
'''simple docstring'''
from __future__ import annotations






class  UpperCAmelCase_					:





 def __init__(     self			,							lowercase_):
     snake_case_			:     Optional[Any]        =    value
     snake_case_			:     int        =    None
     snake_case_			:     Optional[int]        =    None






class  UpperCAmelCase_					:
 UpperCAmelCase_		     =    42





 def __init__(     self):
     snake_case_			:     Tuple        =    0





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_):
     if node is None:
         return

     if path_sum == self.target:
         self.paths += 1

     if node.left:
         self.depth_first_search(node.left			,							path_sum + node.left.value)
     if node.right:
         self.depth_first_search(node.right			,							path_sum + node.right.value)





 def 							snake_case__		(     self			,							lowercase_			,							lowercase_ = None):
     if node is None:
         return 0
     if target is not None:
         snake_case_			:     Any        =    target

     self.depth_first_search(A_			,							node.value)
     self.path_sum(node.left)
     self.path_sum(node.right)

     return self.paths


if __name__ == "__main__":
  import doctest

  doctest.testmod()







from sklearn.metrics import accuracy_score

import datasets


A__										=      "\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"


A__										=      "\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"


A__										=      "\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  UpperCamelCase_      (	datasets.Metric      ):


  """simple docstring"""



  def   _UpperCamelCase     (						self     :					int						)       ->			List[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     :					Dict  ,	A_     :					List[Any]  ,	A_     :					Tuple  ,	A_     :					Optional[Any]=True  ,	A_     :					Tuple=None						)       ->			Any:
      return {
          "accuracy": float(
              accuracy_score(A_  ,	A_  ,	normalize=A_  ,	sample_weight=A_						)						)
      }


