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
  
  
  __A       					=		logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  def  __a  (					self     :      Tuple    ,lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Optional[int]    ,lowerCAmelCase_     :      Tuple       )   ->				Tuple:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     image.to(device=lowerCAmelCase_       )
  						UpperCAmelCase_=     image  # take image as image_embeddings
  						UpperCAmelCase_=     image_embeddings.unsqueeze(1       )
  
  						# duplicate image embeddings for each generation per prompt, using mps friendly method
  						UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     image_embeddings.shape
  						UpperCAmelCase_=     image_embeddings.repeat(1    ,lowerCAmelCase_    ,1       )
  						UpperCAmelCase_=     image_embeddings.view(bs_embed * num_images_per_prompt    ,lowerCAmelCase_    ,-1       )
  
  						if do_classifier_free_guidance:
  												UpperCAmelCase_=     torch.zeros_like(lowerCAmelCase_       )
  
  												# 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
  												UpperCAmelCase_=     torch.cat([uncond_embeddings, image_embeddings]       )
  
  						return image_embeddings
  
  
  
  
  
  
  class 	lowercase		(				lowerCAmelCase__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	def __init__(			self			:		Optional[Any]		,						__UpperCAmelCase			:		PriorTransformer		,						__UpperCAmelCase			:		CLIPTokenizer		,						__UpperCAmelCase			:		CLIPTextModelWithProjection		,						__UpperCAmelCase			:		UnCLIPScheduler		,						__UpperCAmelCase			:		Optional[dict] = None		,						)    ->				Any:
  							super().__init__()
  
  							UpperCAmelCase_=     {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  							UpperCAmelCase_=     decoder_pipe_kwargs.get("""torch_dtype"""		,						lowerCAmelCase_  ) or prior.dtype
  
  							UpperCAmelCase_=     StableDiffusionImageVariationPipeline.from_pretrained(
  							    """lambdalabs/sd-image-variations-diffusers"""		,						**lowerCAmelCase_  )
  
  							# replace `_encode_image` method
  							UpperCAmelCase_=     types.MethodType(_encode_image		,						self.decoder_pipe  )
  
  							self.register_modules(
  							    prior=lowerCAmelCase_		,						tokenizer=lowerCAmelCase_		,						text_encoder=lowerCAmelCase_		,						prior_scheduler=lowerCAmelCase_		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[Any]		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		List[Any]		,						__UpperCAmelCase			:		List[str]		,						__UpperCAmelCase			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Union[CLIPTextModelOutput, Tuple]] = None		,						__UpperCAmelCase			:		Optional[torch.Tensor] = None		,						)    ->				Tuple:
  
  
  
  
  
  
  							if text_model_output is None:
  													UpperCAmelCase_=     len(lowerCAmelCase_  ) if isinstance(lowerCAmelCase_		,						lowerCAmelCase_  ) else 1
  													# get prompt text embeddings
  													UpperCAmelCase_=     self.tokenizer(
  													    lowerCAmelCase_		,						padding="""max_length"""		,						max_length=self.tokenizer.model_max_length		,						return_tensors="""pt"""		,						)
  													UpperCAmelCase_=     text_inputs.input_ids
  													UpperCAmelCase_=     text_inputs.attention_mask.bool().to(lowerCAmelCase_  )
  
  													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  																			UpperCAmelCase_=     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}"""  )
  																			UpperCAmelCase_=     text_input_ids[:, : self.tokenizer.model_max_length]
  
  													UpperCAmelCase_=     self.text_encoder(text_input_ids.to(lowerCAmelCase_  )  )
  
  													UpperCAmelCase_=     text_encoder_output.text_embeds
  													UpperCAmelCase_=     text_encoder_output.last_hidden_state
  
  							else:
  													UpperCAmelCase_=     text_model_output[0].shape[0]
  													UpperCAmelCase_,     UpperCAmelCase_=     text_model_output[0], text_model_output[1]
  													UpperCAmelCase_=     text_attention_mask
  
