




"""simple docstring"""



import cva
import numpy as np




class 	UpperCAmelCase_  :




    def __init__(    self						:       List[Any]	, snake_case_						:       int	, snake_case_						:       Optional[int]      )      ->							List[str]:




     '''simple docstring'''

     if k in (0.04, 0.06):
      A__       =				k
      A__       =				window_size
     else:
      raise ValueError("invalid k value"      )




    def __str__(    self						:       List[str]      )      ->							str:




     '''simple docstring'''

     return str(self.k      )




    def 							__magic_name__							(    self						:       str	, snake_case_						:       Optional[Any]      )      ->							tuple[cva.Mat, list[list[int]]]:




     '''simple docstring'''

     A__       =				cva.imread(lowerCAmelCase_	, 0      )
     A__,      A__       =				img.shape
     A__       =				[]
     A__       =				img.copy()
     A__       =				cva.cvtColor(lowerCAmelCase_	, cva.COLOR_GRAY2RGB      )
     A__,      A__       =				np.gradient(lowerCAmelCase_      )
     A__       =				dx**2
     A__       =				dy**2
     A__       =				dx * dy
     A__       =				0.04
     A__       =				self.window_size // 2
     for y in range(lowerCAmelCase_	, h - offset      ):
      for x in range(lowerCAmelCase_	, w - offset      ):
       A__       =				ixx[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       A__       =				iyy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()
       A__       =				ixy[
           y - offset : y + offset + 1, x - offset : x + offset + 1
       ].sum()

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






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


_SCREAMING_SNAKE_CASE										=						logging.get_logger(__name__)

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



class 				a							(			__lowerCAmelCase       ):







 """simple docstring"""






 lowerCamelCase	:Any    			=		'''biogpt'''



 def __init__(					self		,				lowerCAmelCase_=4_23_84		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=24		,				lowerCAmelCase_=16		,				lowerCAmelCase_=40_96		,				lowerCAmelCase_="gelu"		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=0.1		,				lowerCAmelCase_=10_24		,				lowerCAmelCase_=0.02		,				lowerCAmelCase_=1E-12		,				lowerCAmelCase_=True		,				lowerCAmelCase_=True		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=0.0		,				lowerCAmelCase_=1		,				lowerCAmelCase_=0		,				lowerCAmelCase_=2		,				**lowerCAmelCase_		,				)  ->					Tuple:
        _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=lowerCAmelCase_		,				bos_token_id=lowerCAmelCase_		,				eos_token_id=lowerCAmelCase_		,				**lowerCAmelCase_							)




