


from functools import lru_cache


@lru_cache
def 				lowerCamelCase__	(      a	:   int				)    ->	int:





			"""simple docstring"""
			if num < 0:
						raise ValueError("Number should not be negative."				)

			return 1 if num in (0, 1) else num * factorial(num - 1				)


if __name__ == "__main__":
		import doctest

		doctest.testmod()






import os
import time

import numpy as np
import onnxruntime as ort


snake_case__			 =						'''1'''
snake_case__			 =						'''0'''
snake_case__			 =						'''1'''

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

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


