


"""simple docstring"""
import warnings

from .state import AcceleratorState, GradientState


warnings.filterwarnings('ignore', category=UserWarning, module='torch.optim.lr_scheduler')





class      __A     :





							'''simple docstring'''

							def __init__(				self							:      Union[str, Any]		,       UpperCAmelCase_							:      Tuple		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      bool = True		,       UpperCAmelCase_							:      bool = False       )					->Dict:



														"""simple docstring"""



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



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



														"""simple docstring"""



														if not self.step_with_optimizer:
																					# No link between scheduler and optimizer -> just step
																					self.scheduler.step(*UpperCAmelCase_		,       **UpperCAmelCase_       )
																					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(*UpperCAmelCase_		,       **UpperCAmelCase_       )
														else:
																					# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
																					# num_processes steps per training step
																					snake_case_           =		AcceleratorState().num_processes
																					for _ in range(UpperCAmelCase_       ):
																												# 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(*UpperCAmelCase_		,       **UpperCAmelCase_       )
																												else:
																																			self.scheduler.step(*UpperCAmelCase_		,       **UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[int]:



														"""simple docstring"""



														return self.scheduler.get_last_lr()



							def 		lowerCAmelCase       (				self							:      Dict       )					->Optional[Any]:



														"""simple docstring"""



														return self.scheduler.state_dict()



							def 		lowerCAmelCase       (				self							:      Tuple		,       UpperCAmelCase_							:      Optional[int]       )					->Any:



														"""simple docstring"""



														self.scheduler.load_state_dict(UpperCAmelCase_       )



							def 		lowerCAmelCase       (				self							:      List[str]       )					->List[str]:



														"""simple docstring"""



														return self.scheduler.get_lr()



							def 		lowerCAmelCase       (				self							:      str		,       *UpperCAmelCase_							:      List[str]		,       **UpperCAmelCase_							:      Optional[int]       )					->Any:



														"""simple docstring"""



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







"""simple docstring"""
def 						_a				(      _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)    ->				int:
							if index == number_of_items:
														return 0
							snake_case_           =		0
							snake_case_           =		0
							snake_case_           =		knapsack(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, index + 1				)
							if weights[index] <= max_weight:
														snake_case_           =		values[index] + knapsack(
														    _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				, max_weight - weights[index]				, index + 1				)
							return max(_SCREAMING_SNAKE_CASE				, _SCREAMING_SNAKE_CASE				)


if __name__ == "__main__":
				import doctest

				doctest.testmod()



