code: 
  
  
  '''simple docstring'''
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	barcode //= 10  # exclude the last digit
  	__magic_name__ :					Optional[Any]  			=  False
  	__magic_name__ :					Tuple  			=  0
  
  	# extract and check each digit
  	while barcode != 0:
  		__magic_name__ :					Optional[int]  			=  1 if checker else 3
  		s += mult * (barcode % 10)
  		barcode //= 10
  		__magic_name__ :					Union[str, Any]  			=  not checker
  
  	return (10 - (s % 10)) % 10
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	return len(str(lowerCAmelCase	)	) == 13 and get_check_digit(lowerCAmelCase	) == barcode % 10
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if str(lowerCAmelCase	).isalpha():
  		__magic_name__ :					Optional[Any]  			=  f'Barcode \'{barcode}\' has alphabetic characters.'
  		raise ValueError(lowerCAmelCase	)
  	elif int(lowerCAmelCase	) < 0:
  		raise ValueError('The entered barcode has a negative value. Try again.'	)
  	else:
  		return int(lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  							lowerCAmelCase								=    get_barcode(input('''Barcode: ''').strip())
  
  							if is_valid(barcode):
  														print(F'\'{barcode}\' is a valid barcode.')
  							else:
  														print(F'\'{barcode}\' is NOT a valid barcode.')
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}