tensor<*xf32> {
  %cst = "tf.Const"() {value = dense<0.000000e+00> : tensor<2xf32>} : () -> tensor<2xf32>

  %5 = "tfl.quantize"(%4) {qtype = tensor<1x34x1x3x!quant.uniform<i8:f32, 1.0>>, volatile} : (tensor<1x34x1x3xf32>) -> tensor<1x34x1x3x!quant.uniform<i8:f32, 1.0>>
  %6 = "tfl.dequantize"(%5) : (tensor<1x34x1x3x!quant.uniform<i8:f32, 1.0>>) -> tensor<1x34x1x3xf32>

  func.return %2#0 : tensor<1x112x112x64xf32>
}

// CHECK-LABEL: func @fold_into_pad_with_extra_uses
func.func @fold_into_pad_with_extra_uses(%arg0: tensor<1x2x4x4x3xf32>) -> (tensor<1x2x3x4x4xf32>, tensor<1x2x3x6x6xf32>) {

  // CHECK: %[[PERM:.*]] = "tf.Const"() <{value = dense<[0, 1, 4, 2, 3]> : tensor<5xi32>}>
  // CHECK: %[[TRANSPOSE_OP:[0-9]*]] = "tf.Transpose"(%arg0, %[[PERM]])

    %1 = "tf.Conv2D"(%0, %cst_0) {data_format = "NHWC", device = "", dilations = [1, 1, 1, 1], explicit_paddings = [], padding = "SAME", strides = [1, 1, 2, 1], use_cudnn_on_gpu = true} : (tensor<1x3x4x3xf32>, tensor<2x3x3x1024xf32>) -> tensor<1x3x2x1024xf32>

  func.return %mm : tensor<1x3x3x3xf32>

// CHECK: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %arg0) <{adj_x = false, adj_y = true}>
// CHECK: return %[[mm:.*]]

// PerTensor: %[[mm:.*]] = "tfl.batch_matmul"(%arg0, %arg0) <{adj_x = false, adj_y = true}>
// PerTensor: return %[[mm:.*]]
}

// CHECK-LABEL: NotQuantizeConst
// Float16-LABEL: NotQuantizeConst

}

func.func @split(%arg0: tensor<i32>, %arg1: tensor<1x4x3x3xf32>) -> tensor<1x4x3xf32> {
  %0:3 = "tf.Split"(%arg0, %arg1) : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)
  func.return %0#0 : tensor<1x4x3xf32>

  // CHECK-LABEL: split
  // CHECK: "tfl.split"(%arg0, %arg1) <{num_splits = 3 : i32}> : (tensor<i32>, tensor<1x4x3x3xf32>) -> (tensor<1x4x3xf32>, tensor<1x4x3xf32>, tensor<1x4x3xf32>)

}

// -----

func.func @transpose_uniform_qtype(%arg0 : tensor<1x3x4x3xf32>) -> tensor<3x4x3x1x!quant.uniform<i8:f32, 0.0078356266021728515:-1>> {
  %cst = arith.constant dense<[1, 2, 3, 0]> : tensor<4xi32>
  %0 = "tfl.quantize"(%arg0) {qtype = tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078356266021728515:-1>>} : (tensor<1x3x4x3xf32>) -> tensor<1x3x4x3x!quant.uniform<i8:f32, 0.0078356266021728515:-1>>
  // CHECK: "tfl.transpose"