






import unittest

import torch

from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow


UpperCAmelCase__      			=						False




class 							snake_case_       (    unittest.TestCase      ):

						"""simple docstring"""



						def        UpperCAmelCase__					(self:					int ,					__UpperCAmelCase:					Optional[Any]=32   )    ->     str:




											'''simple docstring'''



											set_seed(0   )
											__a					:						List[str]      =					UNetaDModel(sample_size=__UpperCAmelCase ,					in_channels=3 ,					out_channels=3   )
											__a					:						Tuple      =					torch.optim.SGD(model.parameters() ,					lr=0.00_01   )
											return model, optimizer



						@slow
						def        UpperCAmelCase__					(self:					Optional[int]   )    ->     Optional[Any]:




											'''simple docstring'''



											__a					:						Tuple      =					"cpu"  # ensure full determinism without setting the CUBLAS_WORKSPACE_CONFIG env variable
											__a					:						Optional[int]      =					DDPMScheduler(
											    num_train_timesteps=1000 ,					beta_start=0.00_01 ,					beta_end=0.02 ,					beta_schedule="linear" ,					clip_sample=__UpperCAmelCase ,					)
											__a					:						str      =					DDIMScheduler(
											    num_train_timesteps=1000 ,					beta_start=0.00_01 ,					beta_end=0.02 ,					beta_schedule="linear" ,					clip_sample=__UpperCAmelCase ,					)

											assert ddpm_scheduler.config.num_train_timesteps == ddim_scheduler.config.num_train_timesteps

											# shared batches for DDPM and DDIM
											set_seed(0   )
											__a					:						List[Any]      =					[torch.randn((4, 3, 32, 32)   ).clip(-1 ,					1   ).to(__UpperCAmelCase   ) for _ in range(4   )]
											__a					:						Dict      =					[torch.randn((4, 3, 32, 32)   ).to(__UpperCAmelCase   ) for _ in range(4   )]
											__a					:						List[str]      =					[torch.randint(0 ,					1000 ,					(4,)   ).long().to(__UpperCAmelCase   ) for _ in range(4   )]

											# train with a DDPM scheduler
											__a      ,			__a					:						Any      =					self.get_model_optimizer(resolution=32   )
											model.train().to(__UpperCAmelCase   )
											for i in range(4   ):
																optimizer.zero_grad()
																__a					:						List[Any]      =					ddpm_scheduler.add_noise(clean_images[i] ,					noise[i] ,					timesteps[i]   )
																__a					:						List[str]      =					model(__UpperCAmelCase ,					timesteps[i]   ).sample
																__a					:						str      =					torch.nn.functional.mse_loss(__UpperCAmelCase ,					noise[i]   )
																loss.backward()
																optimizer.step()
											del model, optimizer

											# recreate the model and optimizer, and retry with DDIM
											__a      ,			__a					:						List[Any]      =					self.get_model_optimizer(resolution=32   )
											model.train().to(__UpperCAmelCase   )
											for i in range(4   ):
																optimizer.zero_grad()
																__a					:						Optional[int]      =					ddim_scheduler.add_noise(clean_images[i] ,					noise[i] ,					timesteps[i]   )
																__a					:						Optional[Any]      =					model(__UpperCAmelCase ,					timesteps[i]   ).sample
																__a					:						Tuple      =					torch.nn.functional.mse_loss(__UpperCAmelCase ,					noise[i]   )
																loss.backward()
																optimizer.step()
											del model, optimizer

											self.assertTrue(torch.allclose(__UpperCAmelCase ,					__UpperCAmelCase ,					atol=1E-5   )   )
											self.assertTrue(torch.allclose(__UpperCAmelCase ,					__UpperCAmelCase ,					atol=1E-5   )   )













UpperCAmelCase__      			=						'''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''







def     a_   ()				->						None:



					"""simple docstring"""
					__a					:						Dict      =					input("Enter message: "      )
					__a					:						Dict      =					input("Enter key [alphanumeric]: "      )
					__a					:						str      =					input("Encrypt/Decrypt [e/d]: "      )

					if mode.lower().startswith("e"      ):
										__a					:						List[str]      =					"encrypt"
										__a					:						Union[str, Any]      =					encrypt_message(__A				,       __A      )
					elif mode.lower().startswith("d"      ):
										__a					:						int      =					"decrypt"
										__a					:						List[str]      =					decrypt_message(__A				,       __A      )

					print(f'\n{mode.title()}ed message:'      )
					print(__A      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "encrypt"      )







def     a_   (__A				,       __A      )				->						str:



					"""simple docstring"""
					return translate_message(__A				,       __A				,       "decrypt"      )







def     a_   (__A				,       __A				,       __A      )				->						str:



					"""simple docstring"""
					__a					:						Union[str, Any]      =					[]
					__a					:						Tuple      =					0
					__a					:						Dict      =					key.upper()

					for symbol in message:
										__a					:						Dict      =					LETTERS.find(symbol.upper()      )
										if num != -1:
															if mode == "encrypt":
																				num += LETTERS.find(key[key_index]      )
															elif mode == "decrypt":
																				num -= LETTERS.find(key[key_index]      )

															num %= len(__A      )

															if symbol.isupper():
																				translated.append(LETTERS[num]      )
															elif symbol.islower():
																				translated.append(LETTERS[num].lower()      )

															key_index += 1
															if key_index == len(__A      ):
																				__a					:						List[str]      =					0
										else:
															translated.append(__A      )
					return "".join(__A      )


if __name__ == "__main__":
			main()





