code: 
  
  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 							_lowerCAmelCase		(     UpperCamelCase__	):
  
  
  
  
  				def __init__( self		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		snake_case_		,		)    ->      Optional[Any]:
  											super().__init__()
  
  											self.register_modules(vae=a_		,		text_encoder=a_		,		tokenizer=a_		,		unet=a_		,		scheduler=a_							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Union[str, Any]:
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: List[Any]	     =self.vae.encode(tfms.ToTensor()(a_							).unsqueeze(0							).to(self.device							) * 2 - 1							)
  																		SCREAMING_SNAKE_CASE			: Tuple	     =0.1_8215 * latent.latent_dist.sample()
  											return latent
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Any:
  											SCREAMING_SNAKE_CASE			: List[Any]	     =(1 / 0.1_8215) * latent
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.vae.decode(a_							).sample
  											SCREAMING_SNAKE_CASE			: str	     =(img / 2 + 0.5).clamp(0		,		1							)
  											SCREAMING_SNAKE_CASE			: int	     =img.detach().cpu().permute(0		,		2		,		3		,		1							).numpy()
  											SCREAMING_SNAKE_CASE			: int	     =(img * 255).round().astype('''uint8'''							)
  											return Image.fromarray(img[0]							)
  
  
  
  
  				def     __a		( self		,		snake_case_							)    ->      Dict:
  											SCREAMING_SNAKE_CASE			: int	     =self.tokenizer(
  											    a_		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=a_		,		return_tensors='''pt'''		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.text_encoder(text_input.input_ids.to(self.device							)							)[0]
  
  											SCREAMING_SNAKE_CASE			: Tuple	     =self.tokenizer(
  											    ''''''		,		padding='''max_length'''		,		max_length=self.tokenizer.model_max_length		,		truncation=a_		,		return_tensors='''pt'''		,		)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.text_encoder(uncond_input.input_ids.to(self.device							)							)[0]
  
  											return torch.cat([uncond_embedding, text_embedding]							)
  
  
  
  
  
  
  
  				def __call__( self		,		snake_case_		,		snake_case_		,		snake_case_ = 0.3		,		snake_case_ = 0.6		,		snake_case_ = 0.5		,		snake_case_ = 42		,		snake_case_ = 50		,		snake_case_ = 7.5		,		)    ->      Image.Image:
  											SCREAMING_SNAKE_CASE			: Any	     =steps - int(kmin * steps							)
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =steps - int(kmax * steps							)
  
  											SCREAMING_SNAKE_CASE			: str	     =self.prep_text(a_							)
  
  											self.scheduler.set_timesteps(a_							)
  
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =img.size
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.encode(a_							)
  
  											torch.manual_seed(a_							)
  											SCREAMING_SNAKE_CASE			: Any	     =torch.randn(
  											    (1, self.unet.config.in_channels, height // 8, width // 8)		,		).to(self.device							)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.add_noise(
  											    a_		,		a_		,		timesteps=self.scheduler.timesteps[tmax]		,		)
  
  											SCREAMING_SNAKE_CASE			: int	     =torch.cat([latents] * 2							)
  
  											SCREAMING_SNAKE_CASE			: Optional[Any]	     =self.scheduler.scale_model_input(a_		,		self.scheduler.timesteps[tmax]							)
  
  											with torch.no_grad():
  																		SCREAMING_SNAKE_CASE			: List[str]	     =self.unet(
  																		    a_		,		self.scheduler.timesteps[tmax]		,		encoder_hidden_states=a_		,		).sample
  
  											SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: List[Any]	     =pred.chunk(2							)
  											SCREAMING_SNAKE_CASE			: Tuple	     =pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  											SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.step(a_		,		self.scheduler.timesteps[tmax]		,		a_							).prev_sample
  
  											for i, t in enumerate(tqdm(self.scheduler.timesteps							)							):
  																		if i > tmax:
  																									if i < tmin:  # layout generation phase
  																																SCREAMING_SNAKE_CASE			: List[Any]	     =self.scheduler.add_noise(
  																																    a_		,		a_		,		timesteps=a_		,		)
  
  																																SCREAMING_SNAKE_CASE			: str	     =(mix_factor * latents) + (
  																																    1 - mix_factor
  																																) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
  																																SCREAMING_SNAKE_CASE			: Union[str, Any]	     =torch.cat([input] * 2							)
  
  																									else:  # content generation phase
  																																SCREAMING_SNAKE_CASE			: Any	     =torch.cat([latents] * 2							)
  
  																									SCREAMING_SNAKE_CASE			: Union[str, Any]	     =self.scheduler.scale_model_input(a_		,		a_							)
  
  																									with torch.no_grad():
  																																SCREAMING_SNAKE_CASE			: Tuple	     =self.unet(
  																																    a_		,		a_		,		encoder_hidden_states=a_		,		).sample
  
  																									SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: str	     =pred.chunk(2							)
  																									SCREAMING_SNAKE_CASE			: Dict	     =pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  																									SCREAMING_SNAKE_CASE			: Tuple	     =self.scheduler.step(a_		,		a_		,		a_							).prev_sample
  
  											return self.decode(a_							)
  
  
  
  
  
  
  
style_context: 
  
  
  
  
  from __future__ import annotations
  def 						lowerCAmelCase_       (	__a	,	__a	,	__a	,	__a )       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE     ,							SCREAMING_SNAKE_CASE			: Any	     =m - 1, 0
  							while i >= 0 and j < n:
  														if key == mat[i][j]:
  																					print(f'Key {key} found at row- {i + 1} column- {j + 1}' )
  																					return
  														if key < mat[i][j]:
  																					i -= 1
  														else:
  																					j += 1
  							print(f'Key {key} not found' )
  def 						lowerCAmelCase_       (	)       ->			None:
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  							SCREAMING_SNAKE_CASE			: int	     =[[2, 5, 7], [4, 8, 13], [9, 11, 15], [12, 17, 20]]
  							SCREAMING_SNAKE_CASE			: List[str]	     =int(input('''Enter the element to be searched:''' ) )
  							print(__a )
  							search_in_a_sorted_matrix(__a	,	len(__a )	,	len(mat[0] )	,	__a )
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()
  							main()
  
  
  
  
  
  
  