






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_			:List[str]	    =		logging.get_logger(__name__)  # pylint: disable=invalid-name






def 				A	(			self				,a_				,a_				,a_				,a_   )	->		str:
					__UpperCamelCase :   Union[str, Any]									=image.to(device=a_   )
					__UpperCamelCase :   Any									=image  # take image as image_embeddings
					__UpperCamelCase :   List[Any]									=image_embeddings.unsqueeze(1   )

					# duplicate image embeddings for each generation per prompt, using mps friendly method
					__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   int									=image_embeddings.shape
					__UpperCamelCase :   int									=image_embeddings.repeat(1				,a_				,1   )
					__UpperCamelCase :   str									=image_embeddings.view(bs_embed * num_images_per_prompt				,a_				,-1   )

					if do_classifier_free_guidance:
										__UpperCamelCase :   List[str]									=torch.zeros_like(a_   )

										# 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
										__UpperCamelCase :   Optional[int]									=torch.cat([uncond_embeddings, image_embeddings]   )

					return image_embeddings




class 							__A	(     a					):





				"""simple docstring"""







				def __init__(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ = None				,    ):




									"""simple docstring"""

									super().__init__()

									__UpperCamelCase :   Optional[int]									={'image_encoder': None} if decoder_pipe_kwargs is None else decoder_pipe_kwargs

									__UpperCamelCase :   Any									=decoder_pipe_kwargs.get('torch_dtype'				,    lowerCamelCase__       ) or prior.dtype

									__UpperCamelCase :   Optional[int]									=StableDiffusionImageVariationPipeline.from_pretrained(
									    'lambdalabs/sd-image-variations-diffusers'				,    **lowerCamelCase__       )

									# replace `_encode_image` method
									__UpperCamelCase :   Any									=types.MethodType(_encode_image				,    self.decoder_pipe       )

									self.register_modules(
									    prior=lowerCamelCase__				,    tokenizer=lowerCamelCase__				,    text_encoder=lowerCamelCase__				,    prior_scheduler=lowerCamelCase__				,    )


				def  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    ):




									"""simple docstring"""

									if text_model_output is None:
														__UpperCamelCase :   Optional[int]									=len(lowerCamelCase__       ) if isinstance(lowerCamelCase__				,    lowerCamelCase__       ) else 1
														# get prompt text embeddings
														__UpperCamelCase :   Any									=self.tokenizer(
														    lowerCamelCase__				,    padding='max_length'				,    max_length=self.tokenizer.model_max_length				,    return_tensors='pt'				,    )
														__UpperCamelCase :   int									=text_inputs.input_ids
														__UpperCamelCase :   Any									=text_inputs.attention_mask.bool().to(lowerCamelCase__       )

														if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
																			__UpperCamelCase :   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}'       )
																			__UpperCamelCase :   List[str]									=text_input_ids[:, : self.tokenizer.model_max_length]

														__UpperCamelCase :   Dict									=self.text_encoder(text_input_ids.to(lowerCamelCase__       )       )

														__UpperCamelCase :   int									=text_encoder_output.text_embeds
														__UpperCamelCase :   List[Any]									=text_encoder_output.last_hidden_state

									else:
														__UpperCamelCase :   Optional[Any]									=text_model_output[0].shape[0]
														__UpperCamelCase						,							__UpperCamelCase :   List[Any]									=text_model_output[0], text_model_output[1]
														__UpperCamelCase :   List[Any]									=text_attention_mask

									__UpperCamelCase :   Optional[int]									=text_embeddings.repeat_interleave(lowerCamelCase__				,    dim=0       )
									__UpperCamelCase :   Dict									=text_encoder_hidden_states.repeat_interleave(lowerCamelCase__				,    dim=0       )
									__UpperCamelCase :   str									=text_mask.repeat_interleave(lowerCamelCase__				,    dim=0       )

									if do_classifier_free_guidance:
														__UpperCamelCase :   Tuple									=[''] * batch_size

														__UpperCamelCase :   Any									=self.tokenizer(
														    lowerCamelCase__				,    padding='max_length'				,    max_length=self.tokenizer.model_max_length				,    truncation=lowerCamelCase__				,    return_tensors='pt'				,    )
														__UpperCamelCase :   Any									=uncond_input.attention_mask.bool().to(lowerCamelCase__       )
														__UpperCamelCase :   List[Any]									=self.text_encoder(uncond_input.input_ids.to(lowerCamelCase__       )       )

														__UpperCamelCase :   Any									=uncond_embeddings_text_encoder_output.text_embeds
														__UpperCamelCase :   Tuple									=uncond_embeddings_text_encoder_output.last_hidden_state

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

														__UpperCamelCase :   str									=uncond_embeddings.shape[1]
														__UpperCamelCase :   Dict									=uncond_embeddings.repeat(1				,    lowerCamelCase__       )
														__UpperCamelCase :   str									=uncond_embeddings.view(batch_size * num_images_per_prompt				,    lowerCamelCase__       )

														__UpperCamelCase :   str									=uncond_text_encoder_hidden_states.shape[1]
														__UpperCamelCase :   Any									=uncond_text_encoder_hidden_states.repeat(1				,    lowerCamelCase__				,    1       )
														__UpperCamelCase :   List[Any]									=uncond_text_encoder_hidden_states.view(
														    batch_size * num_images_per_prompt				,    lowerCamelCase__				,    -1       )
														__UpperCamelCase :   Union[str, Any]									=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
														__UpperCamelCase :   Dict									=torch.cat([uncond_embeddings, text_embeddings]       )
														__UpperCamelCase :   str									=torch.cat([uncond_text_encoder_hidden_states, text_encoder_hidden_states]       )

														__UpperCamelCase :   Dict									=torch.cat([uncond_text_mask, text_mask]       )

									return text_embeddings, text_encoder_hidden_states, text_mask


				@property
				def  __lowercase				(					self       ):




									"""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(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  __lowercase				(					self				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       ):




									"""simple docstring"""

									if latents is None:
														__UpperCamelCase :   Any									=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}'       )
														__UpperCamelCase :   List[str]									=latents.to(lowerCamelCase__       )

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


				def  __lowercase				(					self				,    lowerCamelCase__ = None       ):




									"""simple docstring"""

									self.decoder_pipe.to(lowerCamelCase__       )
									super().to(lowerCamelCase__       )







				@torch.no_grad()
				def __call__(					self				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = 1				,    lowerCamelCase__ = 25				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = None				,    lowerCamelCase__ = 4.0				,    lowerCamelCase__ = 8.0				,    lowerCamelCase__ = 50				,    lowerCamelCase__ = 1				,    lowerCamelCase__ = 0.0				,    lowerCamelCase__ = "pil"				,    lowerCamelCase__ = True				,    ):




									"""simple docstring"""

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

									__UpperCamelCase :   Union[str, Any]									=self._execution_device

									__UpperCamelCase :   Dict									=batch_size * num_images_per_prompt

									__UpperCamelCase :   List[str]									=prior_guidance_scale > 1.0 or decoder_guidance_scale > 1.0

									__UpperCamelCase						,							__UpperCamelCase						,							__UpperCamelCase :   str									=self._encode_prompt(
									    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__				,    lowerCamelCase__       )

									# prior

									self.prior_scheduler.set_timesteps(lowerCamelCase__				,    device=lowerCamelCase__       )
									__UpperCamelCase :   Any									=self.prior_scheduler.timesteps

									__UpperCamelCase :   Optional[int]									=self.prior.config.embedding_dim

									__UpperCamelCase :   List[str]									=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
														__UpperCamelCase :   Optional[Any]									=torch.cat([prior_latents] * 2       ) if do_classifier_free_guidance else prior_latents

														__UpperCamelCase :   Optional[int]									=self.prior(
														    lowerCamelCase__				,    timestep=lowerCamelCase__				,    proj_embedding=lowerCamelCase__				,    encoder_hidden_states=lowerCamelCase__				,    attention_mask=lowerCamelCase__				,    ).predicted_image_embedding

														if do_classifier_free_guidance:
																			__UpperCamelCase						,							__UpperCamelCase :   Any									=predicted_image_embedding.chunk(2       )
																			__UpperCamelCase :   str									=predicted_image_embedding_uncond + prior_guidance_scale * (
																			    predicted_image_embedding_text - predicted_image_embedding_uncond
																			)

														if i + 1 == prior_timesteps_tensor.shape[0]:
																			__UpperCamelCase :   str									=None
														else:
																			__UpperCamelCase :   List[Any]									=prior_timesteps_tensor[i + 1]

														__UpperCamelCase :   List[str]									=self.prior_scheduler.step(
														    lowerCamelCase__				,    timestep=lowerCamelCase__				,    sample=lowerCamelCase__				,    generator=lowerCamelCase__				,    prev_timestep=lowerCamelCase__				,    ).prev_sample

									__UpperCamelCase :   Dict									=self.prior.post_process_latents(lowerCamelCase__       )

									__UpperCamelCase :   Optional[Any]									=prior_latents

									__UpperCamelCase :   List[str]									=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









from __future__ import annotations






def 				A	(			a_				,a_   )	->		list[int]:
					__UpperCamelCase :   dict[int, int]									={}
					for index, val in enumerate(a_   ):
										__UpperCamelCase :   Union[str, Any]									=target - val
										if compl in chk_map:
															return [chk_map[compl], index]
										__UpperCamelCase :   List[Any]									=index
					return []


if __name__ == "__main__":
							import doctest

							doctest.testmod()
							print(f"{two_sum([2, 7, 11, 15], 9) = }")

