
"""simple docstring"""

def      lowercase__    (       lowerCAmelCase					:       list[int]			)			->   list[int]:






    """simple docstring"""




    UpperCAmelCase          =       len(lowerCAmelCase			)
    for i in range(lowerCAmelCase			):
        for j in range(i + 1			,					lowerCAmelCase			):
            if numbers[j] < numbers[i]:
                UpperCAmelCase      ,				UpperCAmelCase          =       numbers[j], numbers[i]
    return numbers


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





"""simple docstring"""

import os
import time

import numpy as np
import onnxruntime as ort


SCREAMING_SNAKE_CASE_            =    '''1'''
SCREAMING_SNAKE_CASE_            =    '''0'''
SCREAMING_SNAKE_CASE_            =    '''1'''

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

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



