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
  
  
  UpperCAmelCase      :						Dict    =		logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  def   __lowerCamelCase	(       self		:	int     ,       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Optional[int]     ,       lowerCamelCase__		:	Tuple     ,       lowerCamelCase__		:	Any			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			lowerCamelCase								=     image.to(device=lowerCamelCase__			)
  			lowerCamelCase								=     image  # take image as image_embeddings
  			lowerCamelCase								=     image_embeddings.unsqueeze(1			)
  
  			# duplicate image embeddings for each generation per prompt, using mps friendly method
  			lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     image_embeddings.shape
  			lowerCamelCase								=     image_embeddings.repeat(1     ,       lowerCamelCase__     ,       1			)
  			lowerCamelCase								=     image_embeddings.view(bs_embed * num_images_per_prompt     ,       lowerCamelCase__     ,       -1			)
  
  			if do_classifier_free_guidance:
  						lowerCamelCase								=     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
  						lowerCamelCase								=     torch.cat([uncond_embeddings, image_embeddings]			)
  
  			return image_embeddings
  
  
  
  
  
  
  
  class 					__lowercase					(       a_     ):
  
  
  
  		"""simple docstring"""
  
  		def __init__(   self		,			A		,			A		,			A		,			A		,			A = None		,			)     ->   Optional[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					super().__init__()
  
  					lowerCamelCase								=     {"""image_encoder""": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  					lowerCamelCase								=     decoder_pipe_kwargs.get("""torch_dtype"""		,			A  ) or prior.dtype
  
  					lowerCamelCase								=     StableDiffusionImageVariationPipeline.from_pretrained(
  					    """lambdalabs/sd-image-variations-diffusers"""		,			**A  )
  
  					# replace `_encode_image` method
  					lowerCamelCase								=     types.MethodType(_encode_image		,			self.decoder_pipe  )
  
  					self.register_modules(
  					    prior=A		,			tokenizer=A		,			text_encoder=A		,			prior_scheduler=A		,			)
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A		,			A		,			A		,			A		,			A = None		,			A = None		,			)     ->   Union[str, Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if text_model_output is None:
  								lowerCamelCase								=     len(A  ) if isinstance(A		,			A  ) else 1
  								# get prompt text embeddings
  								lowerCamelCase								=     self.tokenizer(
  								    A		,			padding="""max_length"""		,			max_length=self.tokenizer.model_max_length		,			return_tensors="""pt"""		,			)
  								lowerCamelCase								=     text_inputs.input_ids
  								lowerCamelCase								=     text_inputs.attention_mask.bool().to(A  )
  
  								if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  											lowerCamelCase								=     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}'  )
  											lowerCamelCase								=     text_input_ids[:, : self.tokenizer.model_max_length]
  
  								lowerCamelCase								=     self.text_encoder(text_input_ids.to(A  )  )
  
  								lowerCamelCase								=     text_encoder_output.text_embeds
  								lowerCamelCase								=     text_encoder_output.last_hidden_state
  
  					else:
  								lowerCamelCase								=     text_model_output[0].shape[0]
  								lowerCamelCase		,		lowerCamelCase								=     text_model_output[0], text_model_output[1]
  								lowerCamelCase								=     text_attention_mask
  
  					lowerCamelCase								=     text_embeddings.repeat_interleave(A		,			dim=0  )
  					lowerCamelCase								=     text_encoder_hidden_states.repeat_interleave(A		,			dim=0  )
  					lowerCamelCase								=     text_mask.repeat_interleave(A		,			dim=0  )
  
  					if do_classifier_free_guidance:
  								lowerCamelCase								=     [""""""] * batch_size
  
  								lowerCamelCase								=     self.tokenizer(
  								    A		,			padding="""max_length"""		,			max_length=self.tokenizer.model_max_length		,			truncation=A		,			return_tensors="""pt"""		,			)
  								lowerCamelCase								=     uncond_input.attention_mask.bool().to(A  )
  								lowerCamelCase								=     self.text_encoder(uncond_input.input_ids.to(A  )  )
  
  								lowerCamelCase								=     uncond_embeddings_text_encoder_output.text_embeds
  								lowerCamelCase								=     uncond_embeddings_text_encoder_output.last_hidden_state
  
