





import os
import time

import numpy as np
import onnxruntime as ort


_A :   Dict  			=  '1'
_A :   str  			=  '0'
_A :   List[str]  			=  '1'

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

_A :   Optional[Any]  			=  1_28
_A :   List[str]  			=  1
_A :   List[Any]  			=  np.ones((batch, sequence), dtype=np.intaa)
_A :   Any  			=  np.ones((batch, sequence), dtype=np.intaa)
_A :   Tuple  			=  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...')
_A :   str  			=  time.time()
_A :   List[str]  			=  20_00
_A :   int  			=  {}
for iter in range(max_iters):
       _A :   int  			=  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) * 10_00 / max_iters))










import pprint

import requests

_A :   Any  			=  'https://zenquotes.io/api'



def 	_a						(   )    ->    list:







   """simple docstring"""
   return requests.get(API_ENDPOINT_URL + '''/today'''					).json()



def 	_a						(   )    ->    list:







   """simple docstring"""
   return requests.get(API_ENDPOINT_URL + '''/random'''					).json()


if __name__ == "__main__":
       _A :   Optional[Any]  			=  random_quotes()
       pprint.pprint(response)



