code: 
  
  
  import types
  from typing import List, Optional, Tuple, Union
  
  import torch
  from transformers import CLIPTextModelWithProjection, CLIPTokenizer
  from transformers.models.clip.modeling_clip import CLIPTextModelOutput
  
  from diffusers.models import PriorTransformer
  from diffusers.pipelines import DiffusionPipeline, StableDiffusionImageVariationPipeline
  from diffusers.schedulers import UnCLIPScheduler
  from diffusers.utils import logging, randn_tensor
  
  
  lowercase_								=    logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  def 							SCREAMING_SNAKE_CASE					(  self							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase							,     _UpperCAmelCase				)    ->  Optional[Any]:
  						_a     =			image.to(device=_UpperCAmelCase				)
  						_a     =			image  # take image as image_embeddings
  						_a     =			image_embeddings.unsqueeze(1				)
  
  						# duplicate image embeddings for each generation per prompt, using mps friendly method
  						_a ,	_a ,	_a     =			image_embeddings.shape
  						_a     =			image_embeddings.repeat(1							,     _UpperCAmelCase							,     1				)
  						_a     =			image_embeddings.view(bs_embed * num_images_per_prompt							,     _UpperCAmelCase							,     -1				)
  
  						if do_classifier_free_guidance:
  												_a     =			torch.zeros_like(_UpperCAmelCase				)
  
  												# For classifier free guidance, we need to do two forward passes.
  												# Here we concatenate the unconditional and text embeddings into a single batch
  												# to avoid doing two forward passes
  												_a     =			torch.cat([uncond_embeddings, image_embeddings]				)
  
  						return image_embeddings
  
  
  
  
  
  
  class 					_UpperCamelCase (      lowerCamelCase__	):
  
  
  
  
  
  
  
  							'''simple docstring'''
  
  
  
  
  
  
  
  							def __init__(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				PriorTransformer		,				SCREAMING_SNAKE_CASE_  :				CLIPTokenizer		,				SCREAMING_SNAKE_CASE_  :				CLIPTextModelWithProjection		,				SCREAMING_SNAKE_CASE_  :				UnCLIPScheduler		,				SCREAMING_SNAKE_CASE_  :				Optional[dict] = None		,				):
  													super().__init__()
  
  													_a     =			{'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  													_a     =			decoder_pipe_kwargs.get('torch_dtype'		,				SCREAMING_SNAKE_CASE_		) or prior.dtype
  
  													_a     =			StableDiffusionImageVariationPipeline.from_pretrained(
  													    'lambdalabs/sd-image-variations-diffusers'		,				**SCREAMING_SNAKE_CASE_		)
  
  													# replace `_encode_image` method
  													_a     =			types.MethodType(_encode_image		,				self.decoder_pipe		)
  
  													self.register_modules(
  													    prior=SCREAMING_SNAKE_CASE_		,				tokenizer=SCREAMING_SNAKE_CASE_		,				text_encoder=SCREAMING_SNAKE_CASE_		,				prior_scheduler=SCREAMING_SNAKE_CASE_		,				)
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				List[str]		,				SCREAMING_SNAKE_CASE_  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[CLIPTextModelOutput, Tuple]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Tensor] = None		,				):
  													if text_model_output is None:
  																			_a     =			len(SCREAMING_SNAKE_CASE_		) if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		) else 1
  																			# get prompt text embeddings
  																			_a     =			self.tokenizer(
  																			    SCREAMING_SNAKE_CASE_		,				padding='max_length'		,				max_length=self.tokenizer.model_max_length		,				return_tensors='pt'		,				)
  																			_a     =			text_inputs.input_ids
  																			_a     =			text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE_		)
  
  																			if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  																									_a     =			self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
  																									logger.warning(
  																									    'The following part of your input was truncated because CLIP can only handle sequences up to'
  																									    f""" {self.tokenizer.model_max_length} tokens: {removed_text}"""		)
  																									_a     =			text_input_ids[:, : self.tokenizer.model_max_length]
  
  																			_a     =			self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE_		)		)
  
  																			_a     =			text_encoder_output.text_embeds
  																			_a     =			text_encoder_output.last_hidden_state
  
  													else:
  																			_a     =			text_model_output[0].shape[0]
  																			_a ,	_a     =			text_model_output[0], text_model_output[1]
  																			_a     =			text_attention_mask
  
