code: 
  
  
  from typing import Optional
  
  import torch
  from PIL import Image
  from tqdm.auto import tqdm
  from transformers import CLIPTextModel, CLIPTokenizer
  
  from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
  from diffusers.image_processor import VaeImageProcessor
  from diffusers.utils import (
      deprecate,
  )
  
  
  class 		_SCREAMING_SNAKE_CASE    (	SCREAMING_SNAKE_CASE__    ):
  
  
       def __init__(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_ = 0.9_3       ,				A_ = True       ,				)     ->   Dict:
         _UpperCAmelCase		:       Union[str, Any]       =    mixing_coeff
         _UpperCAmelCase		:       Union[str, Any]       =    leapfrog_steps
  
         super().__init__()
         self.register_modules(
             vae=A_       ,				text_encoder=A_       ,				tokenizer=A_       ,				unet=A_       ,				scheduler=A_       ,				)
  
         _UpperCAmelCase		:       Any       =    2 ** (len(self.vae.config.block_out_channels						) - 1)
         _UpperCAmelCase		:       List[Any]       =    VaeImageProcessor(vae_scale_factor=self.vae_scale_factor						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_ = None       ,				A_ = False						)     ->   Any:
         _UpperCAmelCase		:       List[Any]       =    self.tokenizer(
             A_       ,				padding="""max_length"""       ,				max_length=self.tokenizer.model_max_length       ,				truncation=A_       ,				return_tensors="""pt"""       ,				)
  
         _UpperCAmelCase		:       int       =    self.text_encoder(text_inputs.input_ids.to(self.device						)						).last_hidden_state
  
         _UpperCAmelCase		:       Any       =    prompt_embeds.to(dtype=self.text_encoder.dtype       ,				device=self.device						)
  
         if do_classifier_free_guidance:
           _UpperCAmelCase		:       Optional[Any]       =    """""" if negative_prompt is None else negative_prompt
  
           _UpperCAmelCase		:       List[Any]       =    self.tokenizer(
               A_       ,				padding="""max_length"""       ,				max_length=self.tokenizer.model_max_length       ,				truncation=A_       ,				return_tensors="""pt"""       ,				)
  
           _UpperCAmelCase		:       Any       =    self.text_encoder(uncond_input.input_ids.to(self.device						)						).last_hidden_state
  
           _UpperCAmelCase		:       Dict       =    torch.cat([negative_prompt_embeds, prompt_embeds]						)
  
         return prompt_embeds
  
  
       def 					__snake_case(     self       ,				A_       ,				A_						)     ->   Any:
         _UpperCAmelCase		:       int       =    self.mixing_coeff * x + (1 - self.mixing_coeff) * y
         _UpperCAmelCase		:       Tuple       =    self.mixing_coeff * y + (1 - self.mixing_coeff) * x
  
         return [x, y]
  
  
       def 					__snake_case(     self       ,				A_       ,				A_						)     ->   Union[str, Any]:
         _UpperCAmelCase		:       Dict       =    (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
         _UpperCAmelCase		:       Any       =    (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff
  
         return [x, y]
  
  
       def 					__snake_case(     self       ,				A_						)     ->   Union[str, Any]:
         _UpperCAmelCase		:       str       =    int(A_						)
  
         _UpperCAmelCase		:       Tuple       =    self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod
  
         return alpha_prod, 1 - alpha_prod
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				)     ->   Dict:
         _UpperCAmelCase		:       List[str]       =    timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
         _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    self._get_alpha_and_beta(A_						)
         _UpperCAmelCase,_UpperCAmelCase		:       int       =    self._get_alpha_and_beta(A_						)
  
         _UpperCAmelCase		:       Dict       =    (alpha_prod_t_prev / alpha_prod_t) ** 0.5
         _UpperCAmelCase		:       Any       =    -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
  
         _UpperCAmelCase		:       List[Any]       =    (base - b_t * model_output) / a_t
  
         return model_input, next_model_input.to(base.dtype						)
  
