
def 		UpperCAmelCase     (      a_		)						->      list:







				"""simple docstring"""



				return [
				    txt[:a] + txt[a].upper() + txt[a + 1 :]
				    for a in range(len(a_		)		)
				    if txt[a].isalpha()
				]


if __name__ == "__main__":
					__import__('doctest').testmod()



# 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 					__lowerCAmelCase	:




  def __init__(							self				,  lowerCAmelCase				,  lowerCAmelCase				,  lowerCAmelCase = True				,  lowerCAmelCase = False   )    ->					str:
        '''simple docstring'''






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




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					List[Any]:
        '''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
              _lowercase      			=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     A__	(							self   )    ->					str:
        '''simple docstring'''






        return self.scheduler.get_last_lr()




  def     A__	(							self   )    ->					Tuple:
        '''simple docstring'''






        return self.scheduler.state_dict()




  def     A__	(							self				,  lowerCAmelCase   )    ->					str:
        '''simple docstring'''






        self.scheduler.load_state_dict(lowerCAmelCase   )




  def     A__	(							self   )    ->					int:
        '''simple docstring'''






        return self.scheduler.get_lr()




  def     A__	(							self				,  *lowerCAmelCase				,  **lowerCAmelCase   )    ->					Optional[int]:
        '''simple docstring'''






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


