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

import datasets


_SCREAMING_SNAKE_CASE    :      List[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'

_SCREAMING_SNAKE_CASE    :      List[str]          =			'\\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'

_SCREAMING_SNAKE_CASE    :      str          =			'\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 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]							,						__UpperCamelCase :						Optional[Any]       ) ->       Union[str, Any]:

				"""simple docstring"""







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

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						Tuple							,						__UpperCamelCase :						int       ) ->       Union[str, Any]:

				"""simple docstring"""







				A__  :	Optional[int]         =							simple_accuracy(__UpperCamelCase							,						__UpperCamelCase       )
				A__  :	Optional[Any]         =							float(fa_score(y_true=__UpperCamelCase							,						y_pred=__UpperCamelCase       )       )
				return {
				    "accuracy": acc,
				    "f1": fa,
				}

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						str							,						__UpperCamelCase :						Tuple       ) ->       int:

				"""simple docstring"""







				A__  :	List[str]         =							float(pearsonr(__UpperCamelCase							,						__UpperCamelCase       )[0]       )
				A__  :	Optional[int]         =							float(spearmanr(__UpperCamelCase							,						__UpperCamelCase       )[0]       )
				return {
				    "pearson": pearson_corr,
				    "spearmanr": spearman_corr,
				}







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







	'''simple docstring'''





	def   __snake_case				(			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   __snake_case				(			self				,       UpperCamelCase__				,       UpperCamelCase__		):
					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"]'''		)

def 				SCREAMING_SNAKE_CASE     (      ) ->       Optional[int]:

				"""simple docstring"""







				A__  :	Optional[Any]         =							0
				for i in range(1							,						10_01       ):
								total += i**i
				return str(__UpperCamelCase       )[-10:]


if __name__ == "__main__":
					print(solution())