



"""simple docstring"""






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

import datasets


__snake_case							:     int   =					"""\
@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}
}
"""

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

__snake_case							:     Any   =					"""
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     _lowercase       (						__snake_case      ,__snake_case			)		->     Optional[Any]:
   return float((preds == labels).mean()			)


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Tuple:
   __lowerCAmelCase   :						int				= simple_accuracy(_lowerCamelCase      ,_lowerCamelCase			)
   __lowerCAmelCase   :						int				= float(fa_score(y_true=_lowerCamelCase      ,y_pred=_lowerCamelCase			)			)
   return {
       "accuracy": acc,
       "f1": fa,
   }


def     _lowercase       (						__snake_case      ,__snake_case			)		->     Any:
   __lowerCAmelCase   :						str				= float(pearsonr(_lowerCamelCase      ,_lowerCamelCase			)[0]			)
   __lowerCAmelCase   :						Dict				= 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 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> List[str]:

    """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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int] ,    _SCREAMING_SNAKE_CASE:	str ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Tuple:

    """simple docstring"""

    if self.config_name == "cola":
       return {"matthews_correlation": matthews_corrcoef(UpperCamelCase__ ,    UpperCamelCase__)}
    elif self.config_name == "stsb":
       return pearson_and_spearman(UpperCamelCase__ ,    UpperCamelCase__)
    elif self.config_name in ["mrpc", "qqp"]:
       return acc_and_fa(UpperCamelCase__ ,    UpperCamelCase__)
    elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
       return {"accuracy": simple_accuracy(UpperCamelCase__ ,    UpperCamelCase__)}
    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     _lowercase       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))