#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_traits.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/xla_sharding_util.h"
#include "xla/client/sharding_builder.h"
#include "xla/xla_data.pb.h"
#include "tsl/platform/errors.h"

namespace tensorflow {
namespace tf2xla {
namespace internal {
namespace {

#define TENSORFLOW_COMPILER_JIT_PJRT_TENSOR_BUFFER_UTIL_H_

#include <memory>

#include "absl/status/statusor.h"
#include "tensorflow/compiler/jit/pjrt_tensor_buffer.h"
#include "xla/pjrt/pjrt_client.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/framework/tensor_shape.h"

namespace tensorflow {

// Takes the device memory pointer from the PjRtBuffer and create a Tensor that

#include "tensorflow/c/experimental/stream_executor/stream_executor.h"
#include "tensorflow/c/tf_status_helper.h"
#include "xla/stream_executor/executor_cache.h"
#include "xla/stream_executor/platform.h"
#include "xla/stream_executor/stream_common.h"
#include "xla/stream_executor/stream_executor.h"
#include "tsl/platform/statusor.h"

namespace stream_executor {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_executor.h"
#include "tensorflow/compiler/mlir/tensorflow/ir/tf_saved_model.h"
#include "tensorflow/compiler/mlir/tensorflow/transforms/passes.h"
#include "xla/mlir_hlo/mhlo/IR/register.h"
#include "xla/mlir_hlo/mhlo/transforms/passes.h"
#include "tensorflow/core/ir/types/dialect.h"

int main(int argc, char** argv) {
  tensorflow::InitMlir y(&argc, &argv);

  mlir::registerAllPasses();

#include "tensorflow/compiler/tf2xla/layout_util.h"
#include "tensorflow/compiler/tf2xla/xla_helpers.h"
#include "xla/client/client_library.h"
#include "xla/shape.h"
#include "xla/stream_executor/platform_manager.h"
#include "xla/translate/mhlo_to_hlo/type_to_shape.h"
#include "tensorflow/core/framework/tensor_shape.h"
#include "tensorflow/core/lib/monitoring/cell_reader.h"

#include <ostream>

#include "tensorflow/compiler/tf2xla/tf2xla.pb.h"
#include "xla/client/xla_computation.h"
#include "tensorflow/core/platform/errors.h"
#include "tensorflow/core/platform/status.h"

namespace tensorflow {
namespace tfcompile {

using QuantizeXlaFn = std::function<Status(const tf2xla::Config& config,
                                           xla::XlaComputation* computation)>;

      llvm::cl::desc("Choose target opset."),
      llvm::cl::values(
          clEnumValN(OpSet::TF, "TF",
                     "Uses TF ops that mimic quantization behavior"),
          clEnumValN(OpSet::XLA, "XLA", "Uses TF XLA ops"),
          clEnumValN(OpSet::UNIFORM_QUANTIZED, "UNIFORM_QUANTIZED",
                     "Uses TF Uniform Quantized ops"))};

  // Initialize for tests.
  void initializeForTest() {

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_ops.h"
#include "tensorflow/compiler/mlir/tf2xla/transforms/utils.h"
#include "xla/mlir_hlo/mhlo/IR/hlo_ops.h"
#include "xla/mlir_hlo/utils/convert_op_folder.h"
#include "xla/mlir_hlo/utils/hlo_utils.h"
#include "xla/xla_data.pb.h"

namespace mlir {
namespace mhlo {

namespace {

constexpr absl::string_view kGroupSizeAttrName =

#include "tensorflow/compiler/mlir/tensorflow/ir/tf_types.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/device_util.h"
#include "tensorflow/compiler/mlir/utils/string_container_utils.h"
#include "xla/array4d.h"
#include "xla/service/computation_placer.h"
#include "xla/xla_data.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/protobuf/tpu/topology.pb.h"
#include "tensorflow/core/util/device_name_utils.h"

#include "tensorflow/compiler/jit/tests/device_compiler_test_helper.h"

#include <string>

#include "absl/strings/match.h"
#include "tensorflow/compiler/jit/xla_compilation_cache.pb.h"
#include "xla/service/hlo.pb.h"
#include "tensorflow/core/platform/path.h"
#include "tensorflow/core/public/session.h"

namespace tensorflow {
namespace {