// Warning: The API is experimental and subject to change.
PYBIND11_MODULE(_pywrap_tac_wrapper, m) {
  m.def(
      "run_tac",
      [](const std::string& model_file_path,
         const std::vector<std::string>& device_specs,
         const std::string& model_output_path) {
        return ::tflite::run_tac(model_file_path, device_specs,
                                 model_output_path);
      },
      R"pbdoc(

"""Target aware conversion for TFLite model."""

from tensorflow.compiler.mlir.lite.experimental.tac.py_wrapper import _pywrap_tac_wrapper


def run_tac(model_path, targets, output_path):
  """Run target aware conversion for the given tflite model file.

  Args:
    model_path: Path to the tflite model file.

# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

#include <Python.h>

namespace tflite {

// Run target-aware-conversion for the given tflite model with the given device
// specs.
// Warning: The API is experimental and subject to change.
bool run_tac(const std::string& model_file_path,
             const std::vector<std::string>& device_specs,
             const std::string& model_output_path);

}  // namespace tflite

}
}  // namespace

// Run target-aware-conversion for the given tflite model with the given device
// specs.
// Warning: The API is experimental and subject to changes.
bool run_tac(const std::string& model_file_path,
             const std::vector<std::string>& device_specs,
             const std::string& model_output_path) {
  mlir::TFL::tac::TacModule::Options options;