code: 
  
  
  
  import argparse
  
  import torch
  
  from transformers import NystromformerConfig, NystromformerForMaskedLM
  
  
  
  
  
  
  def 			__lowercase     (				a__     )       ->				Tuple:
  							if "model" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('model.'							,     ''     )
  							if "norm1" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('norm1'							,     'attention.output.LayerNorm'     )
  							if "norm2" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('norm2'							,     'output.LayerNorm'     )
  							if "norm" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('norm'							,     'LayerNorm'     )
  							if "transformer" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.split('.'     )[0].split('_'     )[-1]
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace(f"""transformer_{layer_num}"""							,     f"""encoder.layer.{layer_num}"""     )
  							if "mha.attn" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('mha.attn'							,     'attention.self'     )
  							if "mha" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('mha'							,     'attention'     )
  							if "W_q" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('W_q'							,     'self.query'     )
  							if "W_k" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('W_k'							,     'self.key'     )
  							if "W_v" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('W_v'							,     'self.value'     )
  							if "ff1" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('ff1'							,     'intermediate.dense'     )
  							if "ff2" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('ff2'							,     'output.dense'     )
  							if "ff" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('ff'							,     'output.dense'     )
  							if "mlm_class" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('mlm.mlm_class'							,     'cls.predictions.decoder'     )
  							if "mlm" in orig_key:
  														__SCREAMING_SNAKE_CASE								=      orig_key.replace('mlm'							,     'cls.predictions.transform'     )
  							if "cls" not in orig_key:
  														__SCREAMING_SNAKE_CASE								=      'nystromformer.' + orig_key
  
  							return orig_key
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__     )       ->				Tuple:
  							for key in orig_state_dict.copy().keys():
  														__SCREAMING_SNAKE_CASE								=      orig_state_dict.pop(a__     )
  
  														if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
  																					continue
  														else:
  																					__SCREAMING_SNAKE_CASE								=      val
  
  							__SCREAMING_SNAKE_CASE								=      orig_state_dict['cls.predictions.decoder.bias']
  							__SCREAMING_SNAKE_CASE								=      (
  							    torch.arange(config.max_position_embeddings     ).expand((1, -1)     ) + 2
  							)
  
  							return orig_state_dict
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      torch.load(a__							,     map_location='cpu'     )['model_state_dict']
  							__SCREAMING_SNAKE_CASE								=      NystromformerConfig.from_json_file(a__     )
  							__SCREAMING_SNAKE_CASE								=      NystromformerForMaskedLM(a__     )
  
  							__SCREAMING_SNAKE_CASE								=      convert_checkpoint_helper(a__							,     a__     )
  
  							model.load_state_dict(a__     )
  							model.eval()
  							model.save_pretrained(a__     )
  
  							print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"""     )
  
  
  if __name__ == "__main__":
  			lowerCAmelCase__	:  Any 		=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__	:  Union[str, Any] 		=parser.parse_args()
  			convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__     )       ->				int:
  							return int(a__     ) if x - int(a__     ) <= 0 else int(a__     ) + 1
  
  
  if __name__ == "__main__":
  			import doctest
  
  			doctest.testmod()
  
  
  
  
  
  
  