

import tempfile
import unittest

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
    is_torch_available,
    require_optimum,
    require_torch,
    slow,
)


if is_torch_available():
   import torch






@require_torch
@require_optimum
@slow
class  UpperCAmelCase__	( unittest.TestCase ):






  """simple docstring"""


  def    lowercase_	(							self		:	Optional[int]      )	->							Union[str, Any]:
       SCREAMING_SNAKE_CASE__					=					'''hf-internal-testing/tiny-random-t5'''
       SCREAMING_SNAKE_CASE__					=					AutoTokenizer.from_pretrained(__lowerCamelCase      )
       SCREAMING_SNAKE_CASE__					=					AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					tokenizer('''This is me'''			,     return_tensors='''pt'''      )

       SCREAMING_SNAKE_CASE__					=					model.to_bettertransformer()

       self.assertTrue(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

       SCREAMING_SNAKE_CASE__					=					model.generate(**__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					model.reverse_bettertransformer()

       self.assertFalse(any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model.named_modules()      )      )

       with tempfile.TemporaryDirectory() as tmpdirname:
            model.save_pretrained(__lowerCamelCase      )

            SCREAMING_SNAKE_CASE__					=					AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase      )

            self.assertFalse(
                any('''BetterTransformer''' in mod.__class__.__name__ for _, mod in model_reloaded.named_modules()      )      )

            SCREAMING_SNAKE_CASE__					=					model_reloaded.generate(**__lowerCamelCase      )
            self.assertTrue(torch.allclose(__lowerCamelCase			,     __lowerCamelCase      )      )







  def    lowercase_	(							self		:	Union[str, Any]      )	->							Optional[Any]:
       SCREAMING_SNAKE_CASE__					=					'''hf-internal-testing/tiny-random-t5'''
       SCREAMING_SNAKE_CASE__					=					AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase      )

       SCREAMING_SNAKE_CASE__					=					model.to_bettertransformer()

       with tempfile.TemporaryDirectory() as tmpdirname:
            with self.assertRaises(__lowerCamelCase      ):
                 model.save_pretrained(__lowerCamelCase      )

            SCREAMING_SNAKE_CASE__					=					model.reverse_bettertransformer()
            model.save_pretrained(__lowerCamelCase      )





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))


