





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

import datasets


a__:      Any									=		'\\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'

a__:      Tuple									=		'\\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'

a__:      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__			:       List[str] ,     UpperCamelCase__			:       Optional[Any]  )->List[Any]:
       return float((preds == labels).mean()  )


def 	UpperCamelCase__(       UpperCamelCase__			:       int ,     UpperCamelCase__			:       Optional[int]  )->str:
       A__											=						simple_accuracy(UpperCamelCase__ ,     UpperCamelCase__  )
       A__											=						float(fa_score(y_true=UpperCamelCase__ ,     y_pred=UpperCamelCase__  )  )
       return {
           "accuracy": acc,
           "f1": fa,
       }


def 	UpperCamelCase__(       UpperCamelCase__			:       List[Any] ,     UpperCamelCase__			:       Union[str, Any]  )->Dict:
       A__											=						float(pearsonr(UpperCamelCase__ ,     UpperCamelCase__  )[0]  )
       A__											=						float(spearmanr(UpperCamelCase__ ,     UpperCamelCase__  )[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 ):
        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"]''' )












import numpy as np


def 	UpperCamelCase__(       UpperCamelCase__			:       np.array  )->np.array:
       return (2 / (1 + np.exp(-2 * vector  ))) - 1


if __name__ == "__main__":
       import doctest

       doctest.testmod()





