


# 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: Dict,A_: Optional[int],A_: Dict,A_: bool = True,A_: bool = False					):






            '''simple docstring'''




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







       def 		snake_case_	(			self: Optional[int],*A_: int,**A_: str					):






            '''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
                 __UpperCamelCase =			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: Optional[Any]					):






            '''simple docstring'''




            return self.scheduler.get_last_lr()







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            return self.scheduler.state_dict()







       def 		snake_case_	(			self: Optional[int],A_: Tuple					):






            '''simple docstring'''




            self.scheduler.load_state_dict(A_					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            return self.scheduler.get_lr()







       def 		snake_case_	(			self: Optional[int],*A_: Union[str, Any],**A_: List[Any]					):






            '''simple docstring'''




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






def 		_A		(		_lowercase = 1_00 )					-> int:


     """simple docstring"""


     __UpperCamelCase =			0
     __UpperCamelCase =			0
     for i in range(1					,  n + 1 ):
          sum_of_squares += i**2
          sum_of_ints += i
     return sum_of_ints**2 - sum_of_squares


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


