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
  
  
  UpperCAmelCase__      =							logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  
  
  
  
  def 	UpperCAmelCase__(							self				:   Any,_SCREAMING_SNAKE_CASE				:   Tuple,_SCREAMING_SNAKE_CASE				:   Dict,_SCREAMING_SNAKE_CASE				:   Optional[Any],_SCREAMING_SNAKE_CASE				:   Dict   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  image.to(device=_SCREAMING_SNAKE_CASE   )
  			__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,_SCREAMING_SNAKE_CASE,1   )
  			__A=  image_embeddings.view(bs_embed * num_images_per_prompt,_SCREAMING_SNAKE_CASE,-1   )
  
  			if do_classifier_free_guidance:
  						__A=  torch.zeros_like(_SCREAMING_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
  						__A=  torch.cat([uncond_embeddings, image_embeddings]   )
  
  			return image_embeddings
  
  
  
  
  
  
  class 	a__				(				lowerCAmelCase__      ):
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Optional[Any] ,						lowerCAmelCase_       : PriorTransformer ,						lowerCAmelCase_       : CLIPTokenizer ,						lowerCAmelCase_       : CLIPTextModelWithProjection ,						lowerCAmelCase_       : UnCLIPScheduler ,						lowerCAmelCase_       : Optional[dict] = None ,						)			->		Any:
  						super().__init__()
  
  						__A=  {'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs
  
  						__A=  decoder_pipe_kwargs.get('torch_dtype' ,						lowerCAmelCase_  ) or prior.dtype
  
  						__A=  StableDiffusionImageVariationPipeline.from_pretrained(
  						    'lambdalabs/sd-image-variations-diffusers' ,						**lowerCAmelCase_  )
  
  						# replace `_encode_image` method
  						__A=  types.MethodType(_encode_image ,						self.decoder_pipe  )
  
  						self.register_modules(
  						    prior=lowerCAmelCase_ ,						tokenizer=lowerCAmelCase_ ,						text_encoder=lowerCAmelCase_ ,						prior_scheduler=lowerCAmelCase_ ,						)
  			def 				lowerCAmelCase    (     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:
  
  
  
  
  
  
  						if text_model_output is None:
  									__A=  len(lowerCAmelCase_  ) if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ) else 1
  									# get prompt text embeddings
  									__A=  self.tokenizer(
  									    lowerCAmelCase_ ,						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(lowerCAmelCase_  )
  
  									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(lowerCAmelCase_  )  )
  
  									__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(lowerCAmelCase_ ,						dim=0  )
  						__A=  text_encoder_hidden_states.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
  						__A=  text_mask.repeat_interleave(lowerCAmelCase_ ,						dim=0  )
  
  						if do_classifier_free_guidance:
  									__A=  [''] * batch_size
  
  									__A=  self.tokenizer(
  									    lowerCAmelCase_ ,						padding='max_length' ,						max_length=self.tokenizer.model_max_length ,						truncation=lowerCAmelCase_ ,						return_tensors='pt' ,						)
  									__A=  uncond_input.attention_mask.bool().to(lowerCAmelCase_  )
  									__A=  self.text_encoder(uncond_input.input_ids.to(lowerCAmelCase_  )  )
  
  									__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 ,						lowerCAmelCase_  )
  									__A=  uncond_embeddings.view(batch_size * num_images_per_prompt ,						lowerCAmelCase_  )
  
  									__A=  uncond_text_encoder_hidden_states.shape[1]
  									__A=  uncond_text_encoder_hidden_states.repeat(1 ,						lowerCAmelCase_ ,						1  )
  									__A=  uncond_text_encoder_hidden_states.view(
  									    batch_size * num_images_per_prompt ,						lowerCAmelCase_ ,						-1  )
  									__A=  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
  									__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 				lowerCAmelCase    (     self       : Dict  )			->		List[str]:
  
  
  
  
  
  
  						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 				lowerCAmelCase    (     self       : Optional[int] ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : str ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Dict ,						lowerCAmelCase_       : Tuple ,						lowerCAmelCase_       : Any  )			->		Dict:
  
  
  
  
  
  
  						if latents is None:
  									__A=  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}"""  )
  									__A=  latents.to(lowerCAmelCase_  )
  
