




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

import datasets


lowercase__							:	List[str]											=		'\\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__							:	Optional[int]											=		'\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				:	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 			snake_case__		(		self				:	Dict			,      __lowercase				:	Union[str, Any]			,      __lowercase				:	Dict		):






							"""simple docstring"""






							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 __future__ import annotations

lowerCAmelCase					:    Union[str, Any]           = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
lowerCAmelCase					:    List[str]           = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							UpperCamelCase   			=     len(A)

							for i in range(A):
														UpperCamelCase   			=     -1
														for j in range(i + 1		,     A):
																					if arr[i] < arr[j]:
																												UpperCamelCase   			=     arr[j]
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     []
							for i, outer in enumerate(A):
														UpperCamelCase   			=     -1
														for inner in arr[i + 1 :]:
																					if outer < inner:
																												UpperCamelCase   			=     inner
																												break
														result.append(A)
							return result







def 		A_(     A						:		list[float]):
							UpperCamelCase   			=     len(A)
							UpperCamelCase   			=     []
							UpperCamelCase   			=     [-1] * arr_size

							for index in reversed(range(A)):
														if stack:
																					while stack[-1] <= arr[index]:
																												stack.pop()
																												if not stack:
																																			break
														if stack:
																					UpperCamelCase   			=     stack[-1]
														stack.append(arr[index])
							return result


if __name__ == "__main__":
						from doctest import testmod
						from timeit import timeit

						testmod()
						print(next_greatest_element_slow(arr))
						print(next_greatest_element_fast(arr))
						print(next_greatest_element(arr))

						lowerCAmelCase					:    Optional[Any]           = (
						    'from __main__ import arr, next_greatest_element_slow, '
						    'next_greatest_element_fast, next_greatest_element'
						)
						print(
						    'next_greatest_element_slow():',
						    timeit('next_greatest_element_slow(arr)', setup=setup),
						)
						print(
						    'next_greatest_element_fast():',
						    timeit('next_greatest_element_fast(arr)', setup=setup),
						)
						print(
						    '     next_greatest_element():',
						    timeit('next_greatest_element(arr)', setup=setup),
						)




