




def 					UpperCAmelCase__     (   lowerCamelCase = 1000      ):
  lowercase			:Dict							=  2**power
  lowercase			:List[Any]							=  str(lowerCamelCase      )
  lowercase			:Tuple							=  list(lowerCamelCase      )
  lowercase			:Any							=  0

  for i in list_num:
    sum_of_num += int(lowerCamelCase      )

  return sum_of_num


if __name__ == "__main__":
     _UpperCAmelCase		:  str	=   int(input("Enter the power of 2: ").strip())
     print("2 ^ ", power, " = ", 2**power)
     _UpperCAmelCase		:  int	=   solution(power)
     print("Sum of the digits is: ", result)









from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_UpperCAmelCase		:  int	=   logging.get_logger(__name__)  # pylint: disable=invalid-name

_UpperCAmelCase		:  Dict	=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"



def 					UpperCAmelCase__     (   lowerCamelCase,	lowerCamelCase,	lowerCamelCase=8      ):
  lowercase			:List[str]							=  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  lowercase			:List[Any]							=  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor







class       __lowerCAmelCase				(							lowerCAmelCase):






       def __init__(      self:    Tuple				,    _lowerCAmelCase:    UNetaDConditionModel				,    _lowerCAmelCase:    DDPMScheduler				,    _lowerCAmelCase:    VQModel				,    ):
         super().__init__()

         self.register_modules(
             unet=_lowerCAmelCase				,    scheduler=_lowerCAmelCase				,    movq=_lowerCAmelCase				,    )
         lowercase			:List[Any]							=  2 ** (len(self.movq.config.block_out_channels ) - 1)






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict				,    _lowerCAmelCase:    Optional[Any]				,    _lowerCAmelCase:    Any				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Tuple				,    _lowerCAmelCase:    Optional[int] ):
         if latents is None:
           lowercase			:int							=  randn_tensor(_lowerCAmelCase				,    generator=_lowerCAmelCase				,    device=_lowerCAmelCase				,    dtype=_lowerCAmelCase )
         else:
           if latents.shape != shape:
             raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {shape}" )
           lowercase			:Optional[Any]							=  latents.to(_lowerCAmelCase )

         lowercase			:int							=  latents * scheduler.init_noise_sigma
         return latents






       def     SCREAMING_SNAKE_CASE			(      self:    List[Any]				,    _lowerCAmelCase:    List[str]=0 ):
         if is_accelerate_available():
           from accelerate import cpu_offload
         else:
           raise ImportError("Please install accelerate via `pip install accelerate`" )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         lowercase			:List[str]							=  [
             self.unet,
             self.movq,
         ]
         for cpu_offloaded_model in models:
           if cpu_offloaded_model is not None:
             cpu_offload(_lowerCAmelCase				,    _lowerCAmelCase )






       def     SCREAMING_SNAKE_CASE			(      self:    List[str]				,    _lowerCAmelCase:    Dict=0 ):
         if is_accelerate_available() and is_accelerate_version(">="				,    "0.17.0.dev0" ):
           from accelerate import cpu_offload_with_hook
         else:
           raise ImportError("`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher." )

         lowercase			:List[Any]							=  torch.device(F"cuda:{gpu_id}" )

         if self.device.type != "cpu":
           self.to("cpu"				,    silence_dtype_warnings=_lowerCAmelCase )
           torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

         lowercase			:List[Any]							=  None
         for cpu_offloaded_model in [self.unet, self.movq]:
           lowercase		,			lowercase			:Dict							=  cpu_offload_with_hook(_lowerCAmelCase				,    _lowerCAmelCase				,    prev_module_hook=_lowerCAmelCase )

         # We'll offload the last model manually.
         lowercase			:Dict							=  hook






       @property
       # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
       def     SCREAMING_SNAKE_CASE			(      self:    int ):
         if not hasattr(self.unet				,    "_hf_hook" ):
           return self.device
         for module in self.unet.modules():
           if (
               hasattr(_lowerCAmelCase				,    "_hf_hook" )
               and hasattr(module._hf_hook				,    "execution_device" )
               and module._hf_hook.execution_device is not None
           ):
             return torch.device(module._hf_hook.execution_device )
         return self.device






