"""simple docstring"""


import sys






def     __UpperCAmelCase					(			lowercase		):







  """simple docstring"""
  _UpperCAmelCase						 =      len(lowercase		)
  _UpperCAmelCase						 =      [[0 for x in range(lowercase		)] for x in range(lowercase		)]
  _UpperCAmelCase						 =      [[0 for x in range(lowercase		)] for x in range(lowercase		)]

  for chain_length in range(2   ,lowercase		):
    for a in range(1   ,n - chain_length + 1		):
      _UpperCAmelCase						 =      a + chain_length - 1

      _UpperCAmelCase						 =      sys.maxsize
      for c in range(lowercase   ,lowercase		):
        _UpperCAmelCase						 =      (
            matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
        )
        if cost < matrix[a][b]:
          _UpperCAmelCase						 =      cost
          _UpperCAmelCase						 =      c
  return matrix, sol






def     __UpperCAmelCase					(			lowercase   ,lowercase   ,lowercase		):







  """simple docstring"""
  if i == j:
    print("""A""" + str(lowercase		)   ,end=""" """		)
  else:
    print("""("""   ,end=""" """		)
    print_optiomal_solution(lowercase   ,lowercase   ,optimal_solution[i][j]		)
    print_optiomal_solution(lowercase   ,optimal_solution[i][j] + 1   ,lowercase		)
    print(""")"""   ,end=""" """		)






def     __UpperCAmelCase					(			):







  """simple docstring"""
  _UpperCAmelCase						 =      [30, 35, 15, 5, 10, 20, 25]
  _UpperCAmelCase						 =      len(lowercase		)
  # Size of matrix created from above array will be
  # 30*35 35*15 15*5 5*10 10*20 20*25
  _UpperCAmelCase ,				_UpperCAmelCase						 =      matrix_chain_order(lowercase		)

  print("""No. of Operation required: """ + str(matrix[1][n - 1]		)		)
  print_optiomal_solution(lowercase   ,1   ,n - 1		)


if __name__ == "__main__":
    main()

"""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     __UpperCAmelCase					(			lowercase   ,lowercase		):







  """simple docstring"""
  return (preds == labels).mean()


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


    def      lowerCAmelCase_  (					self	:  Union[str, Any]					):
      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	:  List[str]   ,				__lowerCAmelCase	:  int   ,				__lowerCAmelCase	:  Any					):
      return {"accuracy": simple_accuracy(__lowerCAmelCase   ,				__lowerCAmelCase					)}
