code: 
  
  
  '''simple docstring'''
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  lowerCamelCase							:     Optional[int]								=			sys.argv[1]
  lowerCamelCase							:     List[str]								=			sys.argv[2]
  lowerCamelCase							:     List[Any]								=			int(sys.argv[3])
  
  lowerCamelCase							:     Dict								=			0
  
  lowerCamelCase							:     Union[str, Any]								=			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:
    lowerCamelCase							:     Tuple								=			line.rstrip()
  
    if not line:
     print(line)
     lowerCamelCase							:     Tuple								=			0
     continue
  
    lowerCamelCase							:     List[str]								=			line.split()[0]
  
    lowerCamelCase							:     int								=			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)
     lowerCamelCase							:     int								=			current_subwords_len
     continue
  
    subword_len_counter += current_subwords_len
  
    print(line)
  
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_torch_available,
      is_vision_available,
  )
  
  
  lowerCamelCase							:     Optional[Any]								=			{
      'configuration_mobilevitv2': [
          'MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP',
          'MobileViTV2Config',
          'MobileViTV2OnnxConfig',
      ],
  }
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   lowerCamelCase							:     Optional[int]								=			[
       'MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MobileViTV2ForImageClassification',
       'MobileViTV2ForSemanticSegmentation',
       'MobileViTV2Model',
       'MobileViTV2PreTrainedModel',
   ]
  
  if TYPE_CHECKING:
   from .configuration_mobilevitva import (
       MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
       MobileViTVaConfig,
       MobileViTVaOnnxConfig,
   )
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )
  
  else:
   import sys
  
   lowerCamelCase							:     Dict								=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  