

'''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      :	List[str]							,					_a      :	Optional[Any]							,					_a      :	str							,					_a      :	bool = True							,					_a      :	bool = False )	->    Any:





       '''simple docstring'''






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





  def 						A  (		self      :	Dict							,					*_a      :	List[Any]							,					**_a      :	Any )	->    Union[str, Any]:





       '''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							    =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 						A  (		self      :	Dict )	->    Tuple:





       '''simple docstring'''






       return self.scheduler.get_last_lr()





  def 						A  (		self      :	Optional[int] )	->    Union[str, Any]:





       '''simple docstring'''






       return self.scheduler.state_dict()





  def 						A  (		self      :	Any							,					_a      :	str )	->    int:





       '''simple docstring'''






       self.scheduler.load_state_dict(_a )





  def 						A  (		self      :	Any )	->    Optional[Any]:





       '''simple docstring'''






       return self.scheduler.get_lr()





  def 						A  (		self      :	List[Any]							,					*_a      :	List[str]							,					**_a      :	int )	->    int:





       '''simple docstring'''






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








"""simple docstring"""



from argparse import ArgumentParser

from .env import EnvironmentCommand
def 						_SCREAMING_SNAKE_CASE (    ):





  '''simple docstring'''





  lowercase							    =       ArgumentParser('Diffusers CLI tool'		,					usage='diffusers-cli <command> [<args>]'    )
  lowercase							    =       parser.add_subparsers(help='diffusers-cli command helpers'    )

  # Register commands
  EnvironmentCommand.register_subcommand(__snake_case    )

  # Let's go
  lowercase							    =       parser.parse_args()

  if not hasattr(__snake_case		,					'func'    ):
    parser.print_help()
    exit(1    )

  # Run
  lowercase							    =       args.func(__snake_case    )
  service.run()


if __name__ == "__main__":
    main()


