

# 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 ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE = True ,					_SCREAMING_SNAKE_CASE = False					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     scheduler
        SCREAMING_SNAKE_CASE_				:  str           =     optimizers if isinstance(a_ ,					(list, tuple)					) else [optimizers]
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     split_batches
        SCREAMING_SNAKE_CASE_				:  Dict           =     step_with_optimizer
        SCREAMING_SNAKE_CASE_				:  Dict           =     GradientState()

  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        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
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     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  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.scheduler.get_last_lr()

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.scheduler.state_dict()

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        self.scheduler.load_state_dict(a_					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        return self.scheduler.get_lr()



  def  UpperCAmelCase    (				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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









def    A_  (							a  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[Any]           =     0
      while num > 0:
            digit_sum += num % 1_0
            num //= 1_0
      return digit_sum

def    A_  (							a = 1_0_0  ):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_				:  List[str]           =     1
      SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     2

      for i in range(2					,   max_n + 1  ):
            SCREAMING_SNAKE_CASE_				:  List[str]           =     pre_numerator
            SCREAMING_SNAKE_CASE_				:  str           =     2 * i // 3 if i % 3 == 0 else 1
            SCREAMING_SNAKE_CASE_				:  Tuple           =     cur_numerator
            SCREAMING_SNAKE_CASE_				:  Tuple           =     e_cont * pre_numerator + temp

      return sum_digits(a  )


if __name__ == "__main__":
    print(F'{solution() = }')






