
"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       Optional[int] ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Dict ,					__lowerCamelCase   :       List[Any]		)     ->					List[Any]:
  # Return True if there is node that has not iterated.
  _snake_case					     =       [False] * len(__lowerCamelCase		)
  _snake_case					     =       []
  queue.append(__lowerCamelCase		)
  _snake_case					     =       True

  while queue:
    _snake_case					     =       queue.pop(0		)
    for ind in range(len(graph[u]		)		):
      if visited[ind] is False and graph[u][ind] > 0:
        queue.append(__lowerCamelCase		)
        _snake_case					     =       True
        _snake_case					     =       u

  return visited[t]
def 					_UpperCAmelCase      ( __lowerCamelCase   :       str ,					__lowerCamelCase   :       Union[str, Any] ,					__lowerCamelCase   :       Optional[int]		)     ->					Optional[Any]:
  # This array is filled by BFS and to store path
  _snake_case					     =       [-1] * (len(__lowerCamelCase		))
  _snake_case					     =       0
  while bfs(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
    _snake_case					     =       float('''Inf'''		)
    _snake_case					     =       sink

    while s != source:
      # Find the minimum value in select path
      _snake_case					     =       min(__lowerCamelCase ,					graph[parent[s]][s]		)
      _snake_case					     =       parent[s]

    max_flow += path_flow
    _snake_case					     =       sink

    while v != source:
      _snake_case					     =       parent[v]
      graph[u][v] -= path_flow
      graph[v][u] += path_flow
      _snake_case					     =       parent[v]
  return max_flow


UpperCAmelCase__          = [
    [0, 16, 13, 0, 0, 0],
    [0, 0, 10, 12, 0, 0],
    [0, 4, 0, 0, 14, 0],
    [0, 0, 9, 0, 0, 20],
    [0, 0, 0, 7, 0, 4],
    [0, 0, 0, 0, 0, 0],
]

UpperCAmelCase__      ,	UpperCAmelCase__          = 0, 5

print(ford_fulkerson(graph, source, sink))





"""simple docstring"""



import os
import time

import numpy as np
import onnxruntime as ort


UpperCAmelCase__          = '1'
UpperCAmelCase__          = '0'
UpperCAmelCase__          = '1'

UpperCAmelCase__          = ort.SessionOptions()
UpperCAmelCase__          = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
UpperCAmelCase__          = ['TensorrtExecutionProvider', 'CUDAExecutionProvider']
UpperCAmelCase__          = ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
UpperCAmelCase__          = ort.RunOptions()

UpperCAmelCase__          = 128
UpperCAmelCase__          = 1
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = np.ones((batch, sequence), dtype=np.intaa)
UpperCAmelCase__          = 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...')
UpperCAmelCase__          = time.time()
UpperCAmelCase__          = 2000
UpperCAmelCase__          = {}
for iter in range(max_iters):
 UpperCAmelCase__          = 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))



