




# 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       __lowerCAmelCase				:






       def __init__(      self:    Dict				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    str				,    _lowerCAmelCase:    bool = True				,    _lowerCAmelCase:    bool = False ):
         lowercase			:Optional[Any]							=  scheduler
         lowercase			:Tuple							=  optimizers if isinstance(_lowerCAmelCase				,    (list, tuple) ) else [optimizers]
         lowercase			:str							=  split_batches
         lowercase			:str							=  step_with_optimizer
         lowercase			:Optional[int]							=  GradientState()






       def     SCREAMING_SNAKE_CASE			(      self:    Optional[int]				,    *_lowerCAmelCase:    int				,    **_lowerCAmelCase:    Dict ):
         if not self.step_with_optimizer:
           # No link between scheduler and optimizer -> just step
           self.scheduler.step(*_lowerCAmelCase				,    **_lowerCAmelCase )
           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(*_lowerCAmelCase				,    **_lowerCAmelCase )
         else:
           # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
           # num_processes steps per training step
           lowercase			:Optional[int]							=  AcceleratorState().num_processes
           for _ in range(_lowerCAmelCase ):
             # 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(*_lowerCAmelCase				,    **_lowerCAmelCase )
             else:
               self.scheduler.step(*_lowerCAmelCase				,    **_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    Union[str, Any] ):
         return self.scheduler.get_last_lr()






       def     SCREAMING_SNAKE_CASE			(      self:    Tuple ):
         return self.scheduler.state_dict()






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Tuple ):
         self.scheduler.load_state_dict(_lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str] ):
         return self.scheduler.get_lr()






       def     SCREAMING_SNAKE_CASE			(      self:    Dict				,    *_lowerCAmelCase:    Union[str, Any]				,    **_lowerCAmelCase:    List[Any] ):
         return self.scheduler.print_lr(*_lowerCAmelCase				,    **_lowerCAmelCase )









import torch



def 					UpperCAmelCase__     (   ):
  if torch.cuda.is_available():
    lowercase			:Optional[int]							=  torch.cuda.device_count()
  else:
    lowercase			:Dict							=  0
  print(F"Successfully ran on {num_gpus} GPUs"      )


if __name__ == "__main__":
     main()



