


'''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







'''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()



