




"""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       SCREAMING_SNAKE_CASE__					:


						def __init__(							self				:			Any						,						lowerCAmelCase				:			List[str]						,						lowerCAmelCase				:			List[Any]						,						lowerCAmelCase				:			bool = True						,						lowerCAmelCase				:			bool = False					):
										lowerCAmelCase  =				scheduler
										lowerCAmelCase  =				optimizers if isinstance(lowerCAmelCase						,						(list, tuple)					) else [optimizers]
										lowerCAmelCase  =				split_batches
										lowerCAmelCase  =				step_with_optimizer
										lowerCAmelCase  =				GradientState()


						def   __lowercase (							self				:			Union[str, Any]						,						*lowerCAmelCase				:			Optional[int]						,						**lowerCAmelCase				:			Union[str, Any]					):
										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
														lowerCAmelCase  =				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   __lowercase (							self				:			str					):
										return self.scheduler.get_last_lr()


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


						def   __lowercase (							self				:			Union[str, Any]						,						lowerCAmelCase				:			Any					):
										self.scheduler.load_state_dict(lowerCAmelCase					)


						def   __lowercase (							self				:			List[str]					):
										return self.scheduler.get_lr()




						def   __lowercase (							self				:			Any						,						*lowerCAmelCase				:			List[Any]						,						**lowerCAmelCase				:			Tuple					):
										return self.scheduler.print_lr(*lowerCAmelCase						,						**lowerCAmelCase					)











"""simple docstring"""







from queue import PriorityQueue
from typing import Any

import numpy as np





def 							lowercase					(snake_case__   : dict      ,       snake_case__   : str      ,       snake_case__   : set      ,       snake_case__   : set      ,       snake_case__   : dict      ,       snake_case__   : dict      ,       snake_case__   : PriorityQueue      ,       snake_case__   : dict      ,       snake_case__   : float | int      ,       )     ->  float | int:


				'''simple docstring'''






				for nxt, d in graph[v]:
								if nxt in visited_forward:
												continue
								lowerCAmelCase  =				cst_fwd.get(snake_case__      ,       np.inf )
								lowerCAmelCase  =				cst_fwd[v] + d
								if new_cost_f < old_cost_f:
												queue.put((new_cost_f, nxt) )
												lowerCAmelCase  =				new_cost_f
												lowerCAmelCase  =				v
								if nxt in visited_backward:
												if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
																lowerCAmelCase  =				cst_fwd[v] + d + cst_bwd[nxt]
				return shortest_distance





def 							lowercase					(snake_case__   : str      ,       snake_case__   : str      ,       snake_case__   : dict      ,       snake_case__   : dict )     ->  int:


				'''simple docstring'''






				lowerCAmelCase  =				-1

				lowerCAmelCase  =				set()
				lowerCAmelCase  =				set()
				lowerCAmelCase  =				{source: 0}
				lowerCAmelCase  =				{destination: 0}
				lowerCAmelCase  =				{source: None}
				lowerCAmelCase  =				{destination: None}
				lowerCAmelCase  =				PriorityQueue()
				lowerCAmelCase  =				PriorityQueue()

				lowerCAmelCase  =				np.inf

				queue_forward.put((0, source) )
				queue_backward.put((0, destination) )

				if source == destination:
								return 0

				while not queue_forward.empty() and not queue_backward.empty():
								lowerCAmelCase ,						lowerCAmelCase  =				queue_forward.get()
								visited_forward.add(snake_case__ )

								lowerCAmelCase ,						lowerCAmelCase  =				queue_backward.get()
								visited_backward.add(snake_case__ )

								lowerCAmelCase  =				pass_and_relaxation(
								    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       )

								lowerCAmelCase  =				pass_and_relaxation(
								    snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       snake_case__      ,       )

								if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
												break

				if shortest_distance != np.inf:
								lowerCAmelCase  =				shortest_distance
				return shortest_path_distance


a      =							{
    'B': [['C', 1]],
    'C': [['D', 1]],
    'D': [['F', 1]],
    'E': [['B', 1], ['G', 2]],
    'F': [],
    'G': [['F', 1]],
}
a      =							{
    'B': [['E', 1]],
    'C': [['B', 1]],
    'D': [['C', 1]],
    'F': [['D', 1], ['G', 1]],
    'E': [[None, np.inf]],
    'G': [['E', 2]],
}

if __name__ == "__main__":
		import doctest

		doctest.testmod()





