content: # 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."""
  
  from __future__ import absolute_import, division, print_function
  
  import argparse
  from io import open
  
  import torch
  
  from pytorch_pretrained_bert.modeling_openai import (CONFIG_NAME, WEIGHTS_NAME,
                                                       OpenAIGPTConfig,
                                                       OpenAIGPTModel,
                                                       load_tf_weights_in_openai_gpt)
  
  
  def convert_openai_checkpoint_to_pytorch(openai_checkpoint_folder_path, openai_config_file, pytorch_dump_folder_path):
      # Construct model
      if openai_config_file == "":
          config = OpenAIGPTConfig()
      else:
          config = OpenAIGPTConfig(openai_config_file)
      model = OpenAIGPTModel(config)
  
      # Load weights from numpy
      load_tf_weights_in_openai_gpt(model, openai_checkpoint_folder_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("--openai_checkpoint_folder_path",
                          default = None,
                          type = str,
                          required = True,
                          help = "Path 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("--openai_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_openai_checkpoint_to_pytorch(args.openai_checkpoint_folder_path,
                                           args.openai_config_file,
                                           args.pytorch_dump_folder_path)
  