code: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if "model" in orig_key:
  		__magic_name__ :					Any  			=  orig_key.replace('model.'	,	''	)
  	if "norm1" in orig_key:
  		__magic_name__ :					Dict  			=  orig_key.replace('norm1'	,	'attention.output.LayerNorm'	)
  	if "norm2" in orig_key:
  		__magic_name__ :					List[str]  			=  orig_key.replace('norm2'	,	'output.LayerNorm'	)
  	if "norm" in orig_key:
  		__magic_name__ :					List[Any]  			=  orig_key.replace('norm'	,	'LayerNorm'	)
  	if "transformer" in orig_key:
  		__magic_name__ :					Dict  			=  orig_key.split('.'	)[0].split('_'	)[-1]
  		__magic_name__ :					Optional[Any]  			=  orig_key.replace(f'transformer_{layer_num}'	,	f'encoder.layer.{layer_num}'	)
  	if "mha.attn" in orig_key:
  		__magic_name__ :					int  			=  orig_key.replace('mha.attn'	,	'attention.self'	)
  	if "mha" in orig_key:
  		__magic_name__ :					Tuple  			=  orig_key.replace('mha'	,	'attention'	)
  	if "W_q" in orig_key:
  		__magic_name__ :					Optional[int]  			=  orig_key.replace('W_q'	,	'self.query'	)
  	if "W_k" in orig_key:
  		__magic_name__ :					str  			=  orig_key.replace('W_k'	,	'self.key'	)
  	if "W_v" in orig_key:
  		__magic_name__ :					Dict  			=  orig_key.replace('W_v'	,	'self.value'	)
  	if "ff1" in orig_key:
  		__magic_name__ :					List[Any]  			=  orig_key.replace('ff1'	,	'intermediate.dense'	)
  	if "ff2" in orig_key:
  		__magic_name__ :					List[str]  			=  orig_key.replace('ff2'	,	'output.dense'	)
  	if "ff" in orig_key:
  		__magic_name__ :					Optional[int]  			=  orig_key.replace('ff'	,	'output.dense'	)
  	if "mlm_class" in orig_key:
  		__magic_name__ :					Any  			=  orig_key.replace('mlm.mlm_class'	,	'cls.predictions.decoder'	)
  	if "mlm" in orig_key:
  		__magic_name__ :					Tuple  			=  orig_key.replace('mlm'	,	'cls.predictions.transform'	)
  	if "cls" not in orig_key:
  		__magic_name__ :					Any  			=  'nystromformer.' + orig_key
  
  	return orig_key
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	,	lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for key in orig_state_dict.copy().keys():
  		__magic_name__ :					str  			=  orig_state_dict.pop(lowerCAmelCase	)
  
  		if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
  			continue
  		else:
  			__magic_name__ :					List[str]  			=  val
  
  	__magic_name__ :					Dict  			=  orig_state_dict['cls.predictions.decoder.bias']
  	__magic_name__ :					Optional[int]  			=  (
  	    torch.arange(config.max_position_embeddings	).expand((1, -1)	) + 2
  	)
  
  	return orig_state_dict
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					str  			=  torch.load(lowerCAmelCase	,	map_location='cpu'	)['model_state_dict']
  	__magic_name__ :					Tuple  			=  NystromformerConfig.from_json_file(lowerCAmelCase	)
  	__magic_name__ :					List[str]  			=  NystromformerForMaskedLM(lowerCAmelCase	)
  
  	__magic_name__ :					Any  			=  convert_checkpoint_helper(lowerCAmelCase	,	lowerCAmelCase	)
  
  	model.load_state_dict(lowerCAmelCase	)
  	model.eval()
  	model.save_pretrained(lowerCAmelCase	)
  
  	print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'	)
  
  
  if __name__ == "__main__":
  							lowerCAmelCase   :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.'''
  							)
  							lowerCAmelCase   :int								=    parser.parse_args()
  							convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  '''simple docstring'''
  
  import math
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
  		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
  		raise TypeError(lowerCAmelCase	)
  	if number < 1:
  		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
  		raise ValueError(lowerCAmelCase	)
  	__magic_name__ :					Tuple  			=  [number]
  	while number != 1:
  		if number % 2 == 0:
  			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
  		else:
  			__magic_name__ :					Union[str, Any]  			=  math.floor(
  			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
  		sequence.append(lowerCAmelCase	)
  	return sequence
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()