code: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  _A							    =	"""\
  @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}
  }
  """
  
  _A							    =	"""\
  Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
  values.
  """
  
  
  _A							    =	"""
  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	):
  
  
  
  
  				def     __a		( self							)    ->      List[str]:
  											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     __a		( self							)    ->      Optional[Any]:
  											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     __a		( self		,		snake_case_		,		snake_case_		,		snake_case_=None		,		snake_case_="uniform_average"							)    ->      str:
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =mean_absolute_error(snake_case_		,		snake_case_		,		sample_weight=snake_case_		,		multioutput=snake_case_							)
  
  											return {"mae": mae_score}
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  import sys
  
  sys.set_int_max_str_digits(0)  # type: ignore
  def 						lowerCAmelCase_       (	__a )       ->			bool:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Optional[int]	     =[0] * 11
  							SCREAMING_SNAKE_CASE			: str	     =[0] * 11
  
  							# mark last 9 numbers
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: List[str]	     =1
  														SCREAMING_SNAKE_CASE			: List[Any]	     =number // 10
  							# flag
  							SCREAMING_SNAKE_CASE			: str	     =True
  
  							# check last 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_last[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Dict	     =False
  							if not f:
  														return f
  
  							# mark first 9 numbers
  							SCREAMING_SNAKE_CASE			: Dict	     =int(str(__a )[:9] )
  
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =1
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =number // 10
  
  							# check first 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_front[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  							return f
  def 						lowerCAmelCase_       (	__a )       ->			bool:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: Any	     =[0] * 11
  
  							# mark last 9 numbers
  							for _ in range(9 ):
  														SCREAMING_SNAKE_CASE			: int	     =1
  														SCREAMING_SNAKE_CASE			: Optional[Any]	     =number // 10
  							# flag
  							SCREAMING_SNAKE_CASE			: Any	     =True
  
  							# check last 9 numbers for pandigitality
  
  							for x in range(9 ):
  														if not check_last[x + 1]:
  																					SCREAMING_SNAKE_CASE			: Any	     =False
  							return f
  def 						lowerCAmelCase_       (	)       ->			int:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =1
  							SCREAMING_SNAKE_CASE			: List[Any]	     =1
  							SCREAMING_SNAKE_CASE			: List[Any]	     =2
  							# temporary Fibonacci numbers
  
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							SCREAMING_SNAKE_CASE			: Dict	     =1
  							SCREAMING_SNAKE_CASE			: Tuple	     =2
  							# temporary Fibonacci numbers mod 1e9
  
  							# mod m=1e9, done for fast optimisation
  							SCREAMING_SNAKE_CASE			: str	     =[0] * 1000000
  							SCREAMING_SNAKE_CASE			: Any	     =1000000000
  
  							for x in range(1000000 ):
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =(aa + ba) % m
  														SCREAMING_SNAKE_CASE			: int	     =ba % m
  														SCREAMING_SNAKE_CASE			: Optional[int]	     =ca % m
  														if checka(__a ):
  																					SCREAMING_SNAKE_CASE			: List[Any]	     =1
  
  							for x in range(1000000 ):
  														SCREAMING_SNAKE_CASE			: Tuple	     =a + b
  														SCREAMING_SNAKE_CASE			: Union[str, Any]	     =b
  														SCREAMING_SNAKE_CASE			: List[str]	     =c
  														# perform check only if in tocheck
  														if tocheck[x + 3] and check(__a ):
  																					return x + 3  # first 2 already done
  							return -1
  
  
  if __name__ == "__main__":
  							print(F'''{solution() = }''')
  
  
  
  
  
  
  