






"""simple docstring"""
import os
import time

import numpy as np
import onnxruntime as ort


lowerCAmelCase__       			=   '''1'''
lowerCAmelCase__       			=   '''0'''
lowerCAmelCase__       			=   '''1'''

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

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











"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey

def        snake_case   (     A__			,A__			,A__			,A__			,A__			,A__		):

			# prepare kernel
			# the kernel size have to be odd
			if (ksize % 2) == 0:
						UpperCAmelCase_  :				List[Any]      =      ksize + 1
			UpperCAmelCase_  :				Optional[Any]      =      np.zeros((ksize, ksize)			,dtype=np.floataa		)

			# each value
			for y in range(A__		):
						for x in range(A__		):
									# distance from center
									UpperCAmelCase_  :				Tuple      =      x - ksize // 2
									UpperCAmelCase_  :				Any      =      y - ksize // 2

									# degree to radiant
									UpperCAmelCase_  :				int      =      theta / 1_80 * np.pi
									UpperCAmelCase_  :				Optional[int]      =      np.cos(_theta		)
									UpperCAmelCase_  :				Union[str, Any]      =      np.sin(_theta		)

									# get kernel x
									UpperCAmelCase_  :				Tuple      =      cos_theta * px + sin_theta * py

									# get kernel y
									UpperCAmelCase_  :				List[str]      =      -sin_theta * px + cos_theta * py

									# fill kernel
									UpperCAmelCase_  :				Dict      =      np.exp(
									    -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)		) * np.cos(2 * np.pi * _x / lambd + psi		)

			return gabor


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			# read original image
			lowerCamelCase_       			=   imread('''../image_data/lena.jpg''')
			# turn image in gray scale value
			lowerCamelCase_       			=   cvtColor(img, COLOR_BGR2GRAY)

			# Apply multiple Kernel to detect edges
			lowerCamelCase_       			=   np.zeros(gray.shape[:2])
			for theta in [0, 30, 60, 90, 120, 150]:
						lowerCamelCase_       			=   gabor_filter_kernel(10, 8, theta, 10, 0, 0)
						out += filteraD(gray, CV_8UC3, kernel_aa)
			lowerCamelCase_       			=   out / out.max() * 255
			lowerCamelCase_       			=   out.astype(np.uinta)

			imshow('''Original''', gray)
			imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

			waitKey(0)



