






# 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     ,						_UpperCamelCase     ,						_UpperCamelCase     ,						_UpperCamelCase = True     ,						_UpperCamelCase = False   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						scheduler
												lowerCAmelCase__    						=						optimizers if isinstance(_UpperCamelCase     ,						(list, tuple)   ) else [optimizers]
												lowerCAmelCase__    						=						split_batches
												lowerCAmelCase__    						=						step_with_optimizer
												lowerCAmelCase__    						=						GradientState()






						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""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
																		lowerCAmelCase__    						=						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       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.scheduler.get_last_lr()






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.scheduler.state_dict()






						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												self.scheduler.load_state_dict(_UpperCamelCase   )






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												return self.scheduler.get_lr()







						def       UpperCamelCase__      (	self     ,						*_UpperCamelCase     ,						**_UpperCamelCase   ):






												"""simple docstring"""
												return self.scheduler.print_lr(*_UpperCamelCase     ,						**_UpperCamelCase   )







from __future__ import annotations

__snake_case    :    Any         =     {
    """A""": ["""B""", """C""", """E"""],
    """B""": ["""A""", """D""", """E"""],
    """C""": ["""A""", """F""", """G"""],
    """D""": ["""B"""],
    """E""": ["""A""", """B""", """D"""],
    """F""": ["""C"""],
    """G""": ["""C"""],
}


class     __SCREAMING_SNAKE_CASE					:






						def __init__(	self     ,						_UpperCamelCase     ,						_UpperCamelCase   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						graph
												# mapping node to its parent in resulting breadth first tree
												lowerCAmelCase__    						=						{}
												lowerCAmelCase__    						=						source_vertex






						def       UpperCamelCase__      (	self   ):






												"""simple docstring"""
												lowerCAmelCase__    						=						{self.source_vertex}
												lowerCAmelCase__    						=						None
												lowerCAmelCase__    						=						[self.source_vertex]  # first in first out queue

												while queue:
																		lowerCAmelCase__    						=						queue.pop(0   )
																		for adjacent_vertex in self.graph[vertex]:
																								if adjacent_vertex not in visited:
																														visited.add(_UpperCamelCase   )
																														lowerCAmelCase__    						=						vertex
																														queue.append(_UpperCamelCase   )







						def       UpperCamelCase__      (	self     ,						_UpperCamelCase   ):






												"""simple docstring"""
												if target_vertex == self.source_vertex:
																		return self.source_vertex

												lowerCAmelCase__    						=						self.parent.get(_UpperCamelCase   )
												if target_vertex_parent is None:
																		lowerCAmelCase__    						=						(
																		    F"No path from vertex: {self.source_vertex} to vertex: {target_vertex}"
																		)
																		raise ValueError(_UpperCamelCase   )

												return self.shortest_path(_UpperCamelCase   ) + F"->{target_vertex}"


if __name__ == "__main__":
					__snake_case    :    Optional[Any]         =     Graph(graph, """G""")
					g.breath_first_search()
					print(g.shortest_path("""D"""))
					print(g.shortest_path("""G"""))
					print(g.shortest_path("""Foo"""))





