




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	int:

    """simple docstring"""


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




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	bool:

    """simple docstring"""


    __UpperCamelCase  		=						0
    __UpperCamelCase  		=						number
    while duplicate > 0:
        __UpperCamelCase		,      __UpperCamelCase  		=						divmod(lowercase_					,     10      )
        fact_sum += factorial(lowercase_      )
    return fact_sum == number


if __name__ == "__main__":
 print("Program to check whether a number is a Krisnamurthy Number or not.")
 a_   		=     int(input("Enter number: ").strip())
 print(
     f"""{number} is {'' if krishnamurthy(number) else 'not '}a Krishnamurthy Number."""
 )












import os
import time

import numpy as np
import onnxruntime as ort


a_   		=     "1"
a_   		=     "0"
a_   		=     "1"

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

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






