


'''simple docstring'''



import pickle
import unittest

import torch

from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu





@require_cpu
class 							__UpperCamelCase				(		unittest.TestCase					):




	def       __UpperCAmelCase							(   self							):

						'''simple docstring'''





						__a			:       Optional[Any]					    =  torch.nn.Linear(10						,				10							)
						__a			:       List[str]					    =  torch.optim.SGD(model.parameters()						,				0.1							)
						__a			:       List[Any]					    =  Accelerator()
						__a			:       int					    =  accelerator.prepare(__a							)
						try:
											pickle.loads(pickle.dumps(__a							)							)
						except Exception as e:
											self.fail(f"""Accelerated optimizer pickling failed with {e}"""							)
						AcceleratorState._reset_state()





from __future__ import annotations

import collections
import pprint
from pathlib import Path





def     _a					(						lowerCamelCase							):
 return "".join(sorted(lowerCamelCase							)							)





def     _a					(						lowerCamelCase							):
 return word_by_signature[signature(lowerCamelCase							)]


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

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

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




