



'''simple docstring'''






from sklearn.metrics import accuracy_score

import datasets


A_							:				Dict											=			"""
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
"""


A_							:				List[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}
"""


A_							:				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 							lowercase (						datasets.Metric       ):


 """simple docstring"""


 def _snake_case				(							self							)	->     Union[str, 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 _snake_case				(							self						,a_						,a_						,a_=True						,a_=None							)	->     Optional[int]:
        return {
            "accuracy": float(
                accuracy_score(a_						,a_						,normalize=a_						,sample_weight=a_							)							)
        }








'''simple docstring'''






from string import ascii_uppercase




def 					snake_case_      (  lowerCAmelCase_ ,     lowerCAmelCase_ ,     lowerCAmelCase_ = False ,     lowerCAmelCase_ = ascii_uppercase	)->	str:





       '''simple docstring'''


       _UpperCAmelCase						:		List[str]      =				keyword.upper()
       _UpperCAmelCase						:		Optional[int]      =				plaintext.upper()
       _UpperCAmelCase						:		Optional[int]      =				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
       _UpperCAmelCase						:		str      =				[]
       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
       _UpperCAmelCase						:		Optional[Any]      =				len(lowerCAmelCase_	)

       # create a shifted version of the alphabet
       _UpperCAmelCase						:		Optional[Any]      =				unique_chars + [
           char for char in alphabet if char not in unique_chars
       ]

       # create a modified alphabet by splitting the shifted alphabet into rows
       _UpperCAmelCase						:		List[str]      =				[
           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
       _UpperCAmelCase						:		int      =				{}
       _UpperCAmelCase						:		int      =				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
                     _UpperCAmelCase						:		Optional[int]      =				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"""))



