

'''simple docstring'''






import cva
import numpy as np






class 			lowercase_	:




  """simple docstring"""




  def __init__(      self   :     str					,lowercase__   :     float					,lowercase__   :     int			):

   if k in (0.0_4, 0.0_6):
    __lowercase							  = k
    __lowercase							  = window_size
   else:
    raise ValueError('''invalid k value'''			)






  def __str__(      self   :     Dict			):
   return str(self.k			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     str			):
   __lowercase							  = cva.imread(lowercase__					,0			)
   __lowercase  ,	__lowercase							  = img.shape
   __lowercase							  = []
   __lowercase							  = img.copy()
   __lowercase							  = cva.cvtColor(lowercase__					,cva.COLOR_GRAY2RGB			)
   __lowercase  ,	__lowercase							  = np.gradient(lowercase__			)
   __lowercase							  = dx**2
   __lowercase							  = dy**2
   __lowercase							  = dx * dy
   __lowercase							  = 0.0_4
   __lowercase							  = self.window_size // 2
   for y in range(lowercase__					,h - offset			):
    for x in range(lowercase__					,w - offset			):
     __lowercase							  = ixx[
         y - offset : y + offset + 1, x - offset : x + offset + 1
     ].sum()
     __lowercase							  = iyy[
         y - offset : y + offset + 1, x - offset : x + offset + 1
     ].sum()
     __lowercase							  = ixy[
         y - offset : y + offset + 1, x - offset : x + offset + 1
     ].sum()

     __lowercase							  = (wxx * wyy) - (wxy**2)
     __lowercase							  = wxx + wyy
     __lowercase							  = 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__":
 lowerCAmelCase__											=	HarrisCorner(0.04, 3)
 lowerCAmelCase__						,			lowerCAmelCase__											=	edge_detect.detect('''path_to_image''')
 cva.imwrite('''detect.png''', color_img)








from typing import List

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


SCREAMING_SNAKE_CASE_						   =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_						   =       {
    """snap-research/efficientformer-l1-300""": (
        """https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
    ),
}

class 		UpperCamelCase__ (      lowerCAmelCase_   ):



     '''simple docstring'''

     __snake_case    :	int			=			"efficientformer"






     def __init__(							self						:    Optional[int]						,lowerCamelCase__						:    List[int] = [3, 2, 6, 4]						,lowerCamelCase__						:    List[int] = [48, 96, 224, 448]						,lowerCamelCase__						:    List[bool] = [True, True, True, True]						,lowerCamelCase__						:    int = 448						,lowerCamelCase__						:    int = 32						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    int = 7						,lowerCamelCase__						:    int = 5						,lowerCamelCase__						:    int = 8						,lowerCamelCase__						:    int = 4						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 16						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 3						,lowerCamelCase__						:    int = 2						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    float = 0.0						,lowerCamelCase__						:    int = 1						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    bool = True						,lowerCamelCase__						:    float = 1e-5						,lowerCamelCase__						:    str = "gelu"						,lowerCamelCase__						:    float = 0.02						,lowerCamelCase__						:    float = 1e-1_2						,lowerCamelCase__						:    int = 224						,lowerCamelCase__						:    float = 1e-0_5						,**lowerCamelCase__						:    str						,)     ->		None:




           '''simple docstring'''


           super().__init__(**lowerCamelCase__       )

           SCREAMING_SNAKE_CASE		=						hidden_act
           SCREAMING_SNAKE_CASE		=						hidden_dropout_prob
           SCREAMING_SNAKE_CASE		=						hidden_sizes
           SCREAMING_SNAKE_CASE		=						num_hidden_layers
           SCREAMING_SNAKE_CASE		=						num_attention_heads
           SCREAMING_SNAKE_CASE		=						initializer_range
           SCREAMING_SNAKE_CASE		=						layer_norm_eps
           SCREAMING_SNAKE_CASE		=						patch_size
           SCREAMING_SNAKE_CASE		=						num_channels
           SCREAMING_SNAKE_CASE		=						depths
           SCREAMING_SNAKE_CASE		=						mlp_expansion_ratio
           SCREAMING_SNAKE_CASE		=						downsamples
           SCREAMING_SNAKE_CASE		=						dim
           SCREAMING_SNAKE_CASE		=						key_dim
           SCREAMING_SNAKE_CASE		=						attention_ratio
           SCREAMING_SNAKE_CASE		=						resolution
           SCREAMING_SNAKE_CASE		=						pool_size
           SCREAMING_SNAKE_CASE		=						downsample_patch_size
           SCREAMING_SNAKE_CASE		=						downsample_stride
           SCREAMING_SNAKE_CASE		=						downsample_pad
           SCREAMING_SNAKE_CASE		=						drop_path_rate
           SCREAMING_SNAKE_CASE		=						num_metaad_blocks
           SCREAMING_SNAKE_CASE		=						distillation
           SCREAMING_SNAKE_CASE		=						use_layer_scale
           SCREAMING_SNAKE_CASE		=						layer_scale_init_value
           SCREAMING_SNAKE_CASE		=						image_size
           SCREAMING_SNAKE_CASE		=						batch_norm_eps


