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 OpenAI GPT checkpoint."""
  
  
  import argparse
  import logging
  
  import torch
  
  from transformers import CONFIG_NAME, WEIGHTS_NAME, GPT2Config, GPT2Model, load_tf_weights_in_gpt2
  
  
  logging.basicConfig(level=logging.INFO)
  
  
  def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
      # Construct model
      if gpt2_config_file == "":
          config = GPT2Config()
      else:
          config = GPT2Config.from_json_file(gpt2_config_file)
      model = GPT2Model(config)
  
      # Load weights from numpy
      load_tf_weights_in_gpt2(model, config, gpt2_checkpoint_path)
  
      # Save pytorch-model
      pytorch_weights_dump_path = pytorch_dump_folder_path + "/" + WEIGHTS_NAME
      pytorch_config_dump_path = pytorch_dump_folder_path + "/" + CONFIG_NAME
      print("Save PyTorch model to {}".format(pytorch_weights_dump_path))
      torch.save(model.state_dict(), pytorch_weights_dump_path)
      print("Save configuration file to {}".format(pytorch_config_dump_path))
      with open(pytorch_config_dump_path, "w", encoding="utf-8") as f:
          f.write(config.to_json_string())
  
  
  if __name__ == "__main__":
      parser = argparse.ArgumentParser()
      # Required parameters
      parser.add_argument(
          "--gpt2_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
      )
      parser.add_argument(
          "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
      )
      parser.add_argument(
          "--gpt2_config_file",
          default="",
          type=str,
          help="An optional config json file corresponding to the pre-trained OpenAI model. \n"
          "This specifies the model architecture.",
      )
      args = parser.parse_args()
      convert_gpt2_checkpoint_to_pytorch(args.gpt2_checkpoint_path, args.gpt2_config_file, args.pytorch_dump_folder_path)
  