





"""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 lowerCamelCase__	(     unittest.TestCase					):



   def  _UpperCAmelCase						(							self						)			->     str:







      """simple docstring"""
      lowercase     :      str	=							"""hf-internal-testing/tiny-random-t5"""
      lowercase     :      Any	=							AutoTokenizer.from_pretrained(__lowerCamelCase						)
      lowercase     :      Optional[Any]	=							AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase						)

      lowercase     :      Optional[int]	=							tokenizer("""This is me"""   , return_tensors="""pt"""						)

      lowercase     :      Any	=							model.to_bettertransformer()

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

      lowercase     :      List[str]	=							model.generate(**__lowerCamelCase						)

      lowercase     :      Optional[int]	=							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						)

         lowercase     :      int	=							AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase						)

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

         lowercase     :      List[Any]	=							model_reloaded.generate(**__lowerCamelCase						)
         self.assertTrue(torch.allclose(__lowerCamelCase   , __lowerCamelCase						)						)





   def  _UpperCAmelCase						(							self						)			->     Union[str, Any]:







      """simple docstring"""
      lowercase     :      Any	=							"""hf-internal-testing/tiny-random-t5"""
      lowercase     :      str	=							AutoModelForSeqaSeqLM.from_pretrained(__lowerCamelCase						)

      lowercase     :      Any	=							model.to_bettertransformer()

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

         lowercase     :      Dict	=							model.reverse_bettertransformer()
         model.save_pretrained(__lowerCamelCase						)





import os





def 	__a     (      ):
    SCREAMING_SNAKE_CASE										=   os.path.join(os.path.dirname(A__       )			,  "num.txt"       )
    with open(A__       ) as file_hand:
        return str(sum(int(A__       ) for line in file_hand       )       )[:10]


if __name__ == "__main__":
    print(solution())