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	(   _lowercase					):
  
  
  
  
  					def __init__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      )						->  List[Any]:
  							super().__init__()
  
  							self.register_modules(vae=__lowerCAmelCase    ,      text_encoder=__lowerCAmelCase    ,      tokenizer=__lowerCAmelCase    ,      unet=__lowerCAmelCase    ,      scheduler=__lowerCAmelCase       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Optional[Any]:
  							with torch.no_grad():
  									UpperCamelCase__         =	self.vae.encode(tfms.ToTensor()(__lowerCAmelCase       ).unsqueeze(0       ).to(self.device       ) * 2 - 1       )
  									UpperCamelCase__         =	0.1_8_2_1_5 * latent.latent_dist.sample()
  							return latent
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Optional[int]:
  							UpperCamelCase__         =	(1 / 0.1_8_2_1_5) * latent
  							with torch.no_grad():
  									UpperCamelCase__         =	self.vae.decode(__lowerCAmelCase       ).sample
  							UpperCamelCase__         =	(img / 2 + 0.5).clamp(0    ,      1       )
  							UpperCamelCase__         =	img.detach().cpu().permute(0    ,      2    ,      3    ,      1       ).numpy()
  							UpperCamelCase__         =	(img * 255).round().astype("""uint8"""       )
  							return Image.fromarray(img[0]       )
  
  
  
  
  					def 	snake_case_		(						self    ,      __lowerCAmelCase       )						->  Any:
  							UpperCamelCase__         =	self.tokenizer(
  							    __lowerCAmelCase    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=__lowerCAmelCase    ,      return_tensors="""pt"""    ,      )
  
  							UpperCamelCase__         =	self.text_encoder(text_input.input_ids.to(self.device       )       )[0]
  
  							UpperCamelCase__         =	self.tokenizer(
  							    """"""    ,      padding="""max_length"""    ,      max_length=self.tokenizer.model_max_length    ,      truncation=__lowerCAmelCase    ,      return_tensors="""pt"""    ,      )
  
  							UpperCamelCase__         =	self.text_encoder(uncond_input.input_ids.to(self.device       )       )[0]
  
  							return torch.cat([uncond_embedding, text_embedding]       )
  
  
  
  
  					def __call__(						self    ,      __lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase = 0.3    ,      __lowerCAmelCase = 0.6    ,      __lowerCAmelCase = 0.5    ,      __lowerCAmelCase = 42    ,      __lowerCAmelCase = 50    ,      __lowerCAmelCase = 7.5    ,      )						->  Image.Image:
  							UpperCamelCase__         =	steps - int(kmin * steps       )
  							UpperCamelCase__         =	steps - int(kmax * steps       )
  
  							UpperCamelCase__         =	self.prep_text(__lowerCAmelCase       )
  
  							self.scheduler.set_timesteps(__lowerCAmelCase       )
  
  							UpperCamelCase__  ,			UpperCamelCase__         =	img.size
  							UpperCamelCase__         =	self.encode(__lowerCAmelCase       )
  
  							torch.manual_seed(__lowerCAmelCase       )
  							UpperCamelCase__         =	torch.randn(
  							    (1, self.unet.config.in_channels, height // 8, width // 8)    ,      ).to(self.device       )
  
  							UpperCamelCase__         =	self.scheduler.add_noise(
  							    __lowerCAmelCase    ,      __lowerCAmelCase    ,      timesteps=self.scheduler.timesteps[tmax]    ,      )
  
  							UpperCamelCase__         =	torch.cat([latents] * 2       )
  
  							UpperCamelCase__         =	self.scheduler.scale_model_input(__lowerCAmelCase    ,      self.scheduler.timesteps[tmax]       )
  
  							with torch.no_grad():
  									UpperCamelCase__         =	self.unet(
  									    __lowerCAmelCase    ,      self.scheduler.timesteps[tmax]    ,      encoder_hidden_states=__lowerCAmelCase    ,      ).sample
  
  							UpperCamelCase__  ,			UpperCamelCase__         =	pred.chunk(2       )
  							UpperCamelCase__         =	pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  							UpperCamelCase__         =	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
  													UpperCamelCase__         =	self.scheduler.add_noise(
  													    __lowerCAmelCase    ,      __lowerCAmelCase    ,      timesteps=__lowerCAmelCase    ,      )
  
  													UpperCamelCase__         =	(mix_factor * latents) + (
  													    1 - mix_factor
  													) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
  													UpperCamelCase__         =	torch.cat([input] * 2       )
  
  											else:  # content generation phase
  													UpperCamelCase__         =	torch.cat([latents] * 2       )
  
  											UpperCamelCase__         =	self.scheduler.scale_model_input(__lowerCAmelCase    ,      __lowerCAmelCase       )
  
  											with torch.no_grad():
  													UpperCamelCase__         =	self.unet(
  													    __lowerCAmelCase    ,      __lowerCAmelCase    ,      encoder_hidden_states=__lowerCAmelCase    ,      ).sample
  
  											UpperCamelCase__  ,			UpperCamelCase__         =	pred.chunk(2       )
  											UpperCamelCase__         =	pred_uncond + guidance_scale * (pred_text - pred_uncond)
  
  											UpperCamelCase__         =	self.scheduler.step(__lowerCAmelCase    ,      __lowerCAmelCase    ,      __lowerCAmelCase       ).prev_sample
  
  							return self.decode(__lowerCAmelCase       )
  
  
  
  
  
  
style_context: 
  
  
  
  
  
  import warnings
  
  from .generation import FlaxGenerationMixin
  
  
  
  
  class   _UpperCAmelCase	(   _lowercase					):
  					# warning at import time
  					warnings.warn(
  					    "Importing `FlaxGenerationMixin` from `src/transformers/generation_flax_utils.py` is deprecated and will "
  					    "be removed in Transformers v5. Import as `from transformers import FlaxGenerationMixin` instead." ,       _lowercase ,       )
  
  
  
  
  
  