


from __future__ import annotations

# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
_UpperCAmelCase			:  Tuple     =10



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				for i in range(lowerCAmelCase_ ,   lowerCAmelCase_ ):
								if array[i] == target:
												return i
				return -1



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				lowerCAmelCase_ :      Any    				=				0
				lowerCAmelCase_ :      int    				=				len(lowerCAmelCase_ )
				while left <= right:
								if right - left < precision:
												return lin_search(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )

								lowerCAmelCase_ :      List[Any]    				=				(left + right) // 3 + 1
								lowerCAmelCase_ :      List[str]    				=				2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												lowerCAmelCase_ :      Dict    				=				one_third - 1
								elif array[two_third] < target:
												lowerCAmelCase_ :      List[Any]    				=				two_third + 1

								else:
												lowerCAmelCase_ :      Union[str, Any]    				=				one_third + 1
												lowerCAmelCase_ :      Tuple    				=				two_third - 1
				else:
								return -1



def 			lowerCAmelCase (		lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )-> int:
				if left < right:
								if right - left < precision:
												return lin_search(lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								lowerCAmelCase_ :      Union[str, Any]    				=				(left + right) // 3 + 1
								lowerCAmelCase_ :      Optional[int]    				=				2 * (left + right) // 3 + 1

								if array[one_third] == target:
												return one_third
								elif array[two_third] == target:
												return two_third

								elif target < array[one_third]:
												return rec_ternary_search(lowerCAmelCase_ ,   one_third - 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								elif array[two_third] < target:
												return rec_ternary_search(two_third + 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
								else:
												return rec_ternary_search(one_third + 1 ,   two_third - 1 ,   lowerCAmelCase_ ,   lowerCAmelCase_ )
				else:
								return -1


if __name__ == "__main__":
						import doctest

						doctest.testmod()

						_UpperCAmelCase			:  Tuple     =input("""Enter numbers separated by comma:\n""").strip()
						_UpperCAmelCase			:  Union[str, Any]     =[int(item.strip()) for item in user_input.split(""",""")]
						assert collection == sorted(collection), f"List must be ordered.\n{collection}."
						_UpperCAmelCase			:  int     =int(input("""Enter the number to be found in the list:\n""").strip())
						_UpperCAmelCase			:  Optional[Any]     =ite_ternary_search(collection, target)
						_UpperCAmelCase			:  List[str]     =rec_ternary_search(0, len(collection) - 1, collection, target)
						if resulta != -1:
												print(f"""Iterative search: {target} found at positions: {resulta}""")
												print(f"""Recursive search: {target} found at positions: {resulta}""")
						else:
												print("""Not found""")



# 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   ,						__lowercase   ,						__lowercase   ,						__lowercase = True   ,						__lowercase = False							)					->    Tuple:
										lowerCAmelCase_ :      Optional[int]    				=				scheduler
										lowerCAmelCase_ :      Dict    				=				optimizers if isinstance(__lowercase   ,						(list, tuple)							) else [optimizers]
										lowerCAmelCase_ :      str    				=				split_batches
										lowerCAmelCase_ :      Any    				=				step_with_optimizer
										lowerCAmelCase_ :      Optional[Any]    				=				GradientState()




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




						def 						lowercase_ (				self							)					->    Union[str, Any]:
										return self.scheduler.get_last_lr()




						def 						lowercase_ (				self							)					->    List[str]:
										return self.scheduler.state_dict()




						def 						lowercase_ (				self   ,						__lowercase							)					->    int:
										self.scheduler.load_state_dict(__lowercase							)




						def 						lowercase_ (				self							)					->    Tuple:
										return self.scheduler.get_lr()




						def 						lowercase_ (				self   ,						*__lowercase   ,						**__lowercase							)					->    int:
										return self.scheduler.print_lr(*__lowercase   ,						**__lowercase							)