






"""simple docstring"""



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

import datasets


__A		:       List[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}
}
'''

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

__A		:       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				: int		,					__snake_case				: Optional[Any]	):
		return float((preds == labels).mean()	)



def 						lowercase    (				__snake_case				: Dict		,					__snake_case				: List[str]	):
		lowercase_ : Union[str, Any]							       =   simple_accuracy(__snake_case		,					__snake_case	)
		lowercase_ : Union[str, Any]							       =   float(fa_score(y_true=__snake_case		,					y_pred=__snake_case	)	)
		return {
		    "accuracy": acc,
		    "f1": fa,
		}



def 						lowercase    (				__snake_case				: List[Any]		,					__snake_case				: List[str]	):
		lowercase_ : Any							       =   float(pearsonr(__snake_case		,					__snake_case	)[0]	)
		lowercase_ : Union[str, Any]							       =   float(spearmanr(__snake_case		,					__snake_case	)[0]	)
		return {
		    "pearson": pearson_corr,
		    "spearmanr": spearman_corr,
		}






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   ,						_KWARGS_DESCRIPTION							)
class 					_UpperCAmelCase       ( datasets.Metric							):




					def   A  (				self     :						Dict   )  ->  List[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   A  (				self     :						Union[str, Any]    ,						A     :						str    ,						A     :						int   )  ->  int:
							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"]'''   )











"""simple docstring"""



from typing import Optional
from urllib.parse import quote

import huggingface_hub as hfh
from packaging import version



def 						lowercase    (				__snake_case				: str		,					__snake_case				: str		,					__snake_case				: Optional[str] = None	):
		if version.parse(hfh.__version__	).release < version.parse('''0.11.0'''	).release:
				# old versions of hfh don't url-encode the file path
				lowercase_ : Union[str, Any]							       =   quote(__snake_case	)
		return hfh.hf_hub_url(__snake_case		,					__snake_case		,					repo_type='''dataset'''		,					revision=__snake_case	)



