



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

import datasets


_lowerCamelCase						    =  "\\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"

_lowerCamelCase						    =  "\\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"

_lowerCamelCase						    =  "\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						(				__lowerCamelCase   :     Any		,	__lowerCamelCase   :     str     ):



	"""simple docstring"""





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






def 	_lowerCAmelCase						(				__lowerCamelCase   :     Dict		,	__lowerCamelCase   :     Tuple     ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE       :    int       =	simple_accuracy(__lowerCamelCase		,	__lowerCamelCase     )
	__SCREAMING_SNAKE_CASE       :    List[Any]       =	float(fa_score(y_true=__lowerCamelCase		,	y_pred=__lowerCamelCase     )     )
	return {
	    "accuracy": acc,
	    "f1": fa,
	}






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int		,	__lowerCamelCase   :     str     ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE       :    List[str]       =	float(pearsonr(__lowerCamelCase		,	__lowerCamelCase     )[0]     )
	__SCREAMING_SNAKE_CASE       :    str       =	float(spearmanr(__lowerCamelCase		,	__lowerCamelCase     )[0]     )
	return {
	    "pearson": pearson_corr,
	    "spearmanr": spearman_corr,
	}






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





					def 	__snake_case    (       self :							Any				)->Optional[Any]:
						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 :							Tuple     ,  UpperCamelCase :							Optional[Any]     ,  UpperCamelCase :							str				)->Any:
						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\"]"				)











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 1000000     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    int       =	0
 __SCREAMING_SNAKE_CASE       :    int       =	0
 __SCREAMING_SNAKE_CASE       :    int

 while num_cuboids <= limit:
  max_cuboid_size += 1
  for sum_shortest_sides in range(2		,	2 * max_cuboid_size + 1     ):
   if sqrt(sum_shortest_sides**2 + max_cuboid_size**2     ).is_integer():
    num_cuboids += (
        min(__lowerCamelCase		,	sum_shortest_sides // 2     )
        - max(1		,	sum_shortest_sides - max_cuboid_size     )
        + 1
    )

 return max_cuboid_size


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






