'''simple docstring'''


def  _lowerCamelCase   (				lowerCamelCase_ :							list     ,					lowerCamelCase_ :							list     ,					lowerCamelCase_ :							int     ,					lowerCamelCase_ :							int     ,					lowerCamelCase_ :							int       ):



     """simple docstring"""





     if index == number_of_items:
          return 0
     UpperCAmelCase_							:       int       =				0
     UpperCAmelCase_							:       Union[str, Any]       =				0
     UpperCAmelCase_							:       List[Any]       =				knapsack(lowerCAmelCase__     ,					lowerCAmelCase__     ,					lowerCAmelCase__     ,					lowerCAmelCase__     ,					index + 1       )
     if weights[index] <= max_weight:
          UpperCAmelCase_							:       List[str]       =				values[index] + knapsack(
              lowerCAmelCase__     ,					lowerCAmelCase__     ,					lowerCAmelCase__     ,					max_weight - weights[index]     ,					index + 1       )
     return max(lowerCAmelCase__     ,					lowerCAmelCase__       )


if __name__ == "__main__":
 import doctest

 doctest.testmod()


'''simple docstring'''


import os
import time

import numpy as np
import onnxruntime as ort


snake_case__					:      Optional[int]				      =  '''1'''
snake_case__					:      str				      =  '''0'''
snake_case__					:      List[str]				      =  '''1'''

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

snake_case__					:      List[Any]				      =  128
snake_case__					:      Union[str, Any]				      =  1
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Tuple				      =  np.ones((batch, sequence), dtype=np.intaa)
snake_case__					:      Union[str, Any]				      =  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__					:      str				      =  2000
snake_case__					:      Tuple				      =  {}
for iter in range(max_iters):
 snake_case__					:      str				      =  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))






