





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

import datasets


lowerCamelCase_								=		"\\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_								=		"\\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_								=		"\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_     :		List[Any] ,   a_     :		Union[str, Any]							)			->      Tuple:
      return float((preds == labels).mean()							)



def      __lowerCamelCase   (							a_     :		Tuple ,   a_     :		List[str]							)			->      List[str]:
      __SCREAMING_SNAKE_CASE   :Tuple						=  simple_accuracy(a_ ,   a_							)
      __SCREAMING_SNAKE_CASE   :Tuple						=  float(fa_score(y_true=a_ ,   y_pred=a_							)							)
      return {
          "accuracy": acc,
          "f1": fa,
      }



def      __lowerCamelCase   (							a_     :		List[Any] ,   a_     :		List[str]							)			->      Union[str, Any]:
      __SCREAMING_SNAKE_CASE   :List[str]						=  float(pearsonr(a_ ,   a_							)[0]							)
      __SCREAMING_SNAKE_CASE   :int						=  float(spearmanr(a_ ,   a_							)[0]							)
      return {
          "pearson": pearson_corr,
          "spearmanr": spearman_corr,
      }







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




       def     _UpperCamelCase     (						self							)			->      Optional[int]:





             """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     _UpperCamelCase     (						self		,SCREAMING_SNAKE_CASE__		,SCREAMING_SNAKE_CASE__							)			->      Any:





             """simple docstring"""






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






"""simple docstring"""
def      __lowerCamelCase   (							a_     :		int ,   a_     :		str							)			->      Optional[int]:
      __SCREAMING_SNAKE_CASE   :Optional[int]						=  [1]
      for i in range(2 ,   a_							):
            factorials.append(factorials[-1] * i							)
      assert 0 <= k < factorials[-1] * n, "k out of bounds"

      __SCREAMING_SNAKE_CASE   :List[str]						=  []
      __SCREAMING_SNAKE_CASE   :Optional[Any]						=  list(range(a_							)							)

      # Find permutation
      while factorials:
            __SCREAMING_SNAKE_CASE   :Optional[int]						=  factorials.pop()
            __SCREAMING_SNAKE_CASE				,__SCREAMING_SNAKE_CASE   :Dict						=  divmod(a_ ,   a_							)
            permutation.append(elements[number]							)
            elements.remove(elements[number]							)
      permutation.append(elements[0]							)

      return permutation


if __name__ == "__main__":
  import doctest

  doctest.testmod()