




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

import datasets


lowerCamelCase__ :   Tuple					=							'\\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'

lowerCamelCase__ :   Optional[int]					=							'\\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'

lowerCamelCase__ :   Dict					=							'\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 					UpperCAmelCase_							(      __UpperCAmelCase						:	Optional[Any]			,			__UpperCAmelCase						:	Optional[Any]							)					-> List[Any]:
      return float((preds == labels).mean()							)

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	str			,			__UpperCAmelCase						:	Optional[Any]							)					-> str:
      SCREAMING_SNAKE_CASE_							=			simple_accuracy(__UpperCAmelCase			,			__UpperCAmelCase							)
      SCREAMING_SNAKE_CASE_							=			float(fa_score(y_true=__UpperCAmelCase			,			y_pred=__UpperCAmelCase							)							)
      return {
          "accuracy": acc,
          "f1": fa,
      }

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	Dict			,			__UpperCAmelCase						:	Dict							)					-> Tuple:
      SCREAMING_SNAKE_CASE_							=			float(pearsonr(__UpperCAmelCase			,			__UpperCAmelCase							)[0]							)
      SCREAMING_SNAKE_CASE_							=			float(spearmanr(__UpperCAmelCase			,			__UpperCAmelCase							)[0]							)
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }





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


 '''simple docstring'''







 def       lowerCAmelCase_  (							self  :		List[Any]						):
       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  :		Any				,				_lowerCAmelCase  :		Tuple				,				_lowerCAmelCase  :		Tuple						):
       if self.config_name == "cola":
             return {"matthews_correlation": matthews_corrcoef(_lowerCAmelCase				,				_lowerCAmelCase						)}
       elif self.config_name == "stsb":
             return pearson_and_spearman(_lowerCAmelCase				,				_lowerCAmelCase						)
       elif self.config_name in ["mrpc", "qqp"]:
             return acc_and_fa(_lowerCAmelCase				,				_lowerCAmelCase						)
       elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
             return {"accuracy": simple_accuracy(_lowerCAmelCase				,				_lowerCAmelCase						)}
       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"]'						)





from math import sqrt

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int							)					-> bool:

      if 1 < number < 4:
            # 2 and 3 are primes
            return True
      elif number < 2 or number % 2 == 0 or number % 3 == 0:
            # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
            return False

      # All primes number are in format of 6k +/- 1
      for i in range(5			,			int(sqrt(__UpperCAmelCase							) + 1							)			,			6							):
            if number % i == 0 or number % (i + 2) == 0:
                  return False
      return True

def 					UpperCAmelCase_							(      __UpperCAmelCase						:	int = 1_00_01							)					-> int:
      SCREAMING_SNAKE_CASE_							=			0
      SCREAMING_SNAKE_CASE_							=			1
      while count != nth and number < 3:
            number += 1
            if is_prime(__UpperCAmelCase							):
                  count += 1
      while count != nth:
            number += 2
            if is_prime(__UpperCAmelCase							):
                  count += 1
      return number


if __name__ == "__main__":
   print(f'''{solution() = }''')