




from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput






class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     def __init__(			self     :      Union[str, Any]		,     A__     :      Dict		,     A__     :      int	) ->				Union[str, Any]:
           super().__init__()
           self.register_modules(unet=A__		,     scheduler=A__	)




     @torch.no_grad()
     def __call__(			self     :      int		,     A__     :      int = 1		,     A__     :      Optional[Union[torch.Generator, List[torch.Generator]]] = None		,     A__     :      int = 1_0_0_0		,     A__     :      Optional[str] = "pil"		,     A__     :      bool = True		,     ) ->				Union[ImagePipelineOutput, Tuple]:
           # Sample gaussian noise to begin loop
           if isinstance(self.unet.config.sample_size		,     A__	):
                 lowerCamelCase__					     = (
                     batch_size,
                     self.unet.config.in_channels,
                     self.unet.config.sample_size,
                     self.unet.config.sample_size,
                 )
           else:
                 lowerCamelCase__					     = (batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

           if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__	)
                 lowerCamelCase__					     = image.to(self.device	)
           else:
                 lowerCamelCase__					     = randn_tensor(A__		,     generator=A__		,     device=self.device	)

           # set step values
           self.scheduler.set_timesteps(A__	)

           for t in self.progress_bar(self.scheduler.timesteps	):
                 # 1. predict noise model_output
                 lowerCamelCase__					     = self.unet(A__		,     A__	).sample

                 # 2. compute previous image: x_t -> x_t-1
                 lowerCamelCase__					     = self.scheduler.step(A__		,     A__		,     A__		,     generator=A__	).prev_sample

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

           if not return_dict:
                 return (image,)

           return ImagePipelineOutput(images=A__	)






"""simple docstring"""
import os
import sys
import time

from . import transposition_cipher as trans_cipher

def 						_SCREAMING_SNAKE_CASE       ():
 lowerCAmelCase  							=      'Prehistoric Men.txt'
 lowerCAmelCase  							=      'Output.txt'
 lowerCAmelCase  							=      int(input('Enter key: '       )       )
 lowerCAmelCase  							=      input('Encrypt/Decrypt [e/d]: '       )

 if not os.path.exists(_UpperCAmelCase       ):
  print(F'File {input_file} does not exist. Quitting...'       )
  sys.exit()
 if os.path.exists(_UpperCAmelCase       ):
  print(F'Overwrite {output_file}? [y/n]'       )
  lowerCAmelCase  							=      input('> '       )
  if not response.lower().startswith('y'       ):
   sys.exit()

 lowerCAmelCase  							=      time.time()
 if mode.lower().startswith('e'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.encrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )
 elif mode.lower().startswith('d'       ):
  with open(_UpperCAmelCase       ) as f:
   lowerCAmelCase  							=      f.read()
  lowerCAmelCase  							=      trans_cipher.decrypt_message(_UpperCAmelCase   ,			_UpperCAmelCase       )

 with open(_UpperCAmelCase   ,			'w'       ) as output_obj:
  output_obj.write(_UpperCAmelCase       )

 lowerCAmelCase  							=      round(time.time() - start_time   ,			2       )
 print(('Done (', total_time, 'seconds )')       )


if __name__ == "__main__":
       main()





