






import os
from datetime import datetime as dt

from github import Github


a__  	=  [
    """good first issue""",
    """feature request""",
    """wip""",
]

def _UpperCAmelCase (						):
							snake_case__							=							Github(os.environ["""GITHUB_TOKEN"""]			)
							snake_case__							=							g.get_repo("""huggingface/accelerate"""			)
							snake_case__							=							repo.get_issues(state="""open"""			)

							for issue in open_issues:
														snake_case__							=							sorted([comment for comment in issue.get_comments()]	,       key=lambda a			: i.created_at	,       reverse=__lowercase			)
														snake_case__							=							comments[0] if len(__lowercase			) > 0 else None
														snake_case__							=							dt.utcnow()
														snake_case__							=							(current_time - issue.updated_at).days
														snake_case__							=							(current_time - issue.created_at).days
														if (
														    last_comment is not None
														    and last_comment.user.login == "github-actions[bot]"
														    and days_since_updated > 7
														    and days_since_creation >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
														):
																					# Close issue since it has been 7 days of inactivity since bot mention.
																					issue.edit(state="""closed"""			)
														elif (
														    days_since_updated > 23
														    and days_since_creation >= 30
														    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()			)
														):
																					# Add stale comment
																					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/accelerate/blob/main/CONTRIBUTING.md) """
																					    """are likely to be ignored."""			)


if __name__ == "__main__":
	main()






'''simple docstring'''







# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation

import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings("""ignore""", category=UserWarning, module="""torch.optim.lr_scheduler""")







class 					_a :





     '''simple docstring'''



     def __init__(				self							,__a							,__a							,__a = True							,__a = False							)      ->				Dict:
          snake_case			:      List[Any]        =			scheduler
          snake_case			:      List[Any]        =			optimizers if isinstance(__a							,(list, tuple)							) else [optimizers]
          snake_case			:      List[str]        =			split_batches
          snake_case			:      List[str]        =			step_with_optimizer
          snake_case			:      int        =			GradientState()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          if not self.step_with_optimizer:
               # No link between scheduler and optimizer -> just step
               self.scheduler.step(*__a							,**__a							)
               return

          # Otherwise, first make sure the optimizer was stepped.
          if not self.gradient_state.sync_gradients:
               if self.gradient_state.adjust_scheduler:
                    self.scheduler._step_count += 1
               return

          for opt in self.optimizers:
               if opt.step_was_skipped:
                    return
          if self.split_batches:
               # Split batches -> the training dataloader batch size is not changed so one step per training step
               self.scheduler.step(*__a							,**__a							)
          else:
               # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
               # num_processes steps per training step
               snake_case			:      List[str]        =			AcceleratorState().num_processes
               for _ in range(__a							):
                    # Special case when using OneCycle and `drop_last` was not used
                    if hasattr(self.scheduler							,"""total_steps"""							):
                         if self.scheduler._step_count <= self.scheduler.total_steps:
                              self.scheduler.step(*__a							,**__a							)
                    else:
                         self.scheduler.step(*__a							,**__a							)







     def 			snake_case_      (				self							)      ->				Dict:
          return self.scheduler.get_last_lr()







     def 			snake_case_      (				self							)      ->				int:
          return self.scheduler.state_dict()







     def 			snake_case_      (				self							,__a							)      ->				Any:
          self.scheduler.load_state_dict(__a							)







     def 			snake_case_      (				self							)      ->				Any:
          return self.scheduler.get_lr()







     def 			snake_case_      (				self							,*__a							,**__a							)      ->				Optional[Any]:
          return self.scheduler.print_lr(*__a							,**__a							)



