code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
  
  
  a__				: str									=    logging.get_logger(__name__)
  
  a__				: Dict									=    {
      '''tau/splinter-base''': '''https://huggingface.co/tau/splinter-base/resolve/main/config.json''',
      '''tau/splinter-base-qass''': '''https://huggingface.co/tau/splinter-base-qass/resolve/main/config.json''',
      '''tau/splinter-large''': '''https://huggingface.co/tau/splinter-large/resolve/main/config.json''',
      '''tau/splinter-large-qass''': '''https://huggingface.co/tau/splinter-large-qass/resolve/main/config.json''',
      # See all Splinter models at https://huggingface.co/models?filter=splinter
  }
  
  
  
  
  
  
  class 							__snake_case    (				__magic_name__       ):
  	__lowerCAmelCase						=     '''splinter'''
  
  
  
  
  
  	def __init__(		self			,					UpperCamelCase_=3_0522			,					UpperCamelCase_=768			,					UpperCamelCase_=12			,					UpperCamelCase_=12			,					UpperCamelCase_=3072			,					UpperCamelCase_="gelu"			,					UpperCamelCase_=0.1			,					UpperCamelCase_=0.1			,					UpperCamelCase_=512			,					UpperCamelCase_=2			,					UpperCamelCase_=0.0_2			,					UpperCamelCase_=1E-1_2			,					UpperCamelCase_=True			,					UpperCamelCase_=0			,					UpperCamelCase_=104			,					**UpperCamelCase_			,					)			->		Any:
  		super().__init__(pad_token_id=UpperCamelCase_			,					**UpperCamelCase_		)
  
  		snake_case__  					=      vocab_size
  		snake_case__  					=      max_position_embeddings
  		snake_case__  					=      hidden_size
  		snake_case__  					=      num_hidden_layers
  		snake_case__  					=      num_attention_heads
  		snake_case__  					=      intermediate_size
  		snake_case__  					=      hidden_act
  		snake_case__  					=      hidden_dropout_prob
  		snake_case__  					=      attention_probs_dropout_prob
  		snake_case__  					=      initializer_range
  		snake_case__  					=      type_vocab_size
  		snake_case__  					=      layer_norm_eps
  		snake_case__  					=      use_cache
  		snake_case__  					=      question_token_id
  
  
  
  
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)
  
  
  
  