
"""simple docstring"""
from .number_theory.prime_numbers import next_prime






class   _UpperCAmelCase					:
    def __init__(     self    ,     snake_case_    ,     snake_case_ = None    ,     snake_case_ = None    ,     ):
      _snake_case  :    Optional[int]            = size_table
      _snake_case  :    Optional[int]            = [None] * self.size_table
      _snake_case  :    int            = 0.75 if lim_charge is None else lim_charge
      _snake_case  :    List[Any]            = 1 if charge_factor is None else charge_factor
      _snake_case  :    list            = []
      _snake_case  :    dict            = {}
    def        lowerCamelCase__						(     self  ):
      return self._keys
    def        lowerCamelCase__						(     self  ):
      return sum(1 for slot in self.values if slot is not None  ) / (
          self.size_table * self.charge_factor
      )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      return key % self.size_table
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      print(F'step {step_ord}'  )
      print(list(range(len(self.values  )  )  )  )
      print(self.values  )
    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    Optional[Any]            = 1
      _snake_case  :    Tuple            = values
      for value in values:
        self.insert_data(snake_case_  )
        self._step_by_step(snake_case_  )
        i += 1
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_  ):
      _snake_case  :    Tuple            = data
      _snake_case  :    str            = data
    def        lowerCamelCase__						(     self    ,     snake_case_    ,     snake_case_=None  ):
      _snake_case  :    Union[str, Any]            = self.hash_function(key + 1  )

      while self.values[new_key] is not None and self.values[new_key] != key:
        if self.values.count(snake_case_  ) > 0:
          _snake_case  :    Optional[int]            = self.hash_function(new_key + 1  )
        else:
          _snake_case  :    List[str]            = None
          break

      return new_key
    def        lowerCamelCase__						(     self  ):
      _snake_case  :    int            = [value for value in self.values if value is not None]
      _snake_case  :    Tuple            = next_prime(self.size_table    ,     factor=2  )
      self._keys.clear()
      _snake_case  :    int            = [None] * self.size_table  # hell's pointers D: don't DRY ;/
      for value in survivor_values:
        self.insert_data(snake_case_  )


    def        lowerCamelCase__						(     self    ,     snake_case_  ):
      _snake_case  :    List[str]            = self.hash_function(snake_case_  )

      if self.values[key] is None:
        self._set_value(snake_case_    ,     snake_case_  )

      elif self.values[key] == data:
        pass

      else:
        _snake_case  :    int            = self._collision_resolution(snake_case_    ,     snake_case_  )
        if collision_resolution is not None:
          self._set_value(snake_case_    ,     snake_case_  )
        else:
          self.rehashing()
          self.insert_data(snake_case_  )




'''simple docstring'''

from sklearn.metrics import accuracy_score

import datasets


lowercase__            =     "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


lowercase__            =     "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    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.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> 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])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


lowercase__            =     "\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n"




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






     '''simple docstring'''



     def   UpperCAmelCase_			(						self					:	int			)		->      str:
      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					:	str	,     lowercase_					:	Union[str, Any]	,     lowercase_					:	Any	,     lowercase_					:	Tuple=True	,     lowercase_					:	Union[str, Any]=None			)		->      Dict:
      return {
          "accuracy": float(
              accuracy_score(lowercase_	,     lowercase_	,     normalize=lowercase_	,     sample_weight=lowercase_			)			)
      }




