






# 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 		_snake_case	:






			def __init__(							self		,       a		,       a		,       a = True		,       a = False)   ->							Optional[int]:
						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  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							List[Any]:
						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  SCREAMING_SNAKE_CASE__     (							self)   ->							Any:
						return self.scheduler.get_last_lr()






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							Optional[int]:
						return self.scheduler.state_dict()






			def  SCREAMING_SNAKE_CASE__     (							self		,       a)   ->							Optional[Any]:
						self.scheduler.load_state_dict(a)






			def  SCREAMING_SNAKE_CASE__     (							self)   ->							List[Any]:
						return self.scheduler.get_lr()







			def  SCREAMING_SNAKE_CASE__     (							self		,       *a		,       **a)   ->							str:
						return self.scheduler.print_lr(*a		,       **a)










'''simple docstring'''

from PIL import Image


def 						__magic_name__       (      __UpperCAmelCase, __UpperCAmelCase      )						->   Image:

					'''simple docstring'''

					def brightness(__UpperCAmelCase      ) -> float:
										return 128 + level + (c - 128)

					if not -2_5_5.0 <= level <= 2_5_5.0:
										raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
					return img.point(__UpperCAmelCase      )


if __name__ == "__main__":
							# Load image
							with Image.open('image_data/lena.jpg') as img:
														# Change brightness to 100
														a    :							int										=    change_brightness(img, 100)
														brigt_img.save('image_data/lena_brightness.png', format='png')

