


'''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


__lowercase  :      Tuple      		=				logging.get_logger(__name__)  # pylint: disable=invalid-name





def 							a_       (						self							,						lowercase							,						lowercase							,						lowercase							,						lowercase    )			->Optional[int]:






				'''simple docstring'''

				__lowerCamelCase		:      Any											=					image.to(device=lowercase    )
				__lowerCamelCase		:      List[Any]											=					image  # take image as image_embeddings
				__lowerCamelCase		:      Optional[int]											=					image_embeddings.unsqueeze(1    )

				# duplicate image embeddings for each generation per prompt, using mps friendly method
				__lowerCamelCase		:      Optional[Any]											=					image_embeddings.shape
				__lowerCamelCase		:      int											=					image_embeddings.repeat(1							,						lowercase							,						1    )
				__lowerCamelCase		:      int											=					image_embeddings.view(bs_embed * num_images_per_prompt							,						lowercase							,						-1    )

				if do_classifier_free_guidance:
								__lowerCamelCase		:      Optional[int]											=					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
								__lowerCamelCase		:      Tuple											=					torch.cat([uncond_embeddings, image_embeddings]    )

				return image_embeddings





class _snake_case   ( a__   ):







							def __init__(   self			,				__A			,				__A			,				__A			,				__A			,				__A = None			,				)     ->List[Any]:



											"""simple docstring"""







											super().__init__()

											__lowerCamelCase		:      str											=					{"image_encoder": None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

											__lowerCamelCase		:      List[str]											=					decoder_pipe_kwargs.get("torch_dtype"			,				__A	) or prior.dtype

											__lowerCamelCase		:      Optional[int]											=					StableDiffusionImageVariationPipeline.from_pretrained(
											    "lambdalabs/sd-image-variations-diffusers"			,				**__A	)

											# replace `_encode_image` method
											__lowerCamelCase		:      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			,				)     ->List[Any]:



											"""simple docstring"""







											if text_model_output is None:
															__lowerCamelCase		:      int											=					len(__A	) if isinstance(__A			,				__A	) else 1
															# get prompt text embeddings
															__lowerCamelCase		:      Any											=					self.tokenizer(
															    __A			,				padding="max_length"			,				max_length=self.tokenizer.model_max_length			,				return_tensors="pt"			,				)
															__lowerCamelCase		:      str											=					text_inputs.input_ids
															__lowerCamelCase		:      List[str]											=					text_inputs.attention_mask.bool().to(__A	)

															if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			__lowerCamelCase		:      List[Any]											=					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}'	)
																			__lowerCamelCase		:      Optional[Any]											=					text_input_ids[:, : self.tokenizer.model_max_length]

															__lowerCamelCase		:      Optional[int]											=					self.text_encoder(text_input_ids.to(__A	)	)

															__lowerCamelCase		:      List[str]											=					text_encoder_output.text_embeds
															__lowerCamelCase		:      Tuple											=					text_encoder_output.last_hidden_state

											else:
															__lowerCamelCase		:      Optional[Any]											=					text_model_output[0].shape[0]
															__lowerCamelCase		:      Any											=					text_model_output[0], text_model_output[1]
															__lowerCamelCase		:      str											=					text_attention_mask

											__lowerCamelCase		:      Dict											=					text_embeddings.repeat_interleave(__A			,				dim=0	)
											__lowerCamelCase		:      Any											=					text_encoder_hidden_states.repeat_interleave(__A			,				dim=0	)
											__lowerCamelCase		:      List[str]											=					text_mask.repeat_interleave(__A			,				dim=0	)

											if do_classifier_free_guidance:
															__lowerCamelCase		:      List[Any]											=					[""] * batch_size

															__lowerCamelCase		:      Optional[Any]											=					self.tokenizer(
															    __A			,				padding="max_length"			,				max_length=self.tokenizer.model_max_length			,				truncation=__A			,				return_tensors="pt"			,				)
															__lowerCamelCase		:      Union[str, Any]											=					uncond_input.attention_mask.bool().to(__A	)
															__lowerCamelCase		:      int											=					self.text_encoder(uncond_input.input_ids.to(__A	)	)

															__lowerCamelCase		:      int											=					uncond_embeddings_text_encoder_output.text_embeds
															__lowerCamelCase		:      Union[str, Any]											=					uncond_embeddings_text_encoder_output.last_hidden_state

