






'''simple docstring'''






import cva
import numpy as np




class a_    :







   def __init__(self					,   __a					,   __a)			->      int:
      """simple docstring"""






      if k in (0.04, 0.06):
         __snake_case				:      List[str]     							= k
         __snake_case				:      str     							= window_size
      else:
         raise ValueError('invalid k value')







   def __str__(self)			->      str:
      """simple docstring"""






      return str(self.k)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      tuple[cva.Mat, list[list[int]]]:
      """simple docstring"""






      __snake_case				:      Any     							= cva.imread(__a					,   0)
      __snake_case				:      Optional[Any]     							= img.shape
      __snake_case				:      list[list[int]]     							= []
      __snake_case				:      str     							= img.copy()
      __snake_case				:      Optional[int]     							= cva.cvtColor(__a					,   cva.COLOR_GRAY2RGB)
      __snake_case				:      List[str]     							= np.gradient(__a)
      __snake_case				:      Optional[Any]     							= dx**2
      __snake_case				:      int     							= dy**2
      __snake_case				:      List[str]     							= dx * dy
      __snake_case				:      Any     							= 0.04
      __snake_case				:      List[Any]     							= self.window_size // 2
      for y in range(__a					,   h - offset):
         for x in range(__a					,   w - offset):
            __snake_case				:      Union[str, Any]     							= ixx[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __snake_case				:      int     							= iyy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()
            __snake_case				:      List[Any]     							= ixy[
                y - offset : y + offset + 1, x - offset : x + offset + 1
            ].sum()

            __snake_case				:      Dict     							= (wxx * wyy) - (wxy**2)
            __snake_case				:      List[str]     							= wxx + wyy
            __snake_case				:      Tuple     							= det - k * (trace**2)
            # Can change the value
            if r > 0.5:
               corner_list.append([x, y, r])
               color_img.itemset((y, x, 0)					,   0)
               color_img.itemset((y, x, 1)					,   0)
               color_img.itemset((y, x, 2)					,   2_5_5)
      return color_img, corner_list


if __name__ == "__main__":
     __A    	=   HarrisCorner(0.04, 3)
     __A	,   __A    	=   edge_detect.detect('''path_to_image''')
     cva.imwrite('''detect.png''', color_img)







'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


__A    	=   logging.get_logger(__name__)

__A    	=   {
    '''sayakpaul/vit-msn-base''': '''https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json''',
    # See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							"""vit_msn"""







   def __init__(self					,   __a=7_6_8					,   __a=1_2					,   __a=1_2					,   __a=3_0_7_2					,   __a="gelu"					,   __a=0.0					,   __a=0.0					,   __a=0.02					,   __a=1E-06					,   __a=2_2_4					,   __a=1_6					,   __a=3					,   __a=True					,   **__a					,   )			->      Any:
      """simple docstring"""






      super().__init__(**__a)

      __snake_case				:      List[str]     							= hidden_size
      __snake_case				:      Optional[int]     							= num_hidden_layers
      __snake_case				:      Optional[Any]     							= num_attention_heads
      __snake_case				:      str     							= intermediate_size
      __snake_case				:      List[str]     							= hidden_act
      __snake_case				:      List[Any]     							= hidden_dropout_prob
      __snake_case				:      Tuple     							= attention_probs_dropout_prob
      __snake_case				:      List[str]     							= initializer_range
      __snake_case				:      Optional[int]     							= layer_norm_eps
      __snake_case				:      Dict     							= image_size
      __snake_case				:      int     							= patch_size
      __snake_case				:      Dict     							= num_channels
      __snake_case				:      Tuple     							= qkv_bias