



"""simple docstring"""


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

import datasets


snake_case_    					=	"""\
@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}
}
"""

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

snake_case_    					=	"""
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 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						return float((preds == labels).mean()						)

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

def 			_lowerCAmelCase				(       lowercase_		,     lowercase_						):
						UpperCAmelCase					=			float(pearsonr(lowercase_		,     lowercase_						)[0]						)
						UpperCAmelCase					=			float(spearmanr(lowercase_		,     lowercase_						)[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      UpperCAmelCase__					( self			:Union[str, Any] )    ->      str:
												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			:Optional[Any]       ,  lowercase_			:List[str]       ,  lowercase_			:Dict )    ->      int:
												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"]' )










"""simple docstring"""


import math

def 			_lowerCAmelCase				(       lowercase_						):
						assert isinstance(lowercase_		,     lowercase_						) and (
						    number >= 0
						), "'number' must been an int and positive"

						if 1 < number < 4:
												# 2 and 3 are primes
												return True
						elif number < 2 or not number % 2:
												# Negatives, 0, 1 and all even numbers are not primes
												return False

						UpperCAmelCase					=			range(3		,     int(math.sqrt(lowercase_						) + 1						)		,     2						)
						return not any(not number % i for i in odd_numbers						)






def 			_lowerCAmelCase				(       lowercase_		,     lowercase_=1		,     **lowercase_						):
						UpperCAmelCase					=			factor * value
						UpperCAmelCase					=			value

						while not is_prime(lowercase_						):
												value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1

						if value == first_value_val:
												return next_prime(value + 1		,     **lowercase_						)
						return value





