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

import datasets


__lowercase										= '''\
@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}
}
'''

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

__lowercase										= '''
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     ):
    '''simple docstring'''







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



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[Any]        =		simple_accuracy(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )
    __UpperCamelCase					:Any        =		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     ):
    '''simple docstring'''







    __UpperCamelCase					:Optional[Any]        =		float(pearsonr(SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE     )[0]     )
    __UpperCamelCase					:Optional[Any]        =		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)	->     Union[str, Any]:
        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			,			__lowercase			,			__lowercase)	->     Tuple:
        if self.config_name == "cola":
            return {"matthews_correlation": matthews_corrcoef(__lowercase			,			__lowercase)}
        elif self.config_name == "stsb":
            return pearson_and_spearman(__lowercase			,			__lowercase)
        elif self.config_name in ["mrpc", "qqp"]:
            return acc_and_fa(__lowercase			,			__lowercase)
        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
            return {"accuracy": simple_accuracy(__lowercase			,			__lowercase)}
        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"]''')






from __future__ import annotations



def lowerCamelCase  (       SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      SCREAMING_SNAKE_CASE  ,      ):
    '''simple docstring'''







    if (stress, tangential_force, area).count(0     ) != 1:
        raise ValueError('''You cannot supply more or less than 2 values'''     )
    elif stress < 0:
        raise ValueError('''Stress cannot be negative'''     )
    elif tangential_force < 0:
        raise ValueError('''Tangential Force cannot be negative'''     )
    elif area < 0:
        raise ValueError('''Area cannot be negative'''     )
    elif stress == 0:
        return (
            "stress",
            tangential_force / area,
        )
    elif tangential_force == 0:
        return (
            "tangential_force",
            stress * area,
        )
    else:
        return (
            "area",
            tangential_force / stress,
        )


if __name__ == "__main__":
    import doctest

    doctest.testmod()





