// -----

// Tests that int constants are not folded.

// CHECK-LABEL: transpose_int
func.func @transpose_int() -> tensor<3x2xi32> {
  %0 = stablehlo.constant dense<0> : tensor<2x3xi32>
  %1 = stablehlo.transpose %0, dims = [1, 0] : (tensor<2x3xi32>) -> tensor<3x2xi32>
  return %1 : tensor<3x2xi32>
}
// CHECK: transpose

// -----

// Tests that transposing an argument cannot be folded.

// CHECK-LABEL: transpose_arg

  // converted to NHWC data format.

  // Padding in spatial dimension (NCHW)
  %0 = "tf.Const"() {value = dense<[[0, 0], [0, 0], [3, 3], [3, 3]]> : tensor<4x2xi32>} : () -> tensor<4x2xi32>

  // Reduce over spatial dimensions (NCHW)
  %1 = "tf.Const"() {value = dense<[2, 3]> : tensor<2xi32>} : () -> tensor<2xi32>

  // Transpose input: NHWC -> NCHW

      %outputs_9, %control_10 = tf_executor.island(%control_4) wraps "tf.Identity"(%outputs_7) {device = ""} : (tensor<2x3xi32>) -> tensor<2x3xi32>
      tf_executor.fetch %outputs_9 : tensor<2x3xi32>
    }
    return %0 : tensor<2x3xi32>
  }
}

// CHECK: module attributes
// CHECK-SAME:  tfl.metadata = {{{.*}}keep_stablehlo_constant = "true"{{.*}}}

func.func @main(%arg0: tensor<3x2xf32>) -> tensor<3x2xi32> {
  // CHECK-LABEL: @main
  // CHECK: "tfl.bucketize"(%arg0) <{boundaries = [0.000000e+00 : f32, 1.000000e+01 : f32, 1.000000e+02 : f32]}> : (tensor<3x2xf32>) -> tensor<3x2xi32>
  %0 = "tfl.bucketize"(%arg0) {boundaries = [0.0 : f32, 10.0 : f32, 100.0 : f32]} : (tensor<3x2xf32>) -> tensor<3x2xi32>
  func.return %0 : tensor<3x2xi32>

  // CHECK: %[[RES_INT_2:.*]] = mhlo.convert %[[RES_INT_1]] : tensor<3x2xi32>
  %result_int = "tf.Cast"(%result) {Truncate = false} : (tensor<3x2x!tf_type.qint32>) -> tensor<3x2xi32>
  // CHECK: return %[[RES_INT_2]] : tensor<3x2xi32>
  func.return %result_int : tensor<3x2xi32>

  func.return %0 : tensor<*xf32>
}

func.func @xla_func_0(%arg0: tensor<7xf32>, %arg1: tensor<f32>) -> tensor<10xf32> {
  %cst = "tf.Const"() {value = dense<0> : tensor<1x2xi32>} : () -> tensor<1x2xi32>
  %cst_0 = "tf.Const"() {value = dense<1> : tensor<1xi32>} : () -> tensor<1xi32>
  %cst_1 = "tf.Const"() {value = dense<2> : tensor<1xi32>} : () -> tensor<1xi32>

  func.return
}

func.func private @write_chain_func(%arg0: tensor<1xf32>) -> (tensor<1xf32>) {
  %cst = "tf.Const"() <{value = dense<[[0, 1]]> : tensor<1x2xi32>}> : () -> tensor<1x2xi32>
  %0 = "tf.XlaAllReduce"(%arg0, %cst) <{mode = "CrossReplica", reduce_op = "Add"}> : (tensor<1xf32>, tensor<1x2xi32>) -> tensor<1xf32>
  return %0 : tensor<1xf32>

  %0 = mhlo.constant dense<[[1, 1], [2, 2]]> : tensor<2x2xi32>
  // NO_FALLBACK: tf.MirrorPad
  // SUPPORTED_FALLBACK_DEVICE-NOT: tf.MirrorPad
  // UNSPECIFIED_FALLBACK_DEVICE: tf.MirrorPad
  // UNSUPPORTED_FALLBACK_DEVICE: tf.MirrorPad
  %1 = "tf.MirrorPad"(%arg0, %0) {mode = "SYMMETRIC"} : (tensor<2x3xcomplex<f64>>, tensor<2x2xi32>) -> tensor<4x7xcomplex<f64>>
  func.return %1 : tensor<4x7xcomplex<f64>>
}

// Note: Argument quantization sequence omitted.
// CHECK: stablehlo.dot_general %{{.+}}, %{{.+}}, contracting_dims = [1] x [0] : (tensor<1x1024xi8>, tensor<1024x3xi8>) -> tensor<1x3xi32>

// Note: Result dequantization sequence omitted.
// CHECK: return %{{.+}} : tensor<1x3xf32>

// -----

// Tests that a simple dot_general (lifted as a function) with CustomAggregators