// RUN: tf-tfrt-opt -tfrt-test-tensor-array-effect -verify-diagnostics %s | FileCheck %s

// CHECK-LABEL: @test_tensor_array_effect
// expected-remark@+1 {{HasAtMostTensorArrayEffect: 1}}

#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/OpImplementation.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tfrt/basic_kernels/opdefs/types.h"  // from @tf_runtime

namespace tfrt {
namespace fallback_common {

template <typename OpTy>
mlir::LogicalResult VerifyExecuteOpCommon(OpTy op) {
  auto op_attr_array = op.getOpAttrs().getValue();

See the License for the specific language governing permissions and
limitations under the License.
==============================================================================*/
#include "tensorflow/compiler/mlir/tfrt/ir/mlrt/mlrt_ops.h"

#include "mlir/IR/Builders.h"  // from @llvm-project
#include "mlir/IR/BuiltinTypes.h"  // from @llvm-project
#include "mlir/IR/DialectImplementation.h"  // from @llvm-project

#ifdef MLRT_TF_OPS
#else
#define MLRT_TF_OPS

include "tensorflow/compiler/mlir/tfrt/ir/mlrt/tf_mlrt_dialect.td"
include "tensorflow/compiler/mlir/tfrt/ir/mlrt/mlrt_dialect.td"
include "tensorflow/compiler/mlir/tensorflow/ir/tf_op_base.td"
include "tfrt/compiler/opdefs/tfrt_op_interfaces.td"  // from @tf_runtime
include "tfrt/compiler/opdefs/tfrt_traits.td"  // from @tf_runtime

#include "mlir/Dialect/Func/IR/FuncOps.h"  // from @llvm-project
#include "mlir/IR/BuiltinOps.h"  // from @llvm-project
#include "tensorflow/core/platform/status.h"
#include "tensorflow/core/tfrt/fallback/cost_recorder.h"
#include "tensorflow/core/tfrt/fallback/op_cost_map.pb.h"

namespace tensorflow {
namespace tfrt_compiler {

// Analyze costs for tensorflow operations.
//

// RUN: tf-tfrt-opt -tf-executor-to-tfrt-pipeline %s | FileCheck %s

module attributes {tf.versions = {bad_consumers = [], min_consumer = 0 : i32, producer = 567 : i32}} {
  // CHECK-LABEL: func @__inference_pruned_35
  func.func @__inference_pruned_35() -> tensor<!tf_type.variant> attributes {tf.entry_function = {control_outputs = "", inputs = "", outputs = "flatmapdataset__4_RetVal"}} {

// RUN: tf-tfrt-opt -tf-executor-to-tfrt-pipeline %s | FileCheck %s --dump-input=always

func.func private @batched_function(%arg0: tensor<1x3xf32> {tf._user_specified_name = "0"}, %arg1: tensor<*x!tf_type.resource>) -> tensor<1x3xf32> attributes {tf._input_shapes = [#tf_type.shape<1x3>, #tf_type.shape<*>], tf.signature.is_stateful} {
  %0 = "tf.ReadVariableOp"(%arg1) {device = "/device:CPU:0"} : (tensor<*x!tf_type.resource>) -> tensor<1x3xf32>

  ASSERT_EQ(nullptr, outputs[0]);
}

// TODO(b/164171226): Enable this test with tfrt after AddInputList is
// supported. It is needed for IdentityN.
#ifdef PLATFORM_GOOGLE
INSTANTIATE_TEST_SUITE_P(
    UnifiedCAPI, CppGradients,
    ::testing::Combine(::testing::Values("graphdef", "mlir"),
                       /*tfrt*/ ::testing::Values(false),
                       /*executing_eagerly*/ ::testing::Values(true, false)));

// RUN: tf-tfrt-opt -split-input-file -tfrt-deduplicate-if-result %s | FileCheck %s -dump-input=fail

func.func private @then(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  func.return %x, %x : tensor<i32>, tensor<i32>
}

func.func private @else(%x: tensor<i32>, %y: tensor<i32>) -> (tensor<i32>, tensor<i32>) {
  func.return %y, %y : tensor<i32>, tensor<i32>
}

// CHECK-LABEL: then/tfrt_dedup_results

// 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"()