

from typing import Union

import torch
from PIL import Image
from torchvision import transforms as tfms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)






class 			__magic_name__					(    lowerCAmelCase_   ):





			def __init__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						__snake_case      ,						)     ->				Any:




						'''simple docstring'''
						super().__init__()

						self.register_modules(vae=__snake_case      ,						text_encoder=__snake_case      ,						tokenizer=__snake_case      ,						unet=__snake_case      ,						scheduler=__snake_case      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[Any]:




						'''simple docstring'''
						with torch.no_grad():
									__a						   =self.vae.encode(tfms.ToTensor()(__snake_case      ).unsqueeze(0      ).to(self.device      ) * 2 - 1      )
									__a						   =0.1_8215 * latent.latent_dist.sample()
						return latent





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				Optional[int]:




						'''simple docstring'''
						__a						   =(1 / 0.1_8215) * latent
						with torch.no_grad():
									__a						   =self.vae.decode(__snake_case      ).sample
						__a						   =(img / 2 + 0.5).clamp(0      ,						1      )
						__a						   =img.detach().cpu().permute(0      ,						2      ,						3      ,						1      ).numpy()
						__a						   =(img * 255).round().astype('uint8'      )
						return Image.fromarray(img[0]      )





			def 	__magic_name__					(	self      ,						__snake_case      )     ->				str:




						'''simple docstring'''
						__a						   =self.tokenizer(
						    __snake_case      ,						padding='max_length'      ,						max_length=self.tokenizer.model_max_length      ,						truncation=__snake_case      ,						return_tensors='pt'      ,						)

						__a						   =self.text_encoder(text_input.input_ids.to(self.device      )      )[0]

						__a						   =self.tokenizer(
						    ''      ,						padding='max_length'      ,						max_length=self.tokenizer.model_max_length      ,						truncation=__snake_case      ,						return_tensors='pt'      ,						)

						__a						   =self.text_encoder(uncond_input.input_ids.to(self.device      )      )[0]

						return torch.cat([uncond_embedding, text_embedding]      )





			def __call__(	self      ,						__snake_case      ,						__snake_case      ,						__snake_case = 0.3      ,						__snake_case = 0.6      ,						__snake_case = 0.5      ,						__snake_case = 42      ,						__snake_case = 50      ,						__snake_case = 7.5      ,						)     ->				Image.Image:




						'''simple docstring'''
						__a						   =steps - int(kmin * steps      )
						__a						   =steps - int(kmax * steps      )

						__a						   =self.prep_text(__snake_case      )

						self.scheduler.set_timesteps(__snake_case      )

						__a   ,					__a						   =img.size
						__a						   =self.encode(__snake_case      )

						torch.manual_seed(__snake_case      )
						__a						   =torch.randn(
						    (1, self.unet.config.in_channels, height // 8, width // 8)      ,						).to(self.device      )

						__a						   =self.scheduler.add_noise(
						    __snake_case      ,						__snake_case      ,						timesteps=self.scheduler.timesteps[tmax]      ,						)

						__a						   =torch.cat([latents] * 2      )

						__a						   =self.scheduler.scale_model_input(__snake_case      ,						self.scheduler.timesteps[tmax]      )

						with torch.no_grad():
									__a						   =self.unet(
									    __snake_case      ,						self.scheduler.timesteps[tmax]      ,						encoder_hidden_states=__snake_case      ,						).sample

						__a   ,					__a						   =pred.chunk(2      )
						__a						   =pred_uncond + guidance_scale * (pred_text - pred_uncond)

						__a						   =self.scheduler.step(__snake_case      ,						self.scheduler.timesteps[tmax]      ,						__snake_case      ).prev_sample

						for i, t in enumerate(tqdm(self.scheduler.timesteps      )      ):
									if i > tmax:
												if i < tmin:  # layout generation phase
															__a						   =self.scheduler.add_noise(
															    __snake_case      ,						__snake_case      ,						timesteps=__snake_case      ,						)

															__a						   =(mix_factor * latents) + (
															    1 - mix_factor
															) * orig_latents  # interpolating between layout noise and conditionally generated noise to preserve layout sematics
															__a						   =torch.cat([input] * 2      )

												else:  # content generation phase
															__a						   =torch.cat([latents] * 2      )

												__a						   =self.scheduler.scale_model_input(__snake_case      ,						__snake_case      )

												with torch.no_grad():
															__a						   =self.unet(
															    __snake_case      ,						__snake_case      ,						encoder_hidden_states=__snake_case      ,						).sample

												__a   ,					__a						   =pred.chunk(2      )
												__a						   =pred_uncond + guidance_scale * (pred_text - pred_uncond)

												__a						   =self.scheduler.step(__snake_case      ,						__snake_case      ,						__snake_case      ).prev_sample

						return self.decode(__snake_case      )









import os
import sys
import time

from . import transposition_cipher as trans_cipher






def  UpperCamelCase_(							):




			"""simple docstring"""






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

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

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

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

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


if __name__ == "__main__":
						main()






