limitations under the License.
==============================================================================*/
#include "tensorflow/compiler/mlir/tensorflow/transforms/lift_variables.h"

#include <algorithm>
#include <iterator>
#include <string>
#include <tuple>
#include <type_traits>
#include <utility>
#include <vector>

#include "llvm/ADT/ArrayRef.h"

namespace mlir {
namespace tf_saved_model {

// Creates GlobalTensorOp for each variable from function arguments and converts
// them to the corresponding saved model arguments.
LogicalResult LiftVariables(ModuleOp module, ::tensorflow::Session* session);

}  // namespace tf_saved_model
}  // namespace mlir

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/transforms/lift_variables.h"
#include "tensorflow/compiler/mlir/tensorflow/utils/fake_session.h"

namespace mlir {
namespace tf_saved_model {
namespace {
using ::tensorflow::Session;

#define GEN_PASS_DEF_LIFTVARIABLESTESTPASS

    # variables found in the graph.
    checkpoint_path = os.path.join(
        self._output_saved_model_path, 'variables', 'variables'
    )
    var_name_and_shapes = checkpoint_utils.list_variables(checkpoint_path)

    # Checks that each variable's name and shape match.
    self.assertEqual(len(variable_node_defs), len(var_name_and_shapes))
    for var_name, shape in var_name_and_shapes:

#include "mlir/Pass/Pass.h"  // from @llvm-project
#include "mlir/Pass/PassManager.h"  // from @llvm-project
#include "mlir/Support/LLVM.h"  // from @llvm-project
#include "tensorflow/compiler/mlir/tensorflow/transforms/lift_variables.h"
#include "tensorflow/core/common_runtime/device_mgr.h"
#include "tensorflow/core/framework/resource_mgr.h"
#include "tensorflow/core/framework/tensor.h"
#include "tensorflow/core/platform/errors.h"

# after absl and tensorflow have run various initialization steps.
def do_test(
    create_signature,
    canonicalize=False,
    show_debug_info=False,
    use_lite=False,
    lift_variables=True,
    include_variables_in_initializers=False,
):
  """Runs test.

  1. Performs absl and tf "main"-like initialization that must run before almost
     anything else.

  bool add_default_attributes = true;

  // If set, promote tf.VarHandleOp to resource arguments for all functions.
  bool lift_variables = true;

  // Keeps the variables in initializers before lifting variables (when
  // `lift_variables == true`) or newly adding variable initialization patterns
  // in the initializer functions. One might want to set this to `true` because

//
// Args:
//   saved_model_path: File path from which to load the SavedModel.
//   tags: Tags to identify MetaGraphDef that need to be loaded.
//   lift_variables: Boolean flag that indicates whether to hoist variables
//                   after loading the SavedModel.
//
// Returns:
//   A string of textual MLIR representing the raw imported SavedModel.

          outputs={'r': tensor_info_r},
          method_name='some_function'))
  }, None, None


if __name__ == '__main__':
  common_v1.set_tf_options()

  mlir::func::registerAllExtensions(registry);
  mlir::MLIRContext context(registry);
  tensorflow::MLIRImportOptions import_options;
  import_options.upgrade_legacy = upgrade_legacy;
  import_options.lift_variables = lift_variables;
  import_options.include_variables_in_initializers =
      include_variables_in_initializers;
  auto module_or =
      ConvertSavedModelV1ToMlir(bundle, absl::Span<std::string>(exported_names),