



"""simple docstring"""







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

def       _lowerCamelCase				(							UpperCAmelCase_ :       Optional[int],	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Union[str, Any]     )	-> List[Any]:






	"""simple docstring"""





	A__											=					set()
	# keep track of all the paths to be checked
	A__											=					[[start]]

	# return path if start is goal
	if start == goal:
		return [start]

	# keeps looping until all possible paths have been checked
	while queue:
		# pop the first path from the queue
		A__											=					queue.pop(0     )
		# get the last node from the path
		A__											=					path[-1]
		if node not in explored:
			A__											=					graph[node]
			# go through all neighbour nodes, construct a new path and
			# push it into the queue
			for neighbour in neighbours:
				A__											=					list(_lowerCAmelCase     )
				new_path.append(_lowerCAmelCase     )
				queue.append(_lowerCAmelCase     )
				# return path if neighbour is goal
				if neighbour == goal:
					return new_path

            # mark node as explored
			explored.add(_lowerCAmelCase     )

    # in case there's no path between the 2 nodes
	return []

def       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       str,	UpperCAmelCase_ :       List[str]     )	-> Dict:






	"""simple docstring"""





	if not graph or start not in graph or target not in graph:
		return -1
	if start == target:
		return 0
	A__											=					[start]
	A__											=					set(_lowerCAmelCase     )
	# Keep tab on distances from `start` node.
	A__											=					{start: 0, target: -1}
	while queue:
		A__											=					queue.pop(0     )
		if node == target:
			A__											=					(
			    dist[node] if dist[target] == -1 else min(dist[target],	dist[node]     )
			)
		for adjacent in graph[node]:
			if adjacent not in visited:
				visited.add(_lowerCAmelCase     )
				queue.append(_lowerCAmelCase     )
				A__											=					dist[node] + 1
	return dist[target]


if __name__ == "__main__":
			print(bfs_shortest_path(demo_graph, """G""", """D"""))  # returns ['G', 'C', 'A', 'B', 'D']
			print(bfs_shortest_path_distance(demo_graph, """G""", """D"""))  # returns 4






"""simple docstring"""
import operator as op


SCREAMING_SNAKE_CASE_					     =	'''scaler.pt'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model'''
SCREAMING_SNAKE_CASE_					     =	'''random_states'''
SCREAMING_SNAKE_CASE_					     =	'''optimizer'''
SCREAMING_SNAKE_CASE_					     =	'''scheduler'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin'''
SCREAMING_SNAKE_CASE_					     =	'''pytorch_model.bin.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors'''
SCREAMING_SNAKE_CASE_					     =	'''model.safetensors.index.json'''
SCREAMING_SNAKE_CASE_					     =	'''1.10.2'''
SCREAMING_SNAKE_CASE_					     =	'''py38'''
SCREAMING_SNAKE_CASE_					     =	'''4.17.0'''
SCREAMING_SNAKE_CASE_					     =	['''ml.p3.16xlarge''', '''ml.p3dn.24xlarge''', '''ml.p4dn.24xlarge''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_SHARD''', '''SHARD_GRAD_OP''', '''NO_SHARD''', '''HYBRID_SHARD''', '''HYBRID_SHARD_ZERO2''']
SCREAMING_SNAKE_CASE_					     =	['''TRANSFORMER_BASED_WRAP''', '''SIZE_BASED_WRAP''', '''NO_WRAP''']
SCREAMING_SNAKE_CASE_					     =	['''BACKWARD_PRE''', '''BACKWARD_POST''', '''NO_PREFETCH''']
SCREAMING_SNAKE_CASE_					     =	['''FULL_STATE_DICT''', '''LOCAL_STATE_DICT''', '''SHARDED_STATE_DICT''']
SCREAMING_SNAKE_CASE_					     =	'''2.0.1'''
SCREAMING_SNAKE_CASE_					     =	['''pdsh''', '''standard''', '''openmpi''', '''mvapich''']
SCREAMING_SNAKE_CASE_					     =	['''default''', '''reduce-overhead''', '''max-autotune''']

SCREAMING_SNAKE_CASE_					     =	{'''>''': op.gt, '''>=''': op.ge, '''==''': op.eq, '''!=''': op.ne, '''<=''': op.le, '''<''': op.lt}

# These are the args for `torch.distributed.launch` for pytorch < 1.9
SCREAMING_SNAKE_CASE_					     =	[
    '''nnodes''',
    '''nproc_per_node''',
    '''rdzv_backend''',
    '''rdzv_endpoint''',
    '''rdzv_id''',
    '''rdzv_conf''',
    '''standalone''',
    '''max_restarts''',
    '''monitor_interval''',
    '''start_method''',
    '''role''',
    '''module''',
    '''m''',
    '''no_python''',
    '''run_path''',
    '''log_dir''',
    '''r''',
    '''redirects''',
    '''t''',
    '''tee''',
    '''node_rank''',
    '''master_addr''',
    '''master_port''',
]

SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_GPU''', '''FSDP''', '''MEGATRON_LM''']
SCREAMING_SNAKE_CASE_					     =	['''DEEPSPEED''', '''MULTI_XPU''', '''FSDP''']
