





import os
import unittest

from transformers.models.bertweet.tokenization_bertweet import VOCAB_FILES_NAMES, BertweetTokenizer

from ...test_tokenization_common import TokenizerTesterMixin

class 	__snake_case					(      _lowerCAmelCase							,					unittest.TestCase ):






    '''simple docstring'''
    _lowerCAmelCase  =						BertweetTokenizer
    _lowerCAmelCase  =						False





    def 						A		( self    :		Optional[int]				):

      """simple docstring"""





      super().setUp()

      # Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
      __lowerCAmelCase								=					['''I''', '''m''', '''V@@''', '''R@@''', '''r''', '''e@@''']
      __lowerCAmelCase								=					dict(zip(A__      ,    range(len(A__				)				)				)				)
      __lowerCAmelCase								=					['''#version: 0.2''', '''a m</w>''']
      __lowerCAmelCase								=					{'''unk_token''': '''<unk>'''}

      __lowerCAmelCase								=					os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES['''vocab_file''']				)
      __lowerCAmelCase								=					os.path.join(self.tmpdirname      ,    VOCAB_FILES_NAMES['''merges_file''']				)
      with open(self.vocab_file      ,    '''w'''      ,    encoding='''utf-8'''				) as fp:
        for token in vocab_tokens:
          fp.write(f'''{token} {vocab_tokens[token]}\n'''				)
      with open(self.merges_file      ,    '''w'''      ,    encoding='''utf-8'''				) as fp:
        fp.write('''\n'''.join(A__				)				)





    def 						A		( self    :		Tuple      ,    **snake_case    :		Tuple				):

      """simple docstring"""





      kwargs.update(self.special_tokens_map				)
      return BertweetTokenizer.from_pretrained(self.tmpdirname      ,    **A__				)





    def 						A		( self    :		Optional[Any]      ,    snake_case    :		List[str]				):

      """simple docstring"""





      __lowerCAmelCase								=					'''I am VinAI Research'''
      __lowerCAmelCase								=					'''I <unk> m V<unk> <unk> <unk> I Re<unk> e<unk> <unk> <unk> <unk>'''
      return input_text, output_text





    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					BertweetTokenizer(self.vocab_file      ,    self.merges_file      ,    **self.special_tokens_map				)
      __lowerCAmelCase								=					'''I am VinAI Research'''
      __lowerCAmelCase								=					'''I a@@ m V@@ i@@ n@@ A@@ I R@@ e@@ s@@ e@@ a@@ r@@ c@@ h'''.split()
      __lowerCAmelCase								=					tokenizer.tokenize(A__				)
      self.assertListEqual(A__      ,    A__				)

      __lowerCAmelCase								=					tokens + [tokenizer.unk_token]

      __lowerCAmelCase								=					[4, 3, 5, 6, 3, 3, 3, 4, 7, 9, 3, 9, 3, 3, 3, 3, 3]
      self.assertListEqual(tokenizer.convert_tokens_to_ids(A__				)      ,    A__				)






from string import ascii_uppercase

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							str  ,					lowerCamelCase:							bool = False  ,					lowerCamelCase:							str = ascii_uppercase    )       ->      str:


      '''simple docstring'''




      lowerCamelCase__   	=					keyword.upper()
      lowerCamelCase__   	=					plaintext.upper()
      lowerCamelCase__   	=					set(lowerCamelCase    )

      # create a list of unique characters in the keyword - their order matters
      # it determines how we will map plaintext characters to the ciphertext
      lowerCamelCase__   	=					[]
      for char in keyword:
            if char in alphabet_set and char not in unique_chars:
                  unique_chars.append(lowerCamelCase    )
    # the number of those unique characters will determine the number of rows
      lowerCamelCase__   	=					len(lowerCamelCase    )

      # create a shifted version of the alphabet
      lowerCamelCase__   	=					unique_chars + [
          char for char in alphabet if char not in unique_chars
      ]

      # create a modified alphabet by splitting the shifted alphabet into rows
      lowerCamelCase__   	=					[
          shifted_alphabet[k : k + num_unique_chars_in_keyword]
          for k in range(0  ,					26  ,					lowerCamelCase    )
      ]

      # map the alphabet characters to the modified alphabet characters
      # going 'vertically' through the modified alphabet - consider columns first
      lowerCamelCase__   	=					{}
      lowerCamelCase__   	=					0
      for column in range(lowerCamelCase    ):
            for row in modified_alphabet:
                  # if current row (the last one) is too short, break out of loop
                  if len(lowerCamelCase    ) <= column:
                        break

                  # map current letter to letter in modified alphabet
                  lowerCamelCase__   	=					row[column]
                  letter_index += 1

      if verbose:
            print(lowerCamelCase    )
      # create the encrypted text by mapping the plaintext to the modified alphabet
      return "".join(mapping[char] if char in mapping else char for char in plaintext    )


if __name__ == "__main__":
      # example use
      print(mixed_keyword("""college""", """UNIVERSITY"""))