






"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


_snake_case          =   logging.get_logger(__name__)

_snake_case          =   {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}






class        UpperCamelCase			(	snake_case_		,     snake_case_      ):
 UpperCamelCase					:       Any			=					'''nat'''

 UpperCamelCase					:       List[Any]			=					{
     '''num_attention_heads''': '''num_heads''',
     '''num_hidden_layers''': '''num_layers''',
 }


 def __init__(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						List[Any]=4							,						UpperCAmelCase__		:						int=3							,						UpperCAmelCase__		:						Union[str, Any]=64							,						UpperCAmelCase__		:						str=[3, 4, 6, 5]							,						UpperCAmelCase__		:						Optional[Any]=[2, 4, 8, 16]							,						UpperCAmelCase__		:						str=7							,						UpperCAmelCase__		:						Optional[Any]=3.0							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						str=0.0							,						UpperCAmelCase__		:						str=0.0							,						UpperCAmelCase__		:						str=0.1							,						UpperCAmelCase__		:						List[Any]="gelu"							,						UpperCAmelCase__		:						List[str]=0.0_2							,						UpperCAmelCase__		:						str=1E-5							,						UpperCAmelCase__		:						List[Any]=0.0							,						UpperCAmelCase__		:						List[str]=None							,						UpperCAmelCase__		:						Union[str, Any]=None							,						**UpperCAmelCase__		:						Union[str, Any]							,						)					->       List[str]:
   super().__init__(**UpperCAmelCase__							)

   _a  :       Any             =	patch_size
   _a  :       Union[str, Any]             =	num_channels
   _a  :       List[str]             =	embed_dim
   _a  :       List[Any]             =	depths
   _a  :       int             =	len(UpperCAmelCase__							)
   _a  :       Optional[Any]             =	num_heads
   _a  :       int             =	kernel_size
   _a  :       List[str]             =	mlp_ratio
   _a  :       Dict             =	qkv_bias
   _a  :       List[Any]             =	hidden_dropout_prob
   _a  :       Optional[Any]             =	attention_probs_dropout_prob
   _a  :       Optional[Any]             =	drop_path_rate
   _a  :       Dict             =	hidden_act
   _a  :       Dict             =	layer_norm_eps
   _a  :       List[str]             =	initializer_range
   # we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
   # this indicates the channel dimension after the last stage of the model
   _a  :       List[Any]             =	int(embed_dim * 2 ** (len(UpperCAmelCase__							) - 1)							)
   _a  :       List[Any]             =	layer_scale_init_value
   _a  :       Dict             =	["""stem"""] + [f"""stage{idx}""" for idx in range(1							,						len(UpperCAmelCase__							) + 1							)]
   _a      ,  _a  :       List[Any]             =	get_aligned_output_features_output_indices(
       out_features=UpperCAmelCase__							,						out_indices=UpperCAmelCase__							,						stage_names=self.stage_names							)









"""simple docstring"""




import cva
import numpy as np






class        UpperCamelCase			:


 def __init__(     self		:						Optional[int]							,						UpperCAmelCase__		:						float							,						UpperCAmelCase__		:						int							)					->       Dict:

   if k in (0.0_4, 0.0_6):
     _a  :       List[str]             =	k
     _a  :       List[Any]             =	window_size
   else:
     raise ValueError("""invalid k value"""							)


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







 def _lowercase	(     self		:						int							,						UpperCAmelCase__		:						str							)					->       tuple[cva.Mat, list[list[int]]]:
   _a  :       Dict             =	cva.imread(UpperCAmelCase__							,						0							)
   _a      ,  _a  :       List[Any]             =	img.shape
   _a  :       list[list[int]]             =	[]
   _a  :       List[Any]             =	img.copy()
   _a  :       int             =	cva.cvtColor(UpperCAmelCase__							,						cva.COLOR_GRAY2RGB							)
   _a      ,  _a  :       Any             =	np.gradient(UpperCAmelCase__							)
   _a  :       Tuple             =	dx**2
   _a  :       Union[str, Any]             =	dy**2
   _a  :       Union[str, Any]             =	dx * dy
   _a  :       int             =	0.0_4
   _a  :       List[str]             =	self.window_size // 2
   for y in range(UpperCAmelCase__							,						h - offset							):
     for x in range(UpperCAmelCase__							,						w - offset							):
       _a  :       str             =	ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       List[Any]             =	iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       _a  :       Tuple             =	ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

       _a  :       Any             =	(wxx * wyy) - (wxy**2)
       _a  :       Tuple             =	wxx + wyy
       _a  :       Any             =	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)							,						255							)
   return color_img, corner_list


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

