


"""simple docstring"""







import cva
import numpy as np






class       __A				:






 """simple docstring"""




 def __init__(		self ,    __A ,    __A						)					->     int:

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


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





 def       SCREAMING_SNAKE_CASE							(		self ,    __A						)					->     tuple[cva.Mat, list[list[int]]]:
     a 		=cva.imread(__A ,    0						)
     a		,      a 		=img.shape
     a 		=[]
     a 		=img.copy()
     a 		=cva.cvtColor(__A ,    cva.COLOR_GRAY2RGB						)
     a		,      a 		=np.gradient(__A						)
     a 		=dx**2
     a 		=dy**2
     a 		=dx * dy
     a 		=0.04
     a 		=self.window_size // 2
     for y in range(__A ,    h - offset						):
         for x in range(__A ,    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__":
       lowerCamelCase_   :   List[Any]									=  HarrisCorner(0.04, 3)
       lowerCamelCase_						,  lowerCamelCase_   :   int									=  edge_detect.detect("""path_to_image""")
       cva.imwrite("""detect.png""", color_img)





"""simple docstring"""






import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel

from ...models.attention import BasicTransformerBlock
from ...utils import logging


lowercase__     						=						logging.get_logger(__name__)  # pylint: disable=invalid-name






class lowerCAmelCase__	(     lowercase						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ,      lowercase=768  ):
							super().__init__(lowercase  )
							_lowerCamelCase     :			Any    =    proj_size

							_lowerCamelCase     :			Dict    =    CLIPVisionModel(lowercase  )
							_lowerCamelCase     :			List[str]    =    PaintByExampleMapper(lowercase  )
							_lowerCamelCase     :			Optional[Any]    =    nn.LayerNorm(config.hidden_size  )
							_lowerCamelCase     :			int    =    nn.Linear(config.hidden_size  ,      self.proj_size  )

							# uncondition for scaling
							_lowerCamelCase     :			str    =    nn.Parameter(torch.randn((1, 1, self.proj_size)  )  )






			def 					A_    (  self  ,      lowercase  ,      lowercase=False  ):
							_lowerCamelCase     :			Union[str, Any]    =    self.model(pixel_values=lowercase  )
							_lowerCamelCase     :			int    =    clip_output.pooler_output
							_lowerCamelCase     :			str    =    self.mapper(latent_states[:, None]  )
							_lowerCamelCase     :			List[Any]    =    self.final_layer_norm(lowercase  )
							_lowerCamelCase     :			Dict    =    self.proj_out(lowercase  )
							if return_uncond_vector:
											return latent_states, self.uncond_vector

							return latent_states






class lowerCAmelCase__	(     nn.Module						):






			'''simple docstring'''



			def __init__(  self  ,      lowercase  ):
							super().__init__()
							_lowerCamelCase     :			Tuple    =    (config.num_hidden_layers + 1) // 5
							_lowerCamelCase     :			int    =    config.hidden_size
							_lowerCamelCase     :			Optional[Any]    =    1
							_lowerCamelCase     :			str    =    nn.ModuleList(
							    [
							        BasicTransformerBlock(lowercase  ,      lowercase  ,      lowercase  ,      activation_fn='gelu'  ,      attention_bias=lowercase  )
							        for _ in range(lowercase  )
							    ]  )






			def 					A_    (  self  ,      lowercase  ):
							for block in self.blocks:
											_lowerCamelCase     :			Tuple    =    block(lowercase  )

							return hidden_states