




import tempfile
import unittest

import numpy as np
import torch

from diffusers import ScoreSdeVeScheduler




class     A					(				unittest.TestCase       ):
							# TODO adapt with class SchedulerCommonTest (scheduler needs Numpy Integration)
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       (ScoreSdeVeScheduler,)
							_SCREAMING_SNAKE_CASE  :		Optional[int]			  =       ()







							@property
							def        lowercase__							(    self   :	Dict				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= 4
										UpperCamelCase_     				= 3
										UpperCamelCase_     				= 8
										UpperCamelCase_     				= 8

										UpperCamelCase_     				= torch.rand((batch_size, num_channels, height, width)				)

										return sample







							@property
							def        lowercase__							(    self   :	Any				)		->					int:

										"""simple docstring"""



										UpperCamelCase_     				= 4
										UpperCamelCase_     				= 3
										UpperCamelCase_     				= 8
										UpperCamelCase_     				= 8

										UpperCamelCase_     				= batch_size * num_channels * height * width
										UpperCamelCase_     				= torch.arange(__UpperCAmelCase				)
										UpperCamelCase_     				= sample.reshape(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase				)
										UpperCamelCase_     				= sample / num_elems
										UpperCamelCase_     				= sample.permute(3   ,     0   ,     1   ,     2				)

										return sample







							def        lowercase__							(    self   :	int				)		->					Dict:

										"""simple docstring"""



										def model(__UpperCAmelCase   :	Optional[int]   ,     __UpperCAmelCase   :	Tuple   ,     *__UpperCAmelCase   :	Optional[int]				):
													return sample * t / (t + 1)

										return model







							def        lowercase__							(    self   :	Tuple   ,     **__UpperCAmelCase   :	Optional[int]				)		->					str:

										"""simple docstring"""



										UpperCamelCase_     				= {
										    'num_train_timesteps': 2000,
										    'snr': 0.15,
										    'sigma_min': 0.01,
										    'sigma_max': 1348,
										    'sampling_eps': 1E-5,
										}

										config.update(**__UpperCAmelCase				)
										return config







							def        lowercase__							(    self   :	Tuple   ,     __UpperCAmelCase   :	Dict=0   ,     **__UpperCAmelCase   :	List[Any]				)		->					Tuple:

										"""simple docstring"""



										UpperCamelCase_     				= dict(self.forward_default_kwargs				)

										for scheduler_class in self.scheduler_classes:
													UpperCamelCase_     				= self.dummy_sample
													UpperCamelCase_     				= 0.1 * sample

													UpperCamelCase_     				= self.get_scheduler_config(**__UpperCAmelCase				)
													UpperCamelCase_     				= scheduler_class(**__UpperCAmelCase				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__UpperCAmelCase				)
																UpperCamelCase_     				= scheduler_class.from_pretrained(__UpperCAmelCase				)

													UpperCamelCase_     				= scheduler.step_pred(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample
													UpperCamelCase_     				= new_scheduler.step_pred(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample

													assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

													UpperCamelCase_     				= scheduler.step_correct(__UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample
													UpperCamelCase_     				= new_scheduler.step_correct(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample

													assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler correction are not identical"







							def        lowercase__							(    self   :	Dict   ,     __UpperCAmelCase   :	Tuple=0   ,     **__UpperCAmelCase   :	List[Any]				)		->					List[str]:

										"""simple docstring"""



										UpperCamelCase_     				= dict(self.forward_default_kwargs				)
										kwargs.update(__UpperCAmelCase				)

										for scheduler_class in self.scheduler_classes:
													UpperCamelCase_     				= self.dummy_sample
													UpperCamelCase_     				= 0.1 * sample

													UpperCamelCase_     				= self.get_scheduler_config()
													UpperCamelCase_     				= scheduler_class(**__UpperCAmelCase				)

													with tempfile.TemporaryDirectory() as tmpdirname:
																scheduler.save_config(__UpperCAmelCase				)
																UpperCamelCase_     				= scheduler_class.from_pretrained(__UpperCAmelCase				)

													UpperCamelCase_     				= scheduler.step_pred(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample
													UpperCamelCase_     				= new_scheduler.step_pred(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample

													assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler outputs are not identical"

													UpperCamelCase_     				= scheduler.step_correct(__UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample
													UpperCamelCase_     				= new_scheduler.step_correct(
													    __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample

													assert torch.sum(torch.abs(output - new_output				)				) < 1E-5, "Scheduler correction are not identical"







							def        lowercase__							(    self   :	Optional[int]				)		->					List[Any]:

										"""simple docstring"""



