





from __future__ import annotations





def     _UpperCamelCase     (a__   :str   ,			a__   :list[str] | None = None       ):






							"""simple docstring"""





							UpperCamelCase__				     =			word_bank or []
							# create a table
							UpperCamelCase__				     =			len(a__       ) + 1

							UpperCamelCase__				     =			[]
							for _ in range(a__       ):
														table.append([]       )
							# seed value
							UpperCamelCase__				     =			[[]]  # because empty string has empty combination

							# iterate through the indices
							for i in range(a__       ):
														# condition
														if table[i] != []:
																					for word in word_bank:
																												# slice condition
																												if target[i : i + len(a__       )] == word:
																																			UpperCamelCase__				     =			[
																																			    [word, *way] for way in table[i]
																																			]
																																			# adds the word to every combination the current position holds
																																			# now,push that combination to the table[i+len(word)]
																																			table[i + len(a__       )] += new_combinations

    # combinations are in reverse order so reverse for better output
							for combination in table[len(a__       )]:
														combination.reverse()

							return table[len(a__       )]


if __name__ == "__main__":
					print(all_construct("jwajalapa", ["jwa", "j", "w", "a", "la", "lapa"]))
					print(all_construct("rajamati", ["s", "raj", "amat", "raja", "ma", "i", "t"]))
					print(
					    all_construct(
					        "hexagonosaurus",
					        ["h", "ex", "hex", "ag", "ago", "ru", "auru", "rus", "go", "no", "o", "s"],
					    )
					)











import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
					UpperCamelCase__											= 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__											= parser.parse_args()

					UpperCamelCase__											= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

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

					UpperCamelCase__											= 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)