  								# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  								lowerCamelCase								=     uncond_embeddings.shape[1]
  								lowerCamelCase								=     uncond_embeddings.repeat(1		,			A  )
  								lowerCamelCase								=     uncond_embeddings.view(batch_size * num_images_per_prompt		,			A  )
  
  								lowerCamelCase								=     uncond_text_encoder_hidden_states.shape[1]
  								lowerCamelCase								=     uncond_text_encoder_hidden_states.repeat(1		,			A		,			1  )
  								lowerCamelCase								=     uncond_text_encoder_hidden_states.view(
  								    batch_size * num_images_per_prompt		,			A		,			-1  )
  								lowerCamelCase								=     uncond_text_mask.repeat_interleave(A		,			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
  								lowerCamelCase								=     torch.cat([uncond_embeddings, text_embeddings]  )
  								lowerCamelCase								=     torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]  )
  
  								lowerCamelCase								=     torch.cat([uncond_text_mask, text_mask]  )
  
  					return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  
  
  
  		@property
  		def 				__A      (   self  )     ->   str:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if self.device != torch.device("""meta"""  ) or not hasattr(self.prior		,			"""_hf_hook"""  ):
  								return self.device
  					for module in self.prior.modules():
  								if (
  								    hasattr(A		,			"""_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 				__A      (   self		,			A		,			A		,			A		,			A		,			A		,			A  )     ->   Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if latents is None:
  								lowerCamelCase								=     randn_tensor(A		,			generator=A		,			device=A		,			dtype=A  )
  					else:
  								if latents.shape != shape:
  											raise ValueError(F'Unexpected latents shape, got {latents.shape}, expected {shape}'  )
  								lowerCamelCase								=     latents.to(A  )
  
  					lowerCamelCase								=     latents * scheduler.init_noise_sigma
  					return latents
  
  
  
  
  
  
  
  		def 				__A      (   self		,			A = None  )     ->   Dict:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					self.decoder_pipe.to(A  )
  					super().to(A  )
  
  
  
  
  
  
  
  		@torch.no_grad()
  		def __call__(   self		,			A = None		,			A = None		,			A = None		,			A = 1		,			A = 25		,			A = None		,			A = None		,			A = None		,			A = None		,			A = 4.0		,			A = 8.0		,			A = 50		,			A = 1		,			A = 0.0		,			A = "pil"		,			A = True		,			)     ->   List[Any]:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  
  					if prompt is not None:
  								if isinstance(A		,			A  ):
  											lowerCamelCase								=     1
  								elif isinstance(A		,			A  ):
  											lowerCamelCase								=     len(A  )
  								else:
  											raise ValueError(F'`prompt` has to be of type `str` or `list` but is {type(A  )}'  )
  					else:
  								lowerCamelCase								=     text_model_output[0].shape[0]
  
  					lowerCamelCase								=     self._execution_device
  
  					lowerCamelCase								=     batch_size * num_images_per_prompt
  
  					lowerCamelCase								=     prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  					lowerCamelCase		,		lowerCamelCase		,		lowerCamelCase								=     self._encode_prompt(
  					    A		,			A		,			A		,			A		,			A		,			A  )
  
  					# prior
  
  					self.prior_scheduler.set_timesteps(A		,			device=A  )
  					lowerCamelCase								=     self.prior_scheduler.timesteps
  
  					lowerCamelCase								=     self.prior.config.embedding_dim
  
  					lowerCamelCase								=     self.prepare_latents(
  					    (batch_size, embedding_dim)		,			text_embeddings.dtype		,			A		,			A		,			A		,			self.prior_scheduler		,			)
  
  					for i, t in enumerate(self.progress_bar(A  )  ):
  								# expand the latents if we are doing classifier free guidance
  								lowerCamelCase								=     torch.cat([prior_latents] * 2  ) if do_classifier_free_guidance else prior_latents
  
  								lowerCamelCase								=     self.prior(
  								    A		,			timestep=A		,			proj_embedding=A		,			encoder_hidden_states=A		,			attention_mask=A		,			).predicted_image_embedding
  
  								if do_classifier_free_guidance:
  											lowerCamelCase		,		lowerCamelCase								=     predicted_image_embedding.chunk(2  )
  											lowerCamelCase								=     predicted_image_embedding_uncond + prior_guidance_scale * (
  											    predicted_image_embedding_text - predicted_image_embedding_uncond
  											)
  
  								if i + 1 == prior_timesteps_tensor.shape[0]:
  											lowerCamelCase								=     None
  								else:
  											lowerCamelCase								=     prior_timesteps_tensor[i + 1]
  
  								lowerCamelCase								=     self.prior_scheduler.step(
  								    A		,			timestep=A		,			sample=A		,			generator=A		,			prev_timestep=A		,			).prev_sample
  
  					lowerCamelCase								=     self.prior.post_process_latents(A  )
  
  					lowerCamelCase								=     prior_latents
  
  					lowerCamelCase								=     self.decoder_pipe(
  					    image=A		,			height=A		,			width=A		,			num_inference_steps=A		,			guidance_scale=A		,			generator=A		,			output_type=A		,			return_dict=A		,			num_images_per_prompt=A		,			eta=A		,			)
  					return output
  
  
style_context: 
  
  
  def   __lowerCamelCase	(       lowerCamelCase__		:	int = 100     ,       lowerCamelCase__		:	int = 100			):
  
  
  
  
  
  			'''simple docstring'''
  
  
  
  
  			return max(
  			    sum(int(lowerCamelCase__			) for x in str(base**power			)			)
  			    for base in range(lowerCamelCase__			)
  			    for power in range(lowerCamelCase__			)			)
  
  
  # Tests
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  
  