code: 
  
  
  
  
  
  '''simple docstring'''
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_       )      ->int:
  
  
  
  
  
  
  
  	if "model" in orig_key:
  		snake_case__  					=      orig_key.replace('model.'				,							''       )
  	if "norm1" in orig_key:
  		snake_case__  					=      orig_key.replace('norm1'				,							'attention.output.LayerNorm'       )
  	if "norm2" in orig_key:
  		snake_case__  					=      orig_key.replace('norm2'				,							'output.LayerNorm'       )
  	if "norm" in orig_key:
  		snake_case__  					=      orig_key.replace('norm'				,							'LayerNorm'       )
  	if "transformer" in orig_key:
  		snake_case__  					=      orig_key.split('.'       )[0].split('_'       )[-1]
  		snake_case__  					=      orig_key.replace(f'''transformer_{layer_num}'''				,							f'''encoder.layer.{layer_num}'''       )
  	if "mha.attn" in orig_key:
  		snake_case__  					=      orig_key.replace('mha.attn'				,							'attention.self'       )
  	if "mha" in orig_key:
  		snake_case__  					=      orig_key.replace('mha'				,							'attention'       )
  	if "W_q" in orig_key:
  		snake_case__  					=      orig_key.replace('W_q'				,							'self.query'       )
  	if "W_k" in orig_key:
  		snake_case__  					=      orig_key.replace('W_k'				,							'self.key'       )
  	if "W_v" in orig_key:
  		snake_case__  					=      orig_key.replace('W_v'				,							'self.value'       )
  	if "ff1" in orig_key:
  		snake_case__  					=      orig_key.replace('ff1'				,							'intermediate.dense'       )
  	if "ff2" in orig_key:
  		snake_case__  					=      orig_key.replace('ff2'				,							'output.dense'       )
  	if "ff" in orig_key:
  		snake_case__  					=      orig_key.replace('ff'				,							'output.dense'       )
  	if "mlm_class" in orig_key:
  		snake_case__  					=      orig_key.replace('mlm.mlm_class'				,							'cls.predictions.decoder'       )
  	if "mlm" in orig_key:
  		snake_case__  					=      orig_key.replace('mlm'				,							'cls.predictions.transform'       )
  	if "cls" not in orig_key:
  		snake_case__  					=      'nystromformer.' + orig_key
  
  	return orig_key
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_       )      ->Any:
  
  
  
  
  
  
  
  	for key in orig_state_dict.copy().keys():
  		snake_case__  					=      orig_state_dict.pop(UpperCAmelCase_       )
  
  		if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
  			continue
  		else:
  			snake_case__  					=      val
  
  	snake_case__  					=      orig_state_dict['cls.predictions.decoder.bias']
  	snake_case__  					=      (
  	    torch.arange(config.max_position_embeddings       ).expand((1, -1)       ) + 2
  	)
  
  	return orig_state_dict
  
  
  
  
  def 	__lowerCamelCase (  UpperCAmelCase_				,							UpperCAmelCase_				,							UpperCAmelCase_       )      ->Dict:
  	snake_case__  					=      torch.load(UpperCAmelCase_				,							map_location='cpu'       )['model_state_dict']
  	snake_case__  					=      NystromformerConfig.from_json_file(UpperCAmelCase_       )
  	snake_case__  					=      NystromformerForMaskedLM(UpperCAmelCase_       )
  
  	snake_case__  					=      convert_checkpoint_helper(UpperCAmelCase_				,							UpperCAmelCase_       )
  
  	model.load_state_dict(UpperCAmelCase_       )
  	model.eval()
  	model.save_pretrained(UpperCAmelCase_       )
  
  	print(f'''Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'''       )
  
  
  if __name__ == "__main__":
  			a__				: List[str]									=    argparse.ArgumentParser()
  			# Required parameters
  			parser.add_argument(
  			    '''--pytorch_model_path''', default=None, type=str, required=True, help='''Path to Nystromformer pytorch checkpoint.'''
  			)
  			parser.add_argument(
  			    '''--config_file''',
  			    default=None,
  			    type=str,
  			    required=True,
  			    help='''The json file for Nystromformer model config.''',
  			)
  			parser.add_argument(
  			    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
  			)
  			a__				: int									=    parser.parse_args()
  			convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
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)
  
  
  
  