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 BERT checkpoint."""
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import re
  import argparse
  import tensorflow as tf
  import torch
  import numpy as np
  
  from pytorch_pretrained_bert.modeling import BertConfig, BertForPreTraining, load_tf_weights_in_bert
  
  def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_path):
      # Initialise PyTorch model
      config = BertConfig.from_json_file(bert_config_file)
      print("Building PyTorch model from configuration: {}".format(str(config)))
      model = BertForPreTraining(config)
  
      # Load weights from tf checkpoint
      load_tf_weights_in_bert(model, 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 the TensorFlow checkpoint path.")
      parser.add_argument("--bert_config_file",
                          default = None,
                          type = str,
                          required = True,
                          help = "The config json file corresponding to the pre-trained BERT 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.bert_config_file,
                                       args.pytorch_dump_path)
  