




from PIL import Image




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	Image:

    """simple docstring"""


    def brightness(lowercase_      ) -> float:
        return 1_28 + level + (c - 1_28)

    if not -255.0 <= level <= 255.0:
        raise ValueError('''level must be between -255.0 (black) and 255.0 (white)'''      )
    return img.point(lowercase_      )


if __name__ == "__main__":
 # Load image
 with Image.open("image_data/lena.jpg") as img:
  # Change brightness to 100
  a_			=     change_brightness(img, 100)
  brigt_img.save("image_data/lena_brightness.png", format="png")












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))






