// CHECK: %[[VAL_0:.*]] = "tf._FusedConv2D"(%arg2, %arg1, %arg0) <{data_format = "NHWC", dilations = [1, 1, 1, 1], epsilon = 0.000000e+00 : f32, explicit_paddings = [], fused_ops = ["BiasAdd", "Elu"], num_args = 1 : i64, operandSegmentSizes = array<i32: 1, 1, 1, 0>, padding = "SAME", strides = [1, 1, 1, 1], use_cudnn_on_gpu = true}> {TArgs = [f32]} : (tensor<8x32x32x3xf32>, tensor<1x1x3x128xf32>, tensor<128xf32>) -> tensor<*xf32>

  %scale = arith.constant 0.1 : f32
  %zp = arith.constant 42 : i64
  %elu_attr = tfr.constant "ELU" -> !tfr.attr
  %min, %max = "tfr.quant_act_range"(%elu_attr, %scale, %zp) : (!tfr.attr, f32, i64) -> (!tfr.tensor, !tfr.tensor)
  func.return %min, %max : !tfr.tensor, !tfr.tensor
// CHECK: %[[elu_attr:.*]] = tfr.constant "ELU" -> !tfr.attr
// CHECK: %[[min:.*]], %[[max:.*]] = tfr.quant_act_range(%[[elu_attr]]

def : Pat<(TF_OnesLikeOp AnyTensor:$arg),
          (MHLO_ConstantLike<"1"> $arg)>;

//===----------------------------------------------------------------------===//
// Elu op patterns.
//===----------------------------------------------------------------------===//

def : Pat<(TF_EluOp AnyTensor:$features),
          (MHLO_SelectOp
           (MHLO_CompareOp
              $features,

  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,

  MIRROR_PAD = 100,
  ABS = 101,
  SPLIT_V = 102,
  UNIQUE = 103,
  CEIL = 104,
  REVERSE_V2 = 105,
  ADD_N = 106,
  GATHER_ND = 107,
  COS = 108,
  WHERE = 109,
  RANK = 110,
  ELU = 111,
  REVERSE_SEQUENCE = 112,
  MATRIX_DIAG = 113,
  QUANTIZE = 114,
  MATRIX_SET_DIAG = 115,
  ROUND = 116,
  HARD_SWISH = 117,
  IF = 118,
  WHILE = 119,
  NON_MAX_SUPPRESSION_V4 = 120,

// CHECK-LABEL:cos
// CHECK:  "tfl.cos"(%arg0) : (tensor<f32>) -> tensor<f32>
}

func.func @elu(%arg0: tensor<11x16xf32>) -> tensor<11x16xf32> {
  %0 = "tf.Elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32>
  func.return %0 : tensor<11x16xf32>

// CHECK-LABEL:elu
// CHECK:  "tfl.elu"(%arg0) : (tensor<11x16xf32>) -> tensor<11x16xf32>
}

}

//===----------------------------------------------------------------------===//
// Elu op legalizations.
//===----------------------------------------------------------------------===//

// -----

// CHECK-LABEL: func @elu
func.func @elu(%arg0: tensor<1xf32>) -> tensor<1xf32> {

  func.return %0 : tensor<? x i32>
}

// -----

// CHECK-LABEL: testEluF32
func.func @testEluF32(%arg0: tensor<? x f32>) -> tensor<? x f32> {
  // CHECK: "tfl.elu"(%arg0)
  %0 = "tfl.elu"(%arg0): (tensor<? x f32>) -> tensor<? x f32>
  func.return %0#0 : tensor<? x f32>
}

// -----

// CHECK-LABEL: testTileF32

            // Others
            "AddN", "Bitcast", "Cast", "ClipByValue", "Const", "Empty",
            "Identity", "IdentityN", "Relu", "Relu6", "ReluGrad", "Relu6Grad",
            "LeakyReluGrad", "Elu", "EluGrad", "Selu", "SeluGrad", "Select",
            "SelectV2", "Transpose", "ConjugateTranspose",
            "_UnaryOpsComposition", "CollectiveReduceV2",
            "CollectiveAssignGroupV2",

}

def TF_EluOp : TF_Op<"Elu", [Pure, TF_SameOperandsAndResultTypeResolveRef]> {
  let summary = "Computes the exponential linear function.";

  let description = [{
The ELU function is defined as:

 * $ e ^ x - 1 $ if $ x < 0 $
 * $ x $ if $ x >= 0 $

Examples:

>>> tf.nn.elu(1.0)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>
>>> tf.nn.elu(0.0)