code: 
  
  
  '''simple docstring'''
  
  
  
  
  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  :      Tuple      		=				logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  def 							a_       (						self							,						lowercase							,						lowercase							,						lowercase							,						lowercase    )			->Optional[int]:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				__lowerCamelCase		:      Any											=					image.to(device=lowercase    )
  				__lowerCamelCase		:      List[Any]											=					image  # take image as image_embeddings
  				__lowerCamelCase		:      Optional[int]											=					image_embeddings.unsqueeze(1    )
  
  				# duplicate image embeddings for each generation per prompt, using mps friendly method
  				__lowerCamelCase		:      Optional[Any]											=					image_embeddings.shape
  				__lowerCamelCase		:      int											=					image_embeddings.repeat(1							,						lowercase							,						1    )
  				__lowerCamelCase		:      int											=					image_embeddings.view(bs_embed * num_images_per_prompt							,						lowercase							,						-1    )
  
  				if do_classifier_free_guidance:
  								__lowerCamelCase		:      Optional[int]											=					torch.zeros_like(lowercase    )
  
  								# 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		:      Tuple											=					torch.cat([uncond_embeddings, image_embeddings]    )
  
  				return image_embeddings
  
  
  
  
  
  class _snake_case   ( a__   ):
  
  
  
  
  
  
  
  							def __init__(   self			,				__A			,				__A			,				__A			,				__A			,				__A = None			,				)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											super().__init__()
  
  											__lowerCamelCase		:      str											=					{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  											__lowerCamelCase		:      List[str]											=					decoder_pipe_kwargs.get("torch_dtype"			,				__A	) or prior.dtype
  
  											__lowerCamelCase		:      Optional[int]											=					StableDiffusionImageVariationPipeline.from_pretrained(
  											    "lambdalabs/sd-image-variations-diffusers"			,				**__A	)
  
  											# replace `_encode_image` method
  											__lowerCamelCase		:      Any											=					types.MethodType(_encode_image			,				self.decoder_pipe	)
  
  											self.register_modules(
  											    prior=__A			,				tokenizer=__A			,				text_encoder=__A			,				prior_scheduler=__A			,				)
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A			,				__A			,				__A			,				__A = None			,				__A = None			,				)     ->List[Any]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if text_model_output is None:
  															__lowerCamelCase		:      int											=					len(__A	) if isinstance(__A			,				__A	) else 1
  															# get prompt text embeddings
  															__lowerCamelCase		:      Any											=					self.tokenizer(
  															    __A			,				padding="max_length"			,				max_length=self.tokenizer.model_max_length			,				return_tensors="pt"			,				)
  															__lowerCamelCase		:      str											=					text_inputs.input_ids
  															__lowerCamelCase		:      List[str]											=					text_inputs.attention_mask.bool().to(__A	)
  
  															if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  																			__lowerCamelCase		:      List[Any]											=					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		:      Optional[Any]											=					text_input_ids[:, : self.tokenizer.model_max_length]
  
  															__lowerCamelCase		:      Optional[int]											=					self.text_encoder(text_input_ids.to(__A	)	)
  
  															__lowerCamelCase		:      List[str]											=					text_encoder_output.text_embeds
  															__lowerCamelCase		:      Tuple											=					text_encoder_output.last_hidden_state
  
  											else:
  															__lowerCamelCase		:      Optional[Any]											=					text_model_output[0].shape[0]
  															__lowerCamelCase		:      Any											=					text_model_output[0], text_model_output[1]
  															__lowerCamelCase		:      str											=					text_attention_mask
  
  											__lowerCamelCase		:      Dict											=					text_embeddings.repeat_interleave(__A			,				dim=0	)
  											__lowerCamelCase		:      Any											=					text_encoder_hidden_states.repeat_interleave(__A			,				dim=0	)
  											__lowerCamelCase		:      List[str]											=					text_mask.repeat_interleave(__A			,				dim=0	)
  
  											if do_classifier_free_guidance:
  															__lowerCamelCase		:      List[Any]											=					[""] * batch_size
  
  															__lowerCamelCase		:      Optional[Any]											=					self.tokenizer(
  															    __A			,				padding="max_length"			,				max_length=self.tokenizer.model_max_length			,				truncation=__A			,				return_tensors="pt"			,				)
  															__lowerCamelCase		:      Union[str, Any]											=					uncond_input.attention_mask.bool().to(__A	)
  															__lowerCamelCase		:      int											=					self.text_encoder(uncond_input.input_ids.to(__A	)	)
  
  															__lowerCamelCase		:      int											=					uncond_embeddings_text_encoder_output.text_embeds
  															__lowerCamelCase		:      Union[str, Any]											=					uncond_embeddings_text_encoder_output.last_hidden_state
  
