//RUN: tf_tfl_translate --enable-stablehlo-conversion --input-mlir %s -o /tmp/temp.stablehlo; [ -f /tmp/temp.stablehlo ]


module {
func.func @main(%arg0: tensor<2xi32>) -> tensor<2xi32> {
  %0 = "tf.Mul"(%arg0, %arg0) : (tensor<2xi32>, tensor<2xi32>) -> tensor<2xi32>
  func.return %0 : tensor<2xi32>
}

// so it must be used with care.
(MUL(Q|L)const [-9] x) => (NEG(Q|L) (LEA(Q|L)8 <v.Type> x x))
(MUL(Q|L)const [-5] x) => (NEG(Q|L) (LEA(Q|L)4 <v.Type> x x))
(MUL(Q|L)const [-3] x) => (NEG(Q|L) (LEA(Q|L)2 <v.Type> x x))
(MUL(Q|L)const [-1] x) => (NEG(Q|L) x)
(MUL(Q|L)const [ 0] _) => (MOV(Q|L)const [0])
(MUL(Q|L)const [ 1] x) => x
(MUL(Q|L)const [ 3] x) => (LEA(Q|L)2 x x)
(MUL(Q|L)const [ 5] x) => (LEA(Q|L)4 x x)

			// avoid introducing spurious rounding-related errors)
			sq := new(Float).SetPrec(prec+32).Mul(got, got)
			diff := new(Float).Sub(sq, x)
			err := diff.Abs(diff).SetPrec(prec)

			// maxErr = 2**(-p+1)*√x
			one := new(Float).SetPrec(prec).SetInt64(1)
			maxErr := new(Float).Mul(new(Float).SetMantExp(one, -int(prec)+1), got)

			if err.Cmp(maxErr) >= 0 {

  %1 = "tf.BiasAdd"(%0, %bias) {T = "tfdtype$DT_FLOAT", data_format = "NHWC"}: (tensor<256x8x7x16xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32>
  %2 = "tf.Mul"(%1, %value) {T = "tfdtype$DT_FLOAT"} : (tensor<256x8x7x16xf32>, tensor<16xf32>) -> tensor<256x8x7x16xf32>
  func.return %2 : tensor<256x8x7x16xf32>

 Const 357
 Conv2D 53
 Conv2DBackpropFilter 53
 Conv2DBackpropInput 52
 DivNoNan 1
 Equal 1
 FusedBatchNorm 53
 FusedBatchNormGrad 53
 Identity 2
 MatMul 3
 MaxPool 1
 MaxPoolGrad 1
 Mean 1
 Mul 164
 Pad 1
 ReadVariableOp 646
 Relu 49
 ReluGrad 49
 Reshape 2
 ResourceApplyKerasMomentum 161
 ShapeN 50
 Softmax 1
 SparseSoftmaxCrossEntropyWithLogits 1
 Square 55
 Squeeze 1
 Sub 106
 Sum 57

  %group_assignment = "tf.Const"() { value = dense<[[0],[1]]> : tensor<2x1xi32> } : () -> tensor<2x1xi32>
  // CHECK: "mhlo.all_reduce"
  // CHECK: mhlo.mul
  %0 = "tf.XlaAllReduce"(%input, %group_assignment) {reduce_op = "Mul", mode = "CrossReplica"} : (tensor<f32>, tensor<2x1xi32>) -> tensor<f32>
  func.return %0 : tensor<f32>
}

// -----

// license that can be found in the LICENSE file.

package bug109

func f(a float64) float64 {
	e := 1.0
	e = e * a
	return e
}

/*
6g bugs/bug109.go
bugs/bug109.go:5: illegal types for operand: MUL
	(<float64>FLOAT64)
	(<float32>FLOAT32)
bugs/bug109.go:5: illegal types for operand: AS
	(<float64>FLOAT64)
bugs/bug109.go:6: illegal types for operand: RETURN
	(<float32>FLOAT32)
	(<float64>FLOAT64)

  %0 = "mhlo.broadcast_in_dim"(%cst0) <{broadcast_dimensions = dense<3> : tensor<1xi64>}> : (tensor<4xi32>) -> tensor<1x1x2x4xi32>
  // CHECK: %[[MUL:.*]] = mhlo.multiply %[[BROADCAST]], %[[ARG]] : tensor<1x1x2x4xi32>
  %1 = mhlo.multiply %0, %arg0 : tensor<1x1x2x4xi32>
  // CHECK:      return %[[MUL]] : tensor<1x1x2x4xi32>
  func.return %1 : tensor<1x1x2x4xi32>

      %outputs_18, %control_19 = "tf_executor.island"() ({
        %0 = "tf.Mul"(%outputs_16#0, %outputs_4) {device = ""} : (tensor<*xf32>, tensor<f32>) -> tensor<*xf32>
        "tf_executor.yield"(%0) : (tensor<*xf32>) -> ()
      }) : () -> (tensor<*xf32>, !tf_executor.control)
      %outputs_20, %control_21 = "tf_executor.island"() ({
        %0 = "tf.Mul"(%outputs_16#1, %outputs_8) {device = ""} : (tensor<*xf32>, tensor<f32>) -> tensor<*xf32>

    %0 = tfl.add %arg0, %arg1 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
    %1 = tfl.mul %0, %arg2 {fused_activation_function = "RELU6", tac.device = "GPU", tac.inference_type = "FLOAT"} : tensor<1xf32>
    func.return %1 : tensor<1xf32>
  }