




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

import datasets


lowercase__							:	Dict											=		"\\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"

lowercase__							:	Optional[Any]											=		"\\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"

lowercase__							:	Any											=		"\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 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





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


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	simple_accuracy(_A	,      _A				)
	snake_case_						       =	float(fa_score(y_true=_A	,      y_pred=_A				)				)
	return {
	    "accuracy": acc,
	    "f1": fa,
	}


def 		lowerCamelCase__       (						_A	,      _A				):







	'''simple docstring'''





	snake_case_						       =	float(pearsonr(_A	,      _A				)[0]				)
	snake_case_						       =	float(spearmanr(_A	,      _A				)[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				:	Optional[int]		):






							"""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 			snake_case__		(		self				:	Union[str, Any]			,      __lowercase				:	List[Any]			,      __lowercase				:	List[str]		):






							"""simple docstring"""






							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\"]"		)






def 		lowerCamelCase__       (						):







	'''simple docstring'''





	for n in range(1	,      1000000				):
		yield n * (n + 1) // 2


def 		lowerCamelCase__       (						_A				):







	'''simple docstring'''





	snake_case_						       =	1
	snake_case_						       =	2
	while i * i <= n:
		snake_case_						       =	0
		while n % i == 0:
			n //= i
			multiplicity += 1
		divisors_count *= multiplicity + 1
		i += 1
	if n > 1:
		divisors_count *= 2
	return divisors_count


def 		lowerCamelCase__       (						):







	'''simple docstring'''





	return next(i for i in triangle_number_generator() if count_divisors(_A				) > 500				)


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