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
  
  
  __A  :					Dict												=	logging.get_logger(__name__)  # pylint: disable=invalid-name
  def      __SCREAMING_SNAKE_CASE		(		self    ,							lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      image.to(device=lowercase__   )
  					A    =      image  # take image as image_embeddings
  					A    =      image_embeddings.unsqueeze(1   )
  
  					# duplicate image embeddings for each generation per prompt, using mps friendly method
  					A    ,					A    ,					A    =      image_embeddings.shape
  					A    =      image_embeddings.repeat(1    ,							lowercase__    ,							1   )
  					A    =      image_embeddings.view(bs_embed * num_images_per_prompt    ,							lowercase__    ,							-1   )
  
  					if do_classifier_free_guidance:
  										A    =      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
  										A    =      torch.cat([uncond_embeddings, image_embeddings]   )
  
  					return image_embeddings
  
  
  
  
  
  class     __UpperCamelCase				(     _A		):
  
  
  		def __init__(self				:	str  ,  __SCREAMING_SNAKE_CASE				:	PriorTransformer  ,  __SCREAMING_SNAKE_CASE				:	CLIPTokenizer  ,  __SCREAMING_SNAKE_CASE				:	CLIPTextModelWithProjection  ,  __SCREAMING_SNAKE_CASE				:	UnCLIPScheduler  ,  __SCREAMING_SNAKE_CASE				:	Optional[dict] = None  ,  ):
  							super().__init__()
  
  							A    =      {"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  							A    =      decoder_pipe_kwargs.get("torch_dtype"  ,  __SCREAMING_SNAKE_CASE) or prior.dtype
  
  							A    =      StableDiffusionImageVariationPipeline.from_pretrained(
  							    "lambdalabs/sd-image-variations-diffusers"  ,  **__SCREAMING_SNAKE_CASE)
  
  							# replace `_encode_image` method
  							A    =      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       SCREAMING_SNAKE_CASE__     (self				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	List[Any]  ,  __SCREAMING_SNAKE_CASE				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[CLIPTextModelOutput, Tuple]] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[torch.Tensor] = None  ,  ):
  							if text_model_output is None:
  												A    =      len(__SCREAMING_SNAKE_CASE) if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE) else 1
  												# get prompt text embeddings
  												A    =      self.tokenizer(
  												    __SCREAMING_SNAKE_CASE  ,  padding="max_length"  ,  max_length=self.tokenizer.model_max_length  ,  return_tensors="pt"  ,  )
  												A    =      text_inputs.input_ids
  												A    =      text_inputs.attention_mask.bool().to(__SCREAMING_SNAKE_CASE)
  
  												if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
  																	A    =      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}""")
  																	A    =      text_input_ids[:, : self.tokenizer.model_max_length]
  
  												A    =      self.text_encoder(text_input_ids.to(__SCREAMING_SNAKE_CASE))
  
  												A    =      text_encoder_output.text_embeds
  												A    =      text_encoder_output.last_hidden_state
  
  							else:
  												A    =      text_model_output[0].shape[0]
  												A    ,					A    =      text_model_output[0], text_model_output[1]
  												A    =      text_attention_mask
  
  							A    =      text_embeddings.repeat_interleave(__SCREAMING_SNAKE_CASE  ,  dim=0)
  							A    =      text_encoder_hidden_states.repeat_interleave(__SCREAMING_SNAKE_CASE  ,  dim=0)
  							A    =      text_mask.repeat_interleave(__SCREAMING_SNAKE_CASE  ,  dim=0)
  
  							if do_classifier_free_guidance:
  												A    =      [""] * batch_size
  
  												A    =      self.tokenizer(
  												    __SCREAMING_SNAKE_CASE  ,  padding="max_length"  ,  max_length=self.tokenizer.model_max_length  ,  truncation=__SCREAMING_SNAKE_CASE  ,  return_tensors="pt"  ,  )
  												A    =      uncond_input.attention_mask.bool().to(__SCREAMING_SNAKE_CASE)
  												A    =      self.text_encoder(uncond_input.input_ids.to(__SCREAMING_SNAKE_CASE))
  
  												A    =      uncond_embeddings_text_encoder_output.text_embeds
  												A    =      uncond_embeddings_text_encoder_output.last_hidden_state
  
  												# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
  
  												A    =      uncond_embeddings.shape[1]
  												A    =      uncond_embeddings.repeat(1  ,  __SCREAMING_SNAKE_CASE)
  												A    =      uncond_embeddings.view(batch_size * num_images_per_prompt  ,  __SCREAMING_SNAKE_CASE)
  
  												A    =      uncond_text_encoder_hidden_states.shape[1]
  												A    =      uncond_text_encoder_hidden_states.repeat(1  ,  __SCREAMING_SNAKE_CASE  ,  1)
  												A    =      uncond_text_encoder_hidden_states.view(
  												    batch_size * num_images_per_prompt  ,  __SCREAMING_SNAKE_CASE  ,  -1)
  												A    =      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
  												A    =      torch.cat([uncond_embeddings, text_embeddings])
  												A    =      torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states])
  
