




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

import datasets


UpperCAmelCase_									=	'\\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'

UpperCAmelCase_									=	'\\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'

UpperCAmelCase_									=	'\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__       (						A__       :					Dict  ,			A__       :					List[Any]	):





       '''simple docstring'''



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




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





       '''simple docstring'''



       __lowerCamelCase   =						simple_accuracy(A__  ,			A__	)
       __lowerCamelCase   =						float(fa_score(y_true=A__  ,			y_pred=A__	)	)
       return {
           "accuracy": acc,
           "f1": fa,
       }




def 					lowerCamelCase__       (						A__       :					str  ,			A__       :					List[str]	):





       '''simple docstring'''



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





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,     _KWARGS_DESCRIPTION)
class     lowerCamelCase__(					datasets.Metric):
      def 			lowerCAmelCase__						(				self: Dict   ):
             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: List[Any]    ,       UpperCamelCase_: str    ,       UpperCamelCase_: List[str]   ):
             if self.config_name == "cola":
                    return {"matthews_correlation": matthews_corrcoef(UpperCamelCase_    ,       UpperCamelCase_   )}
             elif self.config_name == "stsb":
                    return pearson_and_spearman(UpperCamelCase_    ,       UpperCamelCase_   )
             elif self.config_name in ["mrpc", "qqp"]:
                    return acc_and_fa(UpperCamelCase_    ,       UpperCamelCase_   )
             elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
                    return {"accuracy": simple_accuracy(UpperCamelCase_    ,       UpperCamelCase_   )}
             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\"]"""   )


"""simple docstring"""






from __future__ import annotations

from fractions import Fraction



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase       ,    __UpperCAmelCase )  ->  bool:
   return (
       num != den and num % 1_0 == den // 1_0 and (num // 1_0) / (den % 1_0) == num / den
   )



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase )  ->  list[str]:
   lowercase__:   str					=      []
   lowercase__:   str					=      1_1
   lowercase__:   str					=      int('''1''' + '''0''' * digit_len )
   for num in range(__UpperCAmelCase       ,    __UpperCAmelCase ):
      while den <= 9_9:
         if (num != den) and (num % 1_0 == den // 1_0) and (den % 1_0 != 0):
            if is_digit_cancelling(__UpperCAmelCase       ,    __UpperCAmelCase ):
               solutions.append(F"""{num}/{den}""" )
         den += 1
      num += 1
      lowercase__:   Dict					=      1_0
   return solutions



def SCREAMING_SNAKE_CASE__			(   __UpperCAmelCase = 2 )  ->  int:
   lowercase__:   List[str]					=      1.0
   for fraction in fraction_list(__UpperCAmelCase ):
      lowercase__:   List[str]					=      Fraction(__UpperCAmelCase )
      result *= frac.denominator / frac.numerator
   return int(__UpperCAmelCase )


if __name__ == "__main__":
       print(solution())
