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 			__lowercase	(						self,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase,    _UpperCamelCase     )    ->int:
  
  
  
  
  
  
  
  						"""simple docstring"""
  
  						lowercase			:   Any					   =		image.to(device=__snake_case     )
  						lowercase			:   int					   =		image  # take image as image_embeddings
  						lowercase			:   List[Any]					   =		image_embeddings.unsqueeze(1     )
  
  						# duplicate image embeddings for each generation per prompt, using mps friendly method
  						lowercase   ,			lowercase   ,			lowercase			:   str					   =		image_embeddings.shape
  						lowercase			:   List[str]					   =		image_embeddings.repeat(1,    __snake_case,    1     )
  						lowercase			:   Dict					   =		image_embeddings.view(bs_embed * num_images_per_prompt,    __snake_case,    -1     )
  
  						if do_classifier_free_guidance:
  												lowercase			:   str					   =		torch.zeros_like(__snake_case     )
  
  												# 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
  												lowercase			:   Optional[int]					   =		torch.cat([uncond_embeddings, image_embeddings]     )
  
  						return image_embeddings
  
  
  
  
  
  
  
  class 				__SCREAMING_SNAKE_CASE   (			A__   ):
  
  
  						def __init__(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						):
  												super().__init__()
  
  												lowercase			:   Any					   =		{'''image_encoder''': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  												lowercase			:   Union[str, Any]					   =		decoder_pipe_kwargs.get('''torch_dtype'''							,						SCREAMING_SNAKE_CASE__		) or prior.dtype
  
  												lowercase			:   Any					   =		StableDiffusionImageVariationPipeline.from_pretrained(
  												    '''lambdalabs/sd-image-variations-diffusers'''							,						**SCREAMING_SNAKE_CASE__		)
  
  												# replace `_encode_image` method
  												lowercase			:   Dict					   =		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        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						):
  
  
  
  
  
  
  
  												if text_model_output is None:
  																		lowercase			:   Union[str, Any]					   =		len(SCREAMING_SNAKE_CASE__		) if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		) else 1
  																		# get prompt text embeddings
  																		lowercase			:   Tuple					   =		self.tokenizer(
  																		    SCREAMING_SNAKE_CASE__							,						padding='''max_length'''							,						max_length=self.tokenizer.model_max_length							,						return_tensors='''pt'''							,						)
  																		lowercase			:   Any					   =		text_inputs.input_ids
  																		lowercase			:   List[str]					   =		text_inputs.attention_mask.bool().to(SCREAMING_SNAKE_CASE__		)
  
  																		if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  																								lowercase			:   Tuple					   =		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}"""		)
  																								lowercase			:   Any					   =		text_input_ids[:, : self.tokenizer.model_max_length]
  
  																		lowercase			:   Dict					   =		self.text_encoder(text_input_ids.to(SCREAMING_SNAKE_CASE__		)		)
  
  																		lowercase			:   Optional[int]					   =		text_encoder_output.text_embeds
  																		lowercase			:   Optional[int]					   =		text_encoder_output.last_hidden_state
  
  												else:
  																		lowercase			:   Optional[int]					   =		text_model_output[0].shape[0]
  																		lowercase   ,			lowercase			:   Any					   =		text_model_output[0], text_model_output[1]
  																		lowercase			:   Optional[Any]					   =		text_attention_mask
  
  												lowercase			:   Optional[int]					   =		text_embeddings.repeat_interleave(SCREAMING_SNAKE_CASE__							,						dim=0		)
  												lowercase			:   Tuple					   =		text_encoder_hidden_states.repeat_interleave(SCREAMING_SNAKE_CASE__							,						dim=0		)
  												lowercase			:   Any					   =		text_mask.repeat_interleave(SCREAMING_SNAKE_CASE__							,						dim=0		)
  
  												if do_classifier_free_guidance:
  																		lowercase			:   Optional[Any]					   =		[''''''] * batch_size
  
  																		lowercase			:   int					   =		self.tokenizer(
  																		    SCREAMING_SNAKE_CASE__							,						padding='''max_length'''							,						max_length=self.tokenizer.model_max_length							,						truncation=SCREAMING_SNAKE_CASE__							,						return_tensors='''pt'''							,						)
  																		lowercase			:   Union[str, Any]					   =		uncond_input.attention_mask.bool().to(SCREAMING_SNAKE_CASE__		)
  																		lowercase			:   Dict					   =		self.text_encoder(uncond_input.input_ids.to(SCREAMING_SNAKE_CASE__		)		)
  
  																		lowercase			:   Optional[Any]					   =		uncond_embeddings_text_encoder_output.text_embeds
  																		lowercase			:   Optional[Any]					   =		uncond_embeddings_text_encoder_output.last_hidden_state
  
  																		# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  																		lowercase			:   Optional[int]					   =		uncond_embeddings.shape[1]
  																		lowercase			:   str					   =		uncond_embeddings.repeat(1							,						SCREAMING_SNAKE_CASE__		)
  																		lowercase			:   Union[str, Any]					   =		uncond_embeddings.view(batch_size * num_images_per_prompt							,						SCREAMING_SNAKE_CASE__		)
  
  																		lowercase			:   List[Any]					   =		uncond_text_encoder_hidden_states.shape[1]
  																		lowercase			:   Tuple					   =		uncond_text_encoder_hidden_states.repeat(1							,						SCREAMING_SNAKE_CASE__							,						1		)
  																		lowercase			:   str					   =		uncond_text_encoder_hidden_states.view(
  																		    batch_size * num_images_per_prompt							,						SCREAMING_SNAKE_CASE__							,						-1		)
  																		lowercase			:   Union[str, Any]					   =		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
  																		lowercase			:   List[Any]					   =		torch.cat([uncond_embeddings, text_embeddings]		)
  																		lowercase			:   Tuple					   =		torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]		)
  
  																		lowercase			:   Optional[Any]					   =		torch.cat([uncond_text_mask, text_mask]		)
  
  												return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  						@property
  						def        __lowerCamelCase			(							self		):
  