  															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  															__lowerCamelCase		:      Optional[int]											=					uncond_embeddings.shape[1]
  															__lowerCamelCase		:      Union[str, Any]											=					uncond_embeddings.repeat(1			,				__A	)
  															__lowerCamelCase		:      Dict											=					uncond_embeddings.view(batch_size * num_images_per_prompt			,				__A	)
  
  															__lowerCamelCase		:      Optional[int]											=					uncond_text_encoder_hidden_states.shape[1]
  															__lowerCamelCase		:      Dict											=					uncond_text_encoder_hidden_states.repeat(1			,				__A			,				1	)
  															__lowerCamelCase		:      Dict											=					uncond_text_encoder_hidden_states.view(
  															    batch_size * num_images_per_prompt			,				__A			,				-1	)
  															__lowerCamelCase		:      Any											=					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		:      Optional[Any]											=					torch.cat([uncond_embeddings, text_embeddings]	)
  															__lowerCamelCase		:      List[str]											=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)
  
  															__lowerCamelCase		:      Optional[int]											=					torch.cat([uncond_text_mask, text_mask]	)
  
  											return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  
  
  
  							@property
  							def 		__SCREAMING_SNAKE_CASE(   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 		__SCREAMING_SNAKE_CASE(   self			,				__A			,				__A			,				__A			,				__A			,				__A			,				__A	)     ->List[str]:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if latents is None:
  															__lowerCamelCase		:      Optional[Any]											=					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		:      Dict											=					latents.to(__A	)
  
  											__lowerCamelCase		:      List[Any]											=					latents * scheduler.init_noise_sigma
  											return latents
  
  
  
  
  
  
  
  							def 		__SCREAMING_SNAKE_CASE(   self			,				__A = None	)     ->Tuple:
  
  
  
  											"""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			,				)     ->Any:
  
  
  
  											"""simple docstring"""
  
  
  
  
  
  
  
  											if prompt is not None:
  															if isinstance(__A			,				__A	):
  																			__lowerCamelCase		:      Tuple											=					1
  															elif isinstance(__A			,				__A	):
  																			__lowerCamelCase		:      int											=					len(__A	)
  															else:
  																			raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__A	)}'	)
  											else:
  															__lowerCamelCase		:      Union[str, Any]											=					text_model_output[0].shape[0]
  
  											__lowerCamelCase		:      Dict											=					self._execution_device
  
  											__lowerCamelCase		:      str											=					batch_size * num_images_per_prompt
  
  											__lowerCamelCase		:      Tuple											=					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  											__lowerCamelCase		:      Dict											=					self._encode_prompt(
  											    __A			,				__A			,				__A			,				__A			,				__A			,				__A	)
  
  											# prior
  
  											self.prior_scheduler.set_timesteps(__A			,				device=__A	)
  											__lowerCamelCase		:      List[str]											=					self.prior_scheduler.timesteps
  
  											__lowerCamelCase		:      Tuple											=					self.prior.config.embedding_dim
  
  											__lowerCamelCase		:      int											=					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		:      Dict											=					torch.cat([prior_latents] * 2	) if do_classifier_free_guidance else prior_latents
  
  															__lowerCamelCase		:      List[Any]											=					self.prior(
  															    __A			,				timestep=__A			,				proj_embedding=__A			,				encoder_hidden_states=__A			,				attention_mask=__A			,				).predicted_image_embedding
  
  															if do_classifier_free_guidance:
  																			__lowerCamelCase		:      List[Any]											=					predicted_image_embedding.chunk(2	)
  																			__lowerCamelCase		:      Union[str, Any]											=					predicted_image_embedding_uncond + prior_guidance_scale * (
  																			    predicted_image_embedding_text - predicted_image_embedding_uncond
  																			)
  
  															if i + 1 == prior_timesteps_tensor.shape[0]:
  																			__lowerCamelCase		:      Dict											=					None
  															else:
  																			__lowerCamelCase		:      Optional[int]											=					prior_timesteps_tensor[i + 1]
  
  															__lowerCamelCase		:      int											=					self.prior_scheduler.step(
  															    __A			,				timestep=__A			,				sample=__A			,				generator=__A			,				prev_timestep=__A			,				).prev_sample
  
  											__lowerCamelCase		:      List[Any]											=					self.prior.post_process_latents(__A	)
  
  											__lowerCamelCase		:      List[str]											=					prior_latents
  
  											__lowerCamelCase		:      Any											=					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: 
  
  
  '''simple docstring'''
  
  
  
  
  def 							a_       (						lowercase    )			->str:
  
  
  
  
  
  
  				'''simple docstring'''
  
  				return " ".join(sorted(set(sentence.split()    )    )    )
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  
  
  
  
  
  