



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	(CMStochasticIterativeScheduler,)
							lowerCamelCase__         =	10







							def      snake_case__   (     self,    **__a):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					{
														    "num_train_timesteps": 201,
														    "sigma_min": 0.002,
														    "sigma_max": 80.0,
														}

														config.update(**__a)
														return config







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					10

														_lowerCAmelCase  :							List[str]				     =					self.get_scheduler_config()
														_lowerCAmelCase  :							Any				     =					self.scheduler_classes[0](**__a)

														scheduler.set_timesteps(__a)

														_lowerCAmelCase  :							Tuple				     =					scheduler.timesteps[0]
														_lowerCAmelCase  :							Tuple				     =					scheduler.timesteps[1]

														_lowerCAmelCase  :							Tuple				     =					self.dummy_sample
														_lowerCAmelCase  :							Dict				     =					0.1 * sample

														_lowerCAmelCase  :							Optional[int]				     =					scheduler.step(__a,    __a,    __a).prev_sample
														_lowerCAmelCase  :							Optional[int]				     =					scheduler.step(__a,    __a,    __a).prev_sample

														self.assertEqual(output_a.shape,    sample.shape)
														self.assertEqual(output_a.shape,    output_a.shape)







							def      snake_case__   (     self):

														'''simple docstring'''

														for timesteps in [10, 50, 100, 1000]:
																					self.check_over_configs(num_train_timesteps=__a)







							def      snake_case__   (     self):

														'''simple docstring'''

														for clip_denoised in [True, False]:
																					self.check_over_configs(clip_denoised=__a)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.scheduler_classes[0]
														_lowerCAmelCase  :							str				     =					self.get_scheduler_config()
														_lowerCAmelCase  :							List[Any]				     =					scheduler_class(**__a)

														_lowerCAmelCase  :							List[str]				     =					1
														scheduler.set_timesteps(__a)
														_lowerCAmelCase  :							Dict				     =					scheduler.timesteps

														_lowerCAmelCase  :							List[str]				     =					torch.manual_seed(0)

														_lowerCAmelCase  :							Tuple				     =					self.dummy_model()
														_lowerCAmelCase  :							Optional[Any]				     =					self.dummy_sample_deter * scheduler.init_noise_sigma

														for i, t in enumerate(__a):
																					# 1. scale model input
																					_lowerCAmelCase  :							Any				     =					scheduler.scale_model_input(__a,    __a)

																					# 2. predict noise residual
																					_lowerCAmelCase  :							Optional[Any]				     =					model(__a,    __a)

																					# 3. predict previous sample x_t-1
																					_lowerCAmelCase  :							Optional[int]				     =					scheduler.step(__a,    __a,    __a,    generator=__a).prev_sample

																					_lowerCAmelCase  :							Any				     =					pred_prev_sample

														_lowerCAmelCase  :							Dict				     =					torch.sum(torch.abs(__a))
														_lowerCAmelCase  :							List[str]				     =					torch.mean(torch.abs(__a))

														assert abs(result_sum.item() - 192.7_614) < 1E-2
														assert abs(result_mean.item() - 0.2_510) < 1E-3







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.scheduler_classes[0]
														_lowerCAmelCase  :							Optional[Any]				     =					self.get_scheduler_config()
														_lowerCAmelCase  :							List[Any]				     =					scheduler_class(**__a)

														_lowerCAmelCase  :							List[str]				     =					[106, 0]
														scheduler.set_timesteps(timesteps=__a)
														_lowerCAmelCase  :							str				     =					scheduler.timesteps

														_lowerCAmelCase  :							Tuple				     =					torch.manual_seed(0)

														_lowerCAmelCase  :							Optional[Any]				     =					self.dummy_model()
														_lowerCAmelCase  :							List[Any]				     =					self.dummy_sample_deter * scheduler.init_noise_sigma

														for t in timesteps:
																					# 1. scale model input
																					_lowerCAmelCase  :							Any				     =					scheduler.scale_model_input(__a,    __a)

																					# 2. predict noise residual
																					_lowerCAmelCase  :							Union[str, Any]				     =					model(__a,    __a)

																					# 3. predict previous sample x_t-1
																					_lowerCAmelCase  :							List[Any]				     =					scheduler.step(__a,    __a,    __a,    generator=__a).prev_sample

																					_lowerCAmelCase  :							int				     =					pred_prev_sample

														_lowerCAmelCase  :							List[str]				     =					torch.sum(torch.abs(__a))
														_lowerCAmelCase  :							Union[str, Any]				     =					torch.mean(torch.abs(__a))

														assert abs(result_sum.item() - 347.6_357) < 1E-2
														assert abs(result_mean.item() - 0.4_527) < 1E-3







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Optional[int]				     =					self.scheduler_classes[0]
														_lowerCAmelCase  :							Dict				     =					self.get_scheduler_config()
														_lowerCAmelCase  :							Union[str, Any]				     =					scheduler_class(**__a)

														_lowerCAmelCase  :							Tuple				     =					[39, 30, 12, 15, 0]

														with self.assertRaises(__a,    msg="`timesteps` must be in descending order."):
																					scheduler.set_timesteps(timesteps=__a)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							int				     =					self.scheduler_classes[0]
														_lowerCAmelCase  :							Optional[int]				     =					self.get_scheduler_config()
														_lowerCAmelCase  :							Tuple				     =					scheduler_class(**__a)

														_lowerCAmelCase  :							Any				     =					[39, 30, 12, 1, 0]
														_lowerCAmelCase  :							Dict				     =					len(__a)

														with self.assertRaises(__a,    msg="Can only pass one of `num_inference_steps` or `timesteps`."):
																					scheduler.set_timesteps(num_inference_steps=__a,    timesteps=__a)







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							List[str]				     =					self.scheduler_classes[0]
														_lowerCAmelCase  :							List[Any]				     =					self.get_scheduler_config()
														_lowerCAmelCase  :							Optional[Any]				     =					scheduler_class(**__a)

														_lowerCAmelCase  :							Optional[Any]				     =					[scheduler.config.num_train_timesteps]

														with self.assertRaises(
														    __a,    msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}",    ):
																					scheduler.set_timesteps(timesteps=__a)





import string






def        A       (      _lowerCamelCase						):





							'''simple docstring'''




							for key in range(len(string.ascii_uppercase						)						):
														_lowerCAmelCase  :							str				     =					""
														for symbol in message:
																					if symbol in string.ascii_uppercase:
																												_lowerCAmelCase  :							List[str]				     =					string.ascii_uppercase.find(_lowerCamelCase						)
																												_lowerCAmelCase  :							Dict				     =					num - key
																												if num < 0:
																																			_lowerCAmelCase  :							Dict				     =					num + len(string.ascii_uppercase						)
																												_lowerCAmelCase  :							Optional[Any]				     =					translated + string.ascii_uppercase[num]
																					else:
																												_lowerCAmelCase  :							int				     =					translated + symbol
														print(F"Decryption using Key #{key}: {translated}"						)






def        A       (      ):





							'''simple docstring'''




							_lowerCAmelCase  :							Tuple				     =					input("Encrypted message: "						)
							_lowerCAmelCase  :							Dict				     =					message.upper()
							decrypt(_lowerCamelCase						)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						main()