  							UpperCAmelCase_=     text_embeddings.repeat_interleave(lowerCAmelCase_		,						dim=0  )
  							UpperCAmelCase_=     text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_		,						dim=0  )
  							UpperCAmelCase_=     text_mask.repeat_interleave(lowerCAmelCase_		,						dim=0  )
  
  							if do_classifier_free_guidance:
  													UpperCAmelCase_=     [""""""] * batch_size
  
  													UpperCAmelCase_=     self.tokenizer(
  													    lowerCAmelCase_		,						padding="""max_length"""		,						max_length=self.tokenizer.model_max_length		,						truncation=lowerCAmelCase_		,						return_tensors="""pt"""		,						)
  													UpperCAmelCase_=     uncond_input.attention_mask.bool().to(lowerCAmelCase_  )
  													UpperCAmelCase_=     self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase_  )  )
  
  													UpperCAmelCase_=     uncond_embeddings_text_encoder_output.text_embeds
  													UpperCAmelCase_=     uncond_embeddings_text_encoder_output.last_hidden_state
  
  													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  													UpperCAmelCase_=     uncond_embeddings.shape[1]
  													UpperCAmelCase_=     uncond_embeddings.repeat(1		,						lowerCAmelCase_  )
  													UpperCAmelCase_=     uncond_embeddings.view(batch_size * num_images_per_prompt		,						lowerCAmelCase_  )
  
  													UpperCAmelCase_=     uncond_text_encoder_hidden_states.shape[1]
  													UpperCAmelCase_=     uncond_text_encoder_hidden_states.repeat(1		,						lowerCAmelCase_		,						1  )
  													UpperCAmelCase_=     uncond_text_encoder_hidden_states.view(
  													    batch_size * num_images_per_prompt		,						lowerCAmelCase_		,						-1  )
  													UpperCAmelCase_=     uncond_text_mask.repeat_interleave(lowerCAmelCase_		,						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
  													UpperCAmelCase_=     torch.cat([uncond_embeddings, text_embeddings]  )
  													UpperCAmelCase_=     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )
  
  													UpperCAmelCase_=     torch.cat([uncond_text_mask, text_mask]  )
  
  							return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  	@property
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		Dict  )    ->				List[str]:
  
  
  
  
  
  
  							if self.device != torch.device("""meta"""  ) or not hasattr(self.prior		,						"""_hf_hook"""  ):
  													return self.device
  							for module in self.prior.modules():
  													if (
  													    hasattr(lowerCAmelCase_		,						"""_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 						_SCREAMING_SNAKE_CASE	(			self			:		Optional[int]		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		str		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Dict		,						__UpperCAmelCase			:		Tuple		,						__UpperCAmelCase			:		Any  )    ->				Dict:
  
  
  
  
  
  
  							if latents is None:
  													UpperCAmelCase_=     randn_tensor(lowerCAmelCase_		,						generator=lowerCAmelCase_		,						device=lowerCAmelCase_		,						dtype=lowerCAmelCase_  )
  							else:
  													if latents.shape != shape:
  																			raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}"""  )
  													UpperCAmelCase_=     latents.to(lowerCAmelCase_  )
  
  							UpperCAmelCase_=     latents * scheduler.init_noise_sigma
  							return latents
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		List[str]		,						__UpperCAmelCase			:		Optional[Union[str, torch.device]] = None  )    ->				Dict:
  							self.decoder_pipe.to(lowerCAmelCase_  )
  							super().to(lowerCAmelCase_  )
  
  
  
  
  	@torch.no_grad()
  	def __call__(			self			:		int		,						__UpperCAmelCase			:		Optional[Union[str, List[str]]] = None		,						__UpperCAmelCase			:		Optional[int] = None		,						__UpperCAmelCase			:		Optional[int] = None		,						__UpperCAmelCase			:		int = 1		,						__UpperCAmelCase			:		int = 25		,						__UpperCAmelCase			:		Optional[torch.Generator] = None		,						__UpperCAmelCase			:		Optional[torch.FloatTensor] = None		,						__UpperCAmelCase			:		Optional[Union[CLIPTextModelOutput, Tuple]] = None		,						__UpperCAmelCase			:		Optional[torch.Tensor] = None		,						__UpperCAmelCase			:		float = 4.0		,						__UpperCAmelCase			:		float = 8.0		,						__UpperCAmelCase			:		int = 50		,						__UpperCAmelCase			:		Optional[int] = 1		,						__UpperCAmelCase			:		float = 0.0		,						__UpperCAmelCase			:		Optional[str] = "pil"		,						__UpperCAmelCase			:		bool = True		,						)    ->				Tuple:
  