  													_a     =			text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)
  													_a     =			text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)
  													_a     =			text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)
  
  													if do_classifier_free_guidance:
  																			_a     =			[''] * batch_size
  
  																			_a     =			self.tokenizer(
  																			    SCREAMING_SNAKE_CASE_		,				padding='max_length'		,				max_length=self.tokenizer.model_max_length		,				truncation=SCREAMING_SNAKE_CASE_		,				return_tensors='pt'		,				)
  																			_a     =			uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE_		)
  																			_a     =			self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE_		)		)
  
  																			_a     =			uncond_embeddings_text_encoder_output.text_embeds
  																			_a     =			uncond_embeddings_text_encoder_output.last_hidden_state
  
  																			# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  																			_a     =			uncond_embeddings.shape[1]
  																			_a     =			uncond_embeddings.repeat(1		,				SCREAMING_SNAKE_CASE_		)
  																			_a     =			uncond_embeddings.view(batch_size * num_images_per_prompt		,				SCREAMING_SNAKE_CASE_		)
  
  																			_a     =			uncond_text_encoder_hidden_states.shape[1]
  																			_a     =			uncond_text_encoder_hidden_states.repeat(1		,				SCREAMING_SNAKE_CASE_		,				1		)
  																			_a     =			uncond_text_encoder_hidden_states.view(
  																			    batch_size * num_images_per_prompt		,				SCREAMING_SNAKE_CASE_		,				-1		)
  																			_a     =			uncond_text_mask.repeat_interleave(SCREAMING_SNAKE_CASE_		,				dim=0		)
  
  																			# done duplicates
  
  																			# For classifier free guidance, we need to do two forward passes.
  																			# Here we concatenate the unconditional and text embeddings into a single batch
  																			# to avoid doing two forward passes
  																			_a     =			torch.cat([uncond_embeddings, text_embeddings]		)
  																			_a     =			torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)
  
  																			_a     =			torch.cat([uncond_text_mask, text_mask]		)
  
  													return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  
  
  
  							@property
  							def 		_UpperCAmelCase	(		self  :				Optional[Any]		):
  													if self.device != torch.device('meta'		) or not hasattr(self.prior		,				'_hf_hook'		):
  																			return self.device
  													for module in self.prior.modules():
  																			if (
  																			    hasattr(SCREAMING_SNAKE_CASE_		,				'_hf_hook'		)
  																			    and hasattr(module._hf_hook		,				'execution_device'		)
  																			    and module._hf_hook.execution_device is not None
  																			):
  																									return torch.device(module._hf_hook.execution_device		)
  													return self.device
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				Union[str, Any]		,				SCREAMING_SNAKE_CASE_  :				Dict		,				SCREAMING_SNAKE_CASE_  :				int		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				str		,				SCREAMING_SNAKE_CASE_  :				Optional[int]		):
  													if latents is None:
  																			_a     =			randn_tensor(SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		,				device=SCREAMING_SNAKE_CASE_		,				dtype=SCREAMING_SNAKE_CASE_		)
  													else:
  																			if latents.shape != shape:
  																									raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}"""		)
  																			_a     =			latents.to(SCREAMING_SNAKE_CASE_		)
  
  													_a     =			latents * scheduler.init_noise_sigma
  													return latents
  
  
  
  
  
  
  
  							def 		_UpperCAmelCase	(		self  :				List[Any]		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, torch.device]] = None		):
  													self.decoder_pipe.to(SCREAMING_SNAKE_CASE_		)
  													super().to(SCREAMING_SNAKE_CASE_		)
  
  
  
  
  
  
  
