





'''simple docstring'''

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

import datasets


__A	: 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'

__A	: Any              =       '\\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'

__A	: Tuple              =       '\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_						(    a					:					List[Any]   , a					:					Union[str, Any]				):
							return float((preds == labels).mean()				)





def 			lowerCAmelCase_						(    a					:					int   , a					:					str				):
							a__            =							simple_accuracy(UpperCAmelCase_   , UpperCAmelCase_				)
							a__            =							float(fa_score(y_true=UpperCAmelCase_   , y_pred=UpperCAmelCase_				)				)
							return {
							    "accuracy": acc,
							    "f1": fa,
							}





def 			lowerCAmelCase_						(    a					:					Optional[int]   , a					:					Dict				):
							a__            =							float(pearsonr(UpperCAmelCase_   , UpperCAmelCase_				)[0]				)
							a__            =							float(spearmanr(UpperCAmelCase_   , UpperCAmelCase_				)[0]				)
							return {
							    "pearson": pearson_corr,
							    "spearmanr": spearman_corr,
							}






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






				'''simple docstring'''





				def        lowercase__   (				self    ):
											"""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        lowercase__   (				self       ,					_a       ,					_a    ):
											"""simple docstring"""
											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"]'    )







import os
def 							SCREAMING_SNAKE_CASE_	(					)			->						List[str]:
       with open(os.path.dirname(UpperCAmelCase_				) + '''/p022_names.txt'''				) as file:
              SCREAMING_SNAKE_CASE_							:				Any		=str(file.readlines()[0]				)
              SCREAMING_SNAKE_CASE_							:				List[Any]		=names.replace('''"'''			,							''''''				).split(''','''				)

       names.sort()

       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0
       SCREAMING_SNAKE_CASE_							:				Optional[int]		=0

       for i, name in enumerate(UpperCAmelCase_				):
              for letter in name:
                     name_score += ord(UpperCAmelCase_				) - 6_4

              total_score += (i + 1) * name_score
              SCREAMING_SNAKE_CASE_							:				Any		=0
       return total_score


if __name__ == "__main__":
 print(solution())
