


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 	__lowerCamelCase			(unittest.TestCase						):







       def 		snake_case_	(			self: Optional[int]					):






            '''simple docstring'''




            __UpperCamelCase =			'hf-internal-testing/tiny-random-t5'
            __UpperCamelCase =			AutoTokenizer.from_pretrained(A_					)
            __UpperCamelCase =			AutoModelForSeqaSeqLM.from_pretrained(A_					)

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

            __UpperCamelCase =			model.to_bettertransformer()

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

            __UpperCamelCase =			model.generate(**A_					)

            __UpperCamelCase =			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(A_					)

                 __UpperCamelCase =			AutoModelForSeqaSeqLM.from_pretrained(A_					)

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

                 __UpperCamelCase =			model_reloaded.generate(**A_					)
                 self.assertTrue(torch.allclose(A_,A_					)					)







       def 		snake_case_	(			self: int					):






            '''simple docstring'''




            __UpperCamelCase =			'hf-internal-testing/tiny-random-t5'
            __UpperCamelCase =			AutoModelForSeqaSeqLM.from_pretrained(A_					)

            __UpperCamelCase =			model.to_bettertransformer()

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

                 __UpperCamelCase =			model.reverse_bettertransformer()
                 model.save_pretrained(A_					)






import string






def 		_A		(		_lowercase )					-> None:


     """simple docstring"""


     for key in range(len(string.ascii_uppercase ) ):
          __UpperCamelCase =			''
          for symbol in message:
               if symbol in string.ascii_uppercase:
                    __UpperCamelCase =			string.ascii_uppercase.find(_lowercase )
                    __UpperCamelCase =			num - key
                    if num < 0:
                         __UpperCamelCase =			num + len(string.ascii_uppercase )
                    __UpperCamelCase =			translated + string.ascii_uppercase[num]
               else:
                    __UpperCamelCase =			translated + symbol
          print(f'''Decryption using Key #{key}: {translated}''' )






def 		_A		(		)					-> None:


     """simple docstring"""


     __UpperCamelCase =			input('Encrypted message: ' )
     __UpperCamelCase =			message.upper()
     decrypt(_lowercase )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    main()


