text: # Copyright 2020 ByteDance Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  import os
  
  import tensorflow as tf
  from absl import logging
  
  from neurst.exps import register_exp
  from neurst.exps.sequence_generator import SequenceGenerator
  from neurst.models.model_utils import summary_model_variables
  from neurst.training import training_utils
  from neurst.utils.configurable import ModelConfigs
  from neurst.utils.flags_core import Flag
  
  
  @register_exp("generation_savedmodel")
  class SequenceGeneratorSavedmodel(SequenceGenerator):
      """ Main exps for exporting savedmodel for sequence generation. """
  
      def __init__(self, args, **kwargs):
          """ Initializes a util class for exporting savedmodel for sequence generation. """
          self._export_path = args["export_path"]
          assert self._export_path, "`export_path` must be provided."
          self._version = args["version"]
          super(SequenceGeneratorSavedmodel, self).__init__(args, **kwargs)
  
      @staticmethod
      def class_or_method_args():
          this_args = super(SequenceGeneratorSavedmodel,
                            SequenceGeneratorSavedmodel).class_or_method_args()
          this_args.extend([
              Flag("export_path", dtype=Flag.TYPE.STRING, default=None,
                   help="The path to the savedmodel."),
              Flag("version", dtype=Flag.TYPE.INTEGER, default=1,
                   help="The version of the model."),
          ])
          return this_args
  
      def run(self):
          """ Export savedmodel for sequence generator.
  
          Step 1: Build model and restore checkpoints.
          Step 2: Export.
          """
          with training_utils.get_strategy_scope(self.strategy):
              model = self._build_and_restore_model()
              keras_model = self.build_generation_model(self.task, model, self._search_layer)
              keras_model.summary()
              summary_model_variables(keras_model)
  
          export_path = os.path.join(self._export_path, str(self._version))
          logging.info("Saving model to {}".format(export_path))
          tf.keras.models.save_model(
              keras_model,
              export_path,
              overwrite=True,
              include_optimizer=False,
              save_format=None,
              signatures=None,
              options=None)
          loaded = tf.saved_model.load(export_path)
          tf.io.gfile.copy(os.path.join(self.model_dir, ModelConfigs.MODEL_CONFIG_YAML_FILENAME),
                           os.path.join(export_path, ModelConfigs.MODEL_CONFIG_YAML_FILENAME),
                           overwrite=True)
          logging.info("========== signatures ==========")
          for x in loaded.signatures.keys():
              logging.info(f"structured outputs for {x}:")
              logging.info("    {}".format(str(loaded.signatures["serving_default"].structured_outputs)))
  