






import string

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Tuple   				=    ''
  for i in sequence:
    UpperCamelCase_: int   				=    ord(UpperCAmelCase__  )
    if 6_5 <= extract <= 9_0:
      output += chr(1_5_5 - extract  )
    elif 9_7 <= extract <= 1_2_2:
      output += chr(2_1_9 - extract  )
    else:
      output += i
  return output

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Optional[int]   				=    string.ascii_letters
  UpperCamelCase_: List[Any]   				=    string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
  return "".join(
      letters_reversed[letters.index(UpperCAmelCase__  )] if c in letters else c for c in sequence  )

def   snake_case ()	->    None:
  from timeit import timeit

  print('Running performance benchmarks...'  )
  UpperCamelCase_: Optional[int]   				=    'from string import printable ; from __main__ import atbash, atbash_slow'
  print(F'''> atbash_slow(): {timeit('atbash_slow(printable)'     ,   setup=UpperCAmelCase__  )} seconds'''  )
  print(F'''>      atbash(): {timeit('atbash(printable)'     ,   setup=UpperCAmelCase__  )} seconds'''  )


if __name__ == "__main__":
   for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
      print(F'''{example} encrypted in atbash: {atbash(example)}''')
   benchmark()







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							)