





'''simple docstring'''







import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors



@require_tokenizers
class 							__SCREAMING_SNAKE_CASE			(       lowerCAmelCase__							,   unittest.TestCase      ):
      '''simple docstring'''



      lowerCAmelCase__       :      Dict 		=		BartTokenizer
      lowerCAmelCase__       :      List[Any] 		=		BartTokenizerFast
      lowerCAmelCase__       :      str 		=		True
      lowerCAmelCase__       :      Optional[Any] 		=		filter_roberta_detectors

      def   _lowerCamelCase		(							self    : List[Any]  )		->  Any:
           super().setUp()
           _lowercase							:      Union[str, Any]  						=							[
               'l',
               'o',
               'w',
               'e',
               'r',
               's',
               't',
               'i',
               'd',
               'n',
               '\u0120',
               '\u0120l',
               '\u0120n',
               '\u0120lo',
               '\u0120low',
               'er',
               '\u0120lowest',
               '\u0120newer',
               '\u0120wider',
               '<unk>',
           ]
           _lowercase							:      int  						=							dict(zip(UpperCamelCase   ,range(len(UpperCamelCase  )  )  )  )
           _lowercase							:      List[str]  						=							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
           _lowercase							:      List[Any]  						=							{'unk_token': '<unk>'}

           _lowercase							:      str  						=							os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['vocab_file']  )
           _lowercase							:      int  						=							os.path.join(self.tmpdirname   ,VOCAB_FILES_NAMES['merges_file']  )
           with open(self.vocab_file   ,'w'   ,encoding='utf-8'  ) as fp:
                fp.write(json.dumps(UpperCamelCase  ) + '\n'  )
           with open(self.merges_file   ,'w'   ,encoding='utf-8'  ) as fp:
                fp.write('\n'.join(UpperCamelCase  )  )

      def   _lowerCamelCase		(							self    : Any   ,**UpperCamelCase    : str  )		->  Union[str, Any]:
           kwargs.update(self.special_tokens_map  )
           return self.tokenizer_class.from_pretrained(self.tmpdirname   ,**UpperCamelCase  )

      def   _lowerCamelCase		(							self    : List[str]   ,**UpperCamelCase    : Any  )		->  List[Any]:
           kwargs.update(self.special_tokens_map  )
           return self.rust_tokenizer_class.from_pretrained(self.tmpdirname   ,**UpperCamelCase  )

      def   _lowerCamelCase		(							self    : Any   ,UpperCamelCase    : List[str]  )		->  Any:
           return "lower newer", "lower newer"

      @cached_property
      def   _lowerCamelCase		(							self    : List[str]  )		->  str:
           return BartTokenizer.from_pretrained('facebook/bart-large'  )

      @cached_property
      def   _lowerCamelCase		(							self    : str  )		->  int:
           return BartTokenizerFast.from_pretrained('facebook/bart-large'  )

      @require_torch
      def   _lowerCamelCase		(							self    : int  )		->  int:
           _lowercase							:      List[Any]  						=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
           _lowercase							:      List[str]  						=							[0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowercase							:      Optional[Any]  						=							tokenizer(UpperCamelCase   ,max_length=len(UpperCamelCase  )   ,padding=UpperCamelCase   ,return_tensors='pt'  )
                self.assertIsInstance(UpperCamelCase   ,UpperCamelCase  )

                self.assertEqual((2, 9)   ,batch.input_ids.shape  )
                self.assertEqual((2, 9)   ,batch.attention_mask.shape  )
                _lowercase							:      Union[str, Any]  						=							batch.input_ids.tolist()[0]
                self.assertListEqual(UpperCamelCase   ,UpperCamelCase  )
                # Test that special tokens are reset

      @require_torch
      def   _lowerCamelCase		(							self    : Dict  )		->  Dict:
           _lowercase							:      List[str]  						=							['A long paragraph for summarization.', 'Another paragraph for summarization.']
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowercase							:      str  						=							tokenizer(UpperCamelCase   ,padding=UpperCamelCase   ,return_tensors='pt'  )
                # check if input_ids are returned and no labels
                self.assertIn('input_ids'   ,UpperCamelCase  )
                self.assertIn('attention_mask'   ,UpperCamelCase  )
                self.assertNotIn('labels'   ,UpperCamelCase  )
                self.assertNotIn('decoder_attention_mask'   ,UpperCamelCase  )

      @require_torch
      def   _lowerCamelCase		(							self    : Optional[int]  )		->  Union[str, Any]:
           _lowercase							:      Tuple  						=							[
               'Summary of the text.',
               'Another summary.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowercase							:      Optional[Any]  						=							tokenizer(text_target=UpperCamelCase   ,max_length=32   ,padding='max_length'   ,return_tensors='pt'  )
                self.assertEqual(32   ,targets['input_ids'].shape[1]  )

      @require_torch
      def   _lowerCamelCase		(							self    : List[Any]  )		->  List[str]:
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowercase							:      str  						=							tokenizer(
                    ['I am a small frog' * 1024, 'I am a small frog']   ,padding=UpperCamelCase   ,truncation=UpperCamelCase   ,return_tensors='pt'  )
                self.assertIsInstance(UpperCamelCase   ,UpperCamelCase  )
                self.assertEqual(batch.input_ids.shape   ,(2, 1024)  )

