
'''simple docstring'''
from __future__ import annotations

from random import choice






def      UpperCamelCase_				(      A__						):
	return choice(A__						)






def      UpperCamelCase_				(      A__  ,      A__						):
	a_      				=						random_pivot(A__						)

	# partition based on pivot
	# linear time
	a_      				=						[e for e in lst if e < pivot]
	a_      				=						[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'''
import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase__											=argparse.ArgumentParser()

		parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')

		parser.add_argument(
		    '--txt2img_unclip',
		    default='kakaobrain/karlo-v1-alpha',
		    type=str,
		    required=False,
		    help='The pretrained txt2img unclip.',
		)

		lowercase__											=parser.parse_args()

		lowercase__											=UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase__											=CLIPImageProcessor()
		lowercase__											=CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase__											=UnCLIPImageVariationPipeline(
		    decoder=txtaimg.decoder,
		    text_encoder=txtaimg.text_encoder,
		    tokenizer=txtaimg.tokenizer,
		    text_proj=txtaimg.text_proj,
		    feature_extractor=feature_extractor,
		    image_encoder=image_encoder,
		    super_res_first=txtaimg.super_res_first,
		    super_res_last=txtaimg.super_res_last,
		    decoder_scheduler=txtaimg.decoder_scheduler,
		    super_res_scheduler=txtaimg.super_res_scheduler,
		)

		imgaimg.save_pretrained(args.dump_path)
