


'''simple docstring'''




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

import datasets


_a   :   Union[str, Any]												=   "\\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   :   Optional[int]												=   "\\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   :   List[str]												=   "\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   _a		(lowercase__			:   int							,   lowercase__			:   List[Any]						)			->			List[str]:






     """simple docstring"""
     return float((preds == labels).mean()						)





def   _a		(lowercase__			:   str							,   lowercase__			:   Optional[Any]						)			->			int:






     """simple docstring"""
     __snake_case           =						simple_accuracy(lowercase__							,   lowercase__						)
     __snake_case           =						float(fa_score(y_true=lowercase__							,   y_pred=lowercase__						)						)
     return {
         "accuracy": acc,
         "f1": fa,
     }





def   _a		(lowercase__			:   Tuple							,   lowercase__			:   List[Any]						)			->			str:






     """simple docstring"""
     __snake_case           =						float(pearsonr(lowercase__							,   lowercase__						)[0]						)
     __snake_case           =						float(spearmanr(lowercase__							,   lowercase__						)[0]						)
     return {
         "pearson": pearson_corr,
         "spearmanr": spearman_corr,
     }




@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION						,					_KWARGS_DESCRIPTION							)
class 			_lowercase (	datasets.Metric							):
   def a  (						self       :      Dict      ) ->							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 a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      Any      ) ->							str:
        if self.config_name == "cola":
             return {"matthews_correlation": matthews_corrcoef(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )}
        elif self.config_name == "stsb":
             return pearson_and_spearman(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        elif self.config_name in ["mrpc", "qqp"]:
             return acc_and_fa(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )
        elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
             return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      )}
        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 datetime import datetime as dt
import os

from github import Github


__A								=       [
    "good first issue",
    "good second issue",
    "good difficult issue",
    "feature request",
    "new model",
    "wip",
]







def      lowerCamelCase_						(			)	->			int:





				"""simple docstring"""


				__lowerCamelCase									=       Github(os.environ['GITHUB_TOKEN'] )
				__lowerCamelCase									=       g.get_repo('huggingface/transformers' )
				__lowerCamelCase									=       repo.get_issues(state='open' )

				for issue in open_issues:
								__lowerCamelCase									=       sorted([comment for comment in issue.get_comments()]					,	key=lambda UpperCamelCase__ : i.created_at					,	reverse=UpperCamelCase__ )
								__lowerCamelCase									=       comments[0] if len(UpperCamelCase__ ) > 0 else None
								if (
								    last_comment is not None
								    and last_comment.user.login == "github-actions[bot]"
								    and (dt.utcnow() - issue.updated_at).days > 7
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
												issue.edit(state='closed' )
								elif (
								    (dt.utcnow() - issue.updated_at).days > 23
								    and (dt.utcnow() - issue.created_at).days >= 30
								    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
								):
												# print(f"Would add stale comment to {issue.number}")
												issue.create_comment(
												    'This issue has been automatically marked as stale because it has not had '
												    'recent activity. If you think this still needs to be addressed '
												    'please comment on this thread.\n\nPlease note that issues that do not follow the '
												    '[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '
												    'are likely to be ignored.' )


if __name__ == "__main__":
					main()


