}
}
)";

static const char *const module_with_sub =
    R"(module {
func.func @main(%arg0: tensor<7x8x9xi8>, %arg1: tensor<7x8x9xi8>) -> tensor<7x8x9xi8> {
  %0 = "tf.Sub"(%arg0, %arg1) : (tensor<7x8x9xi8>, tensor<7x8x9xi8>) -> tensor<7x8x9xi8>
  func.return %0 : tensor<7x8x9xi8>
}
}
)";

// Test pass filter.
TEST(BridgeLoggerFilters, TestPassFilter) {
  mlir::DialectRegistry mlir_registry;

// CHECK:           return %[[VAL_2]] : tensor<1x4xi8>
// CHECK:         }
func.func @bitwise_or_broadcast_chlo(%arg0: tensor<1xi8>, %arg1: tensor<1x4xi8>) -> tensor<1x4xi8> {
  %0 = "chlo.broadcast_or"(%arg0, %arg1) {broadcast_dimensions = array<i64: 1>} : (tensor<1xi8>, tensor<1x4xi8>) -> tensor<1x4xi8>
  func.return %0 : tensor<1x4xi8>
}

// CHECK-LABEL:   func @bitwise_or_dynamic(

}

func.func @RemoveTrivialSubInt8(%arg0: tensor<2x2xi8>) -> tensor<2x2xi8> {
  %cst = arith.constant dense<0> : tensor<2x2xi8>
  %0 = "tf.Sub"(%arg0, %cst) : (tensor<2x2xi8>, tensor<2x2xi8>) -> tensor<2x2xi8>
  func.return %0 : tensor<2x2xi8>

  // CHECK-LABEL: RemoveTrivialSubInt8
  // CHECK-NEXT: return %arg0 : tensor<2x2xi8>
}

func.func @RemoveTrivialMul(%arg0: tensor<2x2xf32>) -> tensor<2x2xf32> {

  %cst = "tfl.pseudo_qconst"() {qtype = tensor<1x2x!quant.uniform<u8:f32, 1.0>>, value = dense<-76> : tensor<1x2xi8>} : () -> tensor<1x2x!quant.uniform<u8:f32, 1.0>>

    return %0 : tensor<1x3xf32>
  }
}

// CHECK-LABEL: quantize_dot_general_per_tensor
// CHECK-SAME: %[[ARG0:.+]]: tensor<1x2xf32>
// CHECK: %[[CST:.+]] = stablehlo.constant() <{value = dense<127> : tensor<2x3xi8>}> : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32, 0.0023622048182750312>>

TEST_F(QuantizationReportTest, InitializeWithModuleOp) {
  constexpr absl::string_view kQuantizedDotGeneral = R"mlir(
    func.func @main(%arg0: tensor<1x2xf32>) -> tensor<1x3xf32> {
      %0 = stablehlo.constant() {value = dense<127> : tensor<2x3xi8>} : () -> tensor<2x3x!quant.uniform<i8<-127:127>:f32:1, {1.000000e+0,2.000000e+0,3.000000e+0}>>
      %1 = stablehlo.uniform_quantize %arg0 : (tensor<1x2xf32>) -> tensor<1x2x!quant.uniform<i8:f32, 4.000000e+0>>

  func.return %1 : tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>

// CHECK:  %[[cst:.*]] = "tfl.pseudo_qconst"() <{qtype = tensor<2x2x!quant.uniform<u8:f32, 7.8431372549019615E-4:128>>, value = dense<0> : tensor<2x2xi8>}>
// CHECK:  return %[[cst]]
}

// CHECK-LABEL: QuantizeFloatConst4Bits
func.func @QuantizeFloatConst4Bits() -> tensor<2x4x!quant.uniform<i4:f32, 2.500000e-01:-1>> {

}

// -----

func.func @batchMatMulV3MatrixInt8(%arg0: tensor<4x5xi8>, %arg1: tensor<5x6xi8>) -> tensor<4x6xi32> {
  %0 = "tf.BatchMatMulV3"(%arg0, %arg1) : (tensor<4x5xi8>, tensor<5x6xi8>) -> tensor<4x6xi32>
  func.return %0 : tensor<4x6xi32>

  // CHECK-LABEL: batchMatMulV3MatrixInt8
  // CHECK: %0 = "tf.BatchMatMulV3"(%arg0, %arg1) : (tensor<4x5xi8>, tensor<5x6xi8>) -> tensor<4x6xi32>
  // CHECK: return %0 : tensor<4x6xi32>

func.func @no_pushdown_gather_with_no_reduction(%arg0: tensor<2xi32>) -> tensor<2x2xf32> {
  %w = "tfl.pseudo_qconst"() {qtype = tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>, value = dense<127> : tensor<2x2xi8>} : () -> tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>
  %dq_w = "tfl.dequantize"(%w) : (tensor<2x2x!quant.uniform<i8<-127:127>:f32, 1.000000e+00>>) -> tensor<2x2xf32>

// CHECK-LABEL: @quantize_constant
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x3xf32>
func.func @quantize_constant(%arg0: tensor<1x3xf32>) -> tensor<1x2xf32> {
  // CHECK-DAG: %[[QCST:.*]] = stablehlo.constant() <{value = dense<-78> : tensor<3x2xi8>}> : () -> tensor<3x2x!quant.uniform<i8<-127:127>:f32, 5.000000e-03>>
  // CHECK-DAG: %[[Q1:.*]] = stablehlo.uniform_quantize %[[ARG0]]
  // CHECK-NOT: "quantfork.qcast"
  // CHECK: %[[DOT:.*]] = stablehlo.dot %[[Q1]], %[[QCST]]