
"""simple docstring"""




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      UpperCamelCase	(       __magic_name__      ):

				"""simple docstring"""



				UpperCAmelCase_				:      UNetaDModel
				UpperCAmelCase_				:      PNDMScheduler


				def __init__(				self		,	lowercase__		,	lowercase__			)    ->    Optional[Any]:
									"""simple docstring"""







									super().__init__()

									SCREAMING_SNAKE_CASE				=	PNDMScheduler.from_config(scheduler.config			)

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




				@torch.no_grad()
				def __call__(				self		,	lowercase__ = 1		,	lowercase__ = 50		,	lowercase__ = None		,	lowercase__ = "pil"		,	lowercase__ = True		,	**lowercase__		,	)    ->    Union[ImagePipelineOutput, Tuple]:
									"""simple docstring"""







									SCREAMING_SNAKE_CASE				=	randn_tensor(
									    (batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size)		,	generator=lowercase__		,	device=self.device		,	)

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

														SCREAMING_SNAKE_CASE				=	self.scheduler.step(lowercase__		,	lowercase__		,	lowercase__			).prev_sample

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

									if not return_dict:
														return (image,)

									return ImagePipelineOutput(images=lowercase__			)


"""simple docstring"""




from functools import lru_cache

@lru_cache
def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_			):
					if not isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
										raise TypeError('k must be an integer.'			)
					if k < 0:
										raise ValueError('k integer must be greater or equal to zero.'			)
					if k == 0:
										return 0
					elif k == 1:
										return 1
					else:
										return fibonacci(k - 1			) + fibonacci(k - 2			)

def   UpperCamelCase_  (		SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_			):
					SCREAMING_SNAKE_CASE				=	len(SCREAMING_SNAKE_CASE_			)
					# Find m such that F_m >= n where F_i is the i_th fibonacci number.
					SCREAMING_SNAKE_CASE				=	0
					while True:
										if fibonacci(SCREAMING_SNAKE_CASE_			) >= len_list:
															SCREAMING_SNAKE_CASE				=	i
															break
										i += 1
					SCREAMING_SNAKE_CASE				=	0
					while fibb_k > 0:
										SCREAMING_SNAKE_CASE				=	min(
										    offset + fibonacci(fibb_k - 1			),     len_list - 1			)  # Prevent out of range
										SCREAMING_SNAKE_CASE				=	arr[index_k]
										if item_k_1 == val:
															return index_k
										elif val < item_k_1:
															fibb_k -= 1
										elif val > item_k_1:
															offset += fibonacci(fibb_k - 1			)
															fibb_k -= 2
					else:
										return -1


if __name__ == "__main__":
	import doctest

	doctest.testmod()
