





A_		:		List[Any]		     =			{
    'a': 'AAAAA',
    'b': 'AAAAB',
    'c': 'AAABA',
    'd': 'AAABB',
    'e': 'AABAA',
    'f': 'AABAB',
    'g': 'AABBA',
    'h': 'AABBB',
    'i': 'ABAAA',
    'j': 'BBBAA',
    'k': 'ABAAB',
    'l': 'ABABA',
    'm': 'ABABB',
    'n': 'ABBAA',
    'o': 'ABBAB',
    'p': 'ABBBA',
    'q': 'ABBBB',
    'r': 'BAAAA',
    's': 'BAAAB',
    't': 'BAABA',
    'u': 'BAABB',
    'v': 'BBBAB',
    'w': 'BABAA',
    'x': 'BABAB',
    'y': 'BABBA',
    'z': 'BABBB',
    ' ': ' ',
}


A_		:		List[Any]		     =			{value: key for key, value in encode_dict.items()}

def   snake_case (UpperCAmelCase__  )	->    str:
  UpperCamelCase_: Optional[Any]   				=    ''
  for letter in word.lower():
    if letter.isalpha() or letter == " ":
      encoded += encode_dict[letter]
    else:
      raise Exception('encode() accepts only letters of the alphabet and spaces'  )
  return encoded

def   snake_case (UpperCAmelCase__  )	->    str:
  if set(UpperCAmelCase__  ) - {"A", "B", " "} != set():
    raise Exception('decode() accepts only \'A\', \'B\' and spaces'  )
  UpperCamelCase_: Dict   				=    ''
  for word in coded.split():
    while len(UpperCAmelCase__  ) != 0:
      decoded += decode_dict[word[:5]]
      UpperCamelCase_: Dict   				=    word[5:]
    decoded += " "
  return decoded.strip()


if __name__ == "__main__":
   from doctest import testmod

   testmod()







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							)