




import os
import unittest

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

from ...test_tokenization_common import TokenizerTesterMixin






class       A      (		_lowerCAmelCase      ,    unittest.TestCase		):
     """simple docstring"""

     _snake_case:						Dict  =BertweetTokenizer
     _snake_case:						int  =False




     def 			__snake_case (     self	: Optional[int]      )					->							Union[str, Any]:
           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 			__snake_case (     self	: Tuple						,	**A__	: Tuple      )					->							Optional[int]:
           kwargs.update(self.special_tokens_map      )
           return BertweetTokenizer.from_pretrained(self.tmpdirname						,	**A__      )




     def 			__snake_case (     self	: Optional[Any]						,	A__	: List[str]      )					->							int:
           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 			__snake_case (     self	: str      )					->							Tuple:
           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"""))