

import torch

from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest



class     _a						(__magic_name__				):


					'''simple docstring'''






					UpperCAmelCase__:   str  							=       (EulerDiscreteScheduler,)
					UpperCAmelCase__:   int  							=       10






					def   __A			( self    ,       **A__			):
						A__							:    Tuple        =		{
						    """num_train_timesteps""": 1100,
						    """beta_start""": 0.0_0_0_1,
						    """beta_end""": 0.0_2,
						    """beta_schedule""": """linear""",
						}

						config.update(**A__			)
						return config






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






					def   __A			( self			):
						for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]    ,       [0.0_0_0_2, 0.0_0_2, 0.0_2]			):
							self.check_over_configs(beta_start=A__    ,       beta_end=A__			)






					def   __A			( self			):
						for schedule in ["linear", "scaled_linear"]:
							self.check_over_configs(beta_schedule=A__			)






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






					def   __A			( self			):
						A__							:    Any        =		self.scheduler_classes[0]
						A__							:    int        =		self.get_scheduler_config()
						A__							:    Any        =		scheduler_class(**A__			)

						scheduler.set_timesteps(self.num_inference_steps			)

						A__							:    List[Any]        =		torch.manual_seed(0			)

						A__							:    Dict        =		self.dummy_model()
						A__							:    List[Any]        =		self.dummy_sample_deter * scheduler.init_noise_sigma
						A__							:    List[str]        =		sample.to(A__			)

						for i, t in enumerate(scheduler.timesteps			):
							A__							:    List[str]        =		scheduler.scale_model_input(A__    ,       A__			)

							A__							:    Optional[Any]        =		model(A__    ,       A__			)

							A__							:    Optional[int]        =		scheduler.step(A__    ,       A__    ,       A__    ,       generator=A__			)
							A__							:    int        =		output.prev_sample

						A__							:    int        =		torch.sum(torch.abs(A__			)			)
						A__							:    str        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1e-2
						assert abs(result_mean.item() - 0.0_1_3_1			) < 1e-3






					def   __A			( self			):
						A__							:    Dict        =		self.scheduler_classes[0]
						A__							:    Optional[Any]        =		self.get_scheduler_config(prediction_type="""v_prediction"""			)
						A__							:    Any        =		scheduler_class(**A__			)

						scheduler.set_timesteps(self.num_inference_steps			)

						A__							:    Union[str, Any]        =		torch.manual_seed(0			)

						A__							:    Any        =		self.dummy_model()
						A__							:    Optional[Any]        =		self.dummy_sample_deter * scheduler.init_noise_sigma
						A__							:    Any        =		sample.to(A__			)

						for i, t in enumerate(scheduler.timesteps			):
							A__							:    int        =		scheduler.scale_model_input(A__    ,       A__			)

							A__							:    Tuple        =		model(A__    ,       A__			)

							A__							:    List[str]        =		scheduler.step(A__    ,       A__    ,       A__    ,       generator=A__			)
							A__							:    int        =		output.prev_sample

						A__							:    List[Any]        =		torch.sum(torch.abs(A__			)			)
						A__							:    str        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 0.0_0_0_2			) < 1e-2
						assert abs(result_mean.item() - 2.2_676e-06			) < 1e-3






					def   __A			( self			):
						A__							:    Dict        =		self.scheduler_classes[0]
						A__							:    List[str]        =		self.get_scheduler_config()
						A__							:    int        =		scheduler_class(**A__			)

						scheduler.set_timesteps(self.num_inference_steps    ,       device=A__			)

						A__							:    Union[str, Any]        =		torch.manual_seed(0			)

						A__							:    List[Any]        =		self.dummy_model()
						A__							:    Dict        =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A__							:    int        =		sample.to(A__			)

						for t in scheduler.timesteps:
							A__							:    str        =		scheduler.scale_model_input(A__    ,       A__			)

							A__							:    Dict        =		model(A__    ,       A__			)

							A__							:    Any        =		scheduler.step(A__    ,       A__    ,       A__    ,       generator=A__			)
							A__							:    List[str]        =		output.prev_sample

						A__							:    Union[str, Any]        =		torch.sum(torch.abs(A__			)			)
						A__							:    Any        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 1_0.0_8_0_7			) < 1e-2
						assert abs(result_mean.item() - 0.0_1_3_1			) < 1e-3






					def   __A			( self			):
						A__							:    Optional[int]        =		self.scheduler_classes[0]
						A__							:    str        =		self.get_scheduler_config()
						A__							:    Optional[Any]        =		scheduler_class(**A__    ,       use_karras_sigmas=A__			)

						scheduler.set_timesteps(self.num_inference_steps    ,       device=A__			)

						A__							:    Optional[Any]        =		torch.manual_seed(0			)

						A__							:    List[Any]        =		self.dummy_model()
						A__							:    List[str]        =		self.dummy_sample_deter * scheduler.init_noise_sigma.cpu()
						A__							:    Optional[Any]        =		sample.to(A__			)

						for t in scheduler.timesteps:
							A__							:    Tuple        =		scheduler.scale_model_input(A__    ,       A__			)

							A__							:    Optional[Any]        =		model(A__    ,       A__			)

							A__							:    List[str]        =		scheduler.step(A__    ,       A__    ,       A__    ,       generator=A__			)
							A__							:    List[Any]        =		output.prev_sample

						A__							:    Optional[Any]        =		torch.sum(torch.abs(A__			)			)
						A__							:    Tuple        =		torch.mean(torch.abs(A__			)			)

						assert abs(result_sum.item() - 1_2_4.5_2_2_9_9_4_9_9_5_1_1_7_1_9			) < 1e-2
						assert abs(result_mean.item() - 0.1_6_2_1_3_9_3_2_6_3_3_3_9_9_9_6_3			) < 1e-3






import string

def 						UpperCamelCase					(lowercase_: str					)     ->      None:
	for key in range(len(string.ascii_uppercase					)					):
		A__							:    Dict        =		""""""
		for symbol in message:
			if symbol in string.ascii_uppercase:
				A__							:    Dict        =		string.ascii_uppercase.find(lowercase_					)
				A__							:    Optional[int]        =		num - key
				if num < 0:
					A__							:    Optional[int]        =		num + len(string.ascii_uppercase					)
				A__							:    Any        =		translated + string.ascii_uppercase[num]
			else:
				A__							:    Optional[Any]        =		translated + symbol
		print(f"""Decryption using Key #{key}: {translated}"""					)

def 						UpperCamelCase					()     ->      None:
	A__							:    Optional[Any]        =		input("""Encrypted message: """					)
	A__							:    Optional[Any]        =		message.upper()
	decrypt(lowercase_					)


if __name__ == "__main__":
	import doctest

	doctest.testmod()
	main()



