




'''simple docstring'''



from __future__ import annotations

from typing import Any





class 			lowerCAmelCase      :


       def __init__(      self			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE     )					->     None:  # noqa: A002



            '''simple docstring'''




            __snake_case			    =     item
            __snake_case			    =     next





class 			lowerCAmelCase      :


       def __init__(      self     )					->     None:



            '''simple docstring'''




            __snake_case			    =     None
            __snake_case			    =     0


       def 					lowerCAmelCase					(      self			,     __SCREAMING_SNAKE_CASE     )					->     None:



            '''simple docstring'''




            __snake_case			    =     Node(__SCREAMING_SNAKE_CASE			,     self.head     )
            self.size += 1


       def 					lowerCAmelCase					(      self     )					->     Any:



            '''simple docstring'''




            if self.head is None:
                 return None
            else:
                 __snake_case			    =     self.head.item
                 __snake_case			    =     self.head.next
                 self.size -= 1
                 return item


       def 					lowerCAmelCase					(      self     )					->     bool:



            '''simple docstring'''




            return self.head is None


       def __str__(      self     )					->     str:



            '''simple docstring'''




            if self.is_empty():
                 return ""
            else:
                 __snake_case			    =     self.head
                 __snake_case			    =     ''''''
                 __snake_case			    =     []
                 while iterate:
                      item_list.append(str(iterate.item     )     )
                      __snake_case			    =     iterate.next

                 __snake_case			    =     ''' --> '''.join(__SCREAMING_SNAKE_CASE     )

                 return item_str




       def __len__(      self     )					->     int:



            '''simple docstring'''




            return self.size












'''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     )     )
            }






