


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import PNDMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput

class 			lowerCamelCase_					(    _SCREAMING_SNAKE_CASE    ):
							_lowerCamelCase				: UNetaDModel
							_lowerCamelCase				: PNDMScheduler





							def __init__(							self			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ):
								super().__init__()

								a_     			=   PNDMScheduler.from_config(scheduler.config    )

								self.register_modules(unet=_SCREAMING_SNAKE_CASE			,       scheduler=_SCREAMING_SNAKE_CASE    )





							@torch.no_grad()
							def __call__(							self			,       _SCREAMING_SNAKE_CASE = 1			,       _SCREAMING_SNAKE_CASE = 50			,       _SCREAMING_SNAKE_CASE = None			,       _SCREAMING_SNAKE_CASE = "pil"			,       _SCREAMING_SNAKE_CASE = True			,       **_SCREAMING_SNAKE_CASE			,       ):
								a_     			=   randn_tensor(
								    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)			,       generator=_SCREAMING_SNAKE_CASE			,       device=self.device			,       )

								self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE    )
								for t in self.progress_bar(self.scheduler.timesteps    ):
									a_     			=   self.unet(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).sample

									a_     			=   self.scheduler.step(_SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE			,       _SCREAMING_SNAKE_CASE    ).prev_sample

								a_     			=   (image / 2 + 0.5).clamp(0			,       1    )
								a_     			=   image.cpu().permute(0			,       2			,       3			,       1    ).numpy()
								if output_type == "pil":
									a_     			=   self.numpy_to_pil(_SCREAMING_SNAKE_CASE    )

								if not return_dict:
									return (image,)

								return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE    )



class 			lowerCamelCase_					:





							def __init__(							self    ):
								a_     			=   [0, 1]





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







def 			__SCREAMING_SNAKE_CASE		(		)     ->	None:



	"""simple docstring"""






	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_     			=   Fibonacci()

	while True:
		a_     			=   input(""">> """	)
		if prompt in {"exit", "quit"}:
			break

		try:
			a_     			=   int(UpperCamelCase	)
		except ValueError:
			print("""Enter a number or 'exit'"""	)
			continue

		print(fibonacci.get(UpperCamelCase	)	)


if __name__ == "__main__":
			main()