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 							UpperCAmelCase				(     self				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase				,UpperCAmelCase      )->		Tuple:
  
  
  
  
  
  
  
  					'''simple docstring'''
  
  
  
  					SCREAMING_SNAKE_CASE_	      =       image.to(device=UpperCAmelCase      )
  					SCREAMING_SNAKE_CASE_	      =       image  # take image as image_embeddings
  					SCREAMING_SNAKE_CASE_	      =       image_embeddings.unsqueeze(1      )
  
  					# duplicate image embeddings for each generation per prompt, using mps friendly method
  					SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       image_embeddings.shape
  					SCREAMING_SNAKE_CASE_	      =       image_embeddings.repeat(1				,UpperCAmelCase				,1      )
  					SCREAMING_SNAKE_CASE_	      =       image_embeddings.view(bs_embed * num_images_per_prompt				,UpperCAmelCase				,-1      )
  
  					if do_classifier_free_guidance:
  										SCREAMING_SNAKE_CASE_	      =       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
  										SCREAMING_SNAKE_CASE_	      =       torch.cat([uncond_embeddings, image_embeddings]      )
  
  					return image_embeddings
  
  
  
  
  
  
  class 						snake_case			(       lowerCAmelCase__	):
  			'''simple docstring'''
  
  			def __init__(	self     :     Optional[Any]							,							lowerCAmelCase_     :     PriorTransformer							,							lowerCAmelCase_     :     CLIPTokenizer							,							lowerCAmelCase_     :     CLIPTextModelWithProjection							,							lowerCAmelCase_     :     UnCLIPScheduler							,							lowerCAmelCase_     :     Optional[dict] = None							,							)					->					Any:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__()
  
  								SCREAMING_SNAKE_CASE_	      =       {'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  								SCREAMING_SNAKE_CASE_	      =       decoder_pipe_kwargs.get('''torch_dtype'''							,							lowerCAmelCase_					) or prior.dtype
  
  								SCREAMING_SNAKE_CASE_	      =       StableDiffusionImageVariationPipeline.from_pretrained(
  								    '''lambdalabs/sd-image-variations-diffusers'''							,							**lowerCAmelCase_					)
  
  								# replace `_encode_image` method
  								SCREAMING_SNAKE_CASE_	      =       types.MethodType(_encode_image							,							self.decoder_pipe					)
  
  								self.register_modules(
  								    prior=lowerCAmelCase_							,							tokenizer=lowerCAmelCase_							,							text_encoder=lowerCAmelCase_							,							prior_scheduler=lowerCAmelCase_							,							)
  
  			def _lowercase (	self     :     List[Any]							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     List[Any]							,							lowerCAmelCase_     :     List[str]							,							lowerCAmelCase_     :     Union[str, Any]							,							lowerCAmelCase_     :     Optional[Union[CLIPTextModelOutput, Tuple]] = None							,							lowerCAmelCase_     :     Optional[torch.Tensor] = None							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if text_model_output is None:
  													SCREAMING_SNAKE_CASE_	      =       len(lowerCAmelCase_					) if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					) else 1
  													# get prompt text embeddings
  													SCREAMING_SNAKE_CASE_	      =       self.tokenizer(
  													    lowerCAmelCase_							,							padding='''max_length'''							,							max_length=self.tokenizer.model_max_length							,							return_tensors='''pt'''							,							)
  													SCREAMING_SNAKE_CASE_	      =       text_inputs.input_ids
  													SCREAMING_SNAKE_CASE_	      =       text_inputs.attention_mask.bool().to(lowerCAmelCase_					)
  
  													if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  																		SCREAMING_SNAKE_CASE_	      =       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}'''					)
  																		SCREAMING_SNAKE_CASE_	      =       text_input_ids[:, : self.tokenizer.model_max_length]
  
  													SCREAMING_SNAKE_CASE_	      =       self.text_encoder(text_input_ids.to(lowerCAmelCase_					)					)
  
  													SCREAMING_SNAKE_CASE_	      =       text_encoder_output.text_embeds
  													SCREAMING_SNAKE_CASE_	      =       text_encoder_output.last_hidden_state
  
  								else:
  													SCREAMING_SNAKE_CASE_	      =       text_model_output[0].shape[0]
  													SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       text_model_output[0], text_model_output[1]
  													SCREAMING_SNAKE_CASE_	      =       text_attention_mask
  
  								SCREAMING_SNAKE_CASE_	      =       text_embeddings.repeat_interleave(lowerCAmelCase_							,							dim=0					)
  								SCREAMING_SNAKE_CASE_	      =       text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_							,							dim=0					)
  								SCREAMING_SNAKE_CASE_	      =       text_mask.repeat_interleave(lowerCAmelCase_							,							dim=0					)
  
  								if do_classifier_free_guidance:
  													SCREAMING_SNAKE_CASE_	      =       [''''''] * batch_size
  
