



lowercase_:  Optional[Any]     						=					{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}
def 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 snake_case__				:							str      							= set()
 # keep track of all the paths to be checked
 snake_case__				:							Union[str, Any]      							= [[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
  snake_case__				:							Any      							= queue.pop(0)
  # get the last node from the path
  snake_case__				:							Tuple      							= path[-1]
  if node not in explored:
   snake_case__				:							Dict      							= graph[node]
   # go through all neighbour nodes, construct a new path and
   # push it into the queue
   for neighbour in neighbours:
    snake_case__				:							Union[str, Any]      							= 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 	_lowercase	(     UpperCAmelCase_						,   UpperCAmelCase_						,   UpperCAmelCase_):




 """simple docstring"""




 if not graph or start not in graph or target not in graph:
  return -1
 if start == target:
  return 0
 snake_case__				:							Dict      							= [start]
 snake_case__				:							str      							= set(lowerCAmelCase__)
 # Keep tab on distances from `start` node.
 snake_case__				:							str      							= {start: 0, target: -1}
 while queue:
  snake_case__				:							List[Any]      							= queue.pop(0)
  if node == target:
   snake_case__				:							str      							= (
       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__)
    snake_case__				:							Any      							= 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









import os
import time

import numpy as np
import onnxruntime as ort


__snake_case     :Any			       ='1'
__snake_case     :List[str]			       ='0'
__snake_case     :Union[str, Any]			       ='1'

__snake_case     :Optional[Any]			       =ort.SessionOptions()
__snake_case     :List[str]			       =ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
__snake_case     :int			       =['TensorrtExecutionProvider', 'CUDAExecutionProvider']
__snake_case     :Optional[Any]			       =ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
__snake_case     :int			       =ort.RunOptions()

__snake_case     :Tuple			       =128
__snake_case     :List[Any]			       =1
__snake_case     :Union[str, Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[Any]			       =np.ones((batch, sequence), dtype=np.intaa)
__snake_case     :Optional[int]			       =np.ones((batch, sequence), dtype=np.intaa)

print('Warm up phase...')
sess.run(
    None,
    {
        sess.get_inputs()[0].name: input_ids,
        sess.get_inputs()[1].name: attention_mask,
        sess.get_inputs()[2].name: token_type_ids,
    },
    run_options=run_opt,
)

print('Start inference...')
__snake_case     :Union[str, Any]			       =time.time()
__snake_case     :int			       =2000
__snake_case     :Optional[int]			       ={}
for iter in range(max_iters):
				__snake_case     :Optional[Any]			       =sess.run(
				    None,
				    {
				        sess.get_inputs()[0].name: input_ids,
				        sess.get_inputs()[1].name: attention_mask,
				        sess.get_inputs()[2].name: token_type_ids,
				    },
				    run_options=run_opt,
				)
print('Average Inference Time = {:.3f} ms'.format((time.time() - start_time) * 1000 / max_iters))