// RUN: tf-tfrt-opt -tf-to-tfrt %s | FileCheck %s --dump-input=fail

// _output_shapes and f.* attributes are removed during tf-to-tfrt lowering.
// CHECK-LABEL: func @remove_unused_attr
func.func @remove_unused_attr() {
  // CHECK: %out_op_chain = tfrt_fallback_async.executeop.seq(%arg0) key(0) cost({{.*}}) device("/device:CPU:0") "tf.SomeOp2"()

    // CHECK: tf_executor.island wraps "tf.VarHandleOp"() <{container = "a", shared_name = "x"}> {_mlir_name = "x", _output_shapes = [#tf_type.shape<>], allowed_devices = [], device = "/device:CPU:0", dtype = i64, shape = #tf_type.shape<>} : () -> tensor<!tf_type.resource<tensor<i64>>>

# Verify importing with _output_shapes enabled works as expected.

node {
  name: "_Arg"
  op: "_Arg"
  attr {
    key: "T"
    value {
      type: DT_STRING
    }
  }
  attr {
    key: "index"
    value {
      i: 0
    }
  }
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 1

  %0 = "tf.ReadVariableOp"(%handle) {_output_shapes = ["tfshape$dim { size: 3 }"], device = "/device:CPU:0", dtype = f32} : (tensor<!tf_type.resource<tensor<3xf32>>>) -> tensor<3xf32>

  // CHECK: device = "gpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>

// CHECK-NEXT:   name: "input0"
// CHECK-NEXT:   op: "_Arg"
// CHECK:          key: "T"
// CHECK-NEXT:     value {
// CHECK-NEXT:       type: DT_INT32
// CHECK-NEXT:     }
// CHECK:          key: "_output_shapes"
// CHECK-NEXT:     value {
// CHECK-NEXT:       list {
// CHECK-NEXT:         shape {
// CHECK-NEXT:           dim {
// CHECK-NEXT:             size: 10
// CHECK:        name: "output0"

  // CHECK: device = "xpu"
  %1 = "tf.MatMul"(%arg0, %0) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  // CHECK: device = "cpu"
  %2 = "tf.Relu"(%1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "cpu"} : (tensor<3x3xf32>) -> tensor<3x3xf32>

# RUN: tf-mlir-translate -graphdef-to-mlir -tf-enable-shape-inference-on-import=false %s -o - | FileCheck %s

node {
  name: "unnamed"
  op: "foo"
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
        }
      }
    }
  }
  attr {
    key: "_disable_call_shape_inference"
    value {
      b: true
    }
  }
  experimental_debug_info {
  }
}
node {
  name: "unnamed1"

# CHECK: tf.Const
# CHECK-SAME: value = dense<""> : tensor<3x!tf_type.string>

node {
  name: "save/SaveV2/shape_and_slices"
  op: "Const"
  attr {
    key: "_output_shapes"
    value {
      list {
        shape {
          dim {
            size: 3
          }
        }
      }
    }
  }
  attr {
    key: "dtype"
    value {
      type: DT_STRING

    %arg1: tensor<1x3xf32> {tf_saved_model.index_path = [0]})
      -> (tensor<3x3xf32> {tf_saved_model.index_path = []}) {
  // CHECK: {{%.*}} = corert.get_op_handler %arg0 "/device:GPU:0"
  %2 = "tf.MatMul"(%arg0, %arg1) {T = f32, _output_shapes = ["tfshape$dim { size: 3 } dim { size: 3 }"], device = "/device:GPU:0", transpose_a = false, transpose_b = false} : (tensor<3x1xf32>, tensor<1x3xf32>) -> tensor<3x3xf32>
  func.return %2 : tensor<3x3xf32>