


"""simple docstring"""







import operator as op


def      lowercase			(				A_       )->      Any:
  '''simple docstring'''







  a						:		Tuple      							=			[]
  a						:		int      							=			lambda A_  ,   A_       : int(x / y       )  # noqa: E731 integer division operation
  a						:		Tuple      							=			{
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8       )  ,   "Action".center(12       )  ,   "Stack"  ,   sep=" | "       )
  print("-" * (30 + len(A_       ))       )

  for x in post_fix:
    if x.isdigit():  # if x in digit
      stack.append(A_       )  # append x to stack
      # output in tabular format
      print(x.rjust(8       )  ,   ("push(" + x + ")").ljust(12       )  ,   ",".join(A_       )  ,   sep=" | "       )
    else:
      a						:		str      							=			stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8       )  ,   ("pop(" + b + ")").ljust(12       )  ,   ",".join(A_       )  ,   sep=" | "       )

      a						:		str      							=			stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8       )  ,   ("pop(" + a + ")").ljust(12       )  ,   ",".join(A_       )  ,   sep=" | "       )

      stack.append(
          str(opr[x](int(A_       )  ,   int(A_       )       )       )       )  # evaluate the 2 values popped from stack & push result to stack
      # output in tabular format
      print(
          x.rjust(8       )  ,   ("push(" + a + x + b + ")").ljust(12       )  ,   ",".join(A_       )  ,   sep=" | "  ,   )

  return int(stack[0]       )


if __name__ == "__main__":
 __lowercase    					=      input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
 print("""\n\tResult = """, solve(Postfix))








"""simple docstring"""





import datasets


_a														=		"""\
@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														=		"""\
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														=		"""
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__		(			__snake_case,     __snake_case	)							->		str:


					"""simple docstring"""





					return (preds == labels).mean()






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



		def 						UpperCAmelCase							(					self)       ->       Dict:






							'''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 						UpperCAmelCase							(					self				,		__a				,		__a)       ->       Dict:






							'''simple docstring'''
							return {"accuracy": simple_accuracy(__a				,		__a)}






