



"""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       __lowerCamelCase	(   __lowercase    ):







   def __init__(self       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase       ,	):




      '''simple docstring'''




      super().__init__()

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







   def 							A__						(self       ,	lowerCamelCase				):




      '''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       ,	lowerCamelCase				):




      '''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 * 255).round().astype("""uint8"""				)
      return Image.fromarray(img[0]				)







   def 							A__						(self       ,	lowerCamelCase				):




      '''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       ,	lowerCamelCase       ,	lowerCamelCase       ,	lowerCamelCase = 0.3       ,	lowerCamelCase = 0.6       ,	lowerCamelCase = 0.5       ,	lowerCamelCase = 42       ,	lowerCamelCase = 50       ,	lowerCamelCase = 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				)




"""simple docstring"""





def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   ,	snake_case_  :  List[str]   )							->	Optional[int]:






   """simple docstring"""


   if n < 0:
      return 0
   # table[i] represents the number of ways to get to amount i
   _lowerCAmelCase					     =		[0] * (n + 1)

   # There is exactly 1 way to get to zero(You pick no coins).
   _lowerCAmelCase					     =		1

   # Pick all coins one by one and update table[] values
   # after the index greater than or equal to the value of the
   # picked coin
   for coin_val in s:
      for j in range(snake_case_   ,	n + 1   ):
         table[j] += table[j - coin_val]

   return table[n]


if __name__ == "__main__":
  import doctest

  doctest.testmod()