




from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)





class 						SCREAMING_SNAKE_CASE__		(_a ):
     def __init__(	self  :		Optional[int]					,	__lowerCamelCase  :		AutoencoderKL					,	__lowerCamelCase  :		CLIPTextModel					,	__lowerCamelCase  :		CLIPTokenizer					,	__lowerCamelCase  :		UNetaDConditionModel					,	__lowerCamelCase  :		Union[PNDMScheduler, LMSDiscreteScheduler, DDIMScheduler]					,	):



       """simple docstring"""
       super().__init__()

       self.register_modules(vae=__lowerCamelCase					,	text_encoder=__lowerCamelCase					,	tokenizer=__lowerCamelCase					,	unet=__lowerCamelCase					,	scheduler=__lowerCamelCase	)
     def 					A__			(	self  :		Tuple					,	__lowerCamelCase  :		Union[str, Any]	):



       """simple docstring"""
       with torch.no_grad():
         lowerCAmelCase__								=			self.vae.encode(tfms.ToTensor()(__lowerCamelCase	).unsqueeze(0	).to(self.device	) * 2 - 1	)
         lowerCAmelCase__								=			0.1_8215 * latent.latent_dist.sample()
       return latent
     def 					A__			(	self  :		List[str]					,	__lowerCamelCase  :		Optional[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			(1 / 0.1_8215) * latent
       with torch.no_grad():
         lowerCAmelCase__								=			self.vae.decode(__lowerCamelCase	).sample
       lowerCAmelCase__								=			(img / 2 + 0.5).clamp(0					,	1	)
       lowerCAmelCase__								=			img.detach().cpu().permute(0					,	2					,	3					,	1	).numpy()
       lowerCAmelCase__								=			(img * 2_55).round().astype('''uint8'''	)
       return Image.fromarray(img[0]	)
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		int	):



       """simple docstring"""
       lowerCAmelCase__								=			self.tokenizer(
           __lowerCamelCase					,	padding='''max_length'''					,	max_length=self.tokenizer.model_max_length					,	truncation=__lowerCamelCase					,	return_tensors='''pt'''					,	)

       lowerCAmelCase__								=			self.text_encoder(text_input.input_ids.to(self.device	)	)[0]

       lowerCAmelCase__								=			self.tokenizer(
           ''''''					,	padding='''max_length'''					,	max_length=self.tokenizer.model_max_length					,	truncation=__lowerCamelCase					,	return_tensors='''pt'''					,	)

       lowerCAmelCase__								=			self.text_encoder(uncond_input.input_ids.to(self.device	)	)[0]

       return torch.cat([uncond_embedding, text_embedding]	)



     def __call__(	self  :		Dict					,	__lowerCamelCase  :		Image.Image					,	__lowerCamelCase  :		str					,	__lowerCamelCase  :		float = 0.3					,	__lowerCamelCase  :		float = 0.6					,	__lowerCamelCase  :		float = 0.5					,	__lowerCamelCase  :		int = 42					,	__lowerCamelCase  :		int = 50					,	__lowerCamelCase  :		float = 7.5					,	):



       """simple docstring"""
       lowerCAmelCase__								=			steps - int(kmin * steps	)
       lowerCAmelCase__								=			steps - int(kmax * steps	)

       lowerCAmelCase__								=			self.prep_text(__lowerCamelCase	)

       self.scheduler.set_timesteps(__lowerCamelCase	)

       lowerCAmelCase__							,			lowerCAmelCase__								=			img.size
       lowerCAmelCase__								=			self.encode(__lowerCamelCase	)

       torch.manual_seed(__lowerCamelCase	)
       lowerCAmelCase__								=			torch.randn(
           (1, self.unet.config.in_channels, height // 8, width // 8)					,	).to(self.device	)

       lowerCAmelCase__								=			self.scheduler.add_noise(
           __lowerCamelCase					,	__lowerCamelCase					,	timesteps=self.scheduler.timesteps[tmax]					,	)

       lowerCAmelCase__								=			torch.cat([latents] * 2	)

       lowerCAmelCase__								=			self.scheduler.scale_model_input(__lowerCamelCase					,	self.scheduler.timesteps[tmax]	)

       with torch.no_grad():
         lowerCAmelCase__								=			self.unet(
             __lowerCamelCase					,	self.scheduler.timesteps[tmax]					,	encoder_hidden_states=__lowerCamelCase					,	).sample

       lowerCAmelCase__							,			lowerCAmelCase__								=			pred.chunk(2	)
       lowerCAmelCase__								=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

       lowerCAmelCase__								=			self.scheduler.step(__lowerCamelCase					,	self.scheduler.timesteps[tmax]					,	__lowerCamelCase	).prev_sample

       for i, t in enumerate(tqdm(self.scheduler.timesteps	)	):
         if i > tmax:
           if i < tmin:  # layout generation phase
             lowerCAmelCase__								=			self.scheduler.add_noise(
                 __lowerCamelCase					,	__lowerCamelCase					,	timesteps=__lowerCamelCase					,	)

             lowerCAmelCase__								=			(mix_factor * latents) + (
                 1 - mix_factor
             ) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
             lowerCAmelCase__								=			torch.cat([input] * 2	)

           else:  # content generation phase
             lowerCAmelCase__								=			torch.cat([latents] * 2	)

           lowerCAmelCase__								=			self.scheduler.scale_model_input(__lowerCamelCase					,	__lowerCamelCase	)

           with torch.no_grad():
             lowerCAmelCase__								=			self.unet(
                 __lowerCamelCase					,	__lowerCamelCase					,	encoder_hidden_states=__lowerCamelCase					,	).sample

           lowerCAmelCase__							,			lowerCAmelCase__								=			pred.chunk(2	)
           lowerCAmelCase__								=			pred_uncond + guidance_scale * (pred_text - pred_uncond)

           lowerCAmelCase__								=			self.scheduler.step(__lowerCamelCase					,	__lowerCamelCase					,	__lowerCamelCase	).prev_sample

       return self.decode(__lowerCamelCase	)







def 		a_       (   __lowerCAmelCase      ,   __lowerCAmelCase							):
  assert isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							), F"""The input value of [set_a={set_a}] is not a set"""
  assert isinstance(__lowerCAmelCase      ,   __lowerCAmelCase							), F"""The input value of [set_b={set_b}] is not a set"""

  return {a + b for a in set_a for b in set_b}


if __name__ == "__main__":
      from doctest import testmod

      testmod()

