


from __future__ import annotations






def 						a__			(				A__,      A__,      A__,      A__				):
			SCREAMING_SNAKE_CASE_		:     List[Any]					=					[]
			SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					input_list[low:mid], input_list[mid : high + 1]
			while left and right:
						result.append((left if left[0] <= right[0] else right).pop(0				)				)
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					result + left + right
			return input_list






def 						a__			(				A__				):
			if len(A__				) <= 1:
						return input_list
			SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					list(A__				)

			# iteration for two-way merging
			SCREAMING_SNAKE_CASE_		:     Any					=					2
			while p <= len(A__				):
						# getting low, high and middle value for merge-sort of single list
						for i in range(0,      len(A__				),      A__				):
									SCREAMING_SNAKE_CASE_		:     str					=					i
									SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					i + p - 1
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					(low + high + 1) // 2
									SCREAMING_SNAKE_CASE_		:     List[Any]					=					merge(A__,      A__,      A__,      A__				)
						# final merge of last two parts
						if p * 2 >= len(A__				):
									SCREAMING_SNAKE_CASE_		:     Optional[int]					=					i
									SCREAMING_SNAKE_CASE_		:     int					=					merge(A__,      0,      A__,      len(A__				) - 1				)
									break
						p *= 2

			return input_list


if __name__ == "__main__":
				lowerCAmelCase__			:      Optional[Any]								=input('Enter numbers separated by a comma:\n').strip()
				if user_input == "":
								lowerCAmelCase__			:      Dict								=[]
				else:
								lowerCAmelCase__			:      Optional[int]								=[int(item.strip()) for item in user_input.split(',')]
				print(iter_merge_sort(unsorted))











'''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							)