       @torch.no_grad()
       @replace_example_docstring(_lowerCAmelCase )
       def __call__(      self:    str				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    Union[torch.FloatTensor, List[torch.FloatTensor]]				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 5_12				,    _lowerCAmelCase:    int = 1_00				,    _lowerCAmelCase:    float = 4.0				,    _lowerCAmelCase:    int = 1				,    _lowerCAmelCase:    Optional[Union[torch.Generator, List[torch.Generator]]] = None				,    _lowerCAmelCase:    Optional[torch.FloatTensor] = None				,    _lowerCAmelCase:    Optional[str] = "pil"				,    _lowerCAmelCase:    bool = True				,    ):
         lowercase			:str							=  self._execution_device

         lowercase			:List[str]							=  guidance_scale > 1.0

         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:Any							=  torch.cat(_lowerCAmelCase				,    dim=0 )
         lowercase			:Any							=  image_embeds.shape[0] * num_images_per_prompt
         if isinstance(_lowerCAmelCase				,    _lowerCAmelCase ):
           lowercase			:List[Any]							=  torch.cat(_lowerCAmelCase				,    dim=0 )

         if do_classifier_free_guidance:
           lowercase			:int							=  image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )
           lowercase			:Any							=  negative_image_embeds.repeat_interleave(_lowerCAmelCase				,    dim=0 )

         lowercase			:Optional[int]							=  torch.cat([negative_image_embeds, image_embeds]				,    dim=0 ).to(dtype=self.unet.dtype				,    device=_lowerCAmelCase )

         self.scheduler.set_timesteps(_lowerCAmelCase				,    device=_lowerCAmelCase )
         lowercase			:Optional[Any]							=  self.scheduler.timesteps

         lowercase			:Tuple							=  self.unet.config.in_channels

         lowercase		,			lowercase			:Optional[int]							=  downscale_height_and_width(_lowerCAmelCase				,    _lowerCAmelCase				,    self.movq_scale_factor )

         # create initial latent
         lowercase			:List[Any]							=  self.prepare_latents(
             (batch_size, num_channels_latents, height, width)				,    image_embeds.dtype				,    _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    self.scheduler				,    )

         for i, t in enumerate(self.progress_bar(_lowerCAmelCase ) ):
           # expand the latents if we are doing classifier free guidance
           lowercase			:Tuple							=  torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents

           lowercase			:List[str]							=  {"image_embeds": image_embeds}
           lowercase			:List[Any]							=  self.unet(
               sample=_lowerCAmelCase				,    timestep=_lowerCAmelCase				,    encoder_hidden_states=_lowerCAmelCase				,    added_cond_kwargs=_lowerCAmelCase				,    return_dict=_lowerCAmelCase				,    )[0]

           if do_classifier_free_guidance:
             lowercase		,			lowercase			:List[Any]							=  noise_pred.split(latents.shape[1]				,    dim=1 )
             lowercase		,			lowercase			:Any							=  noise_pred.chunk(2 )
             lowercase		,			lowercase			:Dict							=  variance_pred.chunk(2 )
             lowercase			:str							=  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
             lowercase			:Any							=  torch.cat([noise_pred, variance_pred_text]				,    dim=1 )

           if not (
               hasattr(self.scheduler.config				,    "variance_type" )
               and self.scheduler.config.variance_type in ["learned", "learned_range"]
           ):
             lowercase		,			lowercase			:str							=  noise_pred.split(latents.shape[1]				,    dim=1 )

           # compute the previous noisy sample x_t -> x_t-1
           lowercase			:Tuple							=  self.scheduler.step(
               _lowerCAmelCase				,    _lowerCAmelCase				,    _lowerCAmelCase				,    generator=_lowerCAmelCase				,    )[0]
         # post-processing
         lowercase			:Dict							=  self.movq.decode(_lowerCAmelCase				,    force_not_quantize=_lowerCAmelCase )["sample"]

         if output_type not in ["pt", "np", "pil"]:
           raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )

         if output_type in ["np", "pil"]:
           lowercase			:Any							=  image * 0.5 + 0.5
           lowercase			:Tuple							=  image.clamp(0				,    1 )
           lowercase			:List[str]							=  image.cpu().permute(0				,    2				,    3				,    1 ).float().numpy()

         if output_type == "pil":
           lowercase			:List[Any]							=  self.numpy_to_pil(_lowerCAmelCase )

         if not return_dict:
           return (image,)

         return ImagePipelineOutput(images=_lowerCAmelCase )



