





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_  ( SCREAMING_SNAKE_CASE__			):


  """simple docstring"""





  def __init__(  self	,  A	,  A	,  A	,  A	,  A	,  )		->   int:





       '''simple docstring'''







       super().__init__()

       self.register_modules(vae=A	,  text_encoder=A	,  tokenizer=A	,  unet=A	,  scheduler=A						)





  def 			__A      (  self	,  A						)		->   Any:





       '''simple docstring'''







       with torch.no_grad():
            __magic_name__				=       self.vae.encode(tfms.ToTensor()(A						).unsqueeze(0						).to(self.device						) * 2 - 1						)
            __magic_name__				=       0.1_82_15 * latent.latent_dist.sample()
       return latent





  def 			__A      (  self	,  A						)		->   Union[str, Any]:





       '''simple docstring'''







       __magic_name__				=       (1 / 0.1_82_15) * latent
       with torch.no_grad():
            __magic_name__				=       self.vae.decode(A						).sample
       __magic_name__				=       (img / 2 + 0.5).clamp(0	,  1						)
       __magic_name__				=       img.detach().cpu().permute(0	,  2	,  3	,  1						).numpy()
       __magic_name__				=       (img * 2_55).round().astype('''uint8'''						)
       return Image.fromarray(img[0]						)





  def 			__A      (  self	,  A						)		->   Dict:





       '''simple docstring'''







       __magic_name__				=       self.tokenizer(
           A	,  padding='''max_length'''	,  max_length=self.tokenizer.model_max_length	,  truncation=A	,  return_tensors='''pt'''	,  )

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

       __magic_name__				=       self.tokenizer(
           ''''''	,  padding='''max_length'''	,  max_length=self.tokenizer.model_max_length	,  truncation=A	,  return_tensors='''pt'''	,  )

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

       return torch.cat([uncond_embedding, text_embedding]						)







  def __call__(  self	,  A	,  A	,  A = 0.3	,  A = 0.6	,  A = 0.5	,  A = 42	,  A = 50	,  A = 7.5	,  )		->   Image.Image:





       '''simple docstring'''







       __magic_name__				=       steps - int(kmin * steps						)
       __magic_name__				=       steps - int(kmax * steps						)

       __magic_name__				=       self.prep_text(A						)

       self.scheduler.set_timesteps(A						)

       __magic_name__		,  __magic_name__				=       img.size
       __magic_name__				=       self.encode(A						)

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

       __magic_name__				=       self.scheduler.add_noise(
           A	,  A	,  timesteps=self.scheduler.timesteps[tmax]	,  )

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

       __magic_name__				=       self.scheduler.scale_model_input(A	,  self.scheduler.timesteps[tmax]						)

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

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

       __magic_name__				=       self.scheduler.step(A	,  self.scheduler.timesteps[tmax]	,  A						).prev_sample

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

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

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

                 __magic_name__				=       self.scheduler.scale_model_input(A	,  A						)

                 with torch.no_grad():
                      __magic_name__				=       self.unet(
                          A	,  A	,  encoder_hidden_states=A	,  ).sample

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

                 __magic_name__				=       self.scheduler.step(A	,  A	,  A						).prev_sample

       return self.decode(A						)






def   _SCREAMING_SNAKE_CASE      (	snake_case_		:						int						):
     if not isinstance(snake_case_ ,     snake_case_						):
          __magic_name__				=       f'Input value of [number={number}] must be an integer'
          raise TypeError(snake_case_						)
     if number < 0 or number % 2 == 1:
          return False
     __magic_name__				=       int(number**0.5						)
     return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
    import doctest

    doctest.testmod()