

'''simple docstring'''


def 						SCREAMING_SNAKE_CASE(  UpperCamelCase = 5_0  )    ->    int:
	UpperCAmelCase_		:      str       	=					[1] * (length + 1)

	for row_length in range(3   ,length + 1  ):
		for block_length in range(3   ,row_length + 1  ):
			for block_start in range(row_length - block_length  ):
				ways_number[row_length] += ways_number[
				    row_length - block_start - block_length - 1
				]

			ways_number[row_length] += 1

	return ways_number[length]


if __name__ == "__main__":
						print(F'{solution() = }')









'''simple docstring'''


import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
						lowerCAmelCase__										= 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.",
						)

						lowerCAmelCase__										= parser.parse_args()

						lowerCAmelCase__										= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

						lowerCAmelCase__										= CLIPImageProcessor()
						lowerCAmelCase__										= CLIPVisionModelWithProjection.from_pretrained("openai/clip-vit-large-patch14")

						lowerCAmelCase__										= 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)






