"""simple docstring"""




from typing import Literal

__lowerCamelCase									=	'ABCDEFGHIJKLMNOPQRSTUVWXYZ'






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							A__						  =				LETTERS if mode == '''decrypt''' else key
							A__						  =				key if mode == '''decrypt''' else LETTERS
							A__						  =				''''''
							# loop through each symbol in the message
							for symbol in message:
														if symbol.upper() in chars_a:
																					# encrypt/decrypt the symbol
																					A__						  =				chars_a.find(symbol.upper()					)
																					if symbol.isupper():
																												translated += chars_b[sym_index].upper()
																					else:
																												translated += chars_b[sym_index].lower()
														else:
																					# symbol is not in LETTERS, just add it
																					translated += symbol
							return translated






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							return translate_message(__lowerCamelCase							,     __lowerCamelCase							,     'encrypt'					)






def 	UpperCAmelCase       (						UpperCamelCase__							,     UpperCamelCase__					):






							"""simple docstring"""





							return translate_message(__lowerCamelCase							,     __lowerCamelCase							,     'decrypt'					)






def 	UpperCAmelCase       (						):






							"""simple docstring"""





							A__						  =				'''Hello World'''
							A__						  =				'''QWERTYUIOPASDFGHJKLZXCVBNM'''
							A__						  =				'''decrypt'''  # set to 'encrypt' or 'decrypt'

							if mode == "encrypt":
														A__						  =				encrypt_message(__lowerCamelCase							,     __lowerCamelCase					)
							elif mode == "decrypt":
														A__						  =				decrypt_message(__lowerCamelCase							,     __lowerCamelCase					)
							print(F'''Using the key {key}, the {mode}ed message is: {translated}'''					)


if __name__ == "__main__":
				import doctest

				doctest.testmod()
				main()









from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[Any]       =      '\\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'

a					:				int       =      '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a					:				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   lowerCamelCase						(	datasets.Metric  ):

       def 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self       ):






        '''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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






