
'''simple docstring'''

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		UpperCAmelCase__ :			Dict											=		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.",
		)

		UpperCAmelCase__ :			str											=		parser.parse_args()

		UpperCAmelCase__ :			List[Any]											=		UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		UpperCAmelCase__ :			str											=		CLIPImageProcessor()
		UpperCAmelCase__ :			List[Any]											=		CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

		UpperCAmelCase__ :			List[Any]											=		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)









'''simple docstring'''

def _SCREAMING_SNAKE_CASE (A    ,					A    ,					A			)			->			list:






       """simple docstring"""







       lowercase__      =					len(A			)
       lowercase__      =					[[0] * n for i in range(A			)]
       for i in range(A			):
              lowercase__      =					y_points[i]

       for i in range(2    ,					A			):
              for j in range(A    ,					A			):
                     lowercase__      =					(
                         (xa - x_points[j - i + 1]) * q[j][i - 1]
                         - (xa - x_points[j]) * q[j - 1][i - 1]
                     ) / (x_points[j] - x_points[j - i + 1])

       return [q[n - 1][n - 1], q]


if __name__ == "__main__":
 import doctest

 doctest.testmod()