															# duplicate unconditional embeddings for each generation per prompt, using mps friendly method

															__lowerCamelCase		:      Optional[int]											=					uncond_embeddings.shape[1]
															__lowerCamelCase		:      Union[str, Any]											=					uncond_embeddings.repeat(1			,				__A	)
															__lowerCamelCase		:      Dict											=					uncond_embeddings.view(batch_size * num_images_per_prompt			,				__A	)

															__lowerCamelCase		:      Optional[int]											=					uncond_text_encoder_hidden_states.shape[1]
															__lowerCamelCase		:      Dict											=					uncond_text_encoder_hidden_states.repeat(1			,				__A			,				1	)
															__lowerCamelCase		:      Dict											=					uncond_text_encoder_hidden_states.view(
															    batch_size * num_images_per_prompt			,				__A			,				-1	)
															__lowerCamelCase		:      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
															__lowerCamelCase		:      Optional[Any]											=					torch.cat([uncond_embeddings, text_embeddings]	)
															__lowerCamelCase		:      List[str]											=					torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]	)

															__lowerCamelCase		:      Optional[int]											=					torch.cat([uncond_text_mask, text_mask]	)

											return text_embeddings, text_encoder_hidden_states, text_mask







							@property
							def 		__SCREAMING_SNAKE_CASE(   self	)     ->str:



											"""simple docstring"""







											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	)     ->List[str]:



											"""simple docstring"""







											if latents is None:
															__lowerCamelCase		:      Optional[Any]											=					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}'	)
															__lowerCamelCase		:      Dict											=					latents.to(__A	)

											__lowerCamelCase		:      List[Any]											=					latents * scheduler.init_noise_sigma
											return latents







							def 		__SCREAMING_SNAKE_CASE(   self			,				__A = None	)     ->Tuple:



											"""simple docstring"""







											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			,				)     ->Any:



											"""simple docstring"""







											if prompt is not None:
															if isinstance(__A			,				__A	):
																			__lowerCamelCase		:      Tuple											=					1
															elif isinstance(__A			,				__A	):
																			__lowerCamelCase		:      int											=					len(__A	)
															else:
																			raise ValueError(f'`prompt` has to be of type `str` or `list` but is {type(__A	)}'	)
											else:
															__lowerCamelCase		:      Union[str, Any]											=					text_model_output[0].shape[0]

											__lowerCamelCase		:      Dict											=					self._execution_device

											__lowerCamelCase		:      str											=					batch_size * num_images_per_prompt

											__lowerCamelCase		:      Tuple											=					prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

											__lowerCamelCase		:      Dict											=					self._encode_prompt(
											    __A			,				__A			,				__A			,				__A			,				__A			,				__A	)

											# prior

											self.prior_scheduler.set_timesteps(__A			,				device=__A	)
											__lowerCamelCase		:      List[str]											=					self.prior_scheduler.timesteps

											__lowerCamelCase		:      Tuple											=					self.prior.config.embedding_dim

											__lowerCamelCase		:      int											=					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
															__lowerCamelCase		:      Dict											=					torch.cat([prior_latents] * 2	) if do_classifier_free_guidance else prior_latents

															__lowerCamelCase		:      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:
																			__lowerCamelCase		:      List[Any]											=					predicted_image_embedding.chunk(2	)
																			__lowerCamelCase		:      Union[str, Any]											=					predicted_image_embedding_uncond + prior_guidance_scale * (
																			    predicted_image_embedding_text - predicted_image_embedding_uncond
																			)

															if i + 1 == prior_timesteps_tensor.shape[0]:
																			__lowerCamelCase		:      Dict											=					None
															else:
																			__lowerCamelCase		:      Optional[int]											=					prior_timesteps_tensor[i + 1]

															__lowerCamelCase		:      int											=					self.prior_scheduler.step(
															    __A			,				timestep=__A			,				sample=__A			,				generator=__A			,				prev_timestep=__A			,				).prev_sample

											__lowerCamelCase		:      List[Any]											=					self.prior.post_process_latents(__A	)

											__lowerCamelCase		:      List[str]											=					prior_latents

											__lowerCamelCase		:      Any											=					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









'''simple docstring'''




def 							a_       (						lowercase    )			->str:






				'''simple docstring'''

				return " ".join(sorted(set(sentence.split()    )    )    )


if __name__ == "__main__":
				import doctest

				doctest.testmod()





