


from sklearn.metrics import mean_absolute_error

import datasets


snake_case__  				=			'\\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'

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


snake_case__  				=			'\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      snake_case__	(   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  ,   _UpperCAmelCase  ,   _UpperCAmelCase  ,   _UpperCAmelCase=None  ,   _UpperCAmelCase="uniform_average"   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   mean_absolute_error(_UpperCAmelCase  ,   _UpperCAmelCase  ,   sample_weight=_UpperCAmelCase  ,   multioutput=_UpperCAmelCase   )

										return {"mae": mae_score}






from __future__ import annotations

import random
import string



class      snake_case__	:
				def __init__(	self  ,   _UpperCAmelCase = None   ):




										"""simple docstring"""




										__magic_name__					:				Union[str, Any]				=   passcode or self.__passcode_creator()
										__magic_name__					:				Any				=   self.__make_key_list()
										__magic_name__					:				Tuple				=   self.__make_shift_key()
				def __str__(	self   ):




										"""simple docstring"""




										return "".join(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										for i in range(1  ,   len(_UpperCAmelCase   )  ,   2   ):
																iterlist[i] *= -1
										return iterlist
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   string.ascii_letters + string.digits
										__magic_name__					:				Optional[Any]				=   [random.choice(_UpperCAmelCase   ) for _ in range(random.randint(10  ,   20   )   )]
										return password
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Any				=   (
										    string.ascii_letters + string.digits + string.punctuation + ' \t\n'
										)

										__magic_name__					:				List[str]				=   []

										# creates points known as breakpoints to break the key_list_options at those
										# points and pivot each substring
										__magic_name__					:				List[Any]				=   sorted(set(self.__passcode   )   )
										__magic_name__					:				list[str]				=   []

										# algorithm for creating a new shuffled list, keys_l, out of key_list_options
										for i in key_list_options:
																temp_list.extend(_UpperCAmelCase   )

																# checking breakpoints at which to pivot temporary sublist and add it into
																# keys_l
																if i in breakpoints or i == key_list_options[-1]:
																						keys_l.extend(temp_list[::-1]   )
																						temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
										return keys_l
				def 			UpperCamelCase_    (	self   ):




										"""simple docstring"""




										__magic_name__					:				Dict				=   sum(self.__neg_pos([ord(_UpperCAmelCase   ) for x in self.__passcode]   )   )
										return num if num > 0 else len(self.__passcode   )
				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# decoding shift like Caesar cipher algorithm implementing negative shift or
										# reverse shift or left shift
										for i in encoded_message:
																__magic_name__					:				int				=   self.__key_list.index(_UpperCAmelCase   )
																decoded_message += self.__key_list[
																    (position - self.__shift_key) % -len(self.__key_list   )
																]

										return decoded_message





				def 			UpperCamelCase_    (	self  ,   _UpperCAmelCase   ):




										"""simple docstring"""




										__magic_name__					:				List[Any]				=   ''

										# encoding shift like Caesar cipher algorithm implementing positive shift or
										# forward shift or right shift
										for i in plaintext:
																__magic_name__					:				Dict				=   self.__key_list.index(_UpperCAmelCase   )
																encoded_message += self.__key_list[
																    (position + self.__shift_key) % len(self.__key_list   )
																]

										return encoded_message






def 			__SCREAMING_SNAKE_CASE							(					lowercase__	:str = "Hello, this is a modified Caesar cipher"					)							->	str:
						__magic_name__					:				List[str]				=   ShuffledShiftCipher()
						return cipa.decrypt(cipa.encrypt(lowercase__					)					)


if __name__ == "__main__":
			import doctest

			doctest.testmod()


