





from __future__ import annotations





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int ,      a__ :int ,      a__ :int							):






							"""simple docstring"""





							if (direction == 1 and array[indexa] > array[indexa]) or (
							    direction == 0 and array[indexa] < array[indexa]
							):
														UpperCamelCase__  ,  UpperCamelCase__					  =      array[indexa], array[indexa]





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int ,      a__ :int ,      a__ :int							):






							"""simple docstring"""





							if length > 1:
														UpperCamelCase__					  =      int(length / 2							)
														for i in range(a__ ,      low + middle							):
																					comp_and_swap(a__ ,      a__ ,      i + middle ,      a__							)
														bitonic_merge(a__ ,      a__ ,      a__ ,      a__							)
														bitonic_merge(a__ ,      low + middle ,      a__ ,      a__							)





def      _UpperCamelCase  (a__ :list[int] ,      a__ :int ,      a__ :int ,      a__ :int							):






							"""simple docstring"""





							if length > 1:
														UpperCamelCase__					  =      int(length / 2							)
														bitonic_sort(a__ ,      a__ ,      a__ ,      1							)
														bitonic_sort(a__ ,      low + middle ,      a__ ,      0							)
														bitonic_merge(a__ ,      a__ ,      a__ ,      a__							)


if __name__ == "__main__":
					UpperCamelCase__           =			input("Enter numbers separated by a comma:\n").strip()
					UpperCamelCase__           =			[int(item.strip()) for item in user_input.split(",")]

					bitonic_sort(unsorted, 0, len(unsorted), 1)
					print("\nSorted array in ascending order is: ", end="")
					print(*unsorted, sep=", ")

					bitonic_merge(unsorted, 0, len(unsorted), 0)
					print("Sorted array in descending order is: ", end="")
					print(*unsorted, sep=", ")











# 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 							__SCREAMING_SNAKE_CASE       :






							def __init__(    self	,			__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase = True	,			__lowerCAmelCase = False	):
														UpperCamelCase__					  =      scheduler
														UpperCamelCase__					  =      optimizers if isinstance(__lowerCAmelCase	,			(list, tuple)	) else [optimizers]
														UpperCamelCase__					  =      split_batches
														UpperCamelCase__					  =      step_with_optimizer
														UpperCamelCase__					  =      GradientState()






							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														if not self.step_with_optimizer:
																					# No link between scheduler and optimizer -> just step
																					self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																					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(*__lowerCAmelCase	,			**__lowerCAmelCase	)
														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(__lowerCAmelCase	):
																												# 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(*__lowerCAmelCase	,			**__lowerCAmelCase	)
																												else:
																																			self.scheduler.step(*__lowerCAmelCase	,			**__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_last_lr()






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.state_dict()






							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase	):
														self.scheduler.load_state_dict(__lowerCAmelCase	)






							def 					_lowerCamelCase		(    self	):
														return self.scheduler.get_lr()







							def 					_lowerCamelCase		(    self	,			*__lowerCAmelCase	,			**__lowerCAmelCase	):
														return self.scheduler.print_lr(*__lowerCAmelCase	,			**__lowerCAmelCase	)




