


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

UpperCamelCase__          = {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}




class      lowerCamelCase_  :






      def __init__(   self							:       Optional[Any]		,		_A							:       dict[str, list[str]]		,		_A							:       str					):


          '''simple docstring'''


          UpperCAmelCase__     :						Tuple						=							graph
          # mapping node to its parent in resulting breadth first tree
          UpperCAmelCase__     :						dict[str, str | None]						=							{}
          UpperCAmelCase__     :						List[Any]						=							source_vertex






      def lowercase_				(   self							:       List[Any]					):


          '''simple docstring'''


          UpperCAmelCase__     :						Dict						=							{self.source_vertex}
          UpperCAmelCase__     :						List[str]						=							None
          UpperCAmelCase__     :						Optional[int]						=							[self.source_vertex]  # first in first out queue

          while queue:
              UpperCAmelCase__     :						int						=							queue.pop(0					)
              for adjacent_vertex in self.graph[vertex]:
                  if adjacent_vertex not in visited:
                      visited.add(_A					)
                      UpperCAmelCase__     :						str						=							vertex
                      queue.append(_A					)






      def lowercase_				(   self							:       Dict		,		_A							:       str					):


          '''simple docstring'''


          if target_vertex == self.source_vertex:
              return self.source_vertex

          UpperCAmelCase__     :						List[str]						=							self.parent.get(_A					)
          if target_vertex_parent is None:
              UpperCAmelCase__     :						str						=							(
                  f"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
              )
              raise ValueError(_A					)

          return self.shortest_path(_A					) + f"""->{target_vertex}"""


if __name__ == "__main__":
  UpperCamelCase__          = Graph(graph, '''G''')
  g.breath_first_search()
  print(g.shortest_path('''D'''))
  print(g.shortest_path('''G'''))
  print(g.shortest_path('''Foo'''))


import datasets


A		: Optional[int]								=     '''\
@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",
}
'''

A		: Optional[int]								=     '''\
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).
'''

A		: str								=     '''
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 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	List[str]		,   SCREAMING_SNAKE_CASE_	:	List[Any]       )  ->     str:
						return (preds == labels).mean()



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






			def    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase  ):
									return {"accuracy": simple_accuracy(__UpperCamelCase				,			__UpperCamelCase  )}