text: # coding=utf-8
  # Copyright 2018 The HugginFace 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 Transformer XL checkpoint and datasets."""
  
  from __future__ import absolute_import, division, print_function
  
  import argparse
  import os
  import sys
  from io import open
  
  import torch
  
  import external.pytorch_pretrained_bert.tokenization_transfo_xl as data_utils
  from external.pytorch_pretrained_bert import (CONFIG_NAME,
                                                WEIGHTS_NAME,
                                                TransfoXLConfig,
                                                TransfoXLLMHeadModel,
                                                load_tf_weights_in_transfo_xl)
  from external.pytorch_pretrained_bert.tokenization_transfo_xl import (CORPUS_NAME,
                                                                        VOCAB_NAME)
  
  if sys.version_info[0] == 2:
      import cPickle as pickle
  else:
      import pickle
  
  # We do this to be able to load python 2 datasets pickles
  # See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
  data_utils.Vocab = data_utils.TransfoXLTokenizer
  data_utils.Corpus = data_utils.TransfoXLCorpus
  sys.modules['data_utils'] = data_utils
  sys.modules['vocabulary'] = data_utils
  
  def convert_transfo_xl_checkpoint_to_pytorch(tf_checkpoint_path,
                                               transfo_xl_config_file,
                                               pytorch_dump_folder_path,
                                               transfo_xl_dataset_file):
      if transfo_xl_dataset_file:
          # Convert a pre-processed corpus (see original TensorFlow repo)
          with open(transfo_xl_dataset_file, "rb") as fp:
              corpus = pickle.load(fp, encoding="latin1")
          # Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
          pytorch_vocab_dump_path = pytorch_dump_folder_path + '/' + VOCAB_NAME
          print("Save vocabulary to {}".format(pytorch_vocab_dump_path))
          corpus_vocab_dict = corpus.vocab.__dict__
          torch.save(corpus_vocab_dict, pytorch_vocab_dump_path)
  
          corpus_dict_no_vocab = corpus.__dict__
          corpus_dict_no_vocab.pop('vocab', None)
          pytorch_dataset_dump_path = pytorch_dump_folder_path + '/' + CORPUS_NAME
          print("Save dataset to {}".format(pytorch_dataset_dump_path))
          torch.save(corpus_dict_no_vocab, pytorch_dataset_dump_path)
  
      if tf_checkpoint_path:
          # Convert a pre-trained TensorFlow model
          config_path = os.path.abspath(transfo_xl_config_file)
          tf_path = os.path.abspath(tf_checkpoint_path)
  
          print("Converting Transformer XL checkpoint from {} with config at {}".format(tf_path, config_path))
          # Initialise PyTorch model
          if transfo_xl_config_file == "":
              config = TransfoXLConfig()
          else:
              config = TransfoXLConfig(transfo_xl_config_file)
          print("Building PyTorch model from configuration: {}".format(str(config)))
          model = TransfoXLLMHeadModel(config)
  
          model = load_tf_weights_in_transfo_xl(model, config, tf_path)
          # Save pytorch-model
          pytorch_weights_dump_path = os.path.join(pytorch_dump_folder_path, WEIGHTS_NAME)
          pytorch_config_dump_path = os.path.join(pytorch_dump_folder_path, CONFIG_NAME)
          print("Save PyTorch model to {}".format(os.path.abspath(pytorch_weights_dump_path)))
          torch.save(model.state_dict(), pytorch_weights_dump_path)
          print("Save configuration file to {}".format(os.path.abspath(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()
      parser.add_argument("--pytorch_dump_folder_path",
                          default = None,
                          type = str,
                          required = True,
                          help = "Path to the folder to store the PyTorch model or dataset/vocab.")
      parser.add_argument("--tf_checkpoint_path",
                          default = "",
                          type = str,
                          help = "An optional path to a TensorFlow checkpoint path to be converted.")
      parser.add_argument("--transfo_xl_config_file",
                          default = "",
                          type = str,
                          help = "An optional config json file corresponding to the pre-trained BERT model. \n"
                              "This specifies the model architecture.")
      parser.add_argument("--transfo_xl_dataset_file",
                          default = "",
                          type = str,
                          help = "An optional dataset file to be converted in a vocabulary.")
      args = parser.parse_args()
      convert_transfo_xl_checkpoint_to_pytorch(args.tf_checkpoint_path,
                                       args.transfo_xl_config_file,
                                       args.pytorch_dump_folder_path,
                                       args.transfo_xl_dataset_file)
  