code: 
  
  
  """simple docstring"""
  
  
  
  
  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 __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			def __init__(		self							:Union[str, Any]				,  UpperCamelCase__							:AutoencoderKL				,  UpperCamelCase__							:CLIPTextModel				,  UpperCamelCase__							:CLIPTokenizer				,  UpperCamelCase__							:UNetaDConditionModel				,  UpperCamelCase__							:DDIMScheduler				,  UpperCamelCase__							:float = 0.93				,  UpperCamelCase__							:bool = True				,  ):
  										_a          =				mixing_coeff
  										_a          =				leapfrog_steps
  
  										super().__init__()
  										self.register_modules(
  										    vae=UpperCamelCase__				,  text_encoder=UpperCamelCase__				,  tokenizer=UpperCamelCase__				,  unet=UpperCamelCase__				,  scheduler=UpperCamelCase__				,  )
  
  										_a          =				2 ** (len(self.vae.config.block_out_channels       ) - 1)
  										_a          =				VaeImageProcessor(vae_scale_factor=self.vae_scale_factor       )
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:List[str]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:Optional[str] = None				,  UpperCamelCase__							:bool = False       ):
  										_a          =				self.tokenizer(
  										    UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  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(
  																	    UpperCamelCase__				,  padding="max_length"				,  max_length=self.tokenizer.model_max_length				,  truncation=UpperCamelCase__				,  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 SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor       ):
  										_a          =				self.mixing_coeff * x + (1 - self.mixing_coeff) * y
  										_a          =				self.mixing_coeff * y + (1 - self.mixing_coeff) * x
  
  										return [x, y]
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:int				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor       ):
  										_a          =				(y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
  										_a          =				(x - (1 - self.mixing_coeff) * y) / self.mixing_coeff
  
  										return [x, y]
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:torch.Tensor       ):
  										# as self.alphas_cumprod is always in cpu
  										_a          =				int(UpperCamelCase__       )
  
  										_a          =				self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod
  
  										return alpha_prod, 1 - alpha_prod
  
  
  
  
  			def SCREAMING_SNAKE_CASE_      (		self							:Optional[int]				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  ):
  										_a          =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
  										_a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )
  										_a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )
  
  										_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 SCREAMING_SNAKE_CASE_      (		self							:Tuple				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  ):
  										_a          =				timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps
  
  										_a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )
  										_a   , _a          =				self._get_alpha_and_beta(UpperCamelCase__       )
  
  										_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 SCREAMING_SNAKE_CASE_      (		self							:Dict				,  UpperCamelCase__							:torch.Tensor       ):
  										_a          =				1 / self.vae.config.scaling_factor * latents
  										_a          =				self.vae.decode(UpperCamelCase__       ).sample
  										_a          =				(image / 2 + 0.5).clamp(0				,  1       )
  										return image
  
  
  
  
  			@torch.no_grad()
  			def SCREAMING_SNAKE_CASE_      (		self							:List[Any]				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:torch.Tensor				,  UpperCamelCase__							:float				,  UpperCamelCase__							:Optional[torch.Generator] = None				,  ):
  										_a          =				guidance_scale > 1.0
  
  										_a          =				image.to(device=self.device				,  dtype=text_embeds.dtype       )
  										_a          =				self.vae.encode(UpperCamelCase__       ).latent_dist.sample(UpperCamelCase__       )
  
  										_a          =				self.vae.config.scaling_factor * latent
  
  										_a          =				[latent.clone(), latent.clone()]
  
  										for i, t in tqdm(enumerate(UpperCamelCase__       )				,  total=len(UpperCamelCase__       )       ):
  																	_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(UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__       ).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=UpperCamelCase__				,  model_input=UpperCamelCase__				,  model_output=UpperCamelCase__				,  timestep=UpperCamelCase__				,  )
  
  																								_a          =				model_input
  
  										return coupled_latents
  
  
  
  
  			@torch.no_grad()
  			def __call__(		self							:List[str]				,  UpperCamelCase__							:str				,  UpperCamelCase__							:str				,  UpperCamelCase__							:Image.Image				,  UpperCamelCase__							:float = 3.0				,  UpperCamelCase__							:int = 50				,  UpperCamelCase__							:float = 0.8				,  UpperCamelCase__							:Optional[str] = None				,  UpperCamelCase__							:Optional[torch.Generator] = None				,  UpperCamelCase__							:Optional[str] = "pil"				,  ):
  										_a          =				guidance_scale > 1.0
  
