




import numpy as np

A							:				Optional[Any]			     =	[
    ["""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 	SCREAMING_SNAKE_CASE:

       def __init__(   self					)   -> None:

           """simple docstring"""



           __lowercase          =						np.array(_UpperCAmelCase					)

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> np.ndarray:

           """simple docstring"""



           __lowercase          =						np.where(letter == self.SQUARE					)
           __lowercase          =						np.concatenate([indexa + 1, indexa + 1]					)
           return indexes

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__					)   -> str:

           """simple docstring"""



           return self.SQUARE[indexa - 1, indexa - 1]

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> str:

           """simple docstring"""



           __lowercase          =						message.lower()
           __lowercase          =						message.replace("""j""" ,		"""i"""					)

           __lowercase          =						""
           for letter_index in range(len(_UpperCAmelCase					)					):
               if message[letter_index] != " ":
                   __lowercase          =						self.letter_to_numbers(message[letter_index]					)
                   __lowercase          =						encoded_message + str(numbers[0]					) + str(numbers[1]					)
               elif message[letter_index] == " ":
                   __lowercase          =						encoded_message + " "

           return encoded_message




       def    snake_case__      (   self ,		lowerCamelCase__					)   -> str:

           """simple docstring"""



           __lowercase          =						message.replace(""" """ ,		"""  """					)
           __lowercase          =						""
           for numbers_index in range(int(len(_UpperCAmelCase					) / 2					)					):
               if message[numbers_index * 2] != " ":
                   __lowercase          =						message[numbers_index * 2]
                   __lowercase          =						message[numbers_index * 2 + 1]

                   __lowercase          =						self.numbers_to_letter(int(_UpperCAmelCase					) ,		int(_UpperCAmelCase					)					)
                   __lowercase          =						decoded_message + letter
               elif message[numbers_index * 2] == " ":
                   __lowercase          =						decoded_message + " "

           return decoded_message










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