  
  
  
  
  
  							if prompt is not None:
  													if isinstance(lowerCAmelCase_		,						lowerCAmelCase_  ):
  																			UpperCAmelCase_=     1
  													elif isinstance(lowerCAmelCase_		,						lowerCAmelCase_  ):
  																			UpperCAmelCase_=     len(lowerCAmelCase_  )
  													else:
  																			raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_  )}"""  )
  							else:
  													UpperCAmelCase_=     text_model_output[0].shape[0]
  
  							UpperCAmelCase_=     self._execution_device
  
  							UpperCAmelCase_=     batch_size * num_images_per_prompt
  
  							UpperCAmelCase_=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  							UpperCAmelCase_,     UpperCAmelCase_,     UpperCAmelCase_=     self._encode_prompt(
  							    lowerCAmelCase_		,						lowerCAmelCase_		,						lowerCAmelCase_		,						lowerCAmelCase_		,						lowerCAmelCase_		,						lowerCAmelCase_  )
  
  							# prior
  
  							self.prior_scheduler.set_timesteps(lowerCAmelCase_		,						device=lowerCAmelCase_  )
  							UpperCAmelCase_=     self.prior_scheduler.timesteps
  
  							UpperCAmelCase_=     self.prior.config.embedding_dim
  
  							UpperCAmelCase_=     self.prepare_latents(
  							    (batch_size, embedding_dim)		,						text_embeddings.dtype		,						lowerCAmelCase_		,						lowerCAmelCase_		,						lowerCAmelCase_		,						self.prior_scheduler		,						)
  
  							for i, t in enumerate(self.progress_bar(lowerCAmelCase_  )  ):
  													# expand the latents if we are doing classifier free guidance
  													UpperCAmelCase_=     torch.cat([prior_latents] * 2  ) if do_classifier_free_guidance else prior_latents
  
  													UpperCAmelCase_=     self.prior(
  													    lowerCAmelCase_		,						timestep=lowerCAmelCase_		,						proj_embedding=lowerCAmelCase_		,						encoder_hidden_states=lowerCAmelCase_		,						attention_mask=lowerCAmelCase_		,						).predicted_image_embedding
  
  													if do_classifier_free_guidance:
  																			UpperCAmelCase_,     UpperCAmelCase_=     predicted_image_embedding.chunk(2  )
  																			UpperCAmelCase_=     predicted_image_embedding_uncond + prior_guidance_scale * (
  																			    predicted_image_embedding_text - predicted_image_embedding_uncond
  																			)
  
  													if i + 1 == prior_timesteps_tensor.shape[0]:
  																			UpperCAmelCase_=     None
  													else:
  																			UpperCAmelCase_=     prior_timesteps_tensor[i + 1]
  
  													UpperCAmelCase_=     self.prior_scheduler.step(
  													    lowerCAmelCase_		,						timestep=lowerCAmelCase_		,						sample=lowerCAmelCase_		,						generator=lowerCAmelCase_		,						prev_timestep=lowerCAmelCase_		,						).prev_sample
  
  							UpperCAmelCase_=     self.prior.post_process_latents(lowerCAmelCase_  )
  
  							UpperCAmelCase_=     prior_latents
  
  							UpperCAmelCase_=     self.decoder_pipe(
  							    image=lowerCAmelCase_		,						height=lowerCAmelCase_		,						width=lowerCAmelCase_		,						num_inference_steps=lowerCAmelCase_		,						guidance_scale=lowerCAmelCase_		,						generator=lowerCAmelCase_		,						output_type=lowerCAmelCase_		,						return_dict=lowerCAmelCase_		,						num_images_per_prompt=lowerCAmelCase_		,						eta=lowerCAmelCase_		,						)
  							return output
  
  
  
  
  
style_context: 
  
  
  
  from .prime_check import Test
  
  Test()
  
  
  
  
  