

lowerCAmelCase__						=	[chr(i) for i in range(3_2, 1_2_6)]
lowerCAmelCase__						=	list(range(len(alphabets)))
lowerCAmelCase__						=	list(range(len(alphabets)))
lowerCAmelCase__						=	list(range(len(alphabets)))
lowerCAmelCase__						=	list(reversed(range(len(alphabets))))
lowerCAmelCase__						=	[]
lowerCAmelCase__						=	lowerCAmelCase__						=	lowerCAmelCase__						=	0



def    lowerCAmelCase__							(		)     -> Optional[Any]:




				'''simple docstring'''





				global gear_one_pos
				global gear_two_pos
				global gear_three_pos
				A__            =     gear_one[0]
				gear_one.append(SCREAMING_SNAKE_CASE_ )
				del gear_one[0]
				gear_one_pos += 1
				if gear_one_pos % int(len(SCREAMING_SNAKE_CASE_ ) ) == 0:
								A__            =     gear_two[0]
								gear_two.append(SCREAMING_SNAKE_CASE_ )
								del gear_two[0]
								gear_two_pos += 1
								if gear_two_pos % int(len(SCREAMING_SNAKE_CASE_ ) ) == 0:
												A__            =     gear_three[0]
												gear_three.append(SCREAMING_SNAKE_CASE_ )
												del gear_three[0]
												gear_three_pos += 1



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple )     -> int:




				'''simple docstring'''





				A__            =     alphabets.index(SCREAMING_SNAKE_CASE_ )
				A__            =     gear_one[target]
				A__            =     gear_two[target]
				A__            =     gear_three[target]
				A__            =     reflector[target]
				A__            =     gear_three.index(SCREAMING_SNAKE_CASE_ )
				A__            =     gear_two.index(SCREAMING_SNAKE_CASE_ )
				A__            =     gear_one.index(SCREAMING_SNAKE_CASE_ )
				code.append(alphabets[target] )
				rotator()


if __name__ == "__main__":
	lowerCAmelCase__						=	list(input("""Type your message:\n"""))
	while True:
		try:
			lowerCAmelCase__						=	int(input("""Please set token:(must be only digits)\n"""))
			break
		except Exception as error:
			print(error)
	for _ in range(token):
		rotator()
	for j in decode:
		engine(j)
	print("""\n""" + """""".join(code))
	print(
	    f"""\nYour Token is {token} please write it down.\nIf you want to decode """
	    """this message again you should input same digits as token!"""
	)




import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest

class    a__  (   snake_case     ):
			"""simple docstring"""







			__lowerCamelCase	    =    (DDIMScheduler,)
			__lowerCamelCase	    =    (('eta', 0.0), ('num_inference_steps', 50))


			def        UpperCamelCase						(      self , **lowercase    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     {
							    "num_train_timesteps": 1000,
							    "beta_start": 0.0001,
							    "beta_end": 0.02,
							    "beta_schedule": "linear",
							    "clip_sample": True,
							}

							config.update(**lowercase    )
							return config


			def        UpperCamelCase						(      self , **lowercase    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config(**lowercase    )
							A__            =     scheduler_class(**lowercase    )

							A__      ,			A__            =     10, 0.0

							A__            =     self.dummy_model()
							A__            =     self.dummy_sample_deter

							scheduler.set_timesteps(lowercase    )

							for t in scheduler.timesteps:
											A__            =     model(lowercase , lowercase    )
											A__            =     scheduler.step(lowercase , lowercase , lowercase , lowercase    ).prev_sample

							return sample


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							for timesteps in [100, 500, 1000]:
											self.check_over_configs(num_train_timesteps=lowercase    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							for steps_offset in [0, 1]:
											self.check_over_configs(steps_offset=lowercase    )

							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config(steps_offset=1    )
							A__            =     scheduler_class(**lowercase    )
							scheduler.set_timesteps(5    )
							assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1]    )    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2]    ):
											self.check_over_configs(beta_start=lowercase , beta_end=lowercase    )


			def        UpperCamelCase						(      self    )					->  Optional[int]:
							'''simple docstring'''






							for schedule in ["linear", "squaredcos_cap_v2"]:
											self.check_over_configs(beta_schedule=lowercase    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for prediction_type in ["epsilon", "v_prediction"]:
											self.check_over_configs(prediction_type=lowercase    )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for clip_sample in [True, False]:
											self.check_over_configs(clip_sample=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							for timestep_spacing in ["trailing", "leading"]:
											self.check_over_configs(timestep_spacing=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							for rescale_betas_zero_snr in [True, False]:
											self.check_over_configs(rescale_betas_zero_snr=lowercase    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							self.check_over_configs(thresholding=lowercase    )
							for threshold in [0.5, 1.0, 2.0]:
											for prediction_type in ["epsilon", "v_prediction"]:
															self.check_over_configs(
															    thresholding=lowercase , prediction_type=lowercase , sample_max_value=lowercase , )


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							for t in [1, 10, 49]:
											self.check_over_forward(time_step=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500]    ):
											self.check_over_forward(time_step=lowercase , num_inference_steps=lowercase    )


			def        UpperCamelCase						(      self    )					->  List[str]:
							'''simple docstring'''






							for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0]    ):
											self.check_over_forward(time_step=lowercase , eta=lowercase    )


			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     self.scheduler_classes[0]
							A__            =     self.get_scheduler_config()
							A__            =     scheduler_class(**lowercase    )

							assert torch.sum(torch.abs(scheduler._get_variance(0 , 0    ) - 0.0    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(420 , 400    ) - 0.1_4771    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(980 , 960    ) - 0.3_2460    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(0 , 0    ) - 0.0    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(487 , 486    ) - 0.0_0979    )    ) < 1e-5
							assert torch.sum(torch.abs(scheduler._get_variance(999 , 998    ) - 0.02    )    ) < 1e-5


			def        UpperCamelCase						(      self    )					->  str:
							'''simple docstring'''






							A__            =     self.full_loop()

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 172.0067    ) < 1e-2
							assert abs(result_mean.item() - 0.22_3967    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__            =     self.full_loop(prediction_type="v_prediction"    )

							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 52.5302    ) < 1e-2
							assert abs(result_mean.item() - 0.0684    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 149.8295    ) < 1e-2
							assert abs(result_mean.item() - 0.1951    ) < 1e-3


			def        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     self.full_loop(set_alpha_to_one=lowercase , beta_start=0.01    )
							A__            =     torch.sum(torch.abs(lowercase    )    )
							A__            =     torch.mean(torch.abs(lowercase    )    )

							assert abs(result_sum.item() - 149.0784    ) < 1e-2
							assert abs(result_mean.item() - 0.1941    ) < 1e-3

