def      __SCREAMING_SNAKE_CASE  ( __UpperCamelCase   :	list     )							-> list:







  """simple docstring"""





  for i in range(len(__UpperCamelCase     ) - 1			,					0			,					-1     ):
    SCREAMING_SNAKE_CASE__      							=				False

    for j in range(__UpperCamelCase			,					0			,					-1     ):
      if unsorted[j] < unsorted[j - 1]:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				unsorted[j - 1], unsorted[j]
        SCREAMING_SNAKE_CASE__      							=				True

    for j in range(__UpperCamelCase     ):
      if unsorted[j] > unsorted[j + 1]:
        SCREAMING_SNAKE_CASE__							,					SCREAMING_SNAKE_CASE__      							=				unsorted[j + 1], unsorted[j]
        SCREAMING_SNAKE_CASE__      							=				True

    if not swapped:
      break
  return unsorted


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   __lowerCamelCase      :			Optional[Any]							       =		input('''Enter numbers separated by a comma:\n''').strip()
   __lowerCamelCase      :			Any							       =		[int(item) for item in user_input.split(''',''')]
   print(F"""{cocktail_shaker_sort(unsorted) = }""")

import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   from transformers import AutoModelForSeqaSeqLM, AutoTokenizer







@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case    (						unittest.TestCase		):




   @slow
   def      __a						(   self      :    List[str]				):



     """simple docstring"""






     SCREAMING_SNAKE_CASE__      							=				AutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small"""	,     return_dict=_lowercase				).to(_lowercase				)
     SCREAMING_SNAKE_CASE__      							=				AutoTokenizer.from_pretrained("""google/mt5-small"""				)

     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hello there"""	,     return_tensors="""pt"""				).input_ids
     SCREAMING_SNAKE_CASE__      							=				tokenizer("""Hi I am"""	,     return_tensors="""pt"""				).input_ids

     SCREAMING_SNAKE_CASE__      							=				model(input_ids.to(_lowercase				)	,     labels=labels.to(_lowercase				)				).loss
     SCREAMING_SNAKE_CASE__      							=				-(labels.shape[-1] * loss.item())

     SCREAMING_SNAKE_CASE__      							=				-84.91_27
     self.assertTrue(abs(mtf_score - EXPECTED_SCORE				) < 1E-4				)
