

"""simple docstring"""
import torch

from transformers import PreTrainedModel

from .custom_configuration import CustomConfig, NoSuperInitConfig

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    CustomConfig



  def __init__( self					: int       ,       snake_case					: Union[str, Any]      )	->			List[str]:


         '''simple docstring'''





         super().__init__(snake_case      )
         __magic_name__			:		Optional[int]        =     torch.nn.Linear(config.hidden_size       ,       config.hidden_size      )



  def 							_UpperCAmelCase      ( self					: int       ,       snake_case					: Optional[int]      )	->			Optional[int]:


         '''simple docstring'''





         return self.linear(snake_case      )




  def 							_UpperCAmelCase      ( self					: Optional[int]       ,       snake_case					: Dict      )	->			List[str]:


         '''simple docstring'''





         pass

class        _UpperCamelCase	(						lowerCamelCase__	):


  """simple docstring"""





  snake_case_        =    NoSuperInitConfig



  def __init__( self					: str       ,       snake_case					: Optional[Any]      )	->			Tuple:


         '''simple docstring'''





         super().__init__(snake_case      )
         __magic_name__			:		List[Any]        =     torch.nn.Linear(config.attribute       ,       config.attribute      )



  def 							_UpperCAmelCase      ( self					: Any       ,       snake_case					: Dict      )	->			List[Any]:


         '''simple docstring'''





         return self.linear(snake_case      )




  def 							_UpperCAmelCase      ( self					: str       ,       snake_case					: List[Any]      )	->			List[str]:


         '''simple docstring'''





         pass



"""simple docstring"""
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import array, dot, pad, ravel, uinta, zeros







def       UpperCamelCase_					(     lowerCamelCase							:					Optional[int]     , lowerCamelCase							:					Tuple   )						->     str:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		Optional[Any]        =     image.shape
       __magic_name__			:		str        =     cols - block_size[1] + 1
       __magic_name__			:		int        =     rows - block_size[0] + 1
       __magic_name__			:		Optional[Any]        =     zeros((dst_height * dst_width, block_size[1] * block_size[0])   )
       __magic_name__			:		List[Any]        =     0
       for i in range(0     , lowerCamelCase   ):
              for j in range(0     , lowerCamelCase   ):
                     __magic_name__			:		List[Any]        =     ravel(image[i : i + block_size[0], j : j + block_size[1]]   )
                     __magic_name__			:		str        =     window
                     row += 1

       return image_array







def       UpperCamelCase_					(     lowerCamelCase							:					str     , lowerCamelCase							:					List[str]   )						->     Tuple:



       """simple docstring"""







       __magic_name__       ,							__magic_name__			:		List[str]        =     image.shape[0], image.shape[1]
       __magic_name__			:		Any        =     filter_kernel.shape[0]
       __magic_name__			:		Optional[Any]        =     k_size // 2
       # Pads image with the edge values of array.
       __magic_name__			:		Union[str, Any]        =     pad(lowerCamelCase     , lowerCamelCase     , mode='''edge'''   )

       # im2col, turn the k_size*k_size pixels into a row and np.vstack all rows
       __magic_name__			:		List[Any]        =     imacol(lowerCamelCase     , (k_size, k_size)   )

       #  turn the kernel into shape(k*k, 1)
       __magic_name__			:		Tuple        =     ravel(lowerCamelCase   )
       # reshape and get the dst image
       __magic_name__			:		int        =     dot(lowerCamelCase     , lowerCamelCase   ).reshape(lowerCamelCase     , lowerCamelCase   )
       return dst


if __name__ == "__main__":
 # read original image
 A									=						imread(R"""../image_data/lena.jpg""")
 # turn image in gray scale value
 A									=						cvtColor(img, COLOR_BGR2GRAY)
 # Laplace operator
 A									=						array([[0, 1, 0], [1, -4, 1], [0, 1, 0]])
 A									=						img_convolve(gray, Laplace_kernel).astype(uinta)
 imshow("""Laplacian""", out)
 waitKey(0)
