code: 
  
  
  """simple docstring"""
  
  
  
  
  import cva
  import numpy as np
  
  from digital_image_processing.filters.convolve import img_convolve
  from digital_image_processing.filters.sobel_filter import sobel_filter
  
  __SCREAMING_SNAKE_CASE					=						1_80
  
  
  
  def 						__a      (  a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				k_size // 2
         _a   , _a          =				np.mgrid[0 - center : k_size - center, 0 - center : k_size - center]
         _a          =				(
             1
             / (2 * np.pi * sigma)
             * np.exp(-(np.square(a					) + np.square(a					)) / (2 * np.square(a					))					)
         )
         return g
  
  
  
  def 						__a      (  a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				np.zeros(a					)
  
         for row in range(1,		image_shape[0] - 1					):
                for col in range(1,		image_shape[1] - 1					):
                       _a          =				gradient_direction[row, col]
  
                       if (
                           0 <= direction < PI / 8
                           or 1_5 * PI / 8 <= direction <= 2 * PI
                           or 7 * PI / 8 <= direction <= 9 * PI / 8
                       ):
                              _a          =				sobel_grad[row, col - 1]
                              _a          =				sobel_grad[row, col + 1]
                              if sobel_grad[row, col] >= w and sobel_grad[row, col] >= e:
                                     _a          =				sobel_grad[row, col]
  
                       elif (
                           PI / 8 <= direction < 3 * PI / 8
                           or 9 * PI / 8 <= direction < 1_1 * PI / 8
                       ):
                              _a          =				sobel_grad[row + 1, col - 1]
                              _a          =				sobel_grad[row - 1, col + 1]
                              if sobel_grad[row, col] >= sw and sobel_grad[row, col] >= ne:
                                     _a          =				sobel_grad[row, col]
  
                       elif (
                           3 * PI / 8 <= direction < 5 * PI / 8
                           or 1_1 * PI / 8 <= direction < 1_3 * PI / 8
                       ):
                              _a          =				sobel_grad[row - 1, col]
                              _a          =				sobel_grad[row + 1, col]
                              if sobel_grad[row, col] >= n and sobel_grad[row, col] >= s:
                                     _a          =				sobel_grad[row, col]
  
                       elif (
                           5 * PI / 8 <= direction < 7 * PI / 8
                           or 1_3 * PI / 8 <= direction < 1_5 * PI / 8
                       ):
                              _a          =				sobel_grad[row - 1, col - 1]
                              _a          =				sobel_grad[row + 1, col + 1]
                              if sobel_grad[row, col] >= nw and sobel_grad[row, col] >= se:
                                     _a          =				sobel_grad[row, col]
  
         return destination
  
  
  
  def 						__a      (  a,		a,		a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for row in range(1,		image_shape[0] - 1					):
                for col in range(1,		image_shape[1] - 1					):
                       if destination[row, col] >= threshold_high:
                              _a          =				strong
                       elif destination[row, col] <= threshold_low:
                              _a          =				0
                       else:
                              _a          =				weak
  
  
  
  def 						__a      (  a,		a,		a,		a					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         for row in range(1,		image_shape[0]					):
                for col in range(1,		image_shape[1]					):
                       if destination[row, col] == weak:
                              if 2_5_5 in (
                                  destination[row, col + 1],
                                  destination[row, col - 1],
                                  destination[row - 1, col],
                                  destination[row + 1, col],
                                  destination[row - 1, col - 1],
                                  destination[row + 1, col - 1],
                                  destination[row - 1, col + 1],
                                  destination[row + 1, col + 1],
                              ):
                                     _a          =				strong
                              else:
                                     _a          =				0
  
  
  
  def 						__a      (  a,		a=1_5,		a=3_0,		a=1_2_8,		a=2_5_5					):
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
  
         _a          =				img_convolve(a,		gen_gaussian_kernel(9,		sigma=1.4					)					)
         # get the gradient and degree by sobel_filter
         _a   , _a          =				sobel_filter(a					)
         _a          =				PI + np.radadeg(a					)
  