  						__A=  latents * scheduler.init_noise_sigma
  						return latents
  			def 				lowerCAmelCase    (     self       : List[str] ,						lowerCAmelCase_       : Optional[Union[str, torch.device]] = None  )			->		Dict:
  						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:
  
  
  
  
  
  
  						if prompt is not None:
  									if isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  												__A=  1
  									elif isinstance(lowerCAmelCase_ ,						lowerCAmelCase_  ):
  												__A=  len(lowerCAmelCase_  )
  									else:
  												raise ValueError(F"""`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase_  )}"""  )
  						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(
  						    lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_ ,						lowerCAmelCase_  )
  
  						# prior
  
  						self.prior_scheduler.set_timesteps(lowerCAmelCase_ ,						device=lowerCAmelCase_  )
  						__A=  self.prior_scheduler.timesteps
  
  						__A=  self.prior.config.embedding_dim
  
  						__A=  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
  									__A=  torch.cat([prior_latents] * 2  ) if do_classifier_free_guidance else prior_latents
  
  									__A=  self.prior(
  									    lowerCAmelCase_ ,						timestep=lowerCAmelCase_ ,						proj_embedding=lowerCAmelCase_ ,						encoder_hidden_states=lowerCAmelCase_ ,						attention_mask=lowerCAmelCase_ ,						).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(
  									    lowerCAmelCase_ ,						timestep=lowerCAmelCase_ ,						sample=lowerCAmelCase_ ,						generator=lowerCAmelCase_ ,						prev_timestep=lowerCAmelCase_ ,						).prev_sample
  
  						__A=  self.prior.post_process_latents(lowerCAmelCase_  )
  
  						__A=  prior_latents
  
  						__A=  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: 
  
  
  '''simple docstring'''
  
  
  
  
  
  UpperCAmelCase__      =							[[0, 1, 1], [0, 0, 1], [1, 0, 0]]
  class 	a__				:
  
  
  
  
  
  			'''simple docstring'''
  
  			def __init__(     self       : Any ,						lowerCAmelCase_       : Optional[int]  )			->		Tuple:
  						__A=  name
  						__A=  []
  						__A=  []
  			def 				lowerCAmelCase    (     self       : Any ,						lowerCAmelCase_       : Dict  )			->		Any:
  						self.inbound.append(lowerCAmelCase_  )
  			def 				lowerCAmelCase    (     self       : str ,						lowerCAmelCase_       : Any  )			->		List[Any]:
  						self.outbound.append(lowerCAmelCase_  )
  
  			def __repr__(     self       : int  )			->		Union[str, Any]:
  						return F"""<node={self.name} inbound={self.inbound} outbound={self.outbound}>"""
  
  
  
  
  
  
  def 	UpperCAmelCase__(							_SCREAMING_SNAKE_CASE				:   Any,_SCREAMING_SNAKE_CASE				:   Optional[int]=3,_SCREAMING_SNAKE_CASE				:   Dict=0.85   ):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  {}
  			for node in nodes:
  						__A=  1
  
  			__A=  {}
  			for node in nodes:
  						__A=  len(node.outbound   )
  
  			for i in range(_SCREAMING_SNAKE_CASE   ):
  						print(f"""======= Iteration {i + 1} ======="""   )
  						for _, node in enumerate(_SCREAMING_SNAKE_CASE   ):
  									__A=  (1 - d) + d * sum(
  									    ranks[ib] / outbounds[ib] for ib in node.inbound   )
  						print(_SCREAMING_SNAKE_CASE   )
  
  
  
  
  
  
  def 	UpperCAmelCase__(							):
  
  
  
  
  
  
  			"""simple docstring"""
  
  
  
  
  			__A=  list(input('Enter Names of the Nodes: '   ).split()   )
  
  			__A=  [Node(_SCREAMING_SNAKE_CASE   ) for name in names]
  
  			for ri, row in enumerate(_SCREAMING_SNAKE_CASE   ):
  						for ci, col in enumerate(_SCREAMING_SNAKE_CASE   ):
  									if col == 1:
  												nodes[ci].add_inbound(names[ri]   )
  												nodes[ri].add_outbound(names[ci]   )
  
  			print('======= Nodes ======='   )
  			for node in nodes:
  						print(_SCREAMING_SNAKE_CASE   )
  
  			page_rank(_SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  					main()
  
  
  
  