





def      __lowercase     (   _SCREAMING_SNAKE_CASE			)				->     list:


      '''simple docstring'''


      if len(_SCREAMING_SNAKE_CASE			) < 2:
            return collection

      def circle_sort_util(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			) -> bool:
            SCREAMING_SNAKE_CASE	=		False

            if low == high:
                  return swapped

            SCREAMING_SNAKE_CASE	=		low
            SCREAMING_SNAKE_CASE	=		high

            while left < right:
                  if collection[left] > collection[right]:
                        SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		(
                            collection[right],
                            collection[left],
                        )
                        SCREAMING_SNAKE_CASE	=		True

                  left += 1
                  right -= 1

            if left == right and collection[left] > collection[right + 1]:
                  SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE	=		(
                      collection[right + 1],
                      collection[left],
                  )

                  SCREAMING_SNAKE_CASE	=		True

            SCREAMING_SNAKE_CASE	=		low + int((high - low) / 2			)
            SCREAMING_SNAKE_CASE	=		circle_sort_util(_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)
            SCREAMING_SNAKE_CASE	=		circle_sort_util(_SCREAMING_SNAKE_CASE				,		mid + 1				,		_SCREAMING_SNAKE_CASE			)

            return swapped or left_swap or right_swap

      SCREAMING_SNAKE_CASE	=		True

      while is_not_sorted is True:
            SCREAMING_SNAKE_CASE	=		circle_sort_util(_SCREAMING_SNAKE_CASE				,		0				,		len(_SCREAMING_SNAKE_CASE			) - 1			)

      return collection


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE_		     = input("""Enter numbers separated by a comma:\n""").strip()
      SCREAMING_SNAKE_CASE_		     = [int(item) for item in user_input.split(""",""")]
      print(circle_sort(unsorted))









# 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 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					Optional[Any]		,lowerCamelCase__		:					Optional[int]		,lowerCamelCase__		:					Tuple		,lowerCamelCase__		:					bool = True		,lowerCamelCase__		:					bool = False )  ->	Tuple:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		scheduler
           SCREAMING_SNAKE_CASE	=		optimizers if isinstance(lowerCamelCase__		,(list, tuple) ) else [optimizers]
           SCREAMING_SNAKE_CASE	=		split_batches
           SCREAMING_SNAKE_CASE	=		step_with_optimizer
           SCREAMING_SNAKE_CASE	=		GradientState()






     def        SCREAMING_SNAKE_CASE__						(							self		:					str		,*lowerCamelCase__		:					Union[str, Any]		,**lowerCamelCase__		:					Optional[int] )  ->	Dict:




           '''simple docstring'''


           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
                 SCREAMING_SNAKE_CASE	=		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		:					int )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_last_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.state_dict()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Optional[int]		,lowerCamelCase__		:					str )  ->	int:




           '''simple docstring'''


           self.scheduler.load_state_dict(lowerCamelCase__ )






     def        SCREAMING_SNAKE_CASE__						(							self		:					List[Any] )  ->	Optional[Any]:




           '''simple docstring'''


           return self.scheduler.get_lr()






     def        SCREAMING_SNAKE_CASE__						(							self		:					Dict		,*lowerCamelCase__		:					int		,**lowerCamelCase__		:					Tuple )  ->	Optional[int]:




           '''simple docstring'''


           return self.scheduler.print_lr(*lowerCamelCase__		,**lowerCamelCase__ )


