




'''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 	SCREAMING_SNAKE_CASE(    __A					):

       def __init__(   self ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__ ,		)   -> List[str]:

           """simple docstring"""



           super().__init__()

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

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> List[str]:

           """simple docstring"""



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

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						(1 / 0.1_82_15) * latent
           with torch.no_grad():
               __lowercase          =						self.vae.decode(lowerCamelCase__					).sample
           __lowercase          =						(img / 2 + 0.5).clamp(0 ,		1					)
           __lowercase          =						img.detach().cpu().permute(0 ,		2 ,		3 ,		1					).numpy()
           __lowercase          =						(img * 255).round().astype("""uint8"""					)
           return Image.fromarray(img[0]					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Optional[Any]:

           """simple docstring"""



           __lowercase          =						self.tokenizer(
               lowerCamelCase__ ,		padding="""max_length""" ,		max_length=self.tokenizer.model_max_length ,		truncation=lowerCamelCase__ ,		return_tensors="""pt""" ,		)

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

           __lowercase          =						self.tokenizer(
               """""" ,		padding="""max_length""" ,		max_length=self.tokenizer.model_max_length ,		truncation=lowerCamelCase__ ,		return_tensors="""pt""" ,		)

           __lowercase          =						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 ,		)   -> Image.Image:

           """simple docstring"""



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

           __lowercase          =						self.prep_text(lowerCamelCase__					)

           self.scheduler.set_timesteps(lowerCamelCase__					)

           __lowercase    ,__lowercase          =						img.size
           __lowercase          =						self.encode(lowerCamelCase__					)

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

           __lowercase          =						self.scheduler.add_noise(
               lowerCamelCase__ ,		lowerCamelCase__ ,		timesteps=self.scheduler.timesteps[tmax] ,		)

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

           __lowercase          =						self.scheduler.scale_model_input(lowerCamelCase__ ,		self.scheduler.timesteps[tmax]					)

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

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

           __lowercase          =						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
                       __lowercase          =						self.scheduler.add_noise(
                           lowerCamelCase__ ,		lowerCamelCase__ ,		timesteps=lowerCamelCase__ ,		)

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

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

                   __lowercase          =						self.scheduler.scale_model_input(lowerCamelCase__ ,		lowerCamelCase__					)

                   with torch.no_grad():
                       __lowercase          =						self.unet(
                           lowerCamelCase__ ,		lowerCamelCase__ ,		encoder_hidden_states=lowerCamelCase__ ,		).sample

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

                   __lowercase          =						self.scheduler.step(lowerCamelCase__ ,		lowerCamelCase__ ,		lowerCamelCase__					).prev_sample

           return self.decode(lowerCamelCase__					)










'''simple docstring'''
A							:				int			     =	(1, 153, 370, 371, 1_634, 24_678_051, 115_132_219_018_763_992_565_095_597_973_971_522_401)
A							:				tuple			     =	(-153, -1, 0, 1.2, 200, "A", [], {}, None)
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Initialization of sum and number of digits.
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    # Calculation of digits of the number
    while temp > 0:
        number_of_digits += 1
        temp //= 10
    # Dividing number into separate digits and find Armstrong number
    __lowercase          =						n
    while temp > 0:
        __lowercase          =						temp % 10
        total += rem**number_of_digits
        temp //= 10
    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False

    # Init a "histogram" of the digits
    __lowercase          =						[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
    __lowercase          =						0
    __lowercase          =						0
    __lowercase          =						n
    while temp > 0:
        __lowercase    ,__lowercase          =						divmod(a__   ,10      )
        digit_histogram[rem] += 1
        digit_total += 1

    for cnt, i in zip(a__   ,range(len(a__      )      )      ):
        total += cnt * i**digit_total

    return n == total
def     snake_case_  (       a__		:     int      ):



    """simple docstring"""




    if not isinstance(a__   ,a__      ) or n < 1:
        return False
    __lowercase          =						len(str(a__      )      )  # the power that all digits will be raised to
    # check if sum of each digit multiplied expo times is equal to number
    return n == sum(int(a__      ) ** expo for i in str(a__      )      )
def     snake_case_  (       ):



    """simple docstring"""




    __lowercase          =						int(input("""Enter an integer to see if it is an Armstrong number: """      ).strip()      )
    print(f'{num} is {"" if armstrong_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if narcissistic_number(a__      ) else "not "}an Armstrong number.'      )
    print(f'{num} is {"" if pluperfect_number(a__      ) else "not "}an Armstrong number.'      )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




