


'''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       :
 def __init__(	self ,			__magic_name__ ,			__magic_name__ ,			__magic_name__ = True ,			__magic_name__ = False  )   ->							str:
      _a      =							scheduler
      _a      =							optimizers if isinstance(__magic_name__ ,			(list, tuple)  ) else [optimizers]
      _a      =							split_batches
      _a      =							step_with_optimizer
      _a      =							GradientState()
 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							List[Any]:
      if not self.step_with_optimizer:
           # No link between scheduler and optimizer -> just step
           self.scheduler.step(*__magic_name__ ,			**__magic_name__  )
           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(*__magic_name__ ,			**__magic_name__  )
      else:
           # Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
           # num_processes steps per training step
           _a      =							AcceleratorState().num_processes
           for _ in range(__magic_name__  ):
                # 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(*__magic_name__ ,			**__magic_name__  )
                else:
                     self.scheduler.step(*__magic_name__ ,			**__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Union[str, Any]:
      return self.scheduler.get_last_lr()
 def 							__UpperCAmelCase						(	self  )   ->							Any:
      return self.scheduler.state_dict()
 def 							__UpperCAmelCase						(	self ,			__magic_name__  )   ->							Any:
      self.scheduler.load_state_dict(__magic_name__  )
 def 							__UpperCAmelCase						(	self  )   ->							Dict:
      return self.scheduler.get_lr()




 def 							__UpperCAmelCase						(	self ,			*__magic_name__ ,			**__magic_name__  )   ->							List[str]:
      return self.scheduler.print_lr(*__magic_name__ ,			**__magic_name__  )





'''simple docstring'''






from timeit import timeit






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     if number < 0:
          raise ValueError('the value of input must not be negative'						)
     _a      =							0
     while number:
          number &= number - 1
          result += 1
     return result






def 		_A			(lowerCAmelCase__					:int						)				->					int:




     '''simple docstring'''



     if number < 0:
          raise ValueError('the value of input must not be negative'						)
     _a      =							0
     while number:
          if number % 2 == 1:
               result += 1
          number >>= 1
     return result






def 		_A			()				->					None:




     '''simple docstring'''



     def do_benchmark(lowerCAmelCase__					:int						) -> None:
          _a      =							'import __main__ as z'
          print(f'Benchmark when {number = }:'						)
          print(f'{get_set_bits_count_using_modulo_operator(lowerCAmelCase__						) = }'						)
          _a      =							timeit('z.get_set_bits_count_using_modulo_operator(25)'			,  setup=lowerCAmelCase__						)
          print(f'timeit() runs in {timing} seconds'						)
          print(f'{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase__						) = }'						)
          _a      =							timeit(
              'z.get_set_bits_count_using_brian_kernighans_algorithm(25)'			,  setup=lowerCAmelCase__			,  )
          print(f'timeit() runs in {timing} seconds'						)

     for number in (25, 37, 58, 0):
          do_benchmark(lowerCAmelCase__						)
          print()


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 benchmark()