         _a          =				suppress_non_maximum(image.shape,		a,		a					)
  
         detect_high_low_threshold(
             image.shape,		a,		a,		a,		a,		a					)
  
         track_edge(image.shape,		a,		a,		a					)
  
         return destination
  
  
  if __name__ == "__main__":
     # read original image in gray mode
     __SCREAMING_SNAKE_CASE					=						cva.imread(r"""../image_data/lena.jpg""", 0)
     # canny edge detection
     __SCREAMING_SNAKE_CASE					=						canny(lena)
     cva.imshow("""canny""", canny_destination)
     cva.waitKey(0)
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  from typing import Union
  
  import torch
  from PIL import Image
  from torchvision import transforms as tfms
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import (
      AutoencoderKL,
      DDIMScheduler,
      DiffusionPipeline,
      LMSDiscreteScheduler,
      PNDMScheduler,
      UNetaDConditionModel,
  )
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     def __init__(		self							:List[Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]				,  ):
            super().__init__()
  
            self.register_modules(vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:str       ):
            with torch.no_grad():
                   _a          =				self.vae.encode(tfms.ToTensor()(UpperCamelCase__       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
                   _a          =				0.18215 * latent.latent_dist.sample()
            return latent
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Any       ):
            _a          =				(1 / 0.18215) * latent
            with torch.no_grad():
                   _a          =				self.vae.decode(UpperCamelCase__       ).sample
            _a          =				(img / 2 + 0.5).clamp(0				,  1       )
            _a          =				img.detach().cpu().permute(0				,  2				,  3				,  1       ).numpy()
            _a          =				(img * 255).round().astype("uint8"       )
            return Image.fromarray(img[0]       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:List[Any]       ):
            _a          =				self.tokenizer(
                UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )
  
            _a          =				self.text_encoder(text_input.input_ids.to(self.device       )       )[0]
  
            _a          =				self.tokenizer(
                ""				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  return_tensors="pt"				,  )
  
            _a          =				self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
  
            return torch.cat([uncond_embedding, text_embedding]       )
  
  
  
  
     def __call__(		self							:Tuple				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:str				,  UpperCamelCase__							:float = 0.3				,  UpperCamelCase__							:float = 0.6				,  UpperCamelCase__							:float = 0.5				,  UpperCamelCase__							:int = 42				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 7.5				,  ):
            _a          =				steps - int(kmin * steps       )
            _a          =				steps - int(kmax * steps       )
  
            _a          =				self.prep_text(UpperCamelCase__       )
  
            self.scheduler.set_timesteps(UpperCamelCase__       )
  
            _a   , _a          =				img.size
            _a          =				self.encode(UpperCamelCase__       )
  
            torch.manual_seed(UpperCamelCase__       )
            _a          =				torch.randn(
                (1, self.unet.config.in_channels, height // 8, width // 8)				,  ).to(self.device       )
  
            _a          =				self.scheduler.add_noise(
                UpperCamelCase__				,  UpperCamelCase__				,  timesteps=self.scheduler.timesteps[tmax]				,  )
  
            _a          =				torch.cat([latents] * 2       )
  
            _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  self.scheduler.timesteps[tmax]       )
  
            with torch.no_grad():
                   _a          =				self.unet(
                       UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
            _a   , _a          =				pred.chunk(2       )
            _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
            _a          =				self.scheduler.step(UpperCamelCase__				,  self.scheduler.timesteps[tmax]				,  UpperCamelCase__       ).prev_sample
  
            for i, t in enumerate(tqdm(self.scheduler.timesteps       )       ):
                   if i > tmax:
                          if i < tmin:  # layout generation phase
                                 _a          =				self.scheduler.add_noise(
                                     UpperCamelCase__				,  UpperCamelCase__				,  timesteps=UpperCamelCase__				,  )
  
                                 _a          =				(mix_factor * latents) + (
                                     1 - mix_factor
                                 ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
                                 _a          =				torch.cat([input] * 2       )
  
                          else:  # content generation phase
                                 _a          =				torch.cat([latents] * 2       )
  
                          _a          =				self.scheduler.scale_model_input(UpperCamelCase__				,  UpperCamelCase__       )
  
                          with torch.no_grad():
                                 _a          =				self.unet(
                                     UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__				,  ).sample
  
                          _a   , _a          =				pred.chunk(2       )
                          _a          =				pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
                          _a          =				self.scheduler.step(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       ).prev_sample
  
            return self.decode(UpperCamelCase__       )
  