new_contents: # coding=utf-8
  # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
  # Copyright (c) 2018 Microsoft and The HuggingFace Inc.  All rights reserved.
  #
  # 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.
  """ Finetuning seq2seq models for sequence generation.
  
  We use the procedure described in [1] to finetune models for sequence
  generation. Let S1 and S2 be the source and target sequence respectively; we
  pack them using the start of sequence [SOS] and end of sequence [EOS] token:
  
      [SOS] S1 [EOS] S2 [EOS]
  
  We then mask a fixed percentage of token from S2 at random and learn to predict
  the masked words. [EOS] can be masked during finetuning so the model learns to
  terminate the generation process.
  
  [1] Dong Li, Nan Yang, Wenhui Wang, Furu Wei, Xiaodong Liu, Yu Wang, Jianfeng
  Gao, Ming Zhou, and Hsiao-Wuen Hon.  “Unified Language Model Pre-Training for
  Natural Language Understanding and Generation.” (May 2019) ArXiv:1905.03197
  """
  
  import logging
  import random
  
  import numpy as np
  import torch
  
  logger = logging.getLogger(__name__)
  
  
  def set_seed(args):
      random.seed(args.seed)
      np.random.seed(args.seed)
      torch.manual_seed(args.seed)
      if args.n_gpu > 0:
          torch.cuda.manual_seed_all(args.seed)
  
  
  def train(args, train_dataset, model, tokenizer):
      """ Fine-tune the pretrained model on the corpus. """
      # Data sampler
      # Data loader
      # Training
      raise NotImplementedError
  
  
  def evaluate(args, model, tokenizer, prefix=""):
      raise NotImplementedError
  
  
  def main():
      raise NotImplementedError
  
  
  def __main__():
      main()
  
subject: add base for seq2seq finetuning