  										_a          =				self.image_processor.preprocess(UpperCamelCase__       )
  
  										_a          =				self._encode_prompt(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
  										_a          =				self._encode_prompt(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
  
  										self.scheduler.set_timesteps(UpperCamelCase__				,  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(UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__				,  UpperCamelCase__       )
  
  										for i, t in tqdm(enumerate(UpperCamelCase__       )				,  total=len(UpperCamelCase__       )       ):
  																	# 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(UpperCamelCase__				,  UpperCamelCase__				,  encoder_hidden_states=UpperCamelCase__       ).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=UpperCamelCase__				,  model_input=UpperCamelCase__				,  model_output=UpperCamelCase__				,  timestep=UpperCamelCase__				,  )
  
  																								_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"				,  UpperCamelCase__				,  standard_warn=UpperCamelCase__       )
  																	_a          =				"np"
  
  										if output_type == "latent":
  																	_a          =				final_latent
  										else:
  																	_a          =				self.decode_latents(UpperCamelCase__       )
  																	_a          =				self.image_processor.postprocess(UpperCamelCase__				,  output_type=UpperCamelCase__       )
  
  										return image
  
style_context: 
  
  
  """simple docstring"""
  
  
  
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  class __snake_case		(					_SCREAMING_SNAKE_CASE      ):
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
  
     lowerCAmelCase_				:   str     	=			'AutoFeatureExtractor'
     lowerCAmelCase_				:   List[Any]     	=			'Speech2Text2Tokenizer'
  
  
  
  
     def __init__(		self							:int				,  UpperCamelCase__							:List[str]				,  UpperCamelCase__							:Union[str, Any]       ):
            super().__init__(UpperCamelCase__				,  UpperCamelCase__       )
            _a          =				self.feature_extractor
            _a          =				False
  
  
  
  
     def __call__(		self							:List[str]				,  *UpperCamelCase__							:List[str]				,  **UpperCamelCase__							:Dict       ):
            # For backward compatibility
            if self._in_target_context_manager:
                   return self.current_processor(*UpperCamelCase__				,  **UpperCamelCase__       )
  
            if "raw_speech" in kwargs:
                   warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."       )
                   _a          =				kwargs.pop("raw_speech"       )
            else:
                   _a          =				kwargs.pop("audio"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("sampling_rate"				,  UpperCamelCase__       )
            _a          =				kwargs.pop("text"				,  UpperCamelCase__       )
            if len(UpperCamelCase__       ) > 0:
                   _a          =				args[0]
                   _a          =				args[1:]
  
            if audio is None and text is None:
                   raise ValueError("You need to specify either an `audio` or `text` input to process."       )
  
            if audio is not None:
                   _a          =				self.feature_extractor(UpperCamelCase__				,  *UpperCamelCase__				,  sampling_rate=UpperCamelCase__				,  **UpperCamelCase__       )
            if text is not None:
                   _a          =				self.tokenizer(UpperCamelCase__				,  **UpperCamelCase__       )
  
            if text is None:
                   return inputs
            elif audio is None:
                   return encodings
            else:
                   _a          =				encodings["input_ids"]
                   return inputs
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Union[str, Any]				,  *UpperCamelCase__							:int				,  **UpperCamelCase__							:Optional[Any]       ):
            return self.tokenizer.batch_decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     def SCREAMING_SNAKE_CASE_      (		self							:Optional[Any]				,  *UpperCamelCase__							:List[Any]				,  **UpperCamelCase__							:List[Any]       ):
            return self.tokenizer.decode(*UpperCamelCase__				,  **UpperCamelCase__       )
  
  
  
  
     @contextmanager
     def SCREAMING_SNAKE_CASE_      (		self							:Dict       ):
            warnings.warn(
                "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
                "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
                "your audio inputs, or in a separate call."       )
            _a          =				True
            _a          =				self.tokenizer
            yield
            _a          =				self.feature_extractor
            _a          =				False
  