'''simple docstring'''

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

import datasets


lowercase_     		= '''\
@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}
}
'''

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

lowercase_     		= '''
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 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






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

def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






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

def 							UpperCamelCase__			(			a__			,      a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=float(pearsonr(a__			,      a__      )[0]      )
						_lowerCAmelCase										=float(spearmanr(a__			,      a__      )[0]      )
						return {
						    "pearson": pearson_corr,
						    "spearmanr": spearman_corr,
						}


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







		"""simple docstring"""






		def 		UpperCamelCase__       (   self				) ->     int:
								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 		UpperCamelCase__       (   self		,						__A		,						__A				) ->     str:
								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



def 							UpperCamelCase__			(			a__      ):


						'''simple docstring'''






						_lowerCAmelCase										=len(a__      ) // 2

						# choose the middle 3 elements
						_lowerCAmelCase										=lst[m - 1 : m + 2]

						# if middle element is peak
						if three[1] > three[0] and three[1] > three[2]:
												return three[1]

						# if increasing, recurse on right
						elif three[0] < three[2]:
												if len(lst[:m]      ) == 2:
																		m -= 1
												return peak(lst[m:]      )

						# decreasing
						else:
												if len(lst[:m]      ) == 2:
																		m += 1
												return peak(lst[:m]      )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





