code: 
  
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  SCREAMING_SNAKE_CASE__				:    Any         =    sys.argv[1]
  SCREAMING_SNAKE_CASE__				:    List[Any]         =    sys.argv[2]
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    int(sys.argv[3])
  
  SCREAMING_SNAKE_CASE__				:    Optional[int]         =    0
  
  SCREAMING_SNAKE_CASE__				:    List[str]         =    AutoTokenizer.from_pretrained(model_name_or_path)
  max_len -= tokenizer.num_special_tokens_to_add()
  
  with open(dataset, 'rt') as f_p:
   for line in f_p:
    SCREAMING_SNAKE_CASE__				:    List[Any]         =    line.rstrip()
  
    if not line:
     print(line)
     SCREAMING_SNAKE_CASE__				:    Optional[int]         =    0
     continue
  
    SCREAMING_SNAKE_CASE__				:    Dict         =    line.split()[0]
  
    SCREAMING_SNAKE_CASE__				:    str         =    len(tokenizer.tokenize(token))
  
    # Token contains strange control characters like \x96 or \x95
    # Just filter out the complete line
    if current_subwords_len == 0:
     continue
  
    if (subword_len_counter + current_subwords_len) > max_len:
     print('')
     print(line)
     SCREAMING_SNAKE_CASE__				:    List[Any]         =    current_subwords_len
     continue
  
    subword_len_counter += current_subwords_len
  
    print(line)
  
  
  
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
  
  
  SCREAMING_SNAKE_CASE__				:    List[str]         =    {
      'configuration_flaubert': ['FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FlaubertConfig', 'FlaubertOnnxConfig'],
      'tokenization_flaubert': ['FlaubertTokenizer'],
  }
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    int         =    [
       'FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'FlaubertForMultipleChoice',
       'FlaubertForQuestionAnswering',
       'FlaubertForQuestionAnsweringSimple',
       'FlaubertForSequenceClassification',
       'FlaubertForTokenClassification',
       'FlaubertModel',
       'FlaubertWithLMHeadModel',
       'FlaubertPreTrainedModel',
   ]
  
  try:
   if not is_tf_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   SCREAMING_SNAKE_CASE__				:    Dict         =    [
       'TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
       'TFFlaubertForMultipleChoice',
       'TFFlaubertForQuestionAnsweringSimple',
       'TFFlaubertForSequenceClassification',
       'TFFlaubertForTokenClassification',
       'TFFlaubertModel',
       'TFFlaubertPreTrainedModel',
       'TFFlaubertWithLMHeadModel',
   ]
  
  
  if TYPE_CHECKING:
   from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
   from .tokenization_flaubert import FlaubertTokenizer
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_flaubert import (
        FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        FlaubertForMultipleChoice,
        FlaubertForQuestionAnswering,
        FlaubertForQuestionAnsweringSimple,
        FlaubertForSequenceClassification,
        FlaubertForTokenClassification,
        FlaubertModel,
        FlaubertPreTrainedModel,
        FlaubertWithLMHeadModel,
    )
  
   try:
    if not is_tf_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_tf_flaubert import (
        TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFFlaubertForMultipleChoice,
        TFFlaubertForQuestionAnsweringSimple,
        TFFlaubertForSequenceClassification,
        TFFlaubertForTokenClassification,
        TFFlaubertModel,
        TFFlaubertPreTrainedModel,
        TFFlaubertWithLMHeadModel,
    )
  
  else:
   import sys
  
   SCREAMING_SNAKE_CASE__				:    List[Any]         =    _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  