from sklearn.metrics import accuracy_score

import datasets


UpperCAmelCase      		=					'''
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      		=					'''
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      		=					'''
@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    ):



 '''simple docstring'''

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





'''simple docstring'''





class 			UpperCamelCase__	:






 def __init__(      self	:Tuple						,					_A	:int      )					->      None:







       '''simple docstring'''



       __A													=      data
       __A													=      None
       __A													=      None





def 							snake_case				(		UpperCAmelCase   ,	UpperCAmelCase						)->      BinaryTreeNode | None:







      """simple docstring"""
      if node is None:
            __A													=      BinaryTreeNode(UpperCAmelCase						)
            return node

      # binary search tree is not empty,
      # so we will insert it into the tree
      # if new_value is less than value of data in node,
      #  add it to left subtree and proceed recursively
      if new_value < node.data:
            __A													=      insert(node.left_child   ,	UpperCAmelCase						)
      else:
            # if new_value is greater than value of data in node,
            #  add it to right subtree and proceed recursively
            __A													=      insert(node.right_child   ,	UpperCAmelCase						)
      return node





def 							snake_case				(		UpperCAmelCase						)->      list[int]:  # if node is None,return







      """simple docstring"""
      if node:
            __A													=      inorder(node.left_child						)
            __A													=      [*inorder_array, node.data]
            __A													=      inorder_array + inorder(node.right_child						)
      else:
            __A													=      []
      return inorder_array





def 							snake_case				(		)->      BinaryTreeNode | None:







      """simple docstring"""
      __A													=      insert(UpperCAmelCase   ,	1_5						)
      insert(UpperCAmelCase   ,	1_0						)
      insert(UpperCAmelCase   ,	2_5						)
      insert(UpperCAmelCase   ,	6						)
      insert(UpperCAmelCase   ,	1_4						)
      insert(UpperCAmelCase   ,	2_0						)
      insert(UpperCAmelCase   ,	6_0						)
      return root





def 							snake_case				(		)->      None:







      """simple docstring"""
      # main function
      __A													=      make_tree()
      print('Printing values of binary search tree in Inorder Traversal.'						)
      inorder(UpperCAmelCase						)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 main()






