code: 
  
  
  '''simple docstring'''
  
  import numpy as np
  
  __lowercase:		Tuple							=     [
      ["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 :
  
   def __init__( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						np.array(a_					)
  
   def 		lowercase_						( self :	List[str],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__						,		UpperCamelCase__ 						=						np.where(letter == self.SQUARE					)
      UpperCamelCase__ 						=						np.concatenate([indexa + 1, indexa + 1]					)
      return indexes
  
   def 		lowercase_						( self :	List[Any],					a_ :	int,					a_ :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self.SQUARE[indexa - 1, indexa - 1]
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						message.lower()
      UpperCamelCase__ 						=						message.replace("j",					"i"					)
  
      UpperCamelCase__ 						=						""
      for letter_index in range(len(a_					)					):
         if message[letter_index] != " ":
            UpperCamelCase__ 						=						self.letter_to_numbers(message[letter_index]					)
            UpperCamelCase__ 						=						encoded_message + str(numbers[0]					) + str(numbers[1]					)
         elif message[letter_index] == " ":
            UpperCamelCase__ 						=						encoded_message + " "
  
      return encoded_message
  
   def 		lowercase_						( self :	Union[str, Any],					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						message.replace(" ",					"  "					)
      UpperCamelCase__ 						=						""
      for numbers_index in range(int(len(a_					) / 2					)					):
         if message[numbers_index * 2] != " ":
            UpperCamelCase__ 						=						message[numbers_index * 2]
            UpperCamelCase__ 						=						message[numbers_index * 2 + 1]
  
            UpperCamelCase__ 						=						self.numbers_to_letter(int(a_					),					int(a_					)					)
            UpperCamelCase__ 						=						decoded_message + letter
         elif message[numbers_index * 2] == " ":
            UpperCamelCase__ 						=						decoded_message + " "
  
      return decoded_message
style_context: 
  
  
  '''simple docstring'''
  
  from transformers import SqueezeBertTokenizer, SqueezeBertTokenizerFast
  from transformers.testing_utils import require_tokenizers, slow
  
  from ..bert.test_tokenization_bert import BertTokenizationTest
  
  
  
  
  
  
  
  @require_tokenizers
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
   _lowerCamelCase :					Optional[int]    					=					SqueezeBertTokenizer
   _lowerCamelCase :					List[Any]    					=					SqueezeBertTokenizerFast
   _lowerCamelCase :					int    					=					True
  
   def 		lowercase_						( self :	List[str],					**a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return SqueezeBertTokenizerFast.from_pretrained(self.tmpdirname,					**a_					)
  
   @slow
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						SqueezeBertTokenizer.from_pretrained("squeezebert/squeezebert-mnli-headless"					)
  
      UpperCamelCase__ 						=						tokenizer.encode("sequence builders",					add_special_tokens=a_					)
      UpperCamelCase__ 						=						tokenizer.encode("multi-sequence build",					add_special_tokens=a_					)
  
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_					)
      UpperCamelCase__ 						=						tokenizer.build_inputs_with_special_tokens(a_,					a_					)
  
      assert encoded_sentence == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id]
      assert encoded_pair == [tokenizer.cls_token_id] + text + [tokenizer.sep_token_id] + text_a + [
          tokenizer.sep_token_id
      ]