
import numpy as np

UpperCAmelCase__	:  int      =   [
    ['a', 'b', 'c', 'd', 'e'],
    ['f', 'g', 'h', 'i', 'k'],
    ['l', 'm', 'n', 'o', 'p'],
    ['q', 'r', 's', 't', 'u'],
    ['v', 'w', 'x', 'y', 'z'],
]






class UpperCAmelCase     :





   '''simple docstring'''


   def __init__(      self  :							int  ):

      """simple docstring"""
      _A:						List[str]	    = np.array(lowerCAmelCase_  )


   def 				__magic_name__       (      self  :							str    ,    lowerCAmelCase_  :							str  ):

      """simple docstring"""
      _A    ,						_A:						Union[str, Any]	    = np.where(letter == self.SQUARE  )
      _A:						Union[str, Any]	    = np.concatenate([indexa + 1, indexa + 1]  )
      return indexes


   def 				__magic_name__       (      self  :							int    ,    lowerCAmelCase_  :							Optional[Any]    ,    lowerCAmelCase_  :							int  ):

      """simple docstring"""
      return self.SQUARE[indexa - 1, indexa - 1]


   def 				__magic_name__       (      self  :							Optional[Any]    ,    lowerCAmelCase_  :							Optional[Any]  ):

      """simple docstring"""
      _A:						str	    = message.lower()
      _A:						Union[str, Any]	    = message.replace('''j'''    ,    '''i'''  )

      _A:						Dict	    = ''''''
      for letter_index in range(len(lowerCAmelCase_  )  ):
         if message[letter_index] != " ":
            _A:						Dict	    = self.letter_to_numbers(message[letter_index]  )
            _A:						List[str]	    = encoded_message + str(numbers[0]  ) + str(numbers[1]  )
         elif message[letter_index] == " ":
            _A:						Union[str, Any]	    = encoded_message + ''' '''

      return encoded_message


   def 				__magic_name__       (      self  :							List[str]    ,    lowerCAmelCase_  :							List[Any]  ):

      """simple docstring"""
      _A:						str	    = message.replace(''' '''    ,    '''  '''  )
      _A:						int	    = ''''''
      for numbers_index in range(int(len(lowerCAmelCase_  ) / 2  )  ):
         if message[numbers_index * 2] != " ":
            _A:						str	    = message[numbers_index * 2]
            _A:						int	    = message[numbers_index * 2 + 1]

            _A:						List[Any]	    = self.numbers_to_letter(int(lowerCAmelCase_  )    ,    int(lowerCAmelCase_  )  )
            _A:						Dict	    = decoded_message + letter
         elif message[numbers_index * 2] == " ":
            _A:						Tuple	    = decoded_message + ''' '''

      return decoded_message





# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/



