




"""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 			_snake_case					:

				'''simple docstring'''




				def __init__(				self   :	str       ,				snake_case   :	str       ,				snake_case   :	List[Any]       ,				snake_case   :	bool = True       ,				snake_case   :	bool = False  ):
								UpperCAmelCase_       :List[str]         =   scheduler
								UpperCAmelCase_       :Tuple         =   optimizers if isinstance(__A       ,				(list, tuple)  ) else [optimizers]
								UpperCAmelCase_       :Union[str, Any]         =   split_batches
								UpperCAmelCase_       :int         =   step_with_optimizer
								UpperCAmelCase_       :int         =   GradientState()





				def 					snake_case_			(				self   :	List[Any]       ,				*snake_case   :	Dict       ,				**snake_case   :	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
												UpperCAmelCase_       :Tuple         =   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   :	Tuple  ):
								return self.scheduler.get_last_lr()





				def 					snake_case_			(				self   :	Union[str, Any]  ):
								return self.scheduler.state_dict()





				def 					snake_case_			(				self   :	Tuple       ,				snake_case   :	Any  ):
								self.scheduler.load_state_dict(__A  )





				def 					snake_case_			(				self   :	Optional[int]  ):
								return self.scheduler.get_lr()





				def 					snake_case_			(				self   :	Any       ,				*snake_case   :	Optional[int]       ,				**snake_case   :	Optional[int]  ):
								return self.scheduler.print_lr(*__A       ,				**__A  )









def 					SCREAMING_SNAKE_CASE__				(							UpperCamelCase__:				List[str]     ):
		SCREAMING_SNAKE_CASE__								=   len(UpperCamelCase__     )
		SCREAMING_SNAKE_CASE__								=   sum(UpperCamelCase__     )

		SCREAMING_SNAKE_CASE__								=   [[False for x in range(s + 1     )] for y in range(n + 1     )]

		for i in range(1       , n + 1     ):
				SCREAMING_SNAKE_CASE__								=   True

		for i in range(1       , s + 1     ):
				SCREAMING_SNAKE_CASE__								=   False

		for i in range(1       , n + 1     ):
				for j in range(1       , s + 1     ):
						SCREAMING_SNAKE_CASE__								=   dp[i][j - 1]

						if arr[i - 1] <= j:
								SCREAMING_SNAKE_CASE__								=   dp[i][j] or dp[i - 1][j - arr[i - 1]]

		for j in range(int(s / 2     )       , -1       , -1     ):
				if dp[n][j] is True:
						SCREAMING_SNAKE_CASE__								=   s - 2 * j
						break

		return diff