// Tests that a float `stablehlo.constant` is not converted into `tfl.qconst`.

// CHECK-LABEL: func @float_constant
func.func @float_constant() -> tensor<1x2x4x5xf32> {
  %0 = stablehlo.constant() {value = dense<1.0> : tensor<1x2x4x5xf32>} : () -> tensor<1x2x4x5xf32>
  return %0 : tensor<1x2x4x5xf32>
}

// CHECK: stablehlo.constant
// CHECK-NOT: tfl.pseudo_qconst
// CHECK-NOT: tfl.pseudo_const
// CHECK-NOT: arith.constant

}

func.func @cast(%arg0: tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32> {
  %0 = "tf.Cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
  func.return %0 : tensor<1x2x2x5xf32>

  // CHECK-LABEL: cast
  // CHECK: "tfl.cast"(%arg0) : (tensor<1x2x2x5xi32>) -> tensor<1x2x2x5xf32>
}

func.func @castFloat32ToI16(%arg0: tensor<1x2x2x5xf32>) -> tensor<1x2x2x5xi16> {

}

// -----

// CHECK-LABEL: testLogistic
func.func @testLogistic(tensor<1x2x3x4x5xf32>) -> tensor<1x2x3x4x5xf32> {
^bb0(%arg0: tensor<1x2x3x4x5xf32>):
  // CHECK: "tfl.logistic"(%arg0)
  %0 = "tfl.logistic"(%arg0): (tensor<1x2x3x4x5xf32>) -> tensor<1x2x3x4x5xf32>
  func.return %0 : tensor<1x2x3x4x5xf32>
}

// -----

// test invalid Logistic input

  %15 = "tf.Identity"(%14) {device = ""} : (tensor<1x2x4x1xf32>) -> tensor<1x2x4x1xf32>
  func.return %15 : tensor<1x2x4x1xf32>
}

// CHECK-LABEL: func @max_unpooling_2d(
// CHECK-SAME:                         %[[VAL_0:.*]]: tensor<1x1x2x1xf32>,
// CHECK-SAME:                         %[[VAL_1:.*]]: tensor<1x1x2x1xi32>) -> tensor<1x2x4x1xf32> attributes {tf._implements = "MaxUnpooling2D"} {

func.func @FakeQuantWithMinMaxVarsPerChannel(tensor<1x2x3x8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<1x2x3x8xf32> {
^bb0(%arg0: tensor<1x2x3x8xf32>, %arg1: tensor<8xf32>, %arg2: tensor<8xf32>):
  %0 = "tf.FakeQuantWithMinMaxVarsPerChannel"(%arg0, %arg1, %arg2) : (tensor<1x2x3x8xf32>, tensor<8xf32>, tensor<8xf32>) -> tensor<1x2x3x8xf32>
  func.return %0 : tensor<1x2x3x8xf32>
}

// -----