  
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				)     ->   Tuple:
         _UpperCAmelCase		:       int       =    timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
         _UpperCAmelCase,_UpperCAmelCase		:       Union[str, Any]       =    self._get_alpha_and_beta(A_						)
         _UpperCAmelCase,_UpperCAmelCase		:       Any       =    self._get_alpha_and_beta(A_						)
  
         _UpperCAmelCase		:       str       =    (alpha_prod_t_prev / alpha_prod_t) ** 0.5
         _UpperCAmelCase		:       Optional[Any]       =    -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
         _UpperCAmelCase		:       Dict       =    a_t * base + b_t * model_output
  
         return model_input, next_model_input.to(base.dtype						)
  
  
       @torch.no_grad()
       def 					__snake_case(     self       ,				A_						)     ->   Any:
         _UpperCAmelCase		:       str       =    1 / self.vae.config.scaling_factor * latents
         _UpperCAmelCase		:       Union[str, Any]       =    self.vae.decode(A_						).sample
         _UpperCAmelCase		:       Optional[int]       =    (image / 2 + 0.5).clamp(0       ,				1						)
         return image
  
  
       @torch.no_grad()
       def 					__snake_case(     self       ,				A_       ,				A_       ,				A_       ,				A_       ,				A_ = None       ,				)     ->   List[Any]:
         _UpperCAmelCase		:       Optional[Any]       =    guidance_scale > 1.0
  
         _UpperCAmelCase		:       Tuple       =    image.to(device=self.device       ,				dtype=text_embeds.dtype						)
         _UpperCAmelCase		:       int       =    self.vae.encode(A_						).latent_dist.sample(A_						)
  
         _UpperCAmelCase		:       Optional[Any]       =    self.vae.config.scaling_factor * latent
  
         _UpperCAmelCase		:       str       =    [latent.clone(), latent.clone()]
  
         for i, t in tqdm(enumerate(A_						)       ,				total=len(A_						)						):
           _UpperCAmelCase		:       Any       =    self.noise_mixing_layer(x=coupled_latents[0]       ,				y=coupled_latents[1]						)
  
           # j - model_input index, k - base index
           for j in range(2						):
             _UpperCAmelCase		:       List[str]       =    j ^ 1
  
             if self.leapfrog_steps:
               if i % 2 == 0:
                 _UpperCAmelCase,_UpperCAmelCase		:       List[str]       =    j, k
  
             _UpperCAmelCase		:       str       =    coupled_latents[j]
             _UpperCAmelCase		:       Tuple       =    coupled_latents[k]
  
             _UpperCAmelCase		:       Any       =    torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input
  
             _UpperCAmelCase		:       List[Any]       =    self.unet(A_       ,				A_       ,				encoder_hidden_states=A_						).sample
  
             if do_classifier_free_guidance:
               _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    noise_pred.chunk(2						)
               _UpperCAmelCase		:       Any       =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
             _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    self.noise_step(
                 base=A_       ,				model_input=A_       ,				model_output=A_       ,				timestep=A_       ,				)
  
             _UpperCAmelCase		:       Optional[Any]       =    model_input
  
         return coupled_latents
  
  
  
  
       @torch.no_grad()
       def __call__(     self       ,				A_       ,				A_       ,				A_       ,				A_ = 3.0       ,				A_ = 50       ,				A_ = 0.8       ,				A_ = None       ,				A_ = None       ,				A_ = "pil"       ,				)     ->   Optional[int]:
         _UpperCAmelCase		:       Any       =    guidance_scale > 1.0
  
         _UpperCAmelCase		:       str       =    self.image_processor.preprocess(A_						)
  
         _UpperCAmelCase		:       Union[str, Any]       =    self._encode_prompt(A_       ,				A_       ,				A_						)
         _UpperCAmelCase		:       Tuple       =    self._encode_prompt(A_       ,				A_       ,				A_						)
  
         self.scheduler.set_timesteps(A_       ,				self.device						)
  
         _UpperCAmelCase		:       Union[str, Any]       =    num_inference_steps - int(num_inference_steps * strength						)
         _UpperCAmelCase		:       Any       =    self.scheduler.timesteps[t_limit:]
         _UpperCAmelCase		:       Dict       =    fwd_timesteps.flip(0						)
  
