




'''simple docstring'''





from __future__ import annotations

import random
import string




class      UpperCamelCase       :



  def __init__(			self		,     __lowerCAmelCase = None ):







      '''simple docstring'''


      _a				:							List[Any]							       =      passcode or self.__passcode_creator()
      _a				:							List[str]							       =      self.__make_key_list()
      _a				:							Optional[int]							       =      self.__make_shift_key()



  def __str__(			self ):







      '''simple docstring'''


      return "".join(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      for i in range(1		,     len(__lowerCAmelCase )		,     2 ):
          iterlist[i] *= -1
      return iterlist



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[int]							       =      string.ascii_letters + string.digits
      _a				:							str							       =      [random.choice(__lowerCAmelCase ) for _ in range(random.randint(10		,     20 ) )]
      return password



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Dict							       =      (
          string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
      )

      _a				:							str							       =      []

      # creates points known as breakpoints to break the key_list_options at those
      # points and pivot each substring
      _a				:							Dict							       =      sorted(set(self.__passcode ) )
      _a				:							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(__lowerCAmelCase )

          # 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     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      sum(self.__neg_pos([ord(__lowerCAmelCase ) for x in self.__passcode] ) )
      return num if num > 0 else len(self.__passcode )



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							Tuple							       =      """"""

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

      return decoded_message



  def     a_							(			self		,     __lowerCAmelCase ):







      '''simple docstring'''


      _a				:							List[str]							       =      """"""

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

      return encoded_message






def UpperCAmelCase    (       UpperCAmelCase_ = "Hello, this is a modified Caesar cipher"						):
    _a				:							Union[str, Any]							       =      ShuffledShiftCipher()
    return cipa.decrypt(cipa.encrypt(UpperCAmelCase_						)						)


if __name__ == "__main__":
  import doctest

  doctest.testmod()







'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


lowerCAmelCase						:		Optional[Any]						      = '''
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
'''


lowerCAmelCase						:		str						      = '''
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {\'accuracy\': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {\'accuracy\': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric("accuracy")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
        >>> print(results)
        {\'accuracy\': 0.8778625954198473}
'''


lowerCAmelCase						:		int						      = '''
@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}
}
'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION				,							_KWARGS_DESCRIPTION			)
class        UpperCAmelCase__						(		datasets.Metric			):



					def 							UpperCAmelCase_			(     self  )  ->      Optional[Any]:
						return datasets.MetricInfo(
						    description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
						        {
						            "predictions": datasets.Sequence(datasets.Value("int32"  )  ),
						            "references": datasets.Sequence(datasets.Value("int32"  )  ),
						        }
						        if self.config_name == "multilabel"
						        else {
						            "predictions": datasets.Value("int32"  ),
						            "references": datasets.Value("int32"  ),
						        }  )      ,					reference_urls=["https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html"]      ,					)



					def 							UpperCAmelCase_			(     self      ,					UpperCamelCase      ,					UpperCamelCase      ,					UpperCamelCase=True      ,					UpperCamelCase=None  )  ->      List[str]:
						return {
						    "accuracy": float(
						        accuracy_score(UpperCamelCase      ,					UpperCamelCase      ,					normalize=UpperCamelCase      ,					sample_weight=UpperCamelCase  )  )
						}