





'''simple docstring'''

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 							snake_case					(     a_							: List[str]    ,   a_							: Dict	)       ->				Union[str, Any]:





      """simple docstring"""

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



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[int]	)       ->				Tuple:





      """simple docstring"""

      UpperCamelCase_				:   int					    =							simple_accuracy(lowerCamelCase_    ,   lowerCamelCase_	)
      UpperCamelCase_				:   Any					    =							float(fa_score(y_true=lowerCamelCase_    ,   y_pred=lowerCamelCase_	)	)
      return {
          "accuracy": acc,
          "f1": fa,
      }



def 							snake_case					(     a_							: List[Any]    ,   a_							: Optional[Any]	)       ->				List[str]:





      """simple docstring"""

      UpperCamelCase_				:   Union[str, Any]					    =							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     A    (      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			,     __lowerCAmelCase			,     __lowerCAmelCase ):






       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\"]""" )














'''simple docstring'''

import random



def 							snake_case					(     a_							: int    ,   a_							: float    ,   a_							: bool = False	)       ->				dict:





      """simple docstring"""

      UpperCamelCase_				:   dict					    =							{i: [] for i in range(a_	)}

      # if probability is greater or equal than 1, then generate a complete graph
      if probability >= 1:
            return complete_graph(a_	)
      # if probability is lower or equal than 0, then return a graph without edges
      if probability <= 0:
            return graph

      # for each couple of nodes, add an edge from u to v
      # if the number randomly generated is greater than probability probability
      for i in range(a_	):
            for j in range(i + 1    ,   a_	):
                  if random.random() < probability:
                        graph[i].append(a_	)
                        if not directed:
                              # if the graph is undirected, add an edge in from j to i, either
                              graph[j].append(a_	)
      return graph



def 							snake_case					(     a_							: int	)       ->				dict:





      """simple docstring"""

      return {
          i: [j for j in range(a_	) if i != j] for i in range(a_	)
      }


if __name__ == "__main__":
      import doctest

      doctest.testmod()






