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
  
  
  snake_case				:	Tuple							   =						logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      self   :						Any   ,     _snake_case   :						List[str]   ,     _snake_case   :						Dict   ,     _snake_case   :						Union[str, Any]   ,     _snake_case   :						List[str]     )       ->	List[Any]:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	__magic_name__	:	Dict			     =  image.to(device=_snake_case     )
  	__magic_name__	:	Optional[int]			     =  image  # take image as image_embeddings
  	__magic_name__	:	str			     =  image_embeddings.unsqueeze(1     )
  
  	# duplicate image embeddings for each generation per prompt, using mps friendly method
  	__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Any			     =  image_embeddings.shape
  	__magic_name__	:	Optional[int]			     =  image_embeddings.repeat(1   ,     _snake_case   ,     1     )
  	__magic_name__	:	int			     =  image_embeddings.view(bs_embed * num_images_per_prompt   ,     _snake_case   ,     -1     )
  
  	if do_classifier_free_guidance:
  		__magic_name__	:	List[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
  		__magic_name__	:	Optional[Any]			     =  torch.cat([uncond_embeddings, image_embeddings]     )
  
  	return image_embeddings
  
  
  
  
  
  class    _snake_case      (  snake_case       ):
  
  
  
  
  
  
  
  			def __init__(       self		,     _a		,     _a		,     _a		,     _a		,     _a = None		,     ):
  				super().__init__()
  
  				__magic_name__	:	List[str]			     =  {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  				__magic_name__	:	int			     =  decoder_pipe_kwargs.get("torch_dtype"		,     _a	) or prior.dtype
  
  				__magic_name__	:	List[str]			     =  StableDiffusionImageVariationPipeline.from_pretrained(
  				    "lambdalabs/sd-image-variations-diffusers"		,     **_a	)
  
  				# replace `_encode_image` method
  				__magic_name__	:	List[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		,     ):
  				if text_model_output is None:
  					__magic_name__	:	Tuple			     =  len(_a	) if isinstance(_a		,     _a	) else 1
  					# get prompt text embeddings
  					__magic_name__	:	Tuple			     =  self.tokenizer(
  					    _a		,     padding="max_length"		,     max_length=self.tokenizer.model_max_length		,     return_tensors="pt"		,     )
  					__magic_name__	:	List[Any]			     =  text_inputs.input_ids
  					__magic_name__	:	List[str]			     =  text_inputs.attention_mask.bool().to(_a	)
  
  					if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  						__magic_name__	:	Dict			     =  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}'''	)
  						__magic_name__	:	str			     =  text_input_ids[:, : self.tokenizer.model_max_length]
  
  					__magic_name__	:	int			     =  self.text_encoder(text_input_ids.to(_a	)	)
  
  					__magic_name__	:	Dict			     =  text_encoder_output.text_embeds
  					__magic_name__	:	Tuple			     =  text_encoder_output.last_hidden_state
  
  				else:
  					__magic_name__	:	Tuple			     =  text_model_output[0].shape[0]
  					__magic_name__     ,					__magic_name__	:	int			     =  text_model_output[0], text_model_output[1]
  					__magic_name__	:	str			     =  text_attention_mask
  
  				__magic_name__	:	List[Any]			     =  text_embeddings.repeat_interleave(_a		,     dim=0	)
  				__magic_name__	:	str			     =  text_encoder_hidden_states.repeat_interleave(_a		,     dim=0	)
  				__magic_name__	:	str			     =  text_mask.repeat_interleave(_a		,     dim=0	)
  
  				if do_classifier_free_guidance:
  					__magic_name__	:	Union[str, Any]			     =  [""] * batch_size
  
  					__magic_name__	:	Tuple			     =  self.tokenizer(
  					    _a		,     padding="max_length"		,     max_length=self.tokenizer.model_max_length		,     truncation=_a		,     return_tensors="pt"		,     )
  					__magic_name__	:	Dict			     =  uncond_input.attention_mask.bool().to(_a	)
  					__magic_name__	:	Dict			     =  self.text_encoder(uncond_input.input_ids.to(_a	)	)
  
  					__magic_name__	:	Union[str, Any]			     =  uncond_embeddings_text_encoder_output.text_embeds
  					__magic_name__	:	List[str]			     =  uncond_embeddings_text_encoder_output.last_hidden_state
  
  					# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  					__magic_name__	:	str			     =  uncond_embeddings.shape[1]
  					__magic_name__	:	Dict			     =  uncond_embeddings.repeat(1		,     _a	)
  					__magic_name__	:	Any			     =  uncond_embeddings.view(batch_size * num_images_per_prompt		,     _a	)
  
