


import torch

from diffusers import DDIMScheduler

from .test_schedulers import SchedulerCommonTest
class 	_lowercase				(      A__	):






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__    :      Union[str, Any]      =		(DDIMScheduler,)
				SCREAMING_SNAKE_CASE__    :      List[Any]      =		(('''eta''', 0.0), ('''num_inference_steps''', 50))


				def 		__magic_name__(	self      :int      ,      **lowerCAmelCase__      :Optional[Any]     )		->      Any:
										__SCREAMING_SNAKE_CASE						:					int		      =		{
										    '''num_train_timesteps''': 1_000,
										    '''beta_start''': 0.0001,
										    '''beta_end''': 0.02,
										    '''beta_schedule''': '''linear''',
										    '''clip_sample''': True,
										}

										config.update(**lowerCAmelCase__     )
										return config


				def 		__magic_name__(	self      :Union[str, Any]      ,      **lowerCAmelCase__      :Optional[int]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.get_scheduler_config(**lowerCAmelCase__     )
										__SCREAMING_SNAKE_CASE						:					Tuple		      =		scheduler_class(**lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE      ,      __SCREAMING_SNAKE_CASE						:					Dict		      =		10, 0.0

										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.dummy_model()
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		self.dummy_sample_deter

										scheduler.set_timesteps(lowerCAmelCase__     )

										for t in scheduler.timesteps:
																__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		model(lowerCAmelCase__      ,      lowerCAmelCase__     )
																__SCREAMING_SNAKE_CASE						:					List[Any]		      =		scheduler.step(lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__      ,      lowerCAmelCase__     ).prev_sample

										return sample


				def 		__magic_name__(	self      :List[Any]     )		->      Tuple:
										for timesteps in [100, 500, 1_000]:
																self.check_over_configs(num_train_timesteps=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Optional[Any]     )		->      Tuple:
										for steps_offset in [0, 1]:
																self.check_over_configs(steps_offset=lowerCAmelCase__     )

										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_scheduler_config(steps_offset=1     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		scheduler_class(**lowerCAmelCase__     )
										scheduler.set_timesteps(5     )
										assert torch.equal(scheduler.timesteps      ,      torch.LongTensor([801, 601, 401, 201, 1]     )     )


				def 		__magic_name__(	self      :Any     )		->      int:
										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=lowerCAmelCase__      ,      beta_end=lowerCAmelCase__     )


				def 		__magic_name__(	self      :str     )		->      Tuple:
										for schedule in ["linear", "squaredcos_cap_v2"]:
																self.check_over_configs(beta_schedule=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Tuple     )		->      Dict:
										for prediction_type in ["epsilon", "v_prediction"]:
																self.check_over_configs(prediction_type=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Dict     )		->      List[str]:
										for clip_sample in [True, False]:
																self.check_over_configs(clip_sample=lowerCAmelCase__     )


				def 		__magic_name__(	self      :int     )		->      Optional[Any]:
										for timestep_spacing in ["trailing", "leading"]:
																self.check_over_configs(timestep_spacing=lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[Any]     )		->      str:
										for rescale_betas_zero_snr in [True, False]:
																self.check_over_configs(rescale_betas_zero_snr=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Dict     )		->      Dict:
										self.check_over_configs(thresholding=lowerCAmelCase__     )
										for threshold in [0.5, 1.0, 2.0]:
																for prediction_type in ["epsilon", "v_prediction"]:
																						self.check_over_configs(
																						    thresholding=lowerCAmelCase__      ,      prediction_type=lowerCAmelCase__      ,      sample_max_value=lowerCAmelCase__      ,      )


				def 		__magic_name__(	self      :int     )		->      Tuple:
										for t in [1, 10, 49]:
																self.check_over_forward(time_step=lowerCAmelCase__     )


				def 		__magic_name__(	self      :List[str]     )		->      Optional[Any]:
										for t, num_inference_steps in zip([1, 10, 50]      ,      [10, 50, 500]     ):
																self.check_over_forward(time_step=lowerCAmelCase__      ,      num_inference_steps=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      List[str]:
										for t, eta in zip([1, 10, 49]      ,      [0.0, 0.5, 1.0]     ):
																self.check_over_forward(time_step=lowerCAmelCase__      ,      eta=lowerCAmelCase__     )


				def 		__magic_name__(	self      :Union[str, Any]     )		->      Union[str, Any]:
										__SCREAMING_SNAKE_CASE						:					str		      =		self.scheduler_classes[0]
										__SCREAMING_SNAKE_CASE						:					Any		      =		self.get_scheduler_config()
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		scheduler_class(**lowerCAmelCase__     )

										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 		__magic_name__(	self      :List[Any]     )		->      str:
										__SCREAMING_SNAKE_CASE						:					Dict		      =		self.full_loop()

										__SCREAMING_SNAKE_CASE						:					Union[str, Any]		      =		torch.sum(torch.abs(lowerCAmelCase__     )     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

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


				def 		__magic_name__(	self      :str     )		->      Tuple:
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		self.full_loop(prediction_type='''v_prediction'''     )

										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.sum(torch.abs(lowerCAmelCase__     )     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

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


				def 		__magic_name__(	self      :List[Any]     )		->      Optional[int]:
										# We specify different beta, so that the first alpha is 0.99
										__SCREAMING_SNAKE_CASE						:					int		      =		self.full_loop(set_alpha_to_one=lowerCAmelCase__      ,      beta_start=0.01     )
										__SCREAMING_SNAKE_CASE						:					List[str]		      =		torch.sum(torch.abs(lowerCAmelCase__     )     )
										__SCREAMING_SNAKE_CASE						:					Optional[int]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

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



				def 		__magic_name__(	self      :List[Any]     )		->      List[Any]:
										# We specify different beta, so that the first alpha is 0.99
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		self.full_loop(set_alpha_to_one=lowerCAmelCase__      ,      beta_start=0.01     )
										__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		torch.sum(torch.abs(lowerCAmelCase__     )     )
										__SCREAMING_SNAKE_CASE						:					List[Any]		      =		torch.mean(torch.abs(lowerCAmelCase__     )     )

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




def 	_UpperCamelCase							(				lowercase__				):
						barcode //= 10  # exclude the last digit
						__SCREAMING_SNAKE_CASE						:					Optional[Any]		      =		False
						__SCREAMING_SNAKE_CASE						:					Dict		      =		0

						# extract and check each digit
						while barcode != 0:
												__SCREAMING_SNAKE_CASE						:					int		      =		1 if checker else 3
												s += mult * (barcode % 10)
												barcode //= 10
												__SCREAMING_SNAKE_CASE						:					Dict		      =		not checker

						return (10 - (s % 10)) % 10


def 	_UpperCamelCase							(				lowercase__				):
						return len(str(lowercase__				)				) == 13 and get_check_digit(lowercase__				) == barcode % 10


def 	_UpperCamelCase							(				lowercase__				):
						if str(lowercase__				).isalpha():
												__SCREAMING_SNAKE_CASE						:					str		      =		F'''Barcode \'{barcode}\' has alphabetic characters.'''
												raise ValueError(lowercase__				)
						elif int(lowercase__				) < 0:
												raise ValueError('''The entered barcode has a negative value. Try again.'''				)
						else:
												return int(lowercase__				)


if __name__ == "__main__":
						import doctest

						doctest.testmod()
						__lowerCAmelCase     :			Union[str, Any]      =get_barcode(input('Barcode: ').strip())

						if is_valid(barcode):
												print(f"""'{barcode}' is a valid barcode.""")
						else:
												print(f"""'{barcode}' is NOT a valid barcode.""")
