
'''simple docstring'''
def      UpperCamelCase_				(      A__						):
	return 10 - x * x






def      UpperCamelCase_				(      A__  ,      A__						):
	# Bolzano theory in order to find if there is a root between a and b
	if equation(A__						) * equation(A__						) >= 0:
		raise ValueError("""Wrong space!"""						)

	a_      				=						a
	while (b - a) >= 0.01:
		# Find middle point
		a_      				=						(a + b) / 2
		# Check if middle point is root
		if equation(A__						) == 0.0:
			break
		# Decide the side to repeat the steps
		if equation(A__						) * equation(A__						) < 0:
			a_      				=						c
		else:
			a_      				=						c
	return c


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		print(bisection(-2, 5))
		print(bisection(0, 6))


'''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)
