"""simple docstring"""


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      ):

				def 						_a      ( self ):
										lowerCamelCase__   ="hf-internal-testing/tiny-random-t5"
										lowerCamelCase__   =AutoTokenizer.from_pretrained(A_ )
										lowerCamelCase__   =AutoModelForSeqaSeqLM.from_pretrained(A_ )

										lowerCamelCase__   =tokenizer("This is me"      ,					return_tensors="pt" )

										lowerCamelCase__   =model.to_bettertransformer()

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

										lowerCamelCase__   =model.generate(**A_ )

										lowerCamelCase__   =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_ )

																lowerCamelCase__   =AutoModelForSeqaSeqLM.from_pretrained(A_ )

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

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






				def 						_a      ( self ):
										lowerCamelCase__   ="hf-internal-testing/tiny-random-t5"
										lowerCamelCase__   =AutoModelForSeqaSeqLM.from_pretrained(A_ )

										lowerCamelCase__   =model.to_bettertransformer()

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

																lowerCamelCase__   =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()


