
'''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  lowerCAmelCase__   :






					def __init__(   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = False		):


											"""simple docstring"""





											lowercase_	:     int													=       scheduler
											lowercase_	:     Optional[int]													=       optimizers if isinstance(__SCREAMING_SNAKE_CASE				,							(list, tuple)		) else [optimizers]
											lowercase_	:     Optional[Any]													=       split_batches
											lowercase_	:     Union[str, Any]													=       step_with_optimizer
											lowercase_	:     List[Any]													=       GradientState()






					def 	_snake_case    (   self				,							*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.scheduler.get_last_lr()






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.scheduler.state_dict()






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											self.scheduler.load_state_dict(__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											return self.scheduler.get_lr()






					def 	_snake_case    (   self				,							*__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





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







import math







class 		_snake_case  :




 def __init__(		self					,  _lowerCamelCase=0    ):  # a graph with Node 0,1,...,N-1
       a						:Optional[int] 							=       n
       a						:Union[str, Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # adjacency matrix for weight
       a						:List[Any] 							=       [
           [math.inf for j in range(0					,  _lowerCamelCase    )] for i in range(0					,  _lowerCamelCase    )
       ]  # dp[i][j] stores minimum distance from i to j




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase					,  _lowerCamelCase    ):
       a						:Tuple 							=       w




 def        SCREAMING_SNAKE_CASE__				(		self    ):
       for k in range(0					,  self.n    ):
             for i in range(0					,  self.n    ):
                   for j in range(0					,  self.n    ):
                         a						:Union[str, Any] 							=       min(self.dp[i][j]					,  self.dp[i][k] + self.dp[k][j]    )




 def        SCREAMING_SNAKE_CASE__				(		self					,  _lowerCamelCase					,  _lowerCamelCase    ):
       return self.dp[u][v]


if __name__ == "__main__":
     snake_case				:    str       				=     Graph(5)
     graph.add_edge(0, 2, 9)
     graph.add_edge(0, 4, 10)
     graph.add_edge(1, 3, 5)
     graph.add_edge(2, 3, 7)
     graph.add_edge(3, 0, 10)
     graph.add_edge(3, 1, 2)
     graph.add_edge(3, 2, 1)
     graph.add_edge(3, 4, 6)
     graph.add_edge(4, 1, 3)
     graph.add_edge(4, 2, 4)
     graph.add_edge(4, 3, 9)
     graph.floyd_warshall()
     graph.show_min(1, 4)
     graph.show_min(0, 3)





