




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	Any:

    """simple docstring"""


    stooge(lowerCamelCase__					,     0					,     len(lowerCamelCase__      ) - 1      )
    return arr




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_      )       ->	List[Any]:

    """simple docstring"""


    if i >= h:
        return

    # If first element is smaller than the last then swap them
    if arr[i] > arr[h]:
        __UpperCamelCase  		=						arr[h], arr[i]

    # If there are more than 2 elements in the array
    if h - i + 1 > 2:
        __UpperCamelCase  		=						(int)((h - i + 1) / 3      )

        # Recursively sort first 2/3 elements
        stooge(lowerCamelCase__					,     lowerCamelCase__					,     (h - t)      )

        # Recursively sort last 2/3 elements
        stooge(lowerCamelCase__					,     i + t					,     (lowerCamelCase__)      )

        # Recursively sort first 2/3 elements
        stooge(lowerCamelCase__					,     lowerCamelCase__					,     (h - t)      )


if __name__ == "__main__":
 a_			=     input("Enter numbers separated by a comma:\n").strip()
 a_			=     [int(item) for item in user_input.split(",")]
 print(stooge_sort(unsorted))












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))






