


'''simple docstring'''






def lowerCamelCase__     (		__lowerCamelCase			:				int			,       __lowerCamelCase			:				bool = False				):







   '''simple docstring'''




   if n == 2:
      return True
   if not n % 2 or n < 2:
      return False
   if n > 5 and n % 1_0 not in (1, 3, 7, 9):  # can quickly check last digit
      return False
   if n > 3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1 and not allow_probable:
      raise ValueError(
          'Warning: upper bound of deterministic test is exceeded. '
          'Pass allow_probable=True to allow probabilistic test. '
          'A return value of True indicates a probable prime.'				)
   # array bounds provided by analysis
   _UpperCAmelCase  :  Any				       =[
       2_0_4_7,
       1_3_7_3_6_5_3,
       2_5_3_2_6_0_0_1,
       3_2_1_5_0_3_1_7_5_1,
       2_1_5_2_3_0_2_8_9_8_7_4_7,
       3_4_7_4_7_4_9_6_6_0_3_8_3,
       3_4_1_5_5_0_0_7_1_7_2_8_3_2_1,
       1,
       3_8_2_5_1_2_3_0_5_6_5_4_6_4_1_3_0_5_1,
       1,
       1,
       3_1_8_6_6_5_8_5_7_8_3_4_0_3_1_1_5_1_1_6_7_4_6_1,
       3_3_1_7_0_4_4_0_6_4_6_7_9_8_8_7_3_8_5_9_6_1_9_8_1,
   ]

   _UpperCAmelCase  :  Optional[int]				       =[2, 3, 5, 7, 1_1, 1_3, 1_7, 1_9, 2_3, 2_9, 3_1, 3_7, 4_1]
   for idx, _p in enumerate(__lowerCamelCase			,       1				):
      if n < _p:
         # then we have our last prime to check
         _UpperCAmelCase  :  Union[str, Any]				       =primes[:idx]
         break
   _UpperCAmelCase       ,			_UpperCAmelCase  :  List[Any]				       =n - 1, 0
   # break up n -1 into a power of 2 (s) and
   # remaining odd component
   # essentially, solve for d * 2 ** s == n - 1
   while d % 2 == 0:
      d //= 2
      s += 1
   for prime in plist:
      _UpperCAmelCase  :  Any				       =False
      for r in range(__lowerCamelCase				):
         _UpperCAmelCase  :  Dict				       =pow(__lowerCamelCase			,       d * 2**r			,       __lowerCamelCase				)
         # see article for analysis explanation for m
         if (r == 0 and m == 1) or ((m + 1) % n == 0):
            _UpperCAmelCase  :  Any				       =True
            # this loop will not determine compositeness
            break
      if pr:
         continue
      # if pr is False, then the above loop never evaluated to true,
      # and the n MUST be composite
      return False
   return True





def lowerCamelCase__     (		):







   '''simple docstring'''




   assert not miller_rabin(5_6_1				)
   assert miller_rabin(5_6_3				)
   # 2047

   assert not miller_rabin(8_3_8_2_0_1				)
   assert miller_rabin(8_3_8_2_0_7				)
   # 1_373_653

   assert not miller_rabin(1_7_3_1_6_0_0_1				)
   assert miller_rabin(1_7_3_1_6_0_1_7				)
   # 25_326_001

   assert not miller_rabin(3_0_7_8_3_8_6_6_4_1				)
   assert miller_rabin(3_0_7_8_3_8_6_6_5_3				)
   # 3_215_031_751

   assert not miller_rabin(1_7_1_3_0_4_5_5_7_4_8_0_1				)
   assert miller_rabin(1_7_1_3_0_4_5_5_7_4_8_1_9				)
   # 2_152_302_898_747

   assert not miller_rabin(2_7_7_9_7_9_9_7_2_8_3_0_7				)
   assert miller_rabin(2_7_7_9_7_9_9_7_2_8_3_2_7				)
   # 3_474_749_660_383

   assert not miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_4_4_1				)
   assert miller_rabin(1_1_3_8_5_0_0_2_3_9_0_9_5_2_7				)
   # 341_550_071_728_321

