

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


lowercase_       		=  logging.get_logger(__name__)

lowercase_       		=  {
    "microsoft/biogpt": "https://huggingface.co/microsoft/biogpt/resolve/main/config.json",
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}


class  A						(							_UpperCAmelCase ):







    """simple docstring"""





    lowerCamelCase			=							'biogpt'


    def __init__(      self :       str,lowercase_ :       Union[str, Any]=4_2_3_8_4,lowercase_ :       List[str]=1_0_2_4,lowercase_ :       Dict=2_4,lowercase_ :       str=1_6,lowercase_ :       Dict=4_0_9_6,lowercase_ :       Optional[Any]="gelu",lowercase_ :       List[str]=0.1,lowercase_ :       List[Any]=0.1,lowercase_ :       List[str]=1_0_2_4,lowercase_ :       Optional[Any]=0.02,lowercase_ :       str=1E-12,lowercase_ :       List[Any]=True,lowercase_ :       Dict=True,lowercase_ :       Union[str, Any]=0.0,lowercase_ :       Optional[Any]=0.0,lowercase_ :       Union[str, Any]=1,lowercase_ :       List[Any]=0,lowercase_ :       Dict=2,**lowercase_ :       List[str],)->    Dict:





        '''simple docstring'''







        A__          =   vocab_size
        A__          =   max_position_embeddings
        A__          =   hidden_size
        A__          =   num_hidden_layers
        A__          =   num_attention_heads
        A__          =   intermediate_size
        A__          =   hidden_act
        A__          =   hidden_dropout_prob
        A__          =   attention_probs_dropout_prob
        A__          =   initializer_range
        A__          =   layer_norm_eps
        A__          =   scale_embedding
        A__          =   use_cache
        A__          =   layerdrop
        A__          =   activation_dropout
        super().__init__(pad_token_id=lowercase_,bos_token_id=lowercase_,eos_token_id=lowercase_,**lowercase_		)









"""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)

