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 					UpperCamelCase	(     snake_case__      ):
  
  
  
  
  					def __init__( self :					List[str]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					Optional[Any]      ,_lowerCAmelCase :					Optional[int]      ,_lowerCAmelCase :					Union[str, Any]      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							super().__init__()
  
  							self.register_modules(vae=_lowerCAmelCase      ,text_encoder=_lowerCAmelCase      ,tokenizer=_lowerCAmelCase      ,unet=_lowerCAmelCase      ,scheduler=_lowerCAmelCase						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					List[Any]      ,_lowerCAmelCase :					Any						):
  
  
  							"""simple docstring"""
  
  
  
  
  							with torch.no_grad():
  									__snake_case		       =     self.vae.encode(tfms.ToTensor()(_lowerCAmelCase						).unsqueeze(0						).to(self.device						) * 2 - 1						)
  									__snake_case		       =     0.1_8_2_1_5 * latent.latent_dist.sample()
  							return latent
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[int]      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     (1 / 0.1_8_2_1_5) * latent
  							with torch.no_grad():
  									__snake_case		       =     self.vae.decode(_lowerCAmelCase						).sample
  							__snake_case		       =     (img / 2 + 0.5).clamp(0      ,1						)
  							__snake_case		       =     img.detach().cpu().permute(0      ,2      ,3      ,1						).numpy()
  							__snake_case		       =     (img * 255).round().astype("uint8"						)
  							return Image.fromarray(img[0]						)
  
  
  
  
  					def 							UpperCamelCase_						( self :					Optional[Any]      ,_lowerCAmelCase :					int						):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     self.tokenizer(
  							    _lowerCAmelCase      ,padding="max_length"      ,max_length=self.tokenizer.model_max_length      ,truncation=_lowerCAmelCase      ,return_tensors="pt"      ,)
  
  							__snake_case		       =     self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
  
  							__snake_case		       =     self.tokenizer(
  							    ""      ,padding="max_length"      ,max_length=self.tokenizer.model_max_length      ,truncation=_lowerCAmelCase      ,return_tensors="pt"      ,)
  
  							__snake_case		       =     self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
  
  							return torch.cat([uncond_embedding, text_embedding]						)
  
  
  
  
  
  
  
  					def __call__( self :					Tuple      ,_lowerCAmelCase :					List[Any]      ,_lowerCAmelCase :					int      ,_lowerCAmelCase :					str = 0.3      ,_lowerCAmelCase :					Optional[int] = 0.6      ,_lowerCAmelCase :					Tuple = 0.5      ,_lowerCAmelCase :					List[Any] = 42      ,_lowerCAmelCase :					Union[str, Any] = 50      ,_lowerCAmelCase :					List[str] = 7.5      ,):
  
  
  							"""simple docstring"""
  
  
  
  
  							__snake_case		       =     steps - int(kmin * steps						)
  							__snake_case		       =     steps - int(kmax * steps						)
  
  							__snake_case		       =     self.prep_text(_lowerCAmelCase						)
  
  							self.scheduler.set_timesteps(_lowerCAmelCase						)
  
  							__snake_case		       =     img.size
  							__snake_case		       =     self.encode(_lowerCAmelCase						)
  
  							torch.manual_seed(_lowerCAmelCase						)
  							__snake_case		       =     torch.randn(
  							    (1, self.unet.config.in_channels, height // 8, width // 8)      ,).to(self.device						)
  
  							__snake_case		       =     self.scheduler.add_noise(
  							    _lowerCAmelCase      ,_lowerCAmelCase      ,timesteps=self.scheduler.timesteps[tmax]      ,)
  
  							__snake_case		       =     torch.cat([latents] * 2						)
  
  							__snake_case		       =     self.scheduler.scale_model_input(_lowerCAmelCase      ,self.scheduler.timesteps[tmax]						)
  
  							with torch.no_grad():
  									__snake_case		       =     self.unet(
  									    _lowerCAmelCase      ,self.scheduler.timesteps[tmax]      ,encoder_hidden_states=_lowerCAmelCase      ,).sample
  
  							__snake_case		       =     pred.chunk(2						)
  							__snake_case		       =     pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  							__snake_case		       =     self.scheduler.step(_lowerCAmelCase      ,self.scheduler.timesteps[tmax]      ,_lowerCAmelCase						).prev_sample
  
  							for i, t in enumerate(tqdm(self.scheduler.timesteps						)						):
  									if i > tmax:
  											if i < tmin:  # layout generation phase
  													__snake_case		       =     self.scheduler.add_noise(
  													    _lowerCAmelCase      ,_lowerCAmelCase      ,timesteps=_lowerCAmelCase      ,)
  
  													__snake_case		       =     (mix_factor * latents) + (
  													    1 - mix_factor
  													) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
  													__snake_case		       =     torch.cat([input] * 2						)
  
  											else:  # content generation phase
  													__snake_case		       =     torch.cat([latents] * 2						)
  
  											__snake_case		       =     self.scheduler.scale_model_input(_lowerCAmelCase      ,_lowerCAmelCase						)
  
  											with torch.no_grad():
  													__snake_case		       =     self.unet(
  													    _lowerCAmelCase      ,_lowerCAmelCase      ,encoder_hidden_states=_lowerCAmelCase      ,).sample
  
  											__snake_case		       =     pred.chunk(2						)
  											__snake_case		       =     pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  											__snake_case		       =     self.scheduler.step(_lowerCAmelCase      ,_lowerCAmelCase      ,_lowerCAmelCase						).prev_sample
  
  							return self.decode(_lowerCAmelCase						)
  
  
  
  
  
  
style_context: import os
  from string import ascii_letters
  
  lowerCamelCase__           =				ascii_letters + ' \t\n'
  
  
  
  def 							_lowerCamelCase(				)  ->   dict[str, None]:
  		__snake_case		       =     os.path.split(os.path.realpath(__snake_case	)	)
  		__snake_case		       =     {}
  		with open(path[0] + "/dictionary.txt"	) as dictionary_file:
  				for word in dictionary_file.read().split("\n"	):
  						__snake_case		       =     None
  		return english_words
  
  
  lowerCamelCase__           =				load_dictionary()
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   float:
  		__snake_case		       =     message.upper()
  		__snake_case		       =     remove_non_letters(__snake_case	)
  		__snake_case		       =     message.split()
  		__snake_case		       =     len([word for word in possible_words if word in ENGLISH_WORDS]	)
  		return float(__snake_case	) / len(__snake_case	)
  
  
  
  def 							_lowerCamelCase(				__snake_case	)  ->   str:
  		return "".join(symbol for symbol in message if symbol in LETTERS_AND_SPACE	)
  
  
  
  def 							_lowerCamelCase(				__snake_case						,						__snake_case = 20						,						__snake_case = 85	)  ->   bool:
  		__snake_case		       =     get_english_count(__snake_case	) * 100 >= word_percentage
  		__snake_case		       =     len(remove_non_letters(__snake_case	)	)
  		__snake_case		       =     (float(__snake_case	) / len(__snake_case	)) * 100
  		__snake_case		       =     message_letters_percentage >= letter_percentage
  		return words_match and letters_match
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  
  
  
  
  