   assert not miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_5_1				)
   assert miller_rabin(1_2_7_5_0_4_1_0_1_8_8_4_8_8_0_4_3_9_1				)
   # 3_825_123_056_546_413_051

   assert not miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_8_6_7				)
   assert miller_rabin(7_9_6_6_6_4_6_4_4_5_8_5_0_7_7_8_7_7_9_1_9_5_1				)
   # 318_665_857_834_031_151_167_461

   assert not miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_3_3				)
   assert miller_rabin(5_5_2_8_4_0_6_7_7_4_4_6_6_4_7_8_9_7_6_6_0_3_5_9				)
   # 3_317_044_064_679_887_385_961_981
   # upper limit for probabilistic test


if __name__ == "__main__":
 test_miller_rabin()





'''simple docstring'''






from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef

import datasets


lowercase				='\\n@inproceedings{wang2019glue,\n  title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n  author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n  note={In the Proceedings of ICLR.},\n  year={2019}\n}\n'

lowercase				='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'

lowercase				='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n    predictions: list of predictions to score.\n        Each translation should be tokenized into a list of tokens.\n    references: list of lists of references for each translation.\n        Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n    "accuracy": Accuracy\n    "f1": F1 score\n    "pearson": Pearson Correlation\n    "spearmanr": Spearman Correlation\n    "matthews_correlation": Matthew Correlation\nExamples:\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'accuracy\': 1.0, \'f1\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n    >>> references = [0., 1., 2., 3., 4., 5.]\n    >>> predictions = [0., 1., 2., 3., 4., 5.]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n    {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n    >>> references = [0, 1]\n    >>> predictions = [0, 1]\n    >>> results = glue_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'matthews_correlation\': 1.0}\n'





def lowerCamelCase__     (		__lowerCamelCase			:				Optional[Any]			,       __lowerCamelCase			:				str				):







   '''simple docstring'''




   return float((preds == labels).mean()				)





def lowerCamelCase__     (		__lowerCamelCase			:				Tuple			,       __lowerCamelCase			:				Union[str, Any]				):







   '''simple docstring'''




   _UpperCAmelCase  :  Union[str, Any]				       =simple_accuracy(__lowerCamelCase			,       __lowerCamelCase				)
   _UpperCAmelCase  :  List[Any]				       =float(fa_score(y_true=__lowerCamelCase			,       y_pred=__lowerCamelCase				)				)
   return {
       "accuracy": acc,
       "f1": fa,
   }





def lowerCamelCase__     (		__lowerCamelCase			:				str			,       __lowerCamelCase			:				int				):







   '''simple docstring'''




   _UpperCAmelCase  :  Dict				       =float(pearsonr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   _UpperCAmelCase  :  int				       =float(spearmanr(__lowerCamelCase			,       __lowerCamelCase				)[0]				)
   return {
       "pearson": pearson_corr,
       "spearmanr": spearman_corr,
   }






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






       def      lowerCAmelCase						(     self)		->    str:




          '''simple docstring'''
          if self.config_name not in [
              "sst2",
              "mnli",
              "mnli_mismatched",
              "mnli_matched",
              "cola",
              "stsb",
              "mrpc",
              "qqp",
              "qnli",
              "rte",
              "wnli",
              "hans",
          ]:
             raise KeyError(
                 'You should supply a configuration name selected in '
                 '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                 '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')
          return datasets.MetricInfo(
              description=_DESCRIPTION    ,				citation=_CITATION    ,				inputs_description=_KWARGS_DESCRIPTION    ,				features=datasets.Features(
                  {
                      'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
                      'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
                  })    ,				codebase_urls=[]    ,				reference_urls=[]    ,				format='numpy'    ,				)






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case)		->    int:




          '''simple docstring'''
          if self.config_name == "cola":
             return {"matthews_correlation": matthews_corrcoef(snake_case    ,				snake_case)}
          elif self.config_name == "stsb":
             return pearson_and_spearman(snake_case    ,				snake_case)
          elif self.config_name in ["mrpc", "qqp"]:
             return acc_and_fa(snake_case    ,				snake_case)
          elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
             return {"accuracy": simple_accuracy(snake_case    ,				snake_case)}
          else:
             raise KeyError(
                 'You should supply a configuration name selected in '
                 '["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
                 '"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')