         _UpperCAmelCase		:       Any       =    self.prepare_latents(A_       ,				A_       ,				A_       ,				A_       ,				A_						)
  
         for i, t in tqdm(enumerate(A_						)       ,				total=len(A_						)						):
           # j - model_input index, k - base index
           for k in range(2						):
             _UpperCAmelCase		:       Dict       =    k ^ 1
  
             if self.leapfrog_steps:
               if i % 2 == 1:
                 _UpperCAmelCase,_UpperCAmelCase		:       Union[str, Any]       =    j, k
  
             _UpperCAmelCase		:       Tuple       =    coupled_latents[j]
             _UpperCAmelCase		:       Any       =    coupled_latents[k]
  
             _UpperCAmelCase		:       Union[str, Any]       =    torch.cat([model_input] * 2						) if do_classifier_free_guidance else model_input
  
             _UpperCAmelCase		:       int       =    self.unet(A_       ,				A_       ,				encoder_hidden_states=A_						).sample
  
             if do_classifier_free_guidance:
               _UpperCAmelCase,_UpperCAmelCase		:       List[Any]       =    noise_pred.chunk(2						)
               _UpperCAmelCase		:       Union[str, Any]       =    noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
             _UpperCAmelCase,_UpperCAmelCase		:       Optional[Any]       =    self.denoise_step(
                 base=A_       ,				model_input=A_       ,				model_output=A_       ,				timestep=A_       ,				)
  
             _UpperCAmelCase		:       Dict       =    model_input
  
           _UpperCAmelCase		:       Optional[Any]       =    self.denoise_mixing_layer(x=coupled_latents[0]       ,				y=coupled_latents[1]						)
  
         # either one is fine
         _UpperCAmelCase		:       Dict       =    coupled_latents[0]
  
         if output_type not in ["latent", "pt", "np", "pil"]:
           _UpperCAmelCase		:       Tuple       =    (
               F'''the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '''
               """`pil`, `np`, `pt`, `latent`"""
           )
           deprecate("""Unsupported output_type"""       ,				"""1.0.0"""       ,				A_       ,				standard_warn=A_						)
           _UpperCAmelCase		:       Optional[int]       =    """np"""
  
         if output_type == "latent":
           _UpperCAmelCase		:       Any       =    final_latent
         else:
           _UpperCAmelCase		:       Dict       =    self.decode_latents(A_						)
           _UpperCAmelCase		:       Optional[int]       =    self.image_processor.postprocess(A_       ,				output_type=A_						)
  
         return image
  
  
  
  
  
  
style_context: 
  
  
  from typing import Any
  
  
  class 		_SCREAMING_SNAKE_CASE    :
  
  
       def __init__(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    []
         _UpperCAmelCase		:       Dict       =    0
  
  
       def __str__(     self						):
         _UpperCAmelCase		:       Union[str, Any]       =    """<""" + str(self.stack						)[1:-1] + """>"""
         return printed
  
  
       def 					__snake_case(     self       ,				A_						):
         self.stack.append(A_						)
         _UpperCAmelCase		:       Optional[Any]       =    self.length + 1
  
  
       def 					__snake_case(     self						):
         self.rotate(1						)
         _UpperCAmelCase		:       List[Any]       =    self.stack[self.length - 1]
         _UpperCAmelCase		:       Optional[Any]       =    self.stack[:-1]
         self.rotate(self.length - 1						)
         _UpperCAmelCase		:       Union[str, Any]       =    self.length - 1
         return dequeued
  
  
       def 					__snake_case(     self       ,				A_						):
         for _ in range(A_						):
           _UpperCAmelCase		:       Dict       =    self.stack[0]
           _UpperCAmelCase		:       List[Any]       =    self.stack[1:]
           self.put(A_						)
           _UpperCAmelCase		:       Dict       =    self.length - 1
  
  
       def 					__snake_case(     self						):
         _UpperCAmelCase		:       Tuple       =    self.get()
         self.put(A_						)
         self.rotate(self.length - 1						)
         return front
  
  
       def 					__snake_case(     self						):
         return self.length
  
  
  
  
  
  