


"""simple docstring"""




def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):

 def count_of_possible_combinations(SCREAMING_SNAKE_CASE__						) -> int:
  if target < 0:
   return 0
  if target == 0:
   return 1
  return sum(count_of_possible_combinations(target - item						) for item in array						)

 return count_of_possible_combinations(SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):

 def count_of_possible_combinations_with_dp_array(
     SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						) -> int:
  if target < 0:
   return 0
  if target == 0:
   return 1
  if dp_array[target] != -1:
   return dp_array[target]
  __lowerCamelCase		:							Any		 =						sum(
      count_of_possible_combinations_with_dp_array(target - item      ,	SCREAMING_SNAKE_CASE__						)
      for item in array						)
  __lowerCamelCase		:							List[str]		 =						answer
  return answer

 __lowerCamelCase		:							List[str]		 =						[-1] * (target + 1)
 return count_of_possible_combinations_with_dp_array(SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						)
def 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 __lowerCamelCase		:							Optional[int]		 =						[0] * (target + 1)
 __lowerCamelCase		:							List[str]		 =						1

 for i in range(1      ,	target + 1						):
  for j in range(SCREAMING_SNAKE_CASE__						):
   if i - array[j] >= 0:
    dp_array[i] += dp_array[i - array[j]]

 return dp_array[target]


if __name__ == "__main__":
       import doctest

       doctest.testmod()
       lowercase_	   =     3
       lowercase_	   =     5
       lowercase_	   =     [1, 2, 5]
       print(combination_sum_iv(n, array, target))





import datasets


lowercase_	   =     '\\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'

lowercase_	   =     '\\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'

lowercase_	   =     '\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 				UpperCamelCase__					(  SCREAMING_SNAKE_CASE__      ,	SCREAMING_SNAKE_CASE__						):
 return (preds == labels).mean()



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




      '''simple docstring'''


      def 					_snake_case     (    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 					_snake_case     (    self:	int						,				a:	Optional[Any]						,				a:	Optional[Any]    ):
       return {"accuracy": simple_accuracy(a						,				a    )}