  													SCREAMING_SNAKE_CASE_	      =       self.tokenizer(
  													    lowerCAmelCase_							,							padding='''max_length'''							,							max_length=self.tokenizer.model_max_length							,							truncation=lowerCAmelCase_							,							return_tensors='''pt'''							,							)
  													SCREAMING_SNAKE_CASE_	      =       uncond_input.attention_mask.bool().to(lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase_					)					)
  
  													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings_text_encoder_output.text_embeds
  													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings_text_encoder_output.last_hidden_state
  
  													# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings.shape[1]
  													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings.repeat(1							,							lowerCAmelCase_					)
  													SCREAMING_SNAKE_CASE_	      =       uncond_embeddings.view(batch_size * num_images_per_prompt							,							lowerCAmelCase_					)
  
  													SCREAMING_SNAKE_CASE_	      =       uncond_text_encoder_hidden_states.shape[1]
  													SCREAMING_SNAKE_CASE_	      =       uncond_text_encoder_hidden_states.repeat(1							,							lowerCAmelCase_							,							1					)
  													SCREAMING_SNAKE_CASE_	      =       uncond_text_encoder_hidden_states.view(
  													    batch_size * num_images_per_prompt							,							lowerCAmelCase_							,							-1					)
  													SCREAMING_SNAKE_CASE_	      =       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
  													SCREAMING_SNAKE_CASE_	      =       torch.cat([uncond_embeddings, text_embeddings]					)
  													SCREAMING_SNAKE_CASE_	      =       torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]					)
  
  													SCREAMING_SNAKE_CASE_	      =       torch.cat([uncond_text_mask, text_mask]					)
  
  								return text_embeddings, text_encoder_hidden_states, text_mask
  
  			@property
  			def _lowercase (	self     :     Dict					)					->					List[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(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 _lowercase (	self     :     Optional[int]							,							lowerCAmelCase_     :     Tuple							,							lowerCAmelCase_     :     str							,							lowerCAmelCase_     :     Tuple							,							lowerCAmelCase_     :     Dict							,							lowerCAmelCase_     :     Tuple							,							lowerCAmelCase_     :     Any					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if latents is None:
  													SCREAMING_SNAKE_CASE_	      =       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}'''					)
  													SCREAMING_SNAKE_CASE_	      =       latents.to(lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       latents * scheduler.init_noise_sigma
  								return latents
  
  			def _lowercase (	self     :     List[str]							,							lowerCAmelCase_     :     Optional[Union[str, torch.device]] = None					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								self.decoder_pipe.to(lowerCAmelCase_					)
  								super().to(lowerCAmelCase_					)
  
  
  
  
  			@torch.no_grad()
  			def __call__(	self     :     int							,							lowerCAmelCase_     :     Optional[Union[str, List[str]]] = None							,							lowerCAmelCase_     :     Optional[int] = None							,							lowerCAmelCase_     :     Optional[int] = None							,							lowerCAmelCase_     :     int = 1							,							lowerCAmelCase_     :     int = 25							,							lowerCAmelCase_     :     Optional[torch.Generator] = None							,							lowerCAmelCase_     :     Optional[torch.FloatTensor] = None							,							lowerCAmelCase_     :     Optional[Union[CLIPTextModelOutput, Tuple]] = None							,							lowerCAmelCase_     :     Optional[torch.Tensor] = None							,							lowerCAmelCase_     :     float = 4.0							,							lowerCAmelCase_     :     float = 8.0							,							lowerCAmelCase_     :     int = 50							,							lowerCAmelCase_     :     Optional[int] = 1							,							lowerCAmelCase_     :     float = 0.0							,							lowerCAmelCase_     :     Optional[str] = "pil"							,							lowerCAmelCase_     :     bool = True							,							)					->					Tuple:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								if prompt is not None:
  													if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       1
  													elif isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  																		SCREAMING_SNAKE_CASE_	      =       len(lowerCAmelCase_					)
  													else:
  																		raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_					)}'''					)
  								else:
  													SCREAMING_SNAKE_CASE_	      =       text_model_output[0].shape[0]
  
  								SCREAMING_SNAKE_CASE_	      =       self._execution_device
  
  								SCREAMING_SNAKE_CASE_	      =       batch_size * num_images_per_prompt
  
  								SCREAMING_SNAKE_CASE_	      =       prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  								SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       self._encode_prompt(
  								    lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_							,							lowerCAmelCase_					)
  
  								# prior
  
  								self.prior_scheduler.set_timesteps(lowerCAmelCase_							,							device=lowerCAmelCase_					)
  								SCREAMING_SNAKE_CASE_	      =       self.prior_scheduler.timesteps
  
