
from __future__ import annotations

import collections
import pprint
from pathlib import Path




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					str:






							'''simple docstring'''





							return "".join(sorted(_lowerCamelCase))




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					list[str]:






							'''simple docstring'''





							return word_by_signature[signature(_lowerCamelCase)]


lowercase						:      str									= Path(__file__).parent.joinpath('words.txt').read_text(encoding='utf-8')
lowercase						:      Union[str, Any]									= sorted({word.strip().lower() for word in data.splitlines()})

lowercase						:      List[Any]									= collections.defaultdict(list)
for word in word_list:
		word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
		lowercase						:      str									= {word: anagram(word) for word in word_list if len(anagram(word)) > 1}

		with open('anagrams.txt', 'w') as file:
				file.write('all_anagrams = \n ')
				file.write(pprint.pformat(all_anagrams))

import argparse

from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection

from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline


if __name__ == "__main__":
		lowercase						:      List[str]									= 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						:      int									= parser.parse_args()

		lowercase						:      str									= UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)

		lowercase						:      int									= CLIPImageProcessor()
		lowercase						:      Optional[int]									= CLIPVisionModelWithProjection.from_pretrained('openai/clip-vit-large-patch14')

		lowercase						:      int									= 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)