



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					(	lowerCamelCase__			):




				def __init__(				self		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case		,							_snake_case		,							):



									"""simple docstring"""





									super().__init__()

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




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									with torch.no_grad():
														_lowerCAmelCase         =       self.vae.encode(tfms.ToTensor()(_snake_case						).unsqueeze(0						).to(self.device						) * 2 - 1						)
														_lowerCAmelCase         =       0.1_8215 * latent.latent_dist.sample()
									return latent




				def 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       (1 / 0.1_8215) * latent
									with torch.no_grad():
														_lowerCAmelCase         =       self.vae.decode(_snake_case						).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 				snake_case  (				self		,							_snake_case						):



									"""simple docstring"""





									_lowerCAmelCase         =       self.tokenizer(
									    _snake_case		,							padding="""max_length"""		,							max_length=self.tokenizer.model_max_length		,							truncation=_snake_case		,							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=_snake_case		,							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		,							_snake_case		,							_snake_case		,							_snake_case = 0.3		,							_snake_case = 0.6		,							_snake_case = 0.5		,							_snake_case = 42		,							_snake_case = 50		,							_snake_case = 7.5		,							):



									"""simple docstring"""





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

									_lowerCAmelCase         =       self.prep_text(_snake_case						)

									self.scheduler.set_timesteps(_snake_case						)

									_lowerCAmelCase      ,      _lowerCAmelCase         =       img.size
									_lowerCAmelCase         =       self.encode(_snake_case						)

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

									_lowerCAmelCase         =       self.scheduler.add_noise(
									    _snake_case		,							_snake_case		,							timesteps=self.scheduler.timesteps[tmax]		,							)

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

									_lowerCAmelCase         =       self.scheduler.scale_model_input(_snake_case		,							self.scheduler.timesteps[tmax]						)

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

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

									_lowerCAmelCase         =       self.scheduler.step(_snake_case		,							self.scheduler.timesteps[tmax]		,							_snake_case						).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(
																								    _snake_case		,							_snake_case		,							timesteps=_snake_case		,							)

																								_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(_snake_case		,							_snake_case						)

																			with torch.no_grad():
																								_lowerCAmelCase         =       self.unet(
																								    _snake_case		,							_snake_case		,							encoder_hidden_states=_snake_case		,							).sample

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

																			_lowerCAmelCase         =       self.scheduler.step(_snake_case		,							_snake_case		,							_snake_case						).prev_sample

									return self.decode(_snake_case						)





from math import sqrt


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      [1]
   for i in range(2,  int(sqrt(__lowerCamelCase						)						) + 1,  1						):
      if number % i == 0:
         values.append(__lowerCamelCase						)
         if int(number // i						) != i:
            values.append(int(number // i						)						)
   return sorted(__lowerCamelCase						)


def  A__ (       __lowerCamelCase						):
   return sum(factors(__lowerCamelCase						)						) > n


def  A__ (       __lowerCamelCase						):
   SCREAMING_SNAKE_CASE_	=      factors(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      len(__lowerCamelCase						)
   SCREAMING_SNAKE_CASE_	=      [[0 for i in range(number + 1						)] for j in range(r + 1						)]
   for i in range(r + 1						):
      SCREAMING_SNAKE_CASE_	=      True

   for i in range(1,  number + 1						):
      SCREAMING_SNAKE_CASE_	=      False

   for i in range(1,  r + 1						):
      for j in range(1,  number + 1						):
         if j < values[i - 1]:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j]
         else:
            SCREAMING_SNAKE_CASE_	=      subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

   return subset[r][number] != 0


def  A__ (       __lowerCamelCase						):
   return abundant(__lowerCamelCase						) and not semi_perfect(__lowerCamelCase						)


if __name__ == "__main__":
   import doctest

   doctest.testmod(verbose=True)
   for number in (69, 70, 71):
      print(F"""{number} is {'' if weird(number) else 'not '}weird.""")


