

"""simple docstring"""


import datasets


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

UpperCAmelCase													="\\nXNLI is a subset of a few thousand examples from MNLI which has been translated\ninto a 14 different languages (some low-ish resource). As with MNLI, the goal is\nto predict textual entailment (does sentence A imply/contradict/neither sentence\nB) and is a classification task (given two sentences, predict one of three\nlabels).\n"

UpperCAmelCase													="\nComputes XNLI score which is just simple accuracy.\nArgs:\n    predictions: Predicted labels.\n    references: Ground truth labels.\nReturns:\n    'accuracy': accuracy\nExamples:\n\n    >>> predictions = [0, 1]\n    >>> references = [0, 1]\n    >>> xnli_metric = datasets.load_metric(\"xnli\")\n    >>> results = xnli_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {'accuracy': 1.0}\n"



def   _A					(	_a	:						Dict     ,   _a	:						Any				):



      """simple docstring"""






      return (preds == labels).mean()





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



    '''simple docstring'''




    def UpperCamelCase__		( 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 UpperCamelCase__		( self ,lowerCamelCase_ ,lowerCamelCase_    )    ->      Dict:
          return {"accuracy": simple_accuracy(lowerCamelCase_ ,lowerCamelCase_    )}







"""simple docstring"""


from __future__ import annotations

from collections.abc import Sequence
from typing import Literal



def   _A					(	_a	:						str     ,   _a	:						str				):



      """simple docstring"""






      A				  =		list(_a				)
      A				  =		list(_a				)
      A				  =		0
      for i in range(len(_a				)				):
            if lista[i] != lista[i]:
                  count += 1
                  A				  =		"""_"""
      if count > 1:
            return False
      else:
            return "".join(_a				)



def   _A					(	_a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      while True:
            A				  =		["""$"""] * len(_a				)
            A				  =		[]
            for i in range(len(_a				)				):
                  for j in range(i + 1     ,   len(_a				)				):
                        A				  =		compare_string(binary[i]     ,   binary[j]				)
                        if k is False:
                              A				  =		"""*"""
                              A				  =		"""*"""
                              temp.append("""X"""				)
            for i in range(len(_a				)				):
                  if checka[i] == "$":
                        pi.append(binary[i]				)
            if len(_a				) == 0:
                  return pi
            A				  =		list(set(_a				)				)



def   _A					(	_a	:						int     ,   _a	:						Sequence[float]				):



      """simple docstring"""






      A				  =		[]
      for minterm in minterms:
            A				  =		""""""
            for _ in range(_a				):
                  A				  =		str(minterm % 2				) + string
                  minterm //= 2
            temp.append(_a				)
      return temp



def   _A					(	_a	:						str     ,   _a	:						str     ,   _a	:						int				):



      """simple docstring"""






      A				  =		list(_a				)
      A				  =		list(_a				)
      A				  =		0
      for i in range(len(_a				)				):
            if lista[i] != lista[i]:
                  count_n += 1
      return count_n == count



def   _A					(	_a	:						list[list[int]]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[]
      A				  =		[0] * len(_a				)
      for i in range(len(chart[0]				)				):
            A				  =		0
            A				  =		-1
            for j in range(len(_a				)				):
                  if chart[j][i] == 1:
                        count += 1
                        A				  =		j
            if count == 1:
                  A				  =		1
      for i in range(len(_a				)				):
            if select[i] == 1:
                  for j in range(len(chart[0]				)				):
                        if chart[i][j] == 1:
                              for k in range(len(_a				)				):
                                    A				  =		0
                  temp.append(prime_implicants[i]				)
      while True:
            A				  =		0
            A				  =		-1
            A				  =		0
            for i in range(len(_a				)				):
                  A				  =		chart[i].count(1				)
                  if count_n > max_n:
                        A				  =		count_n
                        A				  =		i

            if max_n == 0:
                  return temp

            temp.append(prime_implicants[rem]				)

            for i in range(len(chart[0]				)				):
                  if chart[rem][i] == 1:
                        for j in range(len(_a				)				):
                              A				  =		0



def   _A					(	_a	:						list[str]     ,   _a	:						list[str]				):



      """simple docstring"""






      A				  =		[[0 for x in range(len(_a				)				)] for x in range(len(_a				)				)]
      for i in range(len(_a				)				):
            A				  =		prime_implicants[i].count("""_"""				)
            for j in range(len(_a				)				):
                  if is_for_table(prime_implicants[i]     ,   binary[j]     ,   _a				):
                        A				  =		1

      return chart



def   _A					(	):



      """simple docstring"""






      A				  =		int(input("""Enter the no. of variables\n"""				)				)
      A				  =		[
          float(_a				)
          for x in input(
              """Enter the decimal representation of Minterms 'Spaces Separated'\n"""				).split()
      ]
      A				  =		decimal_to_binary(_a     ,   _a				)

      A				  =		check(_a				)
      print("""Prime Implicants are:"""				)
      print(_a				)
      A				  =		prime_implicant_chart(_a     ,   _a				)

      A				  =		selection(_a     ,   _a				)
      print("""Essential Prime Implicants are:"""				)
      print(_a				)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




