



from sklearn.metrics import matthews_corrcoef

import datasets


__snake_case		:Tuple   	=  '''
Compute the Matthews correlation coefficient (MCC)

The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction.  The statistic
is also known as the phi coefficient. [source: Wikipedia]
'''

__snake_case		:Union[str, Any]   	=  '''
Args:
    predictions (list of int): Predicted labels, as returned by a model.
    references (list of int): Ground truth labels.
    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
    matthews_correlation (dict containing float): Matthews correlation.
Examples:
    Example 1, a basic example with only predictions and references as inputs:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.54

    Example 2, the same example as above, but also including sample weights:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])
        >>> print(round(results[\'matthews_correlation\'], 2))
        0.1

    Example 3, the same example as above, but with sample weights that cause a negative correlation:
        >>> matthews_metric = datasets.load_metric("matthews_correlation")
        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
        ...                                     predictions=[1, 2, 2, 0, 3, 3],
        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])
        >>> print(round(results[\'matthews_correlation\'], 2))
        -0.25
'''

__snake_case		:Optional[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 					_A (			datasets.Metric					):



       def 	_lowerCamelCase    (	self							:  Tuple):


           '''simple docstring'''

           return datasets.MetricInfo(
               description=_DESCRIPTION  ,	citation=_CITATION  ,	inputs_description=_KWARGS_DESCRIPTION  ,	features=datasets.Features(
                   {
                       '''predictions''': datasets.Value('''int32'''),
                       '''references''': datasets.Value('''int32'''),
                   })  ,	reference_urls=[
                   '''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'''
               ]  ,	)





       def 	_lowerCamelCase    (	self							:  Optional[Any]  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Tuple=None):


           '''simple docstring'''

           return {
               "matthews_correlation": float(matthews_corrcoef(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	sample_weight=__SCREAMING_SNAKE_CASE)),
           }










def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       ''''''
    for ch in key:
        if ch == " " or ch not in key_no_dups and ch.isalpha():
            key_no_dups += ch
    return key_no_dups





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       [chr(i + 65       ) for i in range(26       )]
    # Remove duplicate characters from key
    __a		 =       remove_duplicates(key.upper()       )
    __a		 =       len(_UpperCAmelCase       )
    # First fill cipher with key characters
    __a		 =       {alphabet[i]: char for i, char in enumerate(_UpperCAmelCase       )}
    # Then map remaining characters in alphabet to
    # the alphabet from the beginning
    for i in range(len(_UpperCAmelCase       )     ,				26       ):
        __a		 =       alphabet[i - offset]
        # Ensure we are not mapping letters to letters previously mapped
        while char in key:
            offset -= 1
            __a		 =       alphabet[i - offset]
        __a		 =       char
    return cipher_alphabet





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    return "".join(cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    _UpperCAmelCase     ,				_UpperCAmelCase       ):
    __a		 =       {v: k for k, v in cipher_map.items()}
    return "".join(rev_cipher_map.get(_UpperCAmelCase     ,				_UpperCAmelCase       ) for ch in message.upper()       )





def   __snake_case			(    ):
    __a		 =       input('''Enter message to encode or decode: '''       ).strip()
    __a		 =       input('''Enter keyword: '''       ).strip()
    __a		 =       input('''Encipher or decipher? E/D:'''       ).strip()[0].lower()
    try:
        __a		 =       {'''e''': encipher, '''d''': decipher}[option]
    except KeyError:
        raise KeyError('''invalid input option'''       )
    __a		 =       create_cipher_map(_UpperCAmelCase       )
    print(func(_UpperCAmelCase     ,				_UpperCAmelCase       )       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()





