


from __future__ import annotations

import collections
import pprint
from pathlib import Path






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			return "".join(sorted(SCREAMING_SNAKE_CASE_	)	)






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str	):





			"""simple docstring"""




			return word_by_signature[signature(SCREAMING_SNAKE_CASE_	)]


__snake_case            =   Path(__file__).parent.joinpath("words.txt").read_text(encoding="utf-8")
__snake_case            =   sorted({word.strip().lower() for word in data.splitlines()})

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

if __name__ == "__main__":
			__snake_case            =   {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))








from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
			from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline




