



'''simple docstring'''







from typing import Optional

import torch
from PIL import Image
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import AutoencoderKL, DDIMScheduler, DiffusionPipeline, UNetaDConditionModel
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import (
    deprecate,
)




class 				__lowerCAmelCase(							lowerCAmelCase__      ):






			def __init__(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							AutoencoderKL      ,       SCREAMING_SNAKE_CASE  :							CLIPTextModel      ,       SCREAMING_SNAKE_CASE  :							CLIPTokenizer      ,       SCREAMING_SNAKE_CASE  :							UNetaDConditionModel      ,       SCREAMING_SNAKE_CASE  :							DDIMScheduler      ,       SCREAMING_SNAKE_CASE  :							float = 0.93      ,       SCREAMING_SNAKE_CASE  :							bool = True      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      mixing_coeff
					SCREAMING_SNAKE_CASE_						:List[str]										=      leapfrog_steps

					super().__init__()
					self.register_modules(
					    vae=SCREAMING_SNAKE_CASE      ,       text_encoder=SCREAMING_SNAKE_CASE      ,       tokenizer=SCREAMING_SNAKE_CASE      ,       unet=SCREAMING_SNAKE_CASE      ,       scheduler=SCREAMING_SNAKE_CASE      ,       )

					SCREAMING_SNAKE_CASE_						:int										=      2 ** (len(self.vae.config.block_out_channels	) - 1)
					SCREAMING_SNAKE_CASE_						:List[Any]										=      VaeImageProcessor(vae_scale_factor=self.vae_scale_factor	)






			def 				_lowercase	(   self  :							Union[str, Any]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None      ,       SCREAMING_SNAKE_CASE  :							bool = False	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Dict										=      self.tokenizer(
					    SCREAMING_SNAKE_CASE      ,       padding='max_length'      ,       max_length=self.tokenizer.model_max_length      ,       truncation=SCREAMING_SNAKE_CASE      ,       return_tensors='pt'      ,       )

					SCREAMING_SNAKE_CASE_						:Any										=      self.text_encoder(text_inputs.input_ids.to(self.device	)	).last_hidden_state

					SCREAMING_SNAKE_CASE_						:List[Any]										=      prompt_embeds.to(dtype=self.text_encoder.dtype      ,       device=self.device	)

					if do_classifier_free_guidance:
							SCREAMING_SNAKE_CASE_						:List[Any]										=      '' if negative_prompt is None else negative_prompt

							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.tokenizer(
							    SCREAMING_SNAKE_CASE      ,       padding='max_length'      ,       max_length=self.tokenizer.model_max_length      ,       truncation=SCREAMING_SNAKE_CASE      ,       return_tensors='pt'      ,       )

							SCREAMING_SNAKE_CASE_						:Dict										=      self.text_encoder(uncond_input.input_ids.to(self.device	)	).last_hidden_state

							SCREAMING_SNAKE_CASE_						:List[Any]										=      torch.cat([negative_prompt_embeds, prompt_embeds]	)

					return prompt_embeds






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:int										=      self.mixing_coeff * x + (1 - self.mixing_coeff) * y
					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.mixing_coeff * y + (1 - self.mixing_coeff) * x

					return [x, y]






			def 				_lowercase	(   self  :							int      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      (y - (1 - self.mixing_coeff) * x) / self.mixing_coeff
					SCREAMING_SNAKE_CASE_						:Dict										=      (x - (1 - self.mixing_coeff) * y) / self.mixing_coeff

					return [x, y]






			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Optional[int]										=      int(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.scheduler.alphas_cumprod[t] if t >= 0 else self.scheduler.final_alpha_cumprod

					return alpha_prod, 1 - alpha_prod






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Tuple										=      self._get_alpha_and_beta(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self._get_alpha_and_beta(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					SCREAMING_SNAKE_CASE_						:str										=      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5

					SCREAMING_SNAKE_CASE_						:Tuple										=      (base - b_t * model_output) / a_t

					return model_input, next_model_input.to(base.dtype	)






			def 				_lowercase	(   self  :							Tuple      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      timestep - self.scheduler.config.num_train_timesteps / self.scheduler.num_inference_steps

					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self._get_alpha_and_beta(SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Optional[int]										=      self._get_alpha_and_beta(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      (alpha_prod_t_prev / alpha_prod_t) ** 0.5
					SCREAMING_SNAKE_CASE_						:str										=      -a_t * (beta_prod_t**0.5) + beta_prod_t_prev**0.5
					SCREAMING_SNAKE_CASE_						:str										=      a_t * base + b_t * model_output

					return model_input, next_model_input.to(base.dtype	)






			@torch.no_grad()
			def 				_lowercase	(   self  :							List[Any]      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor	):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:Tuple										=      1 / self.vae.config.scaling_factor * latents
					SCREAMING_SNAKE_CASE_						:int										=      self.vae.decode(SCREAMING_SNAKE_CASE	).sample
					SCREAMING_SNAKE_CASE_						:str										=      (image / 2 + 0.5).clamp(0      ,       1	)
					return image






			@torch.no_grad()
			def 				_lowercase	(   self  :							Optional[int]      ,       SCREAMING_SNAKE_CASE  :							Image.Image      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							torch.Tensor      ,       SCREAMING_SNAKE_CASE  :							float      ,       SCREAMING_SNAKE_CASE  :							Optional[torch.Generator] = None      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      guidance_scale > 1.0

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      image.to(device=self.device      ,       dtype=text_embeds.dtype	)
					SCREAMING_SNAKE_CASE_						:List[str]										=      self.vae.encode(SCREAMING_SNAKE_CASE	).latent_dist.sample(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.vae.config.scaling_factor * latent

					SCREAMING_SNAKE_CASE_						:int										=      [latent.clone(), latent.clone()]

					for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE	)      ,       total=len(SCREAMING_SNAKE_CASE	)	):
							SCREAMING_SNAKE_CASE_						:List[Any]										=      self.noise_mixing_layer(x=coupled_latents[0]      ,       y=coupled_latents[1]	)

							# j - model_input index, k - base index
							for j in range(2	):
									SCREAMING_SNAKE_CASE_						:Tuple										=      j ^ 1

									if self.leapfrog_steps:
											if i % 2 == 0:
													SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      j, k

									SCREAMING_SNAKE_CASE_						:Dict										=      coupled_latents[j]
									SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      coupled_latents[k]

									SCREAMING_SNAKE_CASE_						:List[str]										=      torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

									SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      self.unet(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       encoder_hidden_states=SCREAMING_SNAKE_CASE	).sample

									if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:int										=      noise_pred.chunk(2	)
											SCREAMING_SNAKE_CASE_						:Optional[int]										=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      self.noise_step(
									    base=SCREAMING_SNAKE_CASE      ,       model_input=SCREAMING_SNAKE_CASE      ,       model_output=SCREAMING_SNAKE_CASE      ,       timestep=SCREAMING_SNAKE_CASE      ,       )

									SCREAMING_SNAKE_CASE_						:Dict										=      model_input

					return coupled_latents






			@torch.no_grad()
			def __call__(   self  :							List[str]      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							str      ,       SCREAMING_SNAKE_CASE  :							Image.Image      ,       SCREAMING_SNAKE_CASE  :							float = 3.0      ,       SCREAMING_SNAKE_CASE  :							int = 50      ,       SCREAMING_SNAKE_CASE  :							float = 0.8      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = None      ,       SCREAMING_SNAKE_CASE  :							Optional[torch.Generator] = None      ,       SCREAMING_SNAKE_CASE  :							Optional[str] = "pil"      ,       ):




					"""simple docstring"""







