






from __future__ import annotations

from dataclasses import dataclass






@dataclass
class 					SCREAMING_SNAKE_CASE_  :
       """simple docstring"""


       __lowerCAmelCase    :		Optional[int]					=4_2
       __lowerCAmelCase    :		Union[str, Any]					=None
       __lowerCAmelCase    :		Dict					=None





def        _snake_case   (_snake_case				:       TreeNode | None)					->	bool:

   # Validation
   def is_valid_tree(_snake_case				:       TreeNode | None) -> bool:
      if node is None:
         return True

      if not isinstance(_snake_case      ,    _snake_case):
         return False

      try:
         float(node.data)
      except (TypeError, ValueError):
         return False

      return is_valid_tree(node.left) and is_valid_tree(node.right)

   if not is_valid_tree(_snake_case):
      raise ValueError(
          'Each node should be type of TreeNode and data should be float.')

   def is_binary_search_tree_recursive_check(
       _snake_case				:       TreeNode | None      ,    _snake_case				:       float      ,    _snake_case				:       float) -> bool:

      if node is None:
         return True

      return (
          left_bound < node.data < right_bound
          and is_binary_search_tree_recursive_check(node.left      ,    _snake_case      ,    node.data)
          and is_binary_search_tree_recursive_check(
              node.right      ,    node.data      ,    _snake_case)
      )

   return is_binary_search_tree_recursive_check(_snake_case      ,    -float('inf')      ,    float('inf'))


if __name__ == "__main__":
    import doctest

    doctest.testmod()





'''simple docstring'''





import datasets


UpperCamelCase_    						=       """\
@InProceedings{conneau2018xnli,
  author = \"Conneau, Alexis
                 and Rinott, Ruty
                 and Lample, Guillaume
                 and Williams, Adina
                 and Bowman, Samuel R.
                 and Schwenk, Holger
                 and Stoyanov, Veselin\",
  title = \"XNLI: Evaluating Cross-lingual Sentence Representations\",
  booktitle = \"Proceedings of the 2018 Conference on Empirical Methods
               in Natural Language Processing\",
  year = \"2018\",
  publisher = \"Association for Computational Linguistics\",
  location = \"Brussels, Belgium\",
}
"""

UpperCamelCase_    						=       """\
XNLI is a subset of a few thousand examples from MNLI which has been translated
into a 14 different languages (some low-ish resource). As with MNLI, the goal is
to predict textual entailment (does sentence A imply/contradict/neither sentence
B) and is a classification task (given two sentences, predict one of three
labels).
"""

UpperCamelCase_    						=       """
Computes XNLI score which is just simple accuracy.
Args:
    predictions: Predicted labels.
    references: Ground truth labels.
Returns:
    'accuracy': accuracy
Examples:

    >>> predictions = [0, 1]
    >>> references = [0, 1]
    >>> xnli_metric = datasets.load_metric(\"xnli\")
    >>> results = xnli_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'accuracy': 1.0}
"""






def 				_lowerCAmelCase			(   __magic_name__						:     Dict   , __magic_name__						:     Union[str, Any]						)				->  Union[str, Any]:
						return (preds == labels).mean()







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







				def  lowerCamelCase_	(       self     :		str   ):
										'''simple docstring'''
										return datasets.MetricInfo(
										    description=_DESCRIPTION		,					citation=_CITATION		,					inputs_description=_KWARGS_DESCRIPTION		,					features=datasets.Features(
										        {
										            '''predictions''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''   ),
										            '''references''': datasets.Value('''int64''' if self.config_name != '''sts-b''' else '''float32'''   ),
										        }   )		,					codebase_urls=[]		,					reference_urls=[]		,					format='''numpy'''		,					)







				def  lowerCamelCase_	(       self     :		int		,					UpperCAmelCase__     :		Dict		,					UpperCAmelCase__     :		Optional[Any]   ):
										'''simple docstring'''
										return {"accuracy": simple_accuracy(UpperCAmelCase__		,					UpperCAmelCase__   )}