  
  
  
  
  
  
  												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        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
  
  
  
  
  
  
  
  												if latents is None:
  																		lowercase			:   str					   =		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}"""		)
  																		lowercase			:   Tuple					   =		latents.to(SCREAMING_SNAKE_CASE__		)
  
  												lowercase			:   List[str]					   =		latents * scheduler.init_noise_sigma
  												return latents
  
  
  						def        __lowerCamelCase			(							self							,						SCREAMING_SNAKE_CASE__ = None		):
  												self.decoder_pipe.to(SCREAMING_SNAKE_CASE__		)
  												super().to(SCREAMING_SNAKE_CASE__		)
  
  
  
  
  
  
  						@torch.no_grad()
  						def __call__(							self							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = 1							,						SCREAMING_SNAKE_CASE__ = 25							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = None							,						SCREAMING_SNAKE_CASE__ = 4.0							,						SCREAMING_SNAKE_CASE__ = 8.0							,						SCREAMING_SNAKE_CASE__ = 50							,						SCREAMING_SNAKE_CASE__ = 1							,						SCREAMING_SNAKE_CASE__ = 0.0							,						SCREAMING_SNAKE_CASE__ = "pil"							,						SCREAMING_SNAKE_CASE__ = True							,						):
  
  
  
  
  
  
  
  												if prompt is not None:
  																		if isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
  																								lowercase			:   Tuple					   =		1
  																		elif isinstance(SCREAMING_SNAKE_CASE__							,						SCREAMING_SNAKE_CASE__		):
  																								lowercase			:   Any					   =		len(SCREAMING_SNAKE_CASE__		)
  																		else:
  																								raise ValueError(f"""`prompt` has to be of type `str` or `list` but is {type(SCREAMING_SNAKE_CASE__		)}"""		)
  												else:
  																		lowercase			:   List[Any]					   =		text_model_output[0].shape[0]
  
  												lowercase			:   Optional[Any]					   =		self._execution_device
  
  												lowercase			:   Optional[int]					   =		batch_size * num_images_per_prompt
  
  												lowercase			:   List[Any]					   =		prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  												lowercase   ,			lowercase   ,			lowercase			:   Dict					   =		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__		)
  												lowercase			:   Union[str, Any]					   =		self.prior_scheduler.timesteps
  
  												lowercase			:   str					   =		self.prior.config.embedding_dim
  
  												lowercase			:   Optional[int]					   =		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
  																		lowercase			:   List[str]					   =		torch.cat([prior_latents] * 2		) if do_classifier_free_guidance else prior_latents
  
  																		lowercase			:   int					   =		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:
  																								lowercase   ,			lowercase			:   List[str]					   =		predicted_image_embedding.chunk(2		)
  																								lowercase			:   List[Any]					   =		predicted_image_embedding_uncond + prior_guidance_scale * (
  																								    predicted_image_embedding_text - predicted_image_embedding_uncond
  																								)
  
  																		if i + 1 == prior_timesteps_tensor.shape[0]:
  																								lowercase			:   Union[str, Any]					   =		None
  																		else:
  																								lowercase			:   List[str]					   =		prior_timesteps_tensor[i + 1]
  
  																		lowercase			:   Dict					   =		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
  
  												lowercase			:   Tuple					   =		self.prior.post_process_latents(SCREAMING_SNAKE_CASE__		)
  
  												lowercase			:   Optional[Any]					   =		prior_latents
  
  												lowercase			:   List[Any]					   =		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: 
  
  
  import numpy as np
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->Dict:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[Any]					   =		abs(predict - actual     )
        lowercase			:   int					   =		difference.mean()
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Tuple					   =		np.array(_UpperCamelCase     )
        lowercase			:   str					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   List[str]					   =		predict - actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
  
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->str:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   Union[str, Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Tuple					   =		predict - actual
        lowercase			:   Dict					   =		np.square(_UpperCamelCase     )
        lowercase			:   Union[str, Any]					   =		square_diff.mean()
        lowercase			:   Union[str, Any]					   =		np.sqrt(_UpperCamelCase     )
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[str]					   =		np.array(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Dict					   =		np.log(predict + 1     )
        lowercase			:   List[str]					   =		np.log(actual + 1     )
  
        lowercase			:   int					   =		log_predict - log_actual
        lowercase			:   Tuple					   =		np.square(_UpperCamelCase     )
        lowercase			:   List[Any]					   =		square_diff.mean()
  
        lowercase			:   int					   =		np.sqrt(_UpperCamelCase     )
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[str]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        lowercase			:   List[Any]					   =		np.array(_UpperCamelCase     )
        lowercase			:   Optional[int]					   =		np.array(_UpperCamelCase     )
  
        lowercase			:   Optional[int]					   =		predict - actual
        lowercase			:   Optional[int]					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        lowercase			:   Dict					   =		np.sum(_UpperCamelCase     ) / len(_UpperCamelCase     )
        # print(numerator, denumerator)
        lowercase			:   int					   =		float(_UpperCamelCase     ) / denumerator * 100
  
        return score
  
  
  
  
  def 			__lowercase	(						_UpperCamelCase,    _UpperCamelCase     )    ->List[Any]:
  
  
  
  
  
  
  
        """simple docstring"""
  
        return np.mean(np.array(_UpperCamelCase     ) == np.array(_UpperCamelCase     )     )
  
  