





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

import torch

from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


class   A      (SCREAMING_SNAKE_CASE   ):
			'''simple docstring'''




			def __init__(      self       :   Optional[int]							,     __lowerCAmelCase       :   VQModel							,     __lowerCAmelCase       :   UNetaDModel							,     __lowerCAmelCase       :   DDIMScheduler       )     ->     str:







					"""simple docstring"""






					super().__init__()
					self.register_modules(vqvae=__lowerCAmelCase							,     unet=__lowerCAmelCase							,     scheduler=__lowerCAmelCase       )







			@torch.no_grad()
			def __call__(      self       :   Dict							,     __lowerCAmelCase       :   int = 1							,     __lowerCAmelCase       :   Optional[Union[torch.Generator, List[torch.Generator]]] = None							,     __lowerCAmelCase       :   float = 0.0							,     __lowerCAmelCase       :   int = 50							,     __lowerCAmelCase       :   Optional[str] = "pil"							,     __lowerCAmelCase       :   bool = True							,     **__lowerCAmelCase       :   List[str]							,     )     ->     Union[Tuple, ImagePipelineOutput]:







					"""simple docstring"""






					A__  				=       randn_tensor(
					    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)							,     generator=__lowerCAmelCase							,     )
					A__  				=       latents.to(self.device       )

					# scale the initial noise by the standard deviation required by the scheduler
					A__  				=       latents * self.scheduler.init_noise_sigma

					self.scheduler.set_timesteps(__lowerCAmelCase       )

					# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
					A__  				=       """eta""" in set(inspect.signature(self.scheduler.step       ).parameters.keys()       )

					A__  				=       {}
					if accepts_eta:
							A__  				=       eta

					for t in self.progress_bar(self.scheduler.timesteps       ):
							A__  				=       self.scheduler.scale_model_input(__lowerCAmelCase							,     __lowerCAmelCase       )
							# predict the noise residual
							A__  				=       self.unet(__lowerCAmelCase							,     __lowerCAmelCase       ).sample
							# compute the previous noisy sample x_t -> x_t-1
							A__  				=       self.scheduler.step(__lowerCAmelCase							,     __lowerCAmelCase							,     __lowerCAmelCase							,     **__lowerCAmelCase       ).prev_sample

					# decode the image latents with the VAE
					A__  				=       self.vqvae.decode(__lowerCAmelCase       ).sample

					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(__lowerCAmelCase       )

					if not return_dict:
							return (image,)

					return ImagePipelineOutput(images=__lowerCAmelCase       )










import math







def        __lowerCamelCase (							)		->    None:






		"""simple docstring"""

		A__  				=       input("""Enter message: """   )
		A__  				=       int(input(F'Enter key [2-{len(__a   ) - 1}]: '   )   )
		A__  				=       input("""Encryption/Decryption [e/d]: """   )

		if mode.lower().startswith("""e"""   ):
				A__  				=       encrypt_message(__a   ,					__a   )
		elif mode.lower().startswith("""d"""   ):
				A__  				=       decrypt_message(__a   ,					__a   )

		# Append pipe symbol (vertical bar) to identify spaces at the end.
		print(F'Output:\n{text + "|"}'   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       [""""""] * key
		for col in range(__a   ):
				A__  				=       col
				while pointer < len(__a   ):
						cipher_text[col] += message[pointer]
						pointer += key
		return "".join(__a   )







def        __lowerCamelCase (							__a       :int   ,					__a       :str   )		->    str:






		"""simple docstring"""

		A__  				=       math.ceil(len(__a   ) / key   )
		A__  				=       key
		A__  				=       (num_cols * num_rows) - len(__a   )
		A__  				=       [""""""] * num_cols
		A__  				=       0
		A__  				=       0

		for symbol in message:
				plain_text[col] += symbol
				col += 1

				if (
				    (col == num_cols)
				    or (col == num_cols - 1)
				    and (row >= num_rows - num_shaded_boxes)
				):
						A__  				=       0
						row += 1

		return "".join(__a   )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()