  												A    =      torch.cat([uncond_text_mask, text_mask])
  
  							return text_embeddings, text_encoder_hidden_states, text_mask
  
  
  		@property
  		def       SCREAMING_SNAKE_CASE__     (self				:	Dict):
  							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       SCREAMING_SNAKE_CASE__     (self				:	Tuple  ,  __SCREAMING_SNAKE_CASE				:	Union[str, Any]  ,  __SCREAMING_SNAKE_CASE				:	Optional[int]  ,  __SCREAMING_SNAKE_CASE				:	str  ,  __SCREAMING_SNAKE_CASE				:	Optional[Any]  ,  __SCREAMING_SNAKE_CASE				:	List[str]  ,  __SCREAMING_SNAKE_CASE				:	Tuple):
  							if latents is None:
  												A    =      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}""")
  												A    =      latents.to(__SCREAMING_SNAKE_CASE)
  
  							A    =      latents * scheduler.init_noise_sigma
  							return latents
  
  
  		def       SCREAMING_SNAKE_CASE__     (self				:	Any  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, torch.device]] = None):
  							self.decoder_pipe.to(__SCREAMING_SNAKE_CASE)
  							super().to(__SCREAMING_SNAKE_CASE)
  
  
  		@torch.no_grad()
  		def __call__(self				:	Dict  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[str, List[str]]] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = None  ,  __SCREAMING_SNAKE_CASE				:	int = 1  ,  __SCREAMING_SNAKE_CASE				:	int = 2_5  ,  __SCREAMING_SNAKE_CASE				:	Optional[torch.Generator] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[torch.FloatTensor] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[Union[CLIPTextModelOutput, Tuple]] = None  ,  __SCREAMING_SNAKE_CASE				:	Optional[torch.Tensor] = None  ,  __SCREAMING_SNAKE_CASE				:	float = 4.0  ,  __SCREAMING_SNAKE_CASE				:	float = 8.0  ,  __SCREAMING_SNAKE_CASE				:	int = 5_0  ,  __SCREAMING_SNAKE_CASE				:	Optional[int] = 1  ,  __SCREAMING_SNAKE_CASE				:	float = 0.0  ,  __SCREAMING_SNAKE_CASE				:	Optional[str] = "pil"  ,  __SCREAMING_SNAKE_CASE				:	bool = True  ,  ):
  							if prompt is not None:
  												if isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	A    =      1
  												elif isinstance(__SCREAMING_SNAKE_CASE  ,  __SCREAMING_SNAKE_CASE):
  																	A    =      len(__SCREAMING_SNAKE_CASE)
  												else:
  																	raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(__SCREAMING_SNAKE_CASE)}""")
  							else:
  												A    =      text_model_output[0].shape[0]
  
  							A    =      self._execution_device
  
  							A    =      batch_size * num_images_per_prompt
  
  							A    =      prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0
  
  							A    ,					A    ,					A    =      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)
  							A    =      self.prior_scheduler.timesteps
  
  							A    =      self.prior.config.embedding_dim
  
  							A    =      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
  												A    =      torch.cat([prior_latents] * 2) if do_classifier_free_guidance else prior_latents
  
  												A    =      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:
  																	A    ,					A    =      predicted_image_embedding.chunk(2)
  																	A    =      predicted_image_embedding_uncond + prior_guidance_scale * (
  																	    predicted_image_embedding_text - predicted_image_embedding_uncond
  																	)
  
  												if i + 1 == prior_timesteps_tensor.shape[0]:
  																	A    =      None
  												else:
  																	A    =      prior_timesteps_tensor[i + 1]
  
  												A    =      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
  
  							A    =      self.prior.post_process_latents(__SCREAMING_SNAKE_CASE)
  
  							A    =      prior_latents
  
  							A    =      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: 
  
  """simple docstring"""
  
  
  
  
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if not isinstance(lowercase__    ,							lowercase__   ):
  										A    =      (
  										    "operation can not be conducted on a object of type "
  										    F"""{type(lowercase__   ).__name__}"""
  										)
  										raise TypeError(lowercase__   )
  					A    =      ""
  					for _ in range(0    ,							32   ):
  										bit_string += str(number % 2   )
  										A    =      number >> 1
  					return bit_string
  def      __SCREAMING_SNAKE_CASE		(		lowercase__   ):
  					"""simple docstring"""
  					if number < 0:
  										raise ValueError("the value of input must be positive"   )
  					elif isinstance(lowercase__    ,							lowercase__   ):
  										raise TypeError("Input value must be a 'int' type"   )
  					elif isinstance(lowercase__    ,							lowercase__   ):
  										raise TypeError("'<' not supported between instances of 'str' and 'int'"   )
  					A    =      0
  					# iterator over [1 to 32],since we are dealing with 32 bit integer
  					for _ in range(1    ,							33   ):
  										# left shift the bits by unity
  										A    =      result << 1
  										# get the end bit
  										A    =      number % 2
  										# right shift the bits by unity
  										A    =      number >> 1
  										# add that bit to our ans
  										A    =      result | end_bit
  					return get_reverse_bit_string(lowercase__   )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  