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

import datasets


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

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

UpperCAmelCase      =   '''
Compute GLUE evaluation metric associated to each GLUE dataset.
Args:
    predictions: list of predictions to score.
        Each translation should be tokenized into a list of tokens.
    references: list of lists of references for each translation.
        Each reference should be tokenized into a list of tokens.
Returns: depending on the GLUE subset, one or several of:
    "accuracy": Accuracy
    "f1": F1 score
    "pearson": Pearson Correlation
    "spearmanr": Spearman Correlation
    "matthews_correlation": Matthew Correlation
Examples:

    >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\')  # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\')  # \'mrpc\' or \'qqp\'
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'accuracy\': 1.0, \'f1\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')
    >>> references = [0., 1., 2., 3., 4., 5.]
    >>> predictions = [0., 1., 2., 3., 4., 5.]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})
    {\'pearson\': 1.0, \'spearmanr\': 1.0}

    >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')
    >>> references = [0, 1]
    >>> predictions = [0, 1]
    >>> results = glue_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'matthews_correlation\': 1.0}
'''






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  int:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  List[Any]:




      """simple docstring"""

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






def 	__lowerCAmelCase   (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )->  Tuple:




      """simple docstring"""

      snake_case_	=				float(pearsonr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )[0] )
      snake_case_	=				float(spearmanr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )[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 						UpperCamelCase__					(     self ):
       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 						UpperCamelCase__					(     self		,				_UpperCAmelCase		,				_UpperCAmelCase ):
       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\"]''' )



import os






def 	__lowerCAmelCase   ()->  List[Any]:




      """simple docstring"""

      snake_case_	=				os.path.join(os.path.dirname(SCREAMING_SNAKE_CASE ) , '''num.txt''' )
      with open(SCREAMING_SNAKE_CASE ) as file_hand:
            return str(sum(int(SCREAMING_SNAKE_CASE ) for line in file_hand ) )[:10]


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