


'''simple docstring'''
import os
from datetime import datetime as dt

from github import Github


UpperCamelCase__          = [
    '''good first issue''',
    '''good second issue''',
    '''good difficult issue''',
    '''enhancement''',
    '''new pipeline/model''',
    '''new scheduler''',
    '''wip''',
]





def    a__			(   )       ->      List[Any]:
    UpperCAmelCase__     :						int						=							Github(os.environ['''GITHUB_TOKEN''']		)
    UpperCAmelCase__     :						List[str]						=							g.get_repo('''huggingface/diffusers'''		)
    UpperCAmelCase__     :						Optional[Any]						=							repo.get_issues(state='''open'''		)

    for issue in open_issues:
        UpperCAmelCase__     :						Union[str, Any]						=							sorted(issue.get_comments()    ,   key=lambda lowerCAmelCase__		: i.created_at    ,   reverse=lowerCAmelCase__		)
        UpperCAmelCase__     :						Optional[int]						=							comments[0] if len(lowerCAmelCase__		) > 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()		)
        ):
            # Closes the issue after 7 days of inactivity since the Stalebot notification.
            issue.edit(state='''closed'''		)
        elif (
            "stale" in issue.get_labels()
            and last_comment is not None
            and last_comment.user.login != "github-actions[bot]"
        ):
            # Opens the issue if someone other than Stalebot commented.
            issue.edit(state='''open'''		)
            issue.remove_from_labels('''stale'''		)
        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()		)
        ):
            # Post a Stalebot notification after 23 days of inactivity.
            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/diffusers/blob/main/CONTRIBUTING.md) '''
                '''are likely to be ignored.'''		)
            issue.add_to_labels('''stale'''		)


if __name__ == "__main__":
  main()







'''simple docstring'''

import math_equivalence  # From: git+https://github.com/hendrycks/math.git

import datasets


lowercase__		:     str			=							'\\n@article{hendrycksmath2021,\n  title={Measuring Mathematical Problem Solving With the MATH Dataset},\n  author={Dan Hendrycks\n    and Collin Burns\n    and Saurav Kadavath\n    and Akul Arora\n    and Steven Basart\n    and Eric Tang\n    and Dawn Song\n    and Jacob Steinhardt},\n  journal={arXiv preprint arXiv:2103.03874},\n  year={2021}\n}\n'


lowercase__		:     str			=							'\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'


lowercase__		:     Dict			=							R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n    predictions: list of predictions to score. Each prediction\n        is a string that contains natural language and LaTex.\n    references: list of reference for each prediction. Each\n        reference is a string that contains natural language\n        and LaTex.\nReturns:\n    accuracy: accuracy after canonicalizing inputs\n        (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n    >>> metric = datasets.load_metric("competition_math")\n    >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n    >>> print(results)\n    {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION		,		_KWARGS_DESCRIPTION					)
class 							__lowerCAmelCase	(       datasets.Metric					):






       """simple docstring"""





       def snake_case__		(				self  :						Dict	)       ->	Any:

            '''simple docstring'''




            return datasets.MetricInfo(
                description=_DESCRIPTION			,					citation=_CITATION			,					inputs_description=_KWARGS_DESCRIPTION			,					features=datasets.Features(
                    {
                        '''predictions''': datasets.Value('''string'''	),
                        '''references''': datasets.Value('''string'''	),
                    }	)			,					homepage='''https://github.com/hendrycks/math'''			,					codebase_urls=['''https://github.com/hendrycks/math''']			,					)







       def snake_case__		(				self  :						List[Any]			,					lowerCAmelCase__  :						Dict			,					lowerCAmelCase__  :						int	)       ->	List[str]:

            '''simple docstring'''




            _UpperCamelCase							       =			0.0
            for i, j in zip(lowerCAmelCase__			,					lowerCAmelCase__	):
                 n_correct += 1.0 if math_equivalence.is_equiv(lowerCAmelCase__			,					lowerCAmelCase__	) else 0.0
            _UpperCamelCase							       =			n_correct / len(lowerCAmelCase__	)
            return {
                "accuracy": accuracy,
            }



