text: # coding=utf-8
  # Copyright 2018 The HuggingFace Inc. team.
  #
  # 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.
  """Convert LXMERT checkpoint."""
  
  
  import argparse
  import logging
  
  import torch
  
  from . import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
  
  
  logging.basicConfig(level=logging.INFO)
  
  
  def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, config_file, pytorch_dump_path):
      # Initialise PyTorch model
      config = LxmertConfig.from_json_file(config_file)
      print("Building PyTorch model from configuration: {}".format(str(config)))
      model = LxmertForPreTraining(config)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_lxmert(model, config, tf_checkpoint_path)
  
      # Save pytorch-model
      print("Save PyTorch model to {}".format(pytorch_dump_path))
      torch.save(model.state_dict(), pytorch_dump_path)
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--config_file",
          default=None,
          type=str,
          required=True,
          help="The config json file corresponding to the pre-trained model. \n"
          "This specifies the model architecture.",
      )
      parser.add_argument(
          "--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      args = parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  