

from __future__ import annotations

from random import choice
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				Optional[Any]:
	return choice(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[int]			,a__    :   int       )					->				int:
	__A :			List[str]		    =			random_pivot(a__       )

	# partition based on pivot
	# linear time
	__A :			Optional[int]		    =			[e for e in lst if e < pivot]
	__A :			Tuple		    =			[e for e in lst if e > pivot]

	# if we get lucky, pivot might be the element we want.
	# we can easily see this:
	# small (elements smaller than k)
	# + pivot (kth element)
	# + big (elements larger than k)
	if len(a__       ) == k - 1:
		return pivot
	# pivot is in elements bigger than k
	elif len(a__       ) < k - 1:
		return kth_number(a__			,k - len(a__       ) - 1       )
	# pivot is in elements smaller than k
	else:
		return kth_number(a__			,a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()









"""simple docstring"""




from dataclasses import dataclass
from typing import List, Optional, Union

import numpy as np
import torch

from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available




@dataclass
class 			__snake_case   (lowerCamelCase  ):
							__a					 =       42


try:
					if not (is_transformers_available() and is_torch_available()):
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					from ...utils.dummy_torch_and_transformers_objects import *  # noqa F403
else:
					from .pipeline_text_to_video_synth import TextToVideoSDPipeline
					from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline  # noqa: F401
					from .pipeline_text_to_video_zero import TextToVideoZeroPipeline