					SCREAMING_SNAKE_CASE_						:List[Any]										=      guidance_scale > 1.0

					SCREAMING_SNAKE_CASE_						:str										=      self.image_processor.preprocess(SCREAMING_SNAKE_CASE	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      self._encode_prompt(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)
					SCREAMING_SNAKE_CASE_						:Tuple										=      self._encode_prompt(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					self.scheduler.set_timesteps(SCREAMING_SNAKE_CASE      ,       self.device	)

					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      num_inference_steps - int(num_inference_steps * strength	)
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.scheduler.timesteps[t_limit:]
					SCREAMING_SNAKE_CASE_						:Tuple										=      fwd_timesteps.flip(0	)

					SCREAMING_SNAKE_CASE_						:List[Any]										=      self.prepare_latents(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE	)

					for i, t in tqdm(enumerate(SCREAMING_SNAKE_CASE	)      ,       total=len(SCREAMING_SNAKE_CASE	)	):
							# j - model_input index, k - base index
							for k in range(2	):
									SCREAMING_SNAKE_CASE_						:Tuple										=      k ^ 1

									if self.leapfrog_steps:
											if i % 2 == 1:
													SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:str										=      j, k

									SCREAMING_SNAKE_CASE_						:Dict										=      coupled_latents[j]
									SCREAMING_SNAKE_CASE_						:Optional[int]										=      coupled_latents[k]

									SCREAMING_SNAKE_CASE_						:List[str]										=      torch.cat([model_input] * 2	) if do_classifier_free_guidance else model_input

									SCREAMING_SNAKE_CASE_						:List[str]										=      self.unet(SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       encoder_hidden_states=SCREAMING_SNAKE_CASE	).sample

									if do_classifier_free_guidance:
											SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:Any										=      noise_pred.chunk(2	)
											SCREAMING_SNAKE_CASE_						:Tuple										=      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

									SCREAMING_SNAKE_CASE_						,		SCREAMING_SNAKE_CASE_						:List[str]										=      self.denoise_step(
									    base=SCREAMING_SNAKE_CASE      ,       model_input=SCREAMING_SNAKE_CASE      ,       model_output=SCREAMING_SNAKE_CASE      ,       timestep=SCREAMING_SNAKE_CASE      ,       )

									SCREAMING_SNAKE_CASE_						:List[Any]										=      model_input

							SCREAMING_SNAKE_CASE_						:Optional[Any]										=      self.denoise_mixing_layer(x=coupled_latents[0]      ,       y=coupled_latents[1]	)

					# either one is fine
					SCREAMING_SNAKE_CASE_						:Optional[Any]										=      coupled_latents[0]

					if output_type not in ["latent", "pt", "np", "pil"]:
							SCREAMING_SNAKE_CASE_						:Union[str, Any]										=      (
							    f'the output_type {output_type} is outdated. Please make sure to set it to one of these instead: '
							    '`pil`, `np`, `pt`, `latent`'
							)
							deprecate('Unsupported output_type'      ,       '1.0.0'      ,       SCREAMING_SNAKE_CASE      ,       standard_warn=SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Tuple										=      'np'

					if output_type == "latent":
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      final_latent
					else:
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.decode_latents(SCREAMING_SNAKE_CASE	)
							SCREAMING_SNAKE_CASE_						:Optional[int]										=      self.image_processor.postprocess(SCREAMING_SNAKE_CASE      ,       output_type=SCREAMING_SNAKE_CASE	)

					return image








from __future__ import annotations

import re


def __A       ( __lowerCAmelCase      )->    list[str]:





						"""simple docstring"""



						def alphanum_key(__lowerCAmelCase      ):
												return [int(__lowerCAmelCase      ) if s.isdigit() else s.lower() for s in re.split('([0-9]+)'					,						__lowerCAmelCase      )]

						return sorted(__lowerCAmelCase					,						key=__lowerCAmelCase      )


if __name__ == "__main__":
		import doctest

		doctest.testmod()
