




import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest







class a		(     _UpperCamelCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       Optional[int]       	=		(CMStochasticIterativeScheduler,)
						UpperCamelCase_	:       str       	=		10






						def     UpperCAmelCase_      ( self :     Tuple				,							**lowerCamelCase__ :     Any				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    {
													    "num_train_timesteps": 201,
													    "sigma_min": 0.0_0_2,
													    "sigma_max": 8_0.0,
													}

													config.update(**lowerCamelCase__				)
													return config






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						List[str]:
													"""simple docstring"""


													__lowercase      =    10

													__lowercase      =    self.get_scheduler_config()
													__lowercase      =    self.scheduler_classes[0](**lowerCamelCase__				)

													scheduler.set_timesteps(lowerCamelCase__				)

													__lowercase      =    scheduler.timesteps[0]
													__lowercase      =    scheduler.timesteps[1]

													__lowercase      =    self.dummy_sample
													__lowercase      =    0.1 * sample

													__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample
													__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				).prev_sample

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






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Union[str, Any]:
													"""simple docstring"""


													for timesteps in [10, 50, 100, 1_000]:
																				self.check_over_configs(num_train_timesteps=lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     str				)    ->						Any:
													"""simple docstring"""


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






						def     UpperCAmelCase_      ( self :     str				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config()
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    1
													scheduler.set_timesteps(lowerCamelCase__				)
													__lowercase      =    scheduler.timesteps

													__lowercase      =    torch.manual_seed(0				)

													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter * scheduler.init_noise_sigma

													for i, t in enumerate(lowerCamelCase__				):
																				# 1. scale model input
																				__lowercase      =    scheduler.scale_model_input(lowerCamelCase__				,							lowerCamelCase__				)

																				# 2. predict noise residual
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)

																				# 3. predict previous sample x_t-1
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				).prev_sample

																				__lowercase      =    pred_prev_sample

													__lowercase      =    torch.sum(torch.abs(lowerCamelCase__				)				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_sum.item() - 1_9_2.7_6_1_4				) < 1e-2
													assert abs(result_mean.item() - 0.2_5_1_0				) < 1e-3






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config()
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    [106, 0]
													scheduler.set_timesteps(timesteps=lowerCamelCase__				)
													__lowercase      =    scheduler.timesteps

													__lowercase      =    torch.manual_seed(0				)

													__lowercase      =    self.dummy_model()
													__lowercase      =    self.dummy_sample_deter * scheduler.init_noise_sigma

													for t in timesteps:
																				# 1. scale model input
																				__lowercase      =    scheduler.scale_model_input(lowerCamelCase__				,							lowerCamelCase__				)

																				# 2. predict noise residual
																				__lowercase      =    model(lowerCamelCase__				,							lowerCamelCase__				)

																				# 3. predict previous sample x_t-1
																				__lowercase      =    scheduler.step(lowerCamelCase__				,							lowerCamelCase__				,							lowerCamelCase__				,							generator=lowerCamelCase__				).prev_sample

																				__lowercase      =    pred_prev_sample

													__lowercase      =    torch.sum(torch.abs(lowerCamelCase__				)				)
													__lowercase      =    torch.mean(torch.abs(lowerCamelCase__				)				)

													assert abs(result_sum.item() - 3_4_7.6_3_5_7				) < 1e-2
													assert abs(result_mean.item() - 0.4_5_2_7				) < 1e-3






						def     UpperCAmelCase_      ( self :     Optional[int]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config()
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    [39, 30, 12, 15, 0]

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






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						str:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config()
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    [39, 30, 12, 1, 0]
													__lowercase      =    len(lowerCamelCase__				)

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






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    self.scheduler_classes[0]
													__lowercase      =    self.get_scheduler_config()
													__lowercase      =    scheduler_class(**lowerCamelCase__				)

													__lowercase      =    [scheduler.config.num_train_timesteps]

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












import math







def 		_A(       )    -> None:
							'''simple docstring'''


							__lowercase      =    input('''Enter message: '''				)
							__lowercase      =    int(input(F'Enter key [2-{len(UpperCamelCase__				) - 1}]: '				)				)
							__lowercase      =    input('''Encryption/Decryption [e/d]: '''				)

							if mode.lower().startswith('''e'''				):
														__lowercase      =    encrypt_message(UpperCamelCase__				,       UpperCamelCase__				)
							elif mode.lower().startswith('''d'''				):
														__lowercase      =    decrypt_message(UpperCamelCase__				,       UpperCamelCase__				)

							# Append pipe symbol (vertical bar) to identify spaces at the end.
							print(F'Output:\n{text + "|"}'				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    [''''''] * key
							for col in range(UpperCamelCase__				):
														__lowercase      =    col
														while pointer < len(UpperCamelCase__				):
																					cipher_text[col] += message[pointer]
																					pointer += key
							return "".join(UpperCamelCase__				)







def 		_A(       UpperCamelCase__							:			int				,       UpperCamelCase__							:			str				)    -> str:
							'''simple docstring'''


							__lowercase      =    math.ceil(len(UpperCamelCase__				) / key				)
							__lowercase      =    key
							__lowercase      =    (num_cols * num_rows) - len(UpperCamelCase__				)
							__lowercase      =    [''''''] * num_cols
							__lowercase      =    0
							__lowercase      =    0

							for symbol in message:
														plain_text[col] += symbol
														col += 1

														if (
														    (col == num_cols)
														    or (col == num_cols - 1)
														    and (row >= num_rows - num_shaded_boxes)
														):
																					__lowercase      =    0
																					row += 1

							return "".join(UpperCamelCase__				)


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()






