






from functools import lru_cache


@lru_cache
def __A   (				__lowerCAmelCase     )->  int:





						"""simple docstring"""



						if not isinstance(__lowerCAmelCase			,		__lowerCAmelCase     ):
												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 __A   (				__lowerCAmelCase			,		__lowerCAmelCase     )->  int:





						"""simple docstring"""



						_UpperCAmelCase     =      len(__lowerCAmelCase     )
						# Find m such that F_m >= n where F_i is the i_th fibonacci number.
						_UpperCAmelCase     =      0
						while True:
												if fibonacci(__lowerCAmelCase     ) >= len_list:
																		_UpperCAmelCase     =      i
																		break
												i += 1
						_UpperCAmelCase     =      0
						while fibb_k > 0:
												_UpperCAmelCase     =      min(
												    offset + fibonacci(fibb_k - 1     )			,		len_list - 1     )  # Prevent out of range
												_UpperCAmelCase     =      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()








import unittest

import torch

from diffusers import DDIMScheduler, TextToVideoZeroPipeline
from diffusers.utils import load_pt, require_torch_gpu, slow

from ..test_pipelines_common import assert_mean_pixel_difference





@slow
@require_torch_gpu
class __lowerCamelCase			(				unittest.TestCase):





	"""simple docstring"""






	def 	UpperCamelCase   (							self   ):
							"""simple docstring"""







							_UpperCAmelCase     =      'runwayml/stable-diffusion-v1-5'
							_UpperCAmelCase     =      TextToVideoZeroPipeline.from_pretrained(UpperCAmelCase	,   torch_dtype=torch.floataa   ).to('cuda'   )
							_UpperCAmelCase     =      DDIMScheduler.from_config(pipe.scheduler.config   )
							_UpperCAmelCase     =      torch.Generator(device='cuda'   ).manual_seed(0   )

							_UpperCAmelCase     =      'A bear is playing a guitar on Times Square'
							_UpperCAmelCase     =      pipe(prompt=UpperCAmelCase	,   generator=UpperCAmelCase   ).images

							_UpperCAmelCase     =      load_pt(
							    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text-to-video/A bear is playing a guitar on Times Square.pt'   )

							assert_mean_pixel_difference(UpperCAmelCase	,   UpperCAmelCase   )
