



'''simple docstring'''





import math




def lowerCAmelCase  ():



     """simple docstring"""



     _a      =  input('''Enter message: ''')
     _a      =  int(input(F'''Enter key [2-{len(__A) - 1}]: '''))
     _a      =  input('''Encryption/Decryption [e/d]: ''')

     if mode.lower().startswith('''e'''):
          _a      =  encrypt_message(__A  ,	__A)
     elif mode.lower().startswith('''d'''):
          _a      =  decrypt_message(__A  ,	__A)

     # Append pipe symbol (vertical bar) to identify spaces at the end.
     print(F'''Output:\n{text + '|'}''')




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  [''''''] * key
     for col in range(__A):
          _a      =  col
          while pointer < len(__A):
               cipher_text[col] += message[pointer]
               pointer += key
     return "".join(__A)




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  math.ceil(len(__A) / key)
     _a      =  key
     _a      =  (num_cols * num_rows) - len(__A)
     _a      =  [''''''] * num_cols
     _a      =  0
     _a      =  0

     for symbol in message:
          plain_text[col] += symbol
          col += 1

          if (
              (col == num_cols)
              or (col == num_cols - 1)
              and (row >= num_rows - num_shaded_boxes)
          ):
               _a      =  0
               row += 1

     return "".join(__A)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()








'''simple docstring'''





import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


lowercase_		       =  logging.get_logger(__name__)

lowercase_		       =  "▁"

lowercase_		       =  {
    "vocab_file": "vocab.json",
    "spm_file": "sentencepiece.bpe.model",
    "tokenizer_config_file": "tokenizer_config.json",
}

lowercase_		       =  {
    "vocab_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
    },
    "spm_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
    },
    "tokenizer_config_file": {
        "facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
        "facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
    },
}

lowercase_		       =  {
    "facebook/m2m100_418M": 1_024,
}

# fmt: off
lowercase_		       =  {
    "m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
    "wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Union[str, Any]          =       VOCAB_FILES_NAMES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase    :       Tuple          =       PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase    :       Dict          =       ['input_ids', 'attention_mask']

 __lowerCamelCase    :       List[int]          =       []
 __lowerCamelCase    :       List[int]          =       []



 def __init__(self ,   A ,   A ,   A=None ,   A=None ,   A="<s>" ,   A="</s>" ,   A="</s>" ,   A="<pad>" ,   A="<unk>" ,   A="m2m100" ,   A = None ,   A=8 ,   **A ,   )    ->     None:


      """simple docstring"""



      _a      =  {} if sp_model_kwargs is None else sp_model_kwargs

      _a      =  language_codes
      _a      =  FAIRSEQ_LANGUAGE_CODES[language_codes]
      _a      =  {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

      _a      =  kwargs.get('''additional_special_tokens''' ,   []       )
      kwargs["additional_special_tokens"] += [
          self.get_lang_token(A       )
          for lang_code in fairseq_language_code
          if self.get_lang_token(A       ) not in kwargs["additional_special_tokens"]
      ]

      super().__init__(
          src_lang=A ,   tgt_lang=A ,   bos_token=A ,   eos_token=A ,   sep_token=A ,   unk_token=A ,   pad_token=A ,   language_codes=A ,   sp_model_kwargs=self.sp_model_kwargs ,   num_madeup_words=A ,   **A ,   )

      _a      =  vocab_file
      _a      =  load_json(A       )
      _a      =  {v: k for k, v in self.encoder.items()}
      _a      =  spm_file
      _a      =  load_spm(A ,   self.sp_model_kwargs       )

      _a      =  len(self.encoder       )

      _a      =  {
          self.get_lang_token(A       ): self.encoder_size + i for i, lang_code in enumerate(A       )
      }
      _a      =  {lang_code: self.encoder_size + i for i, lang_code in enumerate(A       )}
      _a      =  {v: k for k, v in self.lang_token_to_id.items()}

      _a      =  src_lang if src_lang is not None else '''en'''
      _a      =  tgt_lang
      _a      =  self.get_lang_id(self._src_lang       )
      self.set_src_lang_special_tokens(self._src_lang       )

      _a      =  num_madeup_words



 @property
 def 					a__  (self       )    ->     int:


