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 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     def __init__(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = 0.93,					lowercase_ = True,					)							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=mixing_coeff
       a__						=leapfrog_steps
  
       super().__init__()
       self.register_modules(
           vae=lowercase_,					text_encoder=lowercase_,					tokenizer=lowercase_,					unet=lowercase_,					scheduler=lowercase_,					)
  
       a__						=2 ** (len(self.vae.config.block_out_channels ) - 1)
       a__						=VaeImageProcessor(vae_scale_factor=self.vae_scale_factor )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ = None,					lowercase_ = False )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.tokenizer(
           lowercase_,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowercase_,					return_tensors='''pt''',					)
  
       a__						=self.text_encoder(text_inputs.input_ids.to(self.device ) ).last_hidden_state
  
       a__						=prompt_embeds.to(dtype=self.text_encoder.dtype,					device=self.device )
  
       if do_classifier_free_guidance:
         a__						='''''' if negative_prompt is None else negative_prompt
  
         a__						=self.tokenizer(
             lowercase_,					padding='''max_length''',					max_length=self.tokenizer.model_max_length,					truncation=lowercase_,					return_tensors='''pt''',					)
  
         a__						=self.text_encoder(uncond_input.input_ids.to(self.device ) ).last_hidden_state
  
         a__						=torch.cat([negative_prompt_embeds, prompt_embeds] )
  
       return prompt_embeds
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=self.mixing_coeff * x + (1 - self.mixing_coeff) * y
       a__						=self.mixing_coeff * y + (1 - self.mixing_coeff) * x
  
       return [x, y]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
       a__						=(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff
  
       return [x, y]
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Any:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=int(lowercase_ )
  
       a__						=self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod
  
       return alpha_prod, 1 - alpha_prod
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					)							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
       a__,   a__						=self._get_alpha_and_beta(lowercase_ )
       a__,   a__						=self._get_alpha_and_beta(lowercase_ )
  
       a__						=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       a__						=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
  
       a__						=(base - b_t * model_output) / a_t
  
       return model_input, next_model_input.to(base.dtype )
  
  
  
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					)							->					Optional[int]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
       a__,   a__						=self._get_alpha_and_beta(lowercase_ )
       a__,   a__						=self._get_alpha_and_beta(lowercase_ )
  
       a__						=(alpha_prod_t_prev / alpha_prod_t) ** 0.5
       a__						=-a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
       a__						=a_t * base + b_t * model_output
  
       return model_input, next_model_input.to(base.dtype )
  
  
  
     @torch.no_grad()
     def 				_UpperCAmelCase				(       self,					lowercase_ )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=1 / self.vae.config.scaling_factor * latents
       a__						=self.vae.decode(lowercase_ ).sample
       a__						=(image / 2 + 0.5).clamp(0,					1 )
       return image
  
  
  
     @torch.no_grad()
     def 				_UpperCAmelCase				(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = None,					)							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=guidance_scale > 1.0
  
       a__						=image.to(device=self.device,					dtype=text_embeds.dtype )
       a__						=self.vae.encode(lowercase_ ).latent_dist.sample(lowercase_ )
  
       a__						=self.vae.config.scaling_factor * latent
  
       a__						=[latent.clone(), latent.clone()]
  
       for i, t in tqdm(enumerate(lowercase_ ),					total=len(lowercase_ ) ):
         a__						=self.noise_mixing_layer(x=coupled_latents[0],					y=coupled_latents[1] )
  
         # j - model_input index, k - base index
         for j in range(2 ):
           a__						=j ^ 1
  
           if self.leapfrog_steps:
             if i % 2 == 0:
               a__,   a__						=j, k
  
           a__						=coupled_latents[j]
           a__						=coupled_latents[k]
  
           a__						=torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input
  
           a__						=self.unet(lowercase_,					lowercase_,					encoder_hidden_states=lowercase_ ).sample
  
           if do_classifier_free_guidance:
             a__,   a__						=noise_pred.chunk(2 )
             a__						=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
           a__,   a__						=self.noise_step(
               base=lowercase_,					model_input=lowercase_,					model_output=lowercase_,					timestep=lowercase_,					)
  
           a__						=model_input
  
       return coupled_latents
  
  
  
     @torch.no_grad()
     def __call__(       self,					lowercase_,					lowercase_,					lowercase_,					lowercase_ = 3.0,					lowercase_ = 50,					lowercase_ = 0.8,					lowercase_ = None,					lowercase_ = None,					lowercase_ = "pil",					)							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=guidance_scale > 1.0
  
       a__						=self.image_processor.preprocess(lowercase_ )
  
       a__						=self._encode_prompt(lowercase_,					lowercase_,					lowercase_ )
       a__						=self._encode_prompt(lowercase_,					lowercase_,					lowercase_ )
  
       self.scheduler.set_timesteps(lowercase_,					self.device )
  
       a__						=num_inference_steps - int(num_inference_steps * strength )
       a__						=self.scheduler.timesteps[t_limit:]
       a__						=fwd_timesteps.flip(0 )
  
       a__						=self.prepare_latents(lowercase_,					lowercase_,					lowercase_,					lowercase_,					lowercase_ )
  
       for i, t in tqdm(enumerate(lowercase_ ),					total=len(lowercase_ ) ):
         # j - model_input index, k - base index
         for k in range(2 ):
           a__						=k ^ 1
  
           if self.leapfrog_steps:
             if i % 2 == 1:
               a__,   a__						=j, k
  
           a__						=coupled_latents[j]
           a__						=coupled_latents[k]
  
           a__						=torch.cat([model_input] * 2 ) if do_classifier_free_guidance else model_input
  
           a__						=self.unet(lowercase_,					lowercase_,					encoder_hidden_states=lowercase_ ).sample
  
           if do_classifier_free_guidance:
             a__,   a__						=noise_pred.chunk(2 )
             a__						=noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
  
           a__,   a__						=self.denoise_step(
               base=lowercase_,					model_input=lowercase_,					model_output=lowercase_,					timestep=lowercase_,					)
  
           a__						=model_input
  
         a__						=self.denoise_mixing_layer(x=coupled_latents[0],					y=coupled_latents[1] )
  
       # either one is fine
       a__						=coupled_latents[0]
  
       if output_type not in ["latent", "pt", "np", "pil"]:
         a__						=(
             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''',					lowercase_,					standard_warn=lowercase_ )
         a__						='''np'''
  
       if output_type == "latent":
         a__						=final_latent
       else:
         a__						=self.decode_latents(lowercase_ )
         a__						=self.image_processor.postprocess(lowercase_,					output_type=lowercase_ )
  
       return image
  
style_context: 
  
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  from PIL import Image
  
  from ...utils import BaseOutput, is_torch_available, is_transformers_available
  
  
  
  
  
  
  
  @dataclass
  # Copied from diffusers.pipelines.stable_diffusion.__init__.StableDiffusionPipelineOutput with Stable->Alt
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[List[PIL.Image.Image], np.ndarray]
     lowerCamelCase__				:			Optional[List[bool]]
  
  
  if is_transformers_available() and is_torch_available():
         from .modeling_roberta_series import RobertaSeriesModelWithTransformation
         from .pipeline_alt_diffusion import AltDiffusionPipeline
         from .pipeline_alt_diffusion_imgaimg import AltDiffusionImgaImgPipeline
  