





'''simple docstring'''



from typing import List, Optional, Tuple, Union

import torch

from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):
       super().__init__()

       # make sure scheduler can always be converted to DDIM
       _snake_case     		=				DDIMScheduler.from_config(scheduler.config	)

       self.register_modules(unet=lowerCamelCase						,   scheduler=lowerCamelCase	)






     @torch.no_grad()
     def __call__(					self						,   lowerCamelCase = 1						,   lowerCamelCase = None						,   lowerCamelCase = 0.0						,   lowerCamelCase = 50						,   lowerCamelCase = None						,   lowerCamelCase = "pil"						,   lowerCamelCase = True						,   ):

       # Sample gaussian noise to begin loop
       if isinstance(self.unet.config.sample_size						,   lowerCamelCase	):
         _snake_case     		=				(
             batch_size,
             self.unet.config.in_channels,
             self.unet.config.sample_size,
             self.unet.config.sample_size,
         )
       else:
         _snake_case     		=				(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

       if isinstance(lowerCamelCase						,   lowerCamelCase	) and len(lowerCamelCase	) != batch_size:
         raise ValueError(
             F'''You have passed a list of generators of length {len(lowerCamelCase	)}, but requested an effective batch'''
             F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''	)

       _snake_case     		=				randn_tensor(lowerCamelCase						,   generator=lowerCamelCase						,   device=self.device						,   dtype=self.unet.dtype	)

       # set step values
       self.scheduler.set_timesteps(lowerCamelCase	)

       for t in self.progress_bar(self.scheduler.timesteps	):
         # 1. predict noise model_output
         _snake_case     		=				self.unet(lowerCamelCase						,   lowerCamelCase	).sample

         # 2. predict previous mean of image x_t-1 and add variance depending on eta
         # eta corresponds to η in paper and should be between [0, 1]
         # do x_t -> x_t-1
         _snake_case     		=				self.scheduler.step(
             lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   eta=lowerCamelCase						,   use_clipped_model_output=lowerCamelCase						,   generator=lowerCamelCase	).prev_sample

       _snake_case     		=				(image / 2 + 0.5).clamp(0						,   1	)
       _snake_case     		=				image.cpu().permute(0						,   2						,   3						,   1	).numpy()
       if output_type == "pil":
         _snake_case     		=				self.numpy_to_pil(lowerCamelCase	)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=lowerCamelCase	)









'''simple docstring'''



import cva
import numpy as np


class       __SCREAMING_SNAKE_CASE	:



     '''simple docstring'''






     def __init__(					self						,   lowerCamelCase						,   lowerCamelCase	):

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

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






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

           _snake_case     		=				(wxx * wyy) - (wxy**2)
           _snake_case     		=				wxx + wyy
           _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__":
 __magic_name__   :						Tuple    =			HarrisCorner(0.04, 3)
 __magic_name__   ,    __magic_name__   :						Any    =			edge_detect.detect("""path_to_image""")
 cva.imwrite("""detect.png""", color_img)


