
"""simple docstring"""





def       __a	(						__lowerCamelCase    ):
   UpperCAmelCase_   :   Union[str, Any]     	=       []
   UpperCAmelCase_   :   str     	=       1

   for i in range(len(__lowerCamelCase    )    ):
      if i + 1 < len(__lowerCamelCase    ) and text[i] == text[i + 1]:
         count += 1
      else:
         encoded.append((text[i], count)    )
         UpperCAmelCase_   :   List[str]     	=       1

   return encoded







def       __a	(						__lowerCamelCase    ):
   return "".join(char * length for char, length in encoded    )


if __name__ == "__main__":
    from doctest import testmod

    testmod(name='run_length_encode', verbose=True)
    testmod(name='run_length_decode', verbose=True)





from sklearn.neural_network import MLPClassifier

__A	:   Tuple	=					[[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
__A	:   Optional[int]	=					[0, 1, 0, 0]


__A	:   int	=					MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


__A	:   str	=					[[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
__A	:   str	=					clf.predict(test)




def        __UpperCamelCase     (      _A					:							int		)				->Union[str, Any]:



							"""simple docstring"""
							return list(_A		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
