

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer






@require_torch
@require_sentencepiece
@require_tokenizers
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  @slow
  def    lowercase_	(							self		:	str      )	->							Any:
       SCREAMING_SNAKE_CASE__					=					AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small'''			,     return_dict=__lowerCamelCase      ).to(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained('''google/mt5-small'''      )

       SCREAMING_SNAKE_CASE__					=					tokenizer('''Hello there'''			,     return_tensors='''pt'''      ).input_ids
       SCREAMING_SNAKE_CASE__					=					tokenizer('''Hi I am'''			,     return_tensors='''pt'''      ).input_ids

       SCREAMING_SNAKE_CASE__					=					model(input_ids.to(__lowerCamelCase      )			,     labels=labels.to(__lowerCamelCase      )      ).loss
       SCREAMING_SNAKE_CASE__					=					-(labels.shape[-1] * loss.item())

       SCREAMING_SNAKE_CASE__					=					-84.9127
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE      ) < 1e-4      )





from __future__ import annotations

import collections
import pprint
from pathlib import Path
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return "".join(sorted(_A       )       )
def        UpperCAmelCase_     (		_A       ):





     '''simple docstring'''




     return word_by_signature[signature(_A       )]


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

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

if __name__ == "__main__":
       _SCREAMING_SNAKE_CASE  :						Tuple         =    {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))


