


'''simple docstring'''

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

import datasets


_UpperCAmelCase :       Optional[int]										= """\\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"""

_UpperCAmelCase :       Tuple										= """\\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"""

_UpperCAmelCase :       Optional[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 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						return float((preds == labels).mean())

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= simple_accuracy(lowerCAmelCase__, lowerCAmelCase__)
						__lowerCAmelCase											= float(fa_score(y_true=lowerCAmelCase__, y_pred=lowerCAmelCase__))
						return {
						    "accuracy": acc,
						    "f1": fa,
						}

def 							__magic_name__(  lowerCamelCase, lowerCamelCase):
						__lowerCAmelCase											= float(pearsonr(lowerCAmelCase__, lowerCAmelCase__)[0])
						__lowerCAmelCase											= float(spearmanr(lowerCAmelCase__, lowerCAmelCase__)[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     _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				,	__lowercase				,	__lowercase						):
										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"""




def        lowercase							(		lowerCAmelCase__ :     list			) ->      bool:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		if len(lowerCAmelCase__			) == 1:
				return True
		__a           =			series[1] - series[0]
		for index in range(len(lowerCAmelCase__			) - 1			):
				if series[index + 1] - series[index] != common_diff:
						return False
		return True

def        lowercase							(		lowerCAmelCase__ :     list			) ->      float:
		if not isinstance(lowerCAmelCase__    ,					lowerCAmelCase__			):
				raise ValueError('''Input series is not valid, valid series - [2, 4, 6]'''			)
		if len(lowerCAmelCase__			) == 0:
				raise ValueError('''Input list must be a non empty list'''			)
		__a           =			0
		for val in series:
				answer += val
		return answer / len(lowerCAmelCase__			)


if __name__ == "__main__":
				import doctest

				doctest.testmod()