  					__magic_name__	:	List[Any]			     =  uncond_text_encoder_hidden_states.shape[1]
  					__magic_name__	:	Optional[Any]			     =  uncond_text_encoder_hidden_states.repeat(1		,     _a		,     1	)
  					__magic_name__	:	int			     =  uncond_text_encoder_hidden_states.view(
  					    batch_size * num_images_per_prompt		,     _a		,     -1	)
  					__magic_name__	:	Union[str, 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
  					__magic_name__	:	List[str]			     =  torch.cat([uncond_embeddings, text_embeddings]	)
  					__magic_name__	:	Union[str, Any]			     =  torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)
  
  					__magic_name__	:	List[Any]			     =  torch.cat([uncond_text_mask, text_mask]	)
  
  				return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  
  
  
  
  
  			@property
  			def     SCREAMING_SNAKE_CASE					(       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(_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	):
  				if latents is None:
  					__magic_name__	:	Tuple			     =  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}'''	)
  					__magic_name__	:	Optional[int]			     =  latents.to(_a	)
  
  				__magic_name__	:	Dict			     =  latents * scheduler.init_noise_sigma
  				return latents
  
  
  
  
  
  
  
  			def     SCREAMING_SNAKE_CASE					(       self		,     _a = None	):
  				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		,     ):
  				if prompt is not None:
  					if isinstance(_a		,     _a	):
  						__magic_name__	:	int			     =  1
  					elif isinstance(_a		,     _a	):
  						__magic_name__	:	List[str]			     =  len(_a	)
  					else:
  						raise ValueError(f'''`prompt` has to be of type `str` or `list` but is {type(_a	)}'''	)
  				else:
  					__magic_name__	:	int			     =  text_model_output[0].shape[0]
  
  				__magic_name__	:	int			     =  self._execution_device
  
  				__magic_name__	:	List[str]			     =  batch_size * num_images_per_prompt
  
  				__magic_name__	:	Dict			     =  prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  				__magic_name__     ,					__magic_name__     ,					__magic_name__	:	Tuple			     =  self._encode_prompt(
  				    _a		,     _a		,     _a		,     _a		,     _a		,     _a	)
  
  				# prior
  
  				self.prior_scheduler.set_timesteps(_a		,     device=_a	)
  				__magic_name__	:	Tuple			     =  self.prior_scheduler.timesteps
  
  				__magic_name__	:	int			     =  self.prior.config.embedding_dim
  
  				__magic_name__	:	List[Any]			     =  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
  					__magic_name__	:	List[str]			     =  torch.cat([prior_latents] * 2	) if do_classifier_free_guidance else prior_latents
  
  					__magic_name__	:	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:
  						__magic_name__     ,					__magic_name__	:	List[Any]			     =  predicted_image_embedding.chunk(2	)
  						__magic_name__	:	str			     =  predicted_image_embedding_uncond + prior_guidance_scale * (
  						    predicted_image_embedding_text - predicted_image_embedding_uncond
  						)
  
  					if i + 1 == prior_timesteps_tensor.shape[0]:
  						__magic_name__	:	Union[str, Any]			     =  None
  					else:
  						__magic_name__	:	Optional[Any]			     =  prior_timesteps_tensor[i + 1]
  
  					__magic_name__	:	List[Any]			     =  self.prior_scheduler.step(
  					    _a		,     timestep=_a		,     sample=_a		,     generator=_a		,     prev_timestep=_a		,     ).prev_sample
  
  				__magic_name__	:	List[Any]			     =  self.prior.post_process_latents(_a	)
  
  				__magic_name__	:	Optional[int]			     =  prior_latents
  
  				__magic_name__	:	Optional[int]			     =  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: 
  from __future__ import annotations
  
  snake_case				:	List[str]							   =						"Alexander Joslin"
  
  
  
  
  
  
  def 						lowerCAmelCase_		(      _snake_case   :						int     )       ->	int:
  
  
  
  
  
  
  
  	'''simple docstring'''
  
  
  	if number <= 0:
  		__magic_name__	:	int			     =  F'''n must be greater than 0. Got n = {number}'''
  		raise ValueError(_snake_case     )
  
  	__magic_name__	:	List[str]			     =  [number + 1] * (number + 1)
  
  	# starting position
  	__magic_name__	:	Any			     =  0
  	for i in range(1   ,     _snake_case     ):
  		__magic_name__	:	Optional[int]			     =  min(table[i + 1]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 2 <= number:
  			__magic_name__	:	str			     =  min(table[i * 2]   ,     table[i] + 1     )
  		# check if out of bounds
  		if i * 3 <= number:
  			__magic_name__	:	Any			     =  min(table[i * 3]   ,     table[i] + 1     )
  	return table[number]
  
  
  if __name__ == "__main__":
  				import doctest
  
  				doctest.testmod()
  