





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

import datasets


A    :					Dict =      '''\
@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}
}
'''

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

A    :					Optional[Any] =      '''
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					(							__a   :Dict    ,     __a   :Optional[int]    )		->				Union[str, Any]:






		"""simple docstring"""

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







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :Any    )		->				List[str]:






		"""simple docstring"""

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







def    __lowerCamelCase					(							__a   :Optional[Any]    ,     __a   :List[str]    )		->				str:






		"""simple docstring"""

		A__        =   float(pearsonr(__a    ,     __a    )[0]    )
		A__        =   float(spearmanr(__a    ,     __a    )[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    a_       (			self   :    Any   )	->	Dict:







					"""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    a_       (			self   :    List[Any]							,	__lowerCAmelCase   :    Tuple							,	__lowerCAmelCase   :    str   )	->	Dict:







					"""simple docstring"""






					if self.config_name == "cola":
							return {"matthews_correlation": matthews_corrcoef(__lowerCAmelCase							,	__lowerCAmelCase   )}
					elif self.config_name == "stsb":
							return pearson_and_spearman(__lowerCAmelCase							,	__lowerCAmelCase   )
					elif self.config_name in ["mrpc", "qqp"]:
							return acc_and_fa(__lowerCAmelCase							,	__lowerCAmelCase   )
					elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
							return {"accuracy": simple_accuracy(__lowerCAmelCase							,	__lowerCAmelCase   )}
					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					(							__a   :int = 1_0_0_0_0_0_0    )		->				int:






		"""simple docstring"""

		A__        =   limit + 1
		A__        =   [0] * limit
		for first_term in range(1    ,     __a    ):
				for n in range(__a    ,     __a    ,     __a    ):
						A__        =   first_term + n / first_term
						if common_difference % 4:  # d must be divisble by 4
								continue
						else:
								common_difference /= 4
								if (
								    first_term > common_difference
								    and first_term < 4 * common_difference
								):  # since x,y,z are positive integers
										frequency[n] += 1  # so z>0 and a>d ,also 4d<a

		A__        =   sum(1 for x in frequency[1:limit] if x == 1_0    )

		return count


if __name__ == "__main__":
					print(F'''{solution() = }''')



