





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

import datasets


UpperCAmelCase						:   Any			  =     '''\
@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}
}
'''

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

UpperCAmelCase						:   List[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  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      Tuple:
					return float((preds == labels).mean()  )







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      int:
					__A	:						Optional[int]       =				simple_accuracy(a					,						a  )
					__A	:						Tuple       =				float(fa_score(y_true=a					,						y_pred=a  )  )
					return {
					    "accuracy": acc,
					    "f1": fa,
					}







def  _SCREAMING_SNAKE_CASE				(   a					,						a  )      ->      List[Any]:
					__A	:						Optional[Any]       =				float(pearsonr(a					,						a  )[0]  )
					__A	:						Tuple       =				float(spearmanr(a					,						a  )[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     ,					_A     ,					_A     ):
									if self.config_name == "cola":
														return {"matthews_correlation": matthews_corrcoef(_A     ,					_A     )}
									elif self.config_name == "stsb":
														return pearson_and_spearman(_A     ,					_A     )
									elif self.config_name in ["mrpc", "qqp"]:
														return acc_and_fa(_A     ,					_A     )
									elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
														return {"accuracy": simple_accuracy(_A     ,					_A     )}
									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  _SCREAMING_SNAKE_CASE				(   a  )      ->      int:
					if not nums:
										return 0
					__A	:						Optional[int]       =				nums[0]
					__A	:						str       =				0
					for num in nums[1:]:
										__A    ,				__A	:						Tuple       =				(
										    max_excluding + num,
										    max(a					,						a  ),
										)
					return max(a					,						a  )


if __name__ == "__main__":
				import doctest

				doctest.testmod()




