






# 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  _lowercase   :
      def __init__(     self				,			a				,			a				,			a = True				,			a = False   ):
       snake_case__  :    List[Any]							       =scheduler
       snake_case__  :    Any							       =optimizers if isinstance(a				,			(list, tuple)   ) else [optimizers]
       snake_case__  :    List[str]							       =split_batches
       snake_case__  :    Tuple							       =step_with_optimizer
       snake_case__  :    Union[str, Any]							       =GradientState()
      def 						lowercase__					(     self				,			*a				,			**a   ):
       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__  :    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 						lowercase__					(     self   ):
       return self.scheduler.get_last_lr()
      def 						lowercase__					(     self   ):
       return self.scheduler.state_dict()
      def 						lowercase__					(     self				,			a   ):
       self.scheduler.load_state_dict(a   )
      def 						lowercase__					(     self   ):
       return self.scheduler.get_lr()



      def 						lowercase__					(     self				,			*a				,			**a   ):
       return self.scheduler.print_lr(*a				,			**a   )













def 			A__							(      _a  :					Any		,  _a  :					Optional[Any]		,  _a  :					int		,  _a  :					Any	):





 '''simple docstring'''







 if height >= 1:
  move_tower(height - 1		,  _a		,  _a		,  _a	)
  move_disk(_a		,  _a	)
  move_tower(height - 1		,  _a		,  _a		,  _a	)







def 			A__							(      _a  :					Optional[int]		,  _a  :					str	):





 '''simple docstring'''







 print("""moving disk from"""		,  _a		,  """to"""		,  _a	)







def 			A__							(      ):





 '''simple docstring'''







 snake_case__  :    Dict							       =int(input("""Height of hanoi: """	).strip()	)
 move_tower(_a		,  """A"""		,  """B"""		,  """C"""	)


if __name__ == "__main__":
       main()





