

from __future__ import annotations

from random import choice
def     UpperCAmelCase_       (						_A    ):





					'''simple docstring'''




					return choice(UpperCAmelCase_    )
def     UpperCAmelCase_       (						_A				,		_A    ):





					'''simple docstring'''




					SCREAMING_SNAKE_CASE__   =   random_pivot(UpperCAmelCase_    )

					# partition based on pivot
					# linear time
					SCREAMING_SNAKE_CASE__   =   [e for e in lst if e < pivot]
					SCREAMING_SNAKE_CASE__   =   [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(UpperCAmelCase_    ) == k - 1:
										return pivot
					# pivot is in elements bigger than k
					elif len(UpperCAmelCase_    ) < k - 1:
										return kth_number(UpperCAmelCase_				,		k - len(UpperCAmelCase_    ) - 1    )
					# pivot is in elements smaller than k
					else:
										return kth_number(UpperCAmelCase_				,		UpperCAmelCase_    )


if __name__ == "__main__":
							import doctest

							doctest.testmod()





from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
       from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline


