





import cva
import numpy as np

class 	UpperCamelCase__				:



     '''simple docstring'''

     def __init__(							self		:					List[str]		,lowerCamelCase__		:					float		,lowerCamelCase__		:					int )  ->	str:




           '''simple docstring'''


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






     def __str__(							self		:					Optional[Any] )  ->	str:




           '''simple docstring'''


           return str(self.k )






     def        SCREAMING_SNAKE_CASE__						(							self		:					Any		,lowerCamelCase__		:					str )  ->	tuple[cva.Mat, list[list[int]]]:




           '''simple docstring'''


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

                       SCREAMING_SNAKE_CASE	=		(wxx * wyy) - (wxy**2)
                       SCREAMING_SNAKE_CASE	=		wxx + wyy
                       SCREAMING_SNAKE_CASE	=		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_		     = {
    """google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 	UpperCamelCase__				(   lowerCAmelCase_     ):



     '''simple docstring'''

     __snake_case					:			str				=    "pegasus"
     __snake_case					:			List[Any]				=    ["past_key_values"]
     __snake_case					:			Any				=    {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}






     def __init__(							self		:					Dict		,lowerCamelCase__		:					List[str]=50265		,lowerCamelCase__		:					int=1024		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					Tuple=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Optional[Any]=12		,lowerCamelCase__		:					str=4096		,lowerCamelCase__		:					Dict=16		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					str=True		,lowerCamelCase__		:					Any=True		,lowerCamelCase__		:					List[Any]="gelu"		,lowerCamelCase__		:					Tuple=1024		,lowerCamelCase__		:					Optional[int]=0.1		,lowerCamelCase__		:					Tuple=0.0		,lowerCamelCase__		:					Dict=0.0		,lowerCamelCase__		:					Optional[int]=0.02		,lowerCamelCase__		:					Optional[int]=0		,lowerCamelCase__		:					List[Any]=False		,lowerCamelCase__		:					Union[str, Any]=0		,lowerCamelCase__		:					Optional[int]=1		,lowerCamelCase__		:					Any=1		,**lowerCamelCase__		:					Dict		,)  ->	str:




           '''simple docstring'''


           SCREAMING_SNAKE_CASE	=		vocab_size
           SCREAMING_SNAKE_CASE	=		max_position_embeddings
           SCREAMING_SNAKE_CASE	=		d_model
           SCREAMING_SNAKE_CASE	=		encoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		encoder_attention_heads
           SCREAMING_SNAKE_CASE	=		decoder_ffn_dim
           SCREAMING_SNAKE_CASE	=		decoder_layers
           SCREAMING_SNAKE_CASE	=		decoder_attention_heads
           SCREAMING_SNAKE_CASE	=		dropout
           SCREAMING_SNAKE_CASE	=		attention_dropout
           SCREAMING_SNAKE_CASE	=		activation_dropout
           SCREAMING_SNAKE_CASE	=		activation_function
           SCREAMING_SNAKE_CASE	=		init_std
           SCREAMING_SNAKE_CASE	=		encoder_layerdrop
           SCREAMING_SNAKE_CASE	=		decoder_layerdrop
           SCREAMING_SNAKE_CASE	=		use_cache
           SCREAMING_SNAKE_CASE	=		encoder_layers
           SCREAMING_SNAKE_CASE	=		scale_embedding  # scale factor will be sqrt(d_model) if True
           super().__init__(
               pad_token_id=lowerCamelCase__		,eos_token_id=lowerCamelCase__		,is_encoder_decoder=lowerCamelCase__		,decoder_start_token_id=lowerCamelCase__		,forced_eos_token_id=lowerCamelCase__		,**lowerCamelCase__		,)






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					Tuple )  ->	int:




           '''simple docstring'''


           return self.encoder_attention_heads






     @property
     def        SCREAMING_SNAKE_CASE__						(							self		:					List[str] )  ->	int:




           '''simple docstring'''


           return self.d_model