  							@torch.no_grad()
  							def __call__(		self  :				Any		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[str, List[str]]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = None		,				SCREAMING_SNAKE_CASE_  :				int = 1		,				SCREAMING_SNAKE_CASE_  :				int = 2_5		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Generator] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.FloatTensor] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[Union[CLIPTextModelOutput, Tuple]] = None		,				SCREAMING_SNAKE_CASE_  :				Optional[torch.Tensor] = None		,				SCREAMING_SNAKE_CASE_  :				float = 4.0		,				SCREAMING_SNAKE_CASE_  :				float = 8.0		,				SCREAMING_SNAKE_CASE_  :				int = 5_0		,				SCREAMING_SNAKE_CASE_  :				Optional[int] = 1		,				SCREAMING_SNAKE_CASE_  :				float = 0.0		,				SCREAMING_SNAKE_CASE_  :				Optional[str] = "pil"		,				SCREAMING_SNAKE_CASE_  :				bool = True		,				):
  													if prompt is not None:
  																			if isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		):
  																									_a     =			1
  																			elif isinstance(SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		):
  																									_a     =			len(SCREAMING_SNAKE_CASE_		)
  																			else:
  																									raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE_		)}"""		)
  													else:
  																			_a     =			text_model_output[0].shape[0]
  
  													_a     =			self._execution_device
  
  													_a     =			batch_size * num_images_per_prompt
  
  													_a     =			prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  													_a ,	_a ,	_a     =			self._encode_prompt(
  													    SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		)
  
  													# prior
  
  													self.prior_scheduler.set_timesteps(SCREAMING_SNAKE_CASE_		,				device=SCREAMING_SNAKE_CASE_		)
  													_a     =			self.prior_scheduler.timesteps
  
  													_a     =			self.prior.config.embedding_dim
  
  													_a     =			self.prepare_latents(
  													    (batch_size, embedding_dim)		,				text_embeddings.dtype		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				SCREAMING_SNAKE_CASE_		,				self.prior_scheduler		,				)
  
  													for i, t in enumerate(self.progress_bar(SCREAMING_SNAKE_CASE_		)		):
  																			# expand the latents if we are doing classifier free guidance
  																			_a     =			torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents
  
  																			_a     =			self.prior(
  																			    SCREAMING_SNAKE_CASE_		,				timestep=SCREAMING_SNAKE_CASE_		,				proj_embedding=SCREAMING_SNAKE_CASE_		,				encoder_hidden_states=SCREAMING_SNAKE_CASE_		,				attention_mask=SCREAMING_SNAKE_CASE_		,				).predicted_image_embedding
  
  																			if do_classifier_free_guidance:
  																									_a ,	_a     =			predicted_image_embedding.chunk(2		)
  																									_a     =			predicted_image_embedding_uncond + prior_guidance_scale * (
  																									    predicted_image_embedding_text - predicted_image_embedding_uncond
  																									)
  
  																			if i + 1 == prior_timesteps_tensor.shape[0]:
  																									_a     =			None
  																			else:
  																									_a     =			prior_timesteps_tensor[i + 1]
  
  																			_a     =			self.prior_scheduler.step(
  																			    SCREAMING_SNAKE_CASE_		,				timestep=SCREAMING_SNAKE_CASE_		,				sample=SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		,				prev_timestep=SCREAMING_SNAKE_CASE_		,				).prev_sample
  
  													_a     =			self.prior.post_process_latents(SCREAMING_SNAKE_CASE_		)
  
  													_a     =			prior_latents
  
  													_a     =			self.decoder_pipe(
  													    image=SCREAMING_SNAKE_CASE_		,				height=SCREAMING_SNAKE_CASE_		,				width=SCREAMING_SNAKE_CASE_		,				num_inference_steps=SCREAMING_SNAKE_CASE_		,				guidance_scale=SCREAMING_SNAKE_CASE_		,				generator=SCREAMING_SNAKE_CASE_		,				output_type=SCREAMING_SNAKE_CASE_		,				return_dict=SCREAMING_SNAKE_CASE_		,				num_images_per_prompt=SCREAMING_SNAKE_CASE_		,				eta=SCREAMING_SNAKE_CASE_		,				)
  													return output
  
  
style_context: 
  
  
  lowercase_								=    [0.485, 0.456, 0.406]
  lowercase_								=    [0.229, 0.224, 0.225]
  lowercase_								=    [0.5, 0.5, 0.5]
  lowercase_								=    [0.5, 0.5, 0.5]
  lowercase_								=    [0.48145466, 0.4578275, 0.40821073]
  lowercase_								=    [0.26862954, 0.26130258, 0.27577711]
  
  
  