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







							def        lowercase__							(    self   :	Optional[int]				)		->					Any:

										"""simple docstring"""



										for sigma_min, sigma_max in zip([0.0_001, 0.001, 0.01]   ,     [1, 100, 1000]				):
													self.check_over_configs(sigma_min=__UpperCAmelCase   ,     sigma_max=__UpperCAmelCase				)







							def        lowercase__							(    self   :	Dict				)		->					List[str]:

										"""simple docstring"""



										for t in [0.1, 0.5, 0.75]:
													self.check_over_forward(time_step=__UpperCAmelCase				)







							def        lowercase__							(    self   :	Optional[int]				)		->					Optional[int]:

										"""simple docstring"""



										UpperCamelCase_     				= dict(self.forward_default_kwargs				)

										UpperCamelCase_     				= self.scheduler_classes[0]
										UpperCamelCase_     				= self.get_scheduler_config()
										UpperCamelCase_     				= scheduler_class(**__UpperCAmelCase				)

										UpperCamelCase_     				= 3

										UpperCamelCase_     				= self.dummy_model()
										UpperCamelCase_     				= self.dummy_sample_deter

										scheduler.set_sigmas(__UpperCAmelCase				)
										scheduler.set_timesteps(__UpperCAmelCase				)
										UpperCamelCase_     				= torch.manual_seed(0				)

										for i, t in enumerate(scheduler.timesteps				):
													UpperCamelCase_     				= scheduler.sigmas[i]

													for _ in range(scheduler.config.correct_steps				):
																with torch.no_grad():
																			UpperCamelCase_     				= model(__UpperCAmelCase   ,     __UpperCAmelCase				)
																UpperCamelCase_     				= scheduler.step_correct(__UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=__UpperCAmelCase   ,     **__UpperCAmelCase				).prev_sample

													with torch.no_grad():
																UpperCamelCase_     				= model(__UpperCAmelCase   ,     __UpperCAmelCase				)

													UpperCamelCase_     				= scheduler.step_pred(__UpperCAmelCase   ,     __UpperCAmelCase   ,     __UpperCAmelCase   ,     generator=__UpperCAmelCase   ,     **__UpperCAmelCase				)
													UpperCamelCase_				,   UpperCamelCase_     				= output.prev_sample, output.prev_sample_mean

										UpperCamelCase_     				= torch.sum(torch.abs(__UpperCAmelCase				)				)
										UpperCamelCase_     				= torch.mean(torch.abs(__UpperCAmelCase				)				)

										assert np.isclose(result_sum.item()   ,     14_372_758_528.0				)
										assert np.isclose(result_mean.item()   ,     18_714_530.0				)







							def        lowercase__							(    self   :	Optional[Any]				)		->					List[Any]:

										"""simple docstring"""



										UpperCamelCase_     				= dict(self.forward_default_kwargs				)

										UpperCamelCase_     				= kwargs.pop('num_inference_steps'   ,     __UpperCAmelCase				)

										for scheduler_class in self.scheduler_classes:
													UpperCamelCase_     				= self.get_scheduler_config()
													UpperCamelCase_     				= scheduler_class(**__UpperCAmelCase				)

													UpperCamelCase_     				= self.dummy_sample
													UpperCamelCase_     				= 0.1 * sample

													if num_inference_steps is not None and hasattr(__UpperCAmelCase   ,     'set_timesteps'				):
																scheduler.set_timesteps(__UpperCAmelCase				)
													elif num_inference_steps is not None and not hasattr(__UpperCAmelCase   ,     'set_timesteps'				):
																UpperCamelCase_     				= num_inference_steps

													UpperCamelCase_     				= scheduler.step_pred(__UpperCAmelCase   ,     0   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample
													UpperCamelCase_     				= scheduler.step_pred(__UpperCAmelCase   ,     1   ,     __UpperCAmelCase   ,     generator=torch.manual_seed(0				)   ,     **__UpperCAmelCase				).prev_sample

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






__a			:	Optional[Any]							     =		[chr(i) for i in range(32, 1_26)]
__a			:	Dict							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(range(len(alphabets)))
__a			:	Tuple							     =		list(range(len(alphabets)))
__a			:	List[Any]							     =		list(reversed(range(len(alphabets))))
__a			:	List[Any]							     =		[]
__a			:	Optional[int]							     =		0




def  a_		(							)	->			Dict:

			'''simple docstring'''


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




def  a_		(							__snake_case					)	->			Any:

			'''simple docstring'''


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


if __name__ == "__main__":
				__a			:	Union[str, Any]							     =		list(input("""Type your message:\n"""))
				while True:
								try:
												__a			:	Optional[Any]							     =		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!"""
				)