  								SCREAMING_SNAKE_CASE_	      =       self.prior.config.embedding_dim
  
  								SCREAMING_SNAKE_CASE_	      =       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
  													SCREAMING_SNAKE_CASE_	      =       torch.cat([prior_latents] * 2					) if do_classifier_free_guidance else prior_latents
  
  													SCREAMING_SNAKE_CASE_	      =       self.prior(
  													    lowerCAmelCase_							,							timestep=lowerCAmelCase_							,							proj_embedding=lowerCAmelCase_							,							encoder_hidden_states=lowerCAmelCase_							,							attention_mask=lowerCAmelCase_							,							).predicted_image_embedding
  
  													if do_classifier_free_guidance:
  																		SCREAMING_SNAKE_CASE_      ,SCREAMING_SNAKE_CASE_	      =       predicted_image_embedding.chunk(2					)
  																		SCREAMING_SNAKE_CASE_	      =       predicted_image_embedding_uncond + prior_guidance_scale * (
  																		    predicted_image_embedding_text - predicted_image_embedding_uncond
  																		)
  
  													if i + 1 == prior_timesteps_tensor.shape[0]:
  																		SCREAMING_SNAKE_CASE_	      =       None
  													else:
  																		SCREAMING_SNAKE_CASE_	      =       prior_timesteps_tensor[i + 1]
  
  													SCREAMING_SNAKE_CASE_	      =       self.prior_scheduler.step(
  													    lowerCAmelCase_							,							timestep=lowerCAmelCase_							,							sample=lowerCAmelCase_							,							generator=lowerCAmelCase_							,							prev_timestep=lowerCAmelCase_							,							).prev_sample
  
  								SCREAMING_SNAKE_CASE_	      =       self.prior.post_process_latents(lowerCAmelCase_					)
  
  								SCREAMING_SNAKE_CASE_	      =       prior_latents
  
  								SCREAMING_SNAKE_CASE_	      =       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: 
  
  
  import unittest
  
  from transformers import load_tool
  from transformers.tools.agent_types import AGENT_TYPE_MAPPING
  
  from .test_tools_common import ToolTesterMixin, output_types
  
  class 						snake_case			(       unittest.TestCase	, lowerCAmelCase__	):
  			'''simple docstring'''
  
  			def _lowercase (	self     :     List[Any]					)					->					Optional[Any]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       load_tool('''translation'''					)
  								self.tool.setup()
  								SCREAMING_SNAKE_CASE_	      =       load_tool('''translation'''							,							remote=lowerCAmelCase_					)
  
  			def _lowercase (	self     :     Tuple					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.tool('''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     Optional[Any]					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.remote_tool('''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					str:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.tool(text='''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     str					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       self.remote_tool(text='''Hey, what\'s up?'''							,							src_lang='''English'''							,							tgt_lang='''French'''					)
  								self.assertEqual(lowerCAmelCase_							,							'''- Hé, comment ça va?'''					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					Dict:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
  								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)
  
  								# There is a single output
  								if len(self.tool.outputs					) == 1:
  													SCREAMING_SNAKE_CASE_	      =       [outputs]
  
  								self.assertListEqual(output_types(lowerCAmelCase_					)							,							self.tool.outputs					)
  
  			def _lowercase (	self     :     Optional[int]					)					->					List[str]:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
  								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)
  
  								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       [outputs]
  
  								self.assertEqual(len(lowerCAmelCase_					)							,							len(self.tool.outputs					)					)
  
  								for output, output_type in zip(lowerCAmelCase_							,							self.tool.outputs					):
  													SCREAMING_SNAKE_CASE_	      =       AGENT_TYPE_MAPPING[output_type]
  													self.assertTrue(isinstance(lowerCAmelCase_							,							lowerCAmelCase_					)					)
  
  
  
  
  			def _lowercase (	self     :     Tuple					)					->					int:
  
  
  
  
  								"""simple docstring"""
  
  
  
  
  
  
  								SCREAMING_SNAKE_CASE_	      =       ['''Hey, what\'s up?''', '''English''', '''Spanish''']
  
  								SCREAMING_SNAKE_CASE_	      =       []
  
  								for _input, input_type in zip(lowerCAmelCase_							,							self.tool.inputs					):
  													if isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  																		_inputs.append([AGENT_TYPE_MAPPING[_input_type](_input					) for _input_type in input_type]					)
  													else:
  																		_inputs.append(AGENT_TYPE_MAPPING[input_type](_input					)					)
  
          # Should not raise an error
  								SCREAMING_SNAKE_CASE_	      =       self.tool(*lowerCAmelCase_					)
  
  								if not isinstance(lowerCAmelCase_							,							lowerCAmelCase_					):
  													SCREAMING_SNAKE_CASE_	      =       [outputs]
  
  								self.assertEqual(len(lowerCAmelCase_					)							,							len(self.tool.outputs					)					)
  
  
  