


'''simple docstring'''







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  lowercase_					(   a__       ):


    def __init__(					self  , a  , a  , a  , a  , a  , ):
          super().__init__()

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


    def        __a							(					self  , a   ):
          with torch.no_grad():
                UpperCamelCase__  =    self.vae.encode(tfms.ToTensor()(a   ).unsqueeze(0   ).to(self.device   ) * 2 - 1   )
                UpperCamelCase__  =    0.1_8215 * latent.latent_dist.sample()
          return latent


    def        __a							(					self  , a   ):
          UpperCamelCase__  =    (1 / 0.1_8215) * latent
          with torch.no_grad():
                UpperCamelCase__  =    self.vae.decode(a   ).sample
          UpperCamelCase__  =    (img / 2 + 0.5).clamp(0  , 1   )
          UpperCamelCase__  =    img.detach().cpu().permute(0  , 2  , 3  , 1   ).numpy()
          UpperCamelCase__  =    (img * 2_55).round().astype("uint8"   )
          return Image.fromarray(img[0]   )


    def        __a							(					self  , a   ):
          UpperCamelCase__  =    self.tokenizer(
              a  , padding="max_length"  , max_length=self.tokenizer.model_max_length  , truncation=a  , return_tensors="pt"  , )

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

          UpperCamelCase__  =    self.tokenizer(
              ""  , padding="max_length"  , max_length=self.tokenizer.model_max_length  , truncation=a  , return_tensors="pt"  , )

          UpperCamelCase__  =    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  , ):
          UpperCamelCase__  =    steps - int(kmin * steps   )
          UpperCamelCase__  =    steps - int(kmax * steps   )

          UpperCamelCase__  =    self.prep_text(a   )

          self.scheduler.set_timesteps(a   )

          UpperCamelCase__ ,     UpperCamelCase__  =    img.size
          UpperCamelCase__  =    self.encode(a   )

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

          UpperCamelCase__  =    self.scheduler.add_noise(
              a  , a  , timesteps=self.scheduler.timesteps[tmax]  , )

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

          UpperCamelCase__  =    self.scheduler.scale_model_input(a  , self.scheduler.timesteps[tmax]   )

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

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

          UpperCamelCase__  =    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
                            UpperCamelCase__  =    self.scheduler.add_noise(
                                a  , a  , timesteps=a  , )

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

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

                      UpperCamelCase__  =    self.scheduler.scale_model_input(a  , a   )

                      with torch.no_grad():
                            UpperCamelCase__  =    self.unet(
                                a  , a  , encoder_hidden_states=a  , ).sample

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

                      UpperCamelCase__  =    self.scheduler.step(a  , a  , a   ).prev_sample

          return self.decode(a   )









'''simple docstring'''







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      if number != int(__A   ):
            raise ValueError("factorial() only accepts integral values"   )
      if number < 0:
            raise ValueError("factorial() not defined for negative values"   )
      UpperCamelCase__  =    1
      for i in range(1				,      number + 1   ):
            value *= i
      return value







def _UpperCamelCase	(					__A   )							->		int:


      '''simple docstring'''



      if not isinstance(__A				,      __A   ):
            raise ValueError("factorial() only accepts integral values"   )
      if n < 0:
            raise ValueError("factorial() not defined for negative values"   )
      return 1 if n == 0 or n == 1 else n * factorial(n - 1   )


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  a__  : str				=	int(input('Enter a positive integer: ').strip() or 0)
  print(F"""factorial{n} is {factorial(n)}""")