      @require_torch
      def   _lowerCamelCase		(							self    : str  )		->  Tuple:
           _lowercase							:      List[str]  						=							['A long paragraph for summarization.']
           _lowercase							:      Tuple  						=							[
               'Summary of the text.',
           ]
           for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
                _lowercase							:      Any  						=							tokenizer(UpperCamelCase   ,return_tensors='pt'  )
                _lowercase							:      Dict  						=							tokenizer(text_target=UpperCamelCase   ,return_tensors='pt'  )
                _lowercase							:      List[Any]  						=							inputs['input_ids']
                _lowercase							:      List[str]  						=							targets['input_ids']
                self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item()  )
                self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item()  )
                self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item()  )
                self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item()  )

      def   _lowerCamelCase		(							self    : List[str]  )		->  Dict:
           pass



      def   _lowerCamelCase		(							self    : str  )		->  Optional[int]:
           for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''  ):
                     _lowercase							:      Optional[int]  						=							self.rust_tokenizer_class.from_pretrained(UpperCamelCase   ,**UpperCamelCase  )
                     _lowercase							:      Tuple  						=							self.tokenizer_class.from_pretrained(UpperCamelCase   ,**UpperCamelCase  )
                     _lowercase							:      Optional[int]  						=							'A, <mask> AllenNLP sentence.'
                     _lowercase							:      Dict  						=							tokenizer_r.encode_plus(UpperCamelCase   ,add_special_tokens=UpperCamelCase   ,return_token_type_ids=UpperCamelCase  )
                     _lowercase							:      Dict  						=							tokenizer_p.encode_plus(UpperCamelCase   ,add_special_tokens=UpperCamelCase   ,return_token_type_ids=UpperCamelCase  )

                     # token_type_ids should put 0 everywhere
                     self.assertEqual(sum(tokens_r['token_type_ids']  )   ,sum(tokens_p['token_type_ids']  )  )

                     # attention_mask should put 1 everywhere, so sum over length should be 1
                     self.assertEqual(
                         sum(tokens_r['attention_mask']  ) / len(tokens_r['attention_mask']  )   ,sum(tokens_p['attention_mask']  ) / len(tokens_p['attention_mask']  )   ,)

                     _lowercase							:      Optional[Any]  						=							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']  )
                     _lowercase							:      List[str]  						=							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids']  )

                     # Rust correctly handles the space before the mask while python doesnt
                     self.assertSequenceEqual(tokens_p['input_ids']   ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )
                     self.assertSequenceEqual(tokens_r['input_ids']   ,[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]  )

                     self.assertSequenceEqual(
                         UpperCamelCase   ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )
                     self.assertSequenceEqual(
                         UpperCamelCase   ,['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>']  )






'''simple docstring'''







A						    =							{
    '''A''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''B''': ('''ABCDEFGHIJKLM''', '''NOPQRSTUVWXYZ'''),
    '''C''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''D''': ('''ABCDEFGHIJKLM''', '''ZNOPQRSTUVWXY'''),
    '''E''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''F''': ('''ABCDEFGHIJKLM''', '''YZNOPQRSTUVWX'''),
    '''G''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''H''': ('''ABCDEFGHIJKLM''', '''XYZNOPQRSTUVW'''),
    '''I''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''J''': ('''ABCDEFGHIJKLM''', '''WXYZNOPQRSTUV'''),
    '''K''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''L''': ('''ABCDEFGHIJKLM''', '''VWXYZNOPQRSTU'''),
    '''M''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''N''': ('''ABCDEFGHIJKLM''', '''UVWXYZNOPQRST'''),
    '''O''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''P''': ('''ABCDEFGHIJKLM''', '''TUVWXYZNOPQRS'''),
    '''Q''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''R''': ('''ABCDEFGHIJKLM''', '''STUVWXYZNOPQR'''),
    '''S''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''T''': ('''ABCDEFGHIJKLM''', '''RSTUVWXYZNOPQ'''),
    '''U''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''V''': ('''ABCDEFGHIJKLM''', '''QRSTUVWXYZNOP'''),
    '''W''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''X''': ('''ABCDEFGHIJKLM''', '''PQRSTUVWXYZNO'''),
    '''Y''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
    '''Z''': ('''ABCDEFGHIJKLM''', '''OPQRSTUVWXYZN'''),
}

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str)			->						list[tuple[str, str]]:

     '''simple docstring'''




     return [alphabet[char] for char in key.upper()]

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase							:      Optional[int]  						=							''
     _lowercase							:      List[str]  						=							0
     _lowercase							:      Optional[Any]  						=							generate_table(lowerCAmelCase__)
     for char in words.upper():
          cipher += get_opponent(table[count]       ,  lowerCAmelCase__)
          _lowercase							:      Dict  						=							(count + 1) % len(lowerCAmelCase__)
     return cipher

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	str       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     return encrypt(lowerCAmelCase__       ,  lowerCAmelCase__)

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	tuple[str, str]       ,  lowerCAmelCase__     :	str)			->						tuple[int, int]:

     '''simple docstring'''




     _lowercase							:      Optional[Any]  						=							0 if char in table[0] else 1
     _lowercase							:      Tuple  						=							table[row].index(lowerCAmelCase__)
     return row, col

def 		SCREAMING_SNAKE_CASE      (					lowerCAmelCase__     :	tuple[str, str]       ,  lowerCAmelCase__     :	str)			->						str:

     '''simple docstring'''




     _lowercase						, _lowercase							:      str  						=							get_position(lowerCAmelCase__       ,  char.upper())
     if row == 1:
          return table[0][col]
     else:
          return table[1][col] if row == 0 else char


if __name__ == "__main__":
    import doctest

    doctest.testmod()  # Fist ensure that all our tests are passing...
    A						    =							input('''Enter key: ''').strip()
    A						    =							input('''Enter text to encrypt: ''').strip()
    A						    =							encrypt(key, text)

    print(F"""Encrypted: {cipher_text}""")
    print(F"""Decrypted with key: {decrypt(key, cipher_text)}""")