






"""simple docstring"""



class 				__SCREAMING_SNAKE_CASE    :


							def __init__(      self   :    Any				):
													'''simple docstring'''





													A__    :	Any			=	[0, 1]


							def     _UpperCamelCase							(      self   :    int      ,     snake_case   :    int				):
													'''simple docstring'''





													if (difference := index - (len(self.sequence				) - 2)) >= 1:
																			for _ in range(snake_case__				):
																									self.sequence.append(self.sequence[-1] + self.sequence[-2]				)
													return self.sequence[:index]




def   _lowerCAmelCase		(				)		->List[str]:
						print(
						    """Fibonacci Series Using Dynamic Programming\n""",    """Enter the index of the Fibonacci number you want to calculate """,    """in the prompt below. (To exit enter exit or Ctrl-C)\n""",    sep="""""",    )
						A__    :	Tuple			=	Fibonacci()

						while True:
												A__    :	List[Any]			=	input(""">> """   )
												if prompt in {"exit", "quit"}:
																		break

												try:
																		A__    :	Dict			=	int(snake_case_   )
												except ValueError:
																		print("""Enter a number or 'exit'"""   )
																		continue

												print(fibonacci.get(snake_case_   )   )


if __name__ == "__main__":
						main()












import torch
import torch.nn.functional as F

from diffusers import VQDiffusionScheduler

from .test_schedulers import SchedulerCommonTest






class        __lowerCAmelCase					(			UpperCAmelCase__					):
			snake_case_ :					List[str]						=	(VQDiffusionScheduler,)







			def UpperCamelCase     (			self       :     Dict       ,       **snake_case__       :     int ):




									"""simple docstring"""


									_UpperCAmelCase						=	{
									    "num_vec_classes": 4_097,
									    "num_train_timesteps": 100,
									}

									config.update(**snake_case__ )
									return config







			def UpperCamelCase     (			self       :     Optional[int]       ,       snake_case__       :     Any ):




									"""simple docstring"""


									_UpperCAmelCase						=	4
									_UpperCAmelCase						=	8
									_UpperCAmelCase						=	8

									_UpperCAmelCase						=	torch.randint(0       ,       snake_case__       ,       (batch_size, height * width) )

									return sample







			@property
			def UpperCamelCase     (			self       :     str ):




									"""simple docstring"""


									assert False







			def UpperCamelCase     (			self       :     List[str]       ,       snake_case__       :     Optional[Any] ):




									"""simple docstring"""


									def model(snake_case__       :     Dict       ,       snake_case__       :     Tuple       ,       *snake_case__       :     str ):
															_UpperCAmelCase				,					_UpperCAmelCase						=	sample.shape
															_UpperCAmelCase						=	torch.rand((batch_size, num_vec_classes - 1, num_latent_pixels) )
															_UpperCAmelCase						=	F.log_softmax(logits.double()       ,       dim=1 ).float()
															return return_value

									return model







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for timesteps in [2, 5, 100, 1_000]:
															self.check_over_configs(num_train_timesteps=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[int] ):




									"""simple docstring"""


									for num_vec_classes in [5, 100, 1_000, 4_000]:
															self.check_over_configs(num_vec_classes=snake_case__ )







			def UpperCamelCase     (			self       :     Optional[Any] ):




									"""simple docstring"""


									for t in [0, 50, 99]:
															self.check_over_forward(time_step=snake_case__ )







			def UpperCamelCase     (			self       :     Tuple ):




									"""simple docstring"""


									pass




