old_contents: # coding=utf-8
  # Copyright 2019-present, 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.
  """
  Preprocessing script before training DistilBERT.
  """
  from collections import Counter
  import argparse
  import pickle
  
  from utils import logger
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)")
      parser.add_argument("--data_file", type=str, default="data/dump.bert-base-uncased.pickle",
                          help="The binarized dataset.")
      parser.add_argument("--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle",
                          help="The dump file.")
      parser.add_argument("--vocab_size", default=30522, type=int)
      args = parser.parse_args()
  
      logger.info(f'Loading data from {args.data_file}')
      with open(args.data_file, 'rb') as fp:
          data = pickle.load(fp)
  
      logger.info('Counting occurences for MLM.')
      counter = Counter()
      for tk_ids in data:
          counter.update(tk_ids)
      counts = [0]*args.vocab_size
      for k, v in counter.items():
          counts[k] = v
  
      logger.info(f'Dump to {args.token_counts_dump}')
      with open(args.token_counts_dump, 'wb') as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
  
new_contents: # coding=utf-8
  # Copyright 2019-present, 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.
  """
  Preprocessing script before training DistilBERT.
  """
  from collections import Counter
  import argparse
  import pickle
  
  from examples.distillation.utils import logger
  
  if __name__ == '__main__':
      parser = argparse.ArgumentParser(description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)")
      parser.add_argument("--data_file", type=str, default="data/dump.bert-base-uncased.pickle",
                          help="The binarized dataset.")
      parser.add_argument("--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle",
                          help="The dump file.")
      parser.add_argument("--vocab_size", default=30522, type=int)
      args = parser.parse_args()
  
      logger.info(f'Loading data from {args.data_file}')
      with open(args.data_file, 'rb') as fp:
          data = pickle.load(fp)
  
      logger.info('Counting occurences for MLM.')
      counter = Counter()
      for tk_ids in data:
          counter.update(tk_ids)
      counts = [0]*args.vocab_size
      for k, v in counter.items():
          counts[k] = v
  
      logger.info(f'Dump to {args.token_counts_dump}')
      with open(args.token_counts_dump, 'wb') as handle:
          pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
  
subject: fix ModuleNotFoundError error in token counts script