code: 
  
  
  from sklearn.metrics import mean_absolute_error
  
  import datasets
  
  
  __lowerCamelCase       :       str				   =			"\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n"
  
  __lowerCamelCase       :       List[str]				   =			"\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n"
  
  
  __lowerCamelCase       :       List[Any]				   =			"\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 \"raw_values\" : Returns a full set of errors in case of multioutput input.\n\n                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric(\"mae\")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.5}\n\n    If you're using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'mae': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')\n    >>> print(results)\n    {'mae': array([0.5, 1. ])}\n"
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,_KWARGS_DESCRIPTION   )
  class 			a      (      datasets.Metric   ):
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						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							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						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	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=None	,     __UpperCamelCase="uniform_average"							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=mean_absolute_error(__UpperCamelCase	,     __UpperCamelCase	,     sample_weight=__UpperCamelCase	,     multioutput=__UpperCamelCase							)
  
  						return {"mae": mae_score}
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				barcode //= 1_0  # exclude the last digit
  				A__     :							List[str]   				=False
  				A__     :							Dict   				=0
  
  				# extract and check each digit
  				while barcode != 0:
  								A__     :							Union[str, Any]   				=1 if checker else 3
  								s += mult * (barcode % 1_0)
  								barcode //= 1_0
  								A__     :							Union[str, Any]   				=not checker
  
  				return (1_0 - (s % 1_0)) % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							bool:
  				return len(str(snake_case_	)	) == 1_3 and get_check_digit(snake_case_	) == barcode % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if str(snake_case_	).isalpha():
  								A__     :							Tuple   				=f'Barcode \'{barcode}\' has alphabetic characters.'
  								raise ValueError(snake_case_	)
  				elif int(snake_case_	) < 0:
  								raise ValueError('''The entered barcode has a negative value. Try again.'''	)
  				else:
  								return int(snake_case_	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  						__lowerCamelCase       :       List[Any]				   =			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.")
  
  
  
  