code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  a__				: Union[str, Any]									=    {
      '''configuration_nystromformer''': ['''NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''NystromformerConfig'''],
  }
  
  try:
  			if not is_torch_available():
  						raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  			pass
  else:
  			a__				: List[str]									=    [
  			    '''NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
  			    '''NystromformerForMaskedLM''',
  			    '''NystromformerForMultipleChoice''',
  			    '''NystromformerForQuestionAnswering''',
  			    '''NystromformerForSequenceClassification''',
  			    '''NystromformerForTokenClassification''',
  			    '''NystromformerLayer''',
  			    '''NystromformerModel''',
  			    '''NystromformerPreTrainedModel''',
  			]
  
  
  if TYPE_CHECKING:
  			from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  			try:
  						if not is_torch_available():
  									raise OptionalDependencyNotAvailable()
  			except OptionalDependencyNotAvailable:
  						pass
  			else:
  						from .modeling_nystromformer import (
  						    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  						    NystromformerForMaskedLM,
  						    NystromformerForMultipleChoice,
  						    NystromformerForQuestionAnswering,
  						    NystromformerForSequenceClassification,
  						    NystromformerForTokenClassification,
  						    NystromformerLayer,
  						    NystromformerModel,
  						    NystromformerPreTrainedModel,
  						)
  
  
  else:
  			import sys
  
  			a__				: Union[str, Any]									=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
style_context: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import sys
  
  from transformers import AutoTokenizer
  
  
  a__				: Optional[int]									=    sys.argv[1]
  a__				: Any									=    sys.argv[2]
  a__				: str									=    int(sys.argv[3])
  
  a__				: str									=    0
  
  a__				: int									=    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:
  						a__				: Tuple									=    line.rstrip()
  
  						if not line:
  									print(line)
  									a__				: List[str]									=    0
  									continue
  
  						a__				: Optional[int]									=    line.split()[0]
  
  						a__				: Tuple									=    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)
  									a__				: List[str]									=    current_subwords_len
  									continue
  
  						subword_len_counter += current_subwords_len
  
  						print(line)
  
  
  
  