
"""simple docstring"""




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



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int       ,    lowerCAmelCase	:       int		):






     '''simple docstring'''

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

     # each value
     for y in range(lowerCAmelCase		):
          for x in range(lowerCAmelCase		):
               # distance from center
               UpperCAmelCase__							: Dict 							=						x - ksize // 2
               UpperCAmelCase__							: str 							=						y - ksize // 2

               # degree to radiant
               UpperCAmelCase__							: Optional[Any] 							=						theta / 180 * np.pi
               UpperCAmelCase__							: str 							=						np.cos(_theta		)
               UpperCAmelCase__							: List[str] 							=						np.sin(_theta		)

               # get kernel x
               UpperCAmelCase__							: Dict 							=						cos_theta * px + sin_theta * py

               # get kernel y
               UpperCAmelCase__							: Any 							=						-sin_theta * px + cos_theta * py

               # fill kernel
               UpperCAmelCase__							: List[Any] 							=						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
    A__      :	Union[str, Any]          =    imread("""../image_data/lena.jpg""")
    # turn image in gray scale value
    A__      :	str          =    cvtColor(img, COLOR_BGR2GRAY)

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

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

    waitKey(0)





"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class   _lowercase						(       lowerCAmelCase_	,   lowerCAmelCase_				):


     '''simple docstring'''





     @register_to_config
     def __init__(      self ,			__UpperCamelCase = 7_68 ,			)->					Union[str, Any]:
          super().__init__()

          UpperCAmelCase__							: str 							=						nn.Parameter(torch.zeros(1 ,			__UpperCamelCase  )  )
          UpperCAmelCase__							: Optional[int] 							=						nn.Parameter(torch.ones(1 ,			__UpperCamelCase  )  )



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase = None ,			__UpperCamelCase = None ,			)->					Any:
          UpperCAmelCase__							: Dict 							=						nn.Parameter(self.mean.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          UpperCAmelCase__							: Any 							=						nn.Parameter(self.std.to(__UpperCamelCase  ).to(__UpperCamelCase  )  )
          return self



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					Union[str, Any]:
          UpperCAmelCase__							: Dict 							=						(embeds - self.mean) * 1.0 / self.std
          return embeds



     def  lowerCAmelCase__       (      self ,			__UpperCamelCase  )->					List[Any]:
          UpperCAmelCase__							: Any 							=						(embeds * self.std) + self.mean
          return embeds



