




import os
import time

import numpy as np
import onnxruntime as ort


lowerCamelCase__					:  int      							=				'1'
lowerCamelCase__					:  Optional[int]      							=				'0'
lowerCamelCase__					:  Optional[Any]      							=				'1'

lowerCamelCase__					:  int      							=				ort.SessionOptions()
lowerCamelCase__					:  List[Any]      							=				ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('Create inference session...')
lowerCamelCase__					:  List[str]      							=				['TensorrtExecutionProvider', 'CUDAExecutionProvider']
lowerCamelCase__					:  List[str]      							=				ort.InferenceSession('model.onnx', sess_options=sess_opt, providers=execution_provider)
lowerCamelCase__					:  Union[str, Any]      							=				ort.RunOptions()

lowerCamelCase__					:  int      							=				128
lowerCamelCase__					:  Dict      							=				1
lowerCamelCase__					:  Tuple      							=				np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase__					:  Union[str, Any]      							=				np.ones((batch, sequence), dtype=np.intaa)
lowerCamelCase__					:  Any      							=				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...')
lowerCamelCase__					:  str      							=				time.time()
lowerCamelCase__					:  int      							=				2_000
lowerCamelCase__					:  Any      							=				{}
for iter in range(max_iters):
   lowerCamelCase__					:  str      							=				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) * 1_000 / max_iters))




from itertools import product

from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__					)	->     Dict:



				"""simple docstring"""





				lowerCAmelCase__		  =  k_size // 2
				lowerCAmelCase__  ,   lowerCAmelCase__		  =  mgrid[0 - center : k_size - center, 0 - center : k_size - center]
				lowerCAmelCase__		  =  1 / (2 * pi * sigma) * exp(-(square(snake_case__					) + square(snake_case__					)) / (2 * square(snake_case__					))					)
				return g







def     UpperCAmelCase_		(					snake_case__    ,			snake_case__    ,			snake_case__					)	->     str:



				"""simple docstring"""





				lowerCAmelCase__  ,   lowerCAmelCase__		  =  image.shape[0], image.shape[1]
				# dst image height and width
				lowerCAmelCase__		  =  height - k_size + 1
				lowerCAmelCase__		  =  width - k_size + 1

				# im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
				lowerCAmelCase__		  =  zeros((dst_height * dst_width, k_size * k_size)					)
				lowerCAmelCase__		  =  0
				for i, j in product(range(snake_case__					)    ,			range(snake_case__					)					):
								lowerCAmelCase__		  =  ravel(image[i : i + k_size, j : j + k_size]					)
								lowerCAmelCase__		  =  window
								row += 1

				#  turn the kernel into shape(k*k, 1)
				lowerCAmelCase__		  =  gen_gaussian_kernel(snake_case__    ,			snake_case__					)
				lowerCAmelCase__		  =  ravel(snake_case__					)

				# reshape and get the dst image
				lowerCAmelCase__		  =  dot(snake_case__    ,			snake_case__					).reshape(snake_case__    ,			snake_case__					).astype(snake_case__					)

				return dst


if __name__ == "__main__":
	# read original image
	_lowerCAmelCase	:   List[str]    =   imread(R"../image_data/lena.jpg")
	# turn image in gray scale value
	_lowerCAmelCase	:   Optional[Any]    =   cvtColor(img, COLOR_BGR2GRAY)

	# get values with two different mask size
	_lowerCAmelCase	:   Optional[int]    =   gaussian_filter(gray, 3, sigma=1)
	_lowerCAmelCase	:   Tuple    =   gaussian_filter(gray, 5, sigma=0.8)

	# show result images
	imshow("gaussian filter with 3x3 mask", gaussianaxa)
	imshow("gaussian filter with 5x5 mask", gaussianaxa)
	waitKey()
