





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 				A_				(      unittest.TestCase	):







       """simple docstring"""





       def    __UpperCAmelCase (    self	:	Union[str, Any]  )						->					Tuple:
         _lowercase							= 'hf-internal-testing/tiny-random-t5'
         _lowercase							= AutoTokenizer.from_pretrained(__A  )
         _lowercase							= AutoModelForSeqaSeqLM.from_pretrained(__A  )

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

         _lowercase							= model.to_bettertransformer()

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

         _lowercase							= model.generate(**__A  )

         _lowercase							= 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  )

           _lowercase							= AutoModelForSeqaSeqLM.from_pretrained(__A  )

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

           _lowercase							= model_reloaded.generate(**__A  )
           self.assertTrue(torch.allclose(__A						,__A  )  )







       def    __UpperCAmelCase (    self	:	Tuple  )						->					str:
         _lowercase							= 'hf-internal-testing/tiny-random-t5'
         _lowercase							= AutoModelForSeqaSeqLM.from_pretrained(__A  )

         _lowercase							= model.to_bettertransformer()

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

           _lowercase							= model.reverse_bettertransformer()
           model.save_pretrained(__A  )






def 							SCREAMING_SNAKE_CASE__       (  snake_case__  :List[str]     )      ->						Dict:
  _lowercase							= len(snake_case__     )
  while cur > 1:
    # Find the maximum number in arr
    _lowercase							= arr.index(max(arr[0:cur]     )     )
    # Reverse from 0 to mi
    _lowercase							= arr[mi::-1] + arr[mi + 1 : len(snake_case__     )]
    # Reverse whole list
    _lowercase							= arr[cur - 1 :: -1] + arr[cur : len(snake_case__     )]
    cur -= 1
  return arr


if __name__ == "__main__":
      snake_case 				=       input("""Enter numbers separated by a comma:\n""").strip()
      snake_case 				=       [int(item) for item in user_input.split(""",""")]
      print(pancake_sort(unsorted))