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								=    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								=    parser.parse_args()
  							convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
style_context: 
  
  
  '''simple docstring'''
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  lowerCAmelCase   :Union[str, Any]								=    '''\
  @article{scikit-learn,
   title={Scikit-learn: Machine Learning in {P}ython},
   author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
   journal={Journal of Machine Learning Research},
   volume={12},
   pages={2825--2830},
   year={2011}
  }
  '''
  
  lowerCAmelCase   :int								=    '''\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  '''
  
  
  lowerCAmelCase   :Optional[int]								=    '''
  Args:
      predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Estimated target values.
      references: array-like of shape (n_samples,) or (n_samples, n_outputs)
          Ground truth (correct) target values.
      sample_weight: array-like of shape (n_samples,), default=None
          Sample weights.
      multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"
          Defines aggregating of multiple output values. Array-like value defines weights used to average errors.
  
                   "raw_values" : Returns a full set of errors in case of multioutput input.
  
                   "uniform_average" : Errors of all outputs are averaged with uniform weight.
  
  Returns:
      mae : mean absolute error.
          If multioutput is "raw_values", then mean absolute error is returned for each output separately. If multioutput is "uniform_average" or an ndarray of weights, then the weighted average of all output errors is returned.
          MAE output is non-negative floating point. The best value is 0.0.
  Examples:
  
      >>> mae_metric = datasets.load_metric("mae")
      >>> predictions = [2.5, 0.0, 2, 8]
      >>> references = [3, -0.5, 2, 7]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {\'mae\': 0.5}
  
      If you\'re using multi-dimensional lists, then set the config as follows :
  
      >>> mae_metric = datasets.load_metric("mae", "multilist")
      >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
      >>> references = [[0, 2], [-1, 2], [8, -5]]
      >>> results = mae_metric.compute(predictions=predictions, references=references)
      >>> print(results)
      {\'mae\': 0.75}
      >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
      >>> print(results)
      {\'mae\': array([0.5, 1. ])}
  '''
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,  _KWARGS_DESCRIPTION							)
  class _lowerCamelCase (		datasets.Metric							):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Union[str, Any]    )						->				int:
  			return datasets.MetricInfo(
  			    description=_DESCRIPTION						,  citation=_CITATION						,  inputs_description=_KWARGS_DESCRIPTION						,  features=datasets.Features(self._get_feature_types()    )						,  reference_urls=[
  			        'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
  			    ]						,  )
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Tuple    )						->				List[str]:
  			if self.config_name == "multilist":
  				return {
  				    "predictions": datasets.Sequence(datasets.Value('float'    )    ),
  				    "references": datasets.Sequence(datasets.Value('float'    )    ),
  				}
  			else:
  				return {
  				    "predictions": datasets.Value('float'    ),
  				    "references": datasets.Value('float'    ),
  				}
  
  
  
  
  
  
  
  		def      __lowerCAmelCase	(      self	:		Dict						,  _A	:		Optional[int]						,  _A	:		Optional[Any]						,  _A	:		Optional[Any]=None						,  _A	:		Tuple="uniform_average"    )						->				Any:
  			__magic_name__ :					Optional[Any]  			=  mean_absolute_error(_A						,  _A						,  sample_weight=_A						,  multioutput=_A    )
  
  			return {"mae": mae_score}