

from __future__ import annotations

from cmath import sqrt

def 				__SCREAMING_SNAKE_CASE				(					lowerCAmelCase:	List[Any]	,   lowerCAmelCase:	Tuple	,   lowerCAmelCase:	List[str]			) ->							Tuple:

				if a == 0:
								raise ValueError("Coefficient 'a' must not be zero."			)
				_UpperCAmelCase							:		Tuple					=     b * b - 4 * a * c

				_UpperCAmelCase							:		Dict					=     (-b + sqrt(_a			)) / (2 * a)
				_UpperCAmelCase							:		List[Any]					=     (-b - sqrt(_a			)) / (2 * a)

				return (
				    root_a.real if not root_a.imag else root_a,
				    root_a.real if not root_a.imag else root_a,
				)

def 				__SCREAMING_SNAKE_CASE				(					) ->							int:
				_UpperCAmelCase							:		Optional[int]					=     quadratic_roots(a=5	,   b=6	,   c=1			)
				print(F'The solutions are: {solutiona} and {solutiona}'			)


if __name__ == "__main__":
					main()





import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
			a_      =     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.',
			)

			a_      =     parser.parse_args()

			a_      =     UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

			a_      =     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)