




import math
import sys

import cva
import numpy as np




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	np.ndarray:

    """simple docstring"""


    __UpperCamelCase  		=						math.sqrt(lowercase_      )
    __UpperCamelCase  		=						1 / (sigma * math.sqrt(2 * math.pi      ))
    return cons * np.exp(-((img / sigma) ** 2) * 0.5      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )       ->	np.ndarray:

    """simple docstring"""


    __UpperCamelCase  		=						kernel_size // 2
    return img[x - half : x + half + 1, y - half : y + half + 1]




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_      )       ->	np.ndarray:

    """simple docstring"""


    __UpperCamelCase  		=						np.zeros((kernel_size, kernel_size)      )
    for i in range(0					,     lowercase_      ):
        for j in range(0					,     lowercase_      ):
            __UpperCamelCase  		=						math.sqrt(
                abs(i - kernel_size // 2      ) ** 2 + abs(j - kernel_size // 2      ) ** 2      )
    return vec_gaussian(lowercase_					,     lowercase_      )




def 					__SCREAMING_SNAKE_CASE   (  lowercase_					,     lowercase_					,     lowercase_					,     lowercase_					,     )       ->	np.ndarray:

    """simple docstring"""


    __UpperCamelCase  		=						np.zeros(img.shape      )
    __UpperCamelCase  		=						get_gauss_kernel(lowercase_					,     lowercase_      )
    __UpperCamelCase		,      __UpperCamelCase  		=						img.shape
    for i in range(kernel_size // 2					,     size_x - kernel_size // 2      ):
        for j in range(kernel_size // 2					,     size_y - kernel_size // 2      ):
            __UpperCamelCase  		=						get_slice(lowercase_					,     lowercase_					,     lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						img_s - img_s[kernel_size // 2, kernel_size // 2]
            __UpperCamelCase  		=						vec_gaussian(lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						np.multiply(lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						np.multiply(lowercase_					,     lowercase_      )
            __UpperCamelCase  		=						np.sum(lowercase_      ) / np.sum(lowercase_      )
            __UpperCamelCase  		=						val
    return imga




def 					__SCREAMING_SNAKE_CASE   (  lowercase_      )       ->	tuple:

    """simple docstring"""


    __UpperCamelCase  		=						args[1] if args[1:] else '''../image_data/lena.jpg'''
    __UpperCamelCase  		=						float(args[2]      ) if args[2:] else 1.0
    __UpperCamelCase  		=						float(args[3]      ) if args[3:] else 1.0
    if args[4:]:
        __UpperCamelCase  		=						int(args[4]      )
        __UpperCamelCase  		=						kernel_size + abs(kernel_size % 2 - 1      )
    else:
        __UpperCamelCase  		=						5
    return filename, spatial_variance, intensity_variance, kernel_size


if __name__ == "__main__":
 a_		,    a_		,    a_		,    a_			=     parse_args(sys.argv)
 a_			=     cva.imread(filename, 0)
 cva.imshow("input image", img)

 a_			=     img / 255
 a_			=     out.astype("float32")
 a_			=     bilateral_filter(out, spatial_variance, intensity_variance, kernel_size)
 a_			=     out * 255
 a_			=     np.uinta(out)
 cva.imshow("output image", out)
 cva.waitKey(0)
 cva.destroyAllWindows()












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))






