

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[List, PIL.Image.Image, torch.Tensor]      )			->      Tuple:




				'''simple docstring'''





				warnings.warn(
				    "The preprocess method is deprecated and will be removed in a future version. Please"
				    " use VaeImageProcessor.preprocess instead"	,       SCREAMING_SNAKE_CASE_	,       )
				if isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
								return image
				elif isinstance(SCREAMING_SNAKE_CASE_	,       PIL.Image.Image      ):
								A__        =			[image]

				if isinstance(image[0]	,       PIL.Image.Image      ):
								A__    ,				A__        =			image[0].size
								A__    ,				A__        =			(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

								A__        =			[np.array(i.resize((w, h)	,       resample=PIL_INTERPOLATION["lanczos"]      )      )[None, :] for i in image]
								A__        =			np.concatenate(SCREAMING_SNAKE_CASE_	,       axis=0      )
								A__        =			np.array(SCREAMING_SNAKE_CASE_      ).astype(np.floataa      ) / 255.0
								A__        =			image.transpose(0	,       3	,       1	,       2      )
								A__        =			2.0 * image - 1.0
								A__        =			torch.from_numpy(SCREAMING_SNAKE_CASE_      )
				elif isinstance(image[0]	,       torch.Tensor      ):
								A__        =			torch.cat(SCREAMING_SNAKE_CASE_	,       dim=0      )
				return image



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							Union[List, PIL.Image.Image, torch.Tensor]      )			->      Any:




				'''simple docstring'''





				if isinstance(SCREAMING_SNAKE_CASE_	,       torch.Tensor      ):
								return mask
				elif isinstance(SCREAMING_SNAKE_CASE_	,       PIL.Image.Image      ):
								A__        =			[mask]

				if isinstance(mask[0]	,       PIL.Image.Image      ):
								A__    ,				A__        =			mask[0].size
								A__    ,				A__        =			(x - x % 3_2 for x in (w, h))  # resize to integer multiple of 32
								A__        =			[np.array(m.convert("L"      ).resize((w, h)	,       resample=PIL_INTERPOLATION["nearest"]      )      )[None, :] for m in mask]
								A__        =			np.concatenate(SCREAMING_SNAKE_CASE_	,       axis=0      )
								A__        =			mask.astype(np.floataa      ) / 255.0
								A__        =			0
								A__        =			1
								A__        =			torch.from_numpy(SCREAMING_SNAKE_CASE_      )
				elif isinstance(mask[0]	,       torch.Tensor      ):
								A__        =			torch.cat(SCREAMING_SNAKE_CASE_	,       dim=0      )
				return mask





class 					a__						(					snake_case			):
			"""simple docstring"""







			__lowerCamelCase        =       42
			__lowerCamelCase        =       42


			def __init__(    self      ,      lowercase      ,      lowercase       )  ->						List[Any]:
							'''simple docstring'''






							super().__init__()
							self.register_modules(unet=lowercase      ,      scheduler=lowercase       )


			@torch.no_grad()
			def __call__(    self      ,      lowercase      ,      lowercase      ,      lowercase = 250      ,      lowercase = 0.0      ,      lowercase = 10      ,      lowercase = 10      ,      lowercase = None      ,      lowercase = "pil"      ,      lowercase = True      ,      )  ->						Union[ImagePipelineOutput, Tuple]:
							'''simple docstring'''






							A__        =			image

							A__        =			_preprocess_image(lowercase       )
							A__        =			original_image.to(device=self.device      ,      dtype=self.unet.dtype       )
							A__        =			_preprocess_mask(lowercase       )
							A__        =			mask_image.to(device=self.device      ,      dtype=self.unet.dtype       )

							A__        =			original_image.shape[0]

							# sample gaussian noise to begin the loop
							if isinstance(lowercase      ,      lowercase       ) and len(lowercase       ) != batch_size:
											raise ValueError(
											    F'You have passed a list of generators of length {len(lowercase       )}, but requested an effective batch'
											    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'       )

							A__        =			original_image.shape
							A__        =			randn_tensor(lowercase      ,      generator=lowercase      ,      device=self.device      ,      dtype=self.unet.dtype       )

							# set step values
							self.scheduler.set_timesteps(lowercase      ,      lowercase      ,      lowercase      ,      self.device       )
							A__        =			eta

							A__        =			self.scheduler.timesteps[0] + 1
							A__        =			generator[0] if isinstance(lowercase      ,      lowercase       ) else generator
							for i, t in enumerate(self.progress_bar(self.scheduler.timesteps       )       ):
											if t < t_last:
															# predict the noise residual
															A__        =			self.unet(lowercase      ,      lowercase       ).sample
															# compute previous image: x_t -> x_t-1
															A__        =			self.scheduler.step(lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase      ,      lowercase       ).prev_sample

											else:
															# compute the reverse: x_t-1 -> x_t
															A__        =			self.scheduler.undo_step(lowercase      ,      lowercase      ,      lowercase       )
											A__        =			t

							A__        =			(image / 2 + 0.5).clamp(0      ,      1       )
							A__        =			image.cpu().permute(0      ,      2      ,      3      ,      1       ).numpy()
							if output_type == "pil":
											A__        =			self.numpy_to_pil(lowercase       )

							if not return_dict:
											return (image,)

							return ImagePipelineOutput(images=lowercase       )




from math import factorial



def 					lowerCAmelCase__							(	SCREAMING_SNAKE_CASE_:							int = 1_0_0      )			->      int:




				'''simple docstring'''





				return sum(map(SCREAMING_SNAKE_CASE_	,       str(factorial(SCREAMING_SNAKE_CASE_      )      )      )      )


if __name__ == "__main__":
	print(solution(int(input("""Enter the Number: """).strip())))

