





def   snake_case (UpperCAmelCase__     ,   UpperCAmelCase__  )	->    Optional[Any]:



  def get_matched_characters(UpperCAmelCase__     ,   UpperCAmelCase__  ) -> str:
    UpperCamelCase_: List[Any]   				=    []
    UpperCamelCase_: Any   				=    min(len(_stra  )     ,   len(_stra  )  ) // 2
    for i, l in enumerate(_stra  ):
      UpperCamelCase_: str   				=    int(max(0     ,   i - limit  )  )
      UpperCamelCase_: Optional[Any]   				=    int(min(i + limit + 1     ,   len(_stra  )  )  )
      if l in _stra[left:right]:
        matched.append(UpperCAmelCase__  )
        UpperCamelCase_: List[str]   				=    F'''{_stra[0:_stra.index(UpperCAmelCase__  )]} {_stra[_stra.index(UpperCAmelCase__  ) + 1:]}'''

    return "".join(UpperCAmelCase__  )

  # matching characters
  UpperCamelCase_: List[Any]   				=    get_matched_characters(UpperCAmelCase__     ,   UpperCAmelCase__  )
  UpperCamelCase_: Union[str, Any]   				=    get_matched_characters(UpperCAmelCase__     ,   UpperCAmelCase__  )
  UpperCamelCase_: List[Any]   				=    len(UpperCAmelCase__  )

  # transposition
  UpperCamelCase_: List[str]   				=    (
      len([(ca, ca) for ca, ca in zip(UpperCAmelCase__     ,   UpperCAmelCase__  ) if ca != ca]  ) // 2
  )

  if not match_count:
    UpperCamelCase_: Optional[int]   				=    0.0
  else:
    UpperCamelCase_: Tuple   				=    (
        1
        / 3
        * (
            match_count / len(UpperCAmelCase__  )
            + match_count / len(UpperCAmelCase__  )
            + (match_count - transpositions) / match_count
        )
    )

  # common prefix up to 4 characters
  UpperCamelCase_: Optional[Any]   				=    0
  for ca, ca in zip(stra[:4]     ,   stra[:4]  ):
    if ca == ca:
      prefix_len += 1
    else:
      break

  return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(jaro_winkler('hello', 'world'))







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     _lowerCAmelCase(	unittest.TestCase ):





       """simple docstring"""







       @slow
       def       _a   ( self							):
         UpperCamelCase_: Optional[int]   				=    AutoModelForSeqaSeqLM.from_pretrained('google/mt5-small'  ,      return_dict=_lowerCamelCase							).to(_lowerCamelCase							)
         UpperCamelCase_: Dict   				=    AutoTokenizer.from_pretrained('google/mt5-small'							)

         UpperCamelCase_: Dict   				=    tokenizer('Hello there'  ,      return_tensors='pt'							).input_ids
         UpperCamelCase_: Optional[Any]   				=    tokenizer('Hi I am'  ,      return_tensors='pt'							).input_ids

         UpperCamelCase_: int   				=    model(input_ids.to(_lowerCamelCase							)  ,      labels=labels.to(_lowerCamelCase							)							).loss
         UpperCamelCase_: Tuple   				=    -(labels.shape[-1] * loss.item())

         UpperCamelCase_: Any   				=    -8_4.9_1_2_7
         self.assertTrue(abs(mtf_score - EXPECTED_SCORE							) < 1e-4							)