




'''simple docstring'''



from string import ascii_uppercase







def 	_UpperCamelCase       (_lowerCamelCase							:   str      ,		_lowerCamelCase							:   str      ,		_lowerCamelCase							:   bool = False      ,		_lowerCamelCase							:   str = ascii_uppercase		)->      str:




     '''simple docstring'''





     __snake_case			    =     keyword.upper()
     __snake_case			    =     plaintext.upper()
     __snake_case			    =     set(_lowerCamelCase		)

     # create a list of unique characters in the keyword - their order matters
     # it determines how we will map plaintext characters to the ciphertext
     __snake_case			    =     []
     for char in keyword:
          if char in alphabet_set and char not in unique_chars:
               unique_chars.append(_lowerCamelCase		)
    # the number of those unique characters will determine the number of rows
     __snake_case			    =     len(_lowerCamelCase		)

     # create a shifted version of the alphabet
     __snake_case			    =     unique_chars + [
         char for char in alphabet if char not in unique_chars
     ]

     # create a modified alphabet by splitting the shifted alphabet into rows
     __snake_case			    =     [
         shifted_alphabet[k : k + num_unique_chars_in_keyword]
         for k in range(0      ,		26      ,		_lowerCamelCase		)
     ]

     # map the alphabet characters to the modified alphabet characters
     # going 'vertically' through the modified alphabet - consider columns first
     __snake_case			    =     {}
     __snake_case			    =     0
     for column in range(_lowerCamelCase		):
          for row in modified_alphabet:
               # if current row (the last one) is too short, break out of loop
               if len(_lowerCamelCase		) <= column:
                    break

               # map current letter to letter in modified alphabet
               __snake_case			    =     row[column]
               letter_index += 1

     if verbose:
          print(_lowerCamelCase		)
     # create the encrypted text by mapping the plaintext to the modified alphabet
     return "".join(mapping[char] if char in mapping else char for char in plaintext		)


if __name__ == "__main__":
     # example use
     print(mixed_keyword('''college''', '''UNIVERSITY'''))












'''simple docstring'''



from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase_				:     List[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
'''


UpperCAmelCase_				:     List[Any]     			=						'''
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}
'''


UpperCAmelCase_				:     Union[str, Any]     			=						'''
@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 			lowerCAmelCase      (       datasets.Metric):


       def 					lowerCAmelCase					(      self     )					->     List[str]:



            '''simple docstring'''




            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 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE=True			,     __SCREAMING_SNAKE_CASE=None     )					->     Any:



            '''simple docstring'''




            return {
                "accuracy": float(
                    accuracy_score(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     normalize=__SCREAMING_SNAKE_CASE			,     sample_weight=__SCREAMING_SNAKE_CASE     )     )
            }






