


from typing import List, Optional, Tuple, Union

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


__snake_case   							=						transforms.Compose(
    [
        transforms.Resize((2_5_6, 2_5_6)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)






def 		_A		(		_lowercase )					-> List[Any]:


     """simple docstring"""


     if isinstance(_lowercase					,  torch.Tensor ):
          return image
     elif isinstance(_lowercase					,  PIL.Image.Image ):
          __UpperCamelCase =			[image]

     __UpperCamelCase =			[trans(img.convert('RGB' ) ) for img in image]
     __UpperCamelCase =			torch.stack(_lowercase )
     return image



class 	__lowerCamelCase			(_a						):







       def __init__(			self: int,A_: Optional[int],A_: Optional[int]					):






            '''simple docstring'''




            super().__init__()

            # make sure scheduler can always be converted to DDIM
            __UpperCamelCase =			DDIMScheduler.from_config(scheduler.config					)

            self.register_modules(unet=A_,scheduler=A_					)







       def 		snake_case_	(			self: Optional[int],A_: Optional[Any]					):






            '''simple docstring'''




            if strength < 0 or strength > 1:
                 raise ValueError(F'''The value of strength should in [0.0, 1.0] but is {strength}'''					)







       def 		snake_case_	(			self: int,A_: Optional[Any],A_: Any,A_: Optional[Any]					):






            '''simple docstring'''




            __UpperCamelCase =			min(int(num_inference_steps * strength					),A_					)

            __UpperCamelCase =			max(num_inference_steps - init_timestep,0					)
            __UpperCamelCase =			self.scheduler.timesteps[t_start:]

            return timesteps, num_inference_steps - t_start







       def 		snake_case_	(			self: Optional[Any],A_: List[str],A_: Optional[Any],A_: Optional[Any],A_: Optional[int],A_: Optional[int],A_: Tuple=None					):






            '''simple docstring'''




            if not isinstance(A_,(torch.Tensor, PIL.Image.Image, list)					):
                 raise ValueError(
                     F'''`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(A_					)}'''					)

            __UpperCamelCase =			image.to(device=A_,dtype=A_					)

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

            __UpperCamelCase =			init_latents.shape
            __UpperCamelCase =			randn_tensor(A_,generator=A_,device=A_,dtype=A_					)

            # get latents
            print('add noise to latents at timestep',A_					)
            __UpperCamelCase =			self.scheduler.add_noise(A_,A_,A_					)
            __UpperCamelCase =			init_latents

            return latents







       @torch.no_grad()
       def __call__(			self: Any,A_: Union[torch.FloatTensor, PIL.Image.Image] = None,A_: float = 0.8,A_: int = 1,A_: Optional[Union[torch.Generator, List[torch.Generator]]] = None,A_: float = 0.0,A_: int = 50,A_: Optional[bool] = None,A_: Optional[str] = "pil",A_: bool = True,):






            '''simple docstring'''




            self.check_inputs(A_					)

            # 2. Preprocess image
            __UpperCamelCase =			preprocess(A_					)

            # 3. set timesteps
            self.scheduler.set_timesteps(A_,device=self.device					)
            __UpperCamelCase,    __UpperCamelCase =			self.get_timesteps(A_,A_,self.device					)
            __UpperCamelCase =			timesteps[:1].repeat(A_					)

            # 4. Prepare latent variables
            __UpperCamelCase =			self.prepare_latents(A_,A_,A_,self.unet.dtype,self.device,A_					)
            __UpperCamelCase =			latents

            # 5. Denoising loop
            for t in self.progress_bar(A_					):
                 # 1. predict noise model_output
                 __UpperCamelCase =			self.unet(A_,A_					).sample

                 # 2. predict previous mean of image x_t-1 and add variance depending on eta
                 # eta corresponds to η in paper and should be between [0, 1]
                 # do x_t -> x_t-1
                 __UpperCamelCase =			self.scheduler.step(
                     A_,A_,A_,eta=A_,use_clipped_model_output=A_,generator=A_,).prev_sample

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

            if not return_dict:
                 return (image, latent_timestep.item())

            return ImagePipelineOutput(images=A_					)






import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