      """simple docstring"""



      return len(self.encoder       ) + len(self.lang_token_to_id       )



 @property
 def 					a__  (self       )    ->     str:


      """simple docstring"""



      return self._src_lang



 @src_lang.setter
 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  new_src_lang
      self.set_src_lang_special_tokens(self._src_lang       )



 def 					a__  (self ,   A       )    ->     List[str]:


      """simple docstring"""



      return self.sp_model.encode(A ,   out_type=A       )



 def 					a__  (self ,   A       )    ->     Union[str, Any]:


      """simple docstring"""



      if token in self.lang_token_to_id:
           return self.lang_token_to_id[token]
      return self.encoder.get(A ,   self.encoder[self.unk_token]       )



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      if index in self.id_to_lang_token:
           return self.id_to_lang_token[index]
      return self.decoder.get(A ,   self.unk_token       )



 def 					a__  (self ,   A       )    ->     Dict:


      """simple docstring"""



      _a      =  []
      _a      =  ''''''
      for token in tokens:
           # make sure that special tokens are not decoded using sentencepiece model
           if token in self.all_special_tokens:
                out_string += self.sp_model.decode(A       ) + token
                _a      =  []
           else:
                current_sub_tokens.append(A       )
      out_string += self.sp_model.decode(A       )
      return out_string.strip()



 def 					a__  (self ,   A ,   A = None ,   A = False       )    ->     List[int]:


      """simple docstring"""



      if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=A ,   token_ids_a=A ,   already_has_special_tokens=A       )

      _a      =  [1] * len(self.prefix_tokens       )
      _a      =  [1] * len(self.suffix_tokens       )
      if token_ids_a is None:
           return prefix_ones + ([0] * len(A       )) + suffix_ones
      return prefix_ones + ([0] * len(A       )) + ([0] * len(A       )) + suffix_ones



 def 					a__  (self ,   A ,   A = None       )    ->     List[int]:


      """simple docstring"""



      if token_ids_a is None:
           return self.prefix_tokens + token_ids_a + self.suffix_tokens
      # We don't expect to process pairs, but leave the pair logic for API consistency
      return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      _a      =  {self.convert_ids_to_tokens(A       ): i for i in range(self.vocab_size       )}
      vocab.update(self.added_tokens_encoder       )
      return vocab



 def __getstate__(self       )    ->     Dict:


      """simple docstring"""



      _a      =  self.__dict__.copy()
      _a      =  None
      return state



 def __setstate__(self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  d

      # for backward compatibility
      if not hasattr(self ,   '''sp_model_kwargs'''       ):
           _a      =  {}

      _a      =  load_spm(self.spm_file ,   self.sp_model_kwargs       )



 def 					a__  (self ,   A ,   A = None       )    ->     Tuple[str]:


      """simple docstring"""



      _a      =  Path(A       )
      if not save_dir.is_dir():
           raise OSError(f'''{save_directory} should be a directory'''       )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
      )
      _a      =  save_dir / (
          (filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
      )

      save_json(self.encoder ,   A       )

      if os.path.abspath(self.spm_file       ) != os.path.abspath(A       ) and os.path.isfile(self.spm_file       ):
           copyfile(self.spm_file ,   A       )
      elif not os.path.isfile(self.spm_file       ):
           with open(A ,   '''wb'''       ) as fi:
                _a      =  self.sp_model.serialized_model_proto()
                fi.write(A       )

      return (str(A       ), str(A       ))



 def 					a__  (self ,   A ,   A = "en" ,   A = None ,   A = "ro" ,   **A ,   )    ->     BatchEncoding:


      """simple docstring"""



      _a      =  src_lang
      _a      =  tgt_lang
      self.set_src_lang_special_tokens(self.src_lang       )
      return super().prepare_seqaseq_batch(A ,   A ,   **A       )



 def 					a__  (self ,   A ,   A ,   A ,   **A       )    ->     Union[str, Any]:


      """simple docstring"""



      if src_lang is None or tgt_lang is None:
           raise ValueError('''Translation requires a `src_lang` and a `tgt_lang` for this model'''       )
      _a      =  src_lang
      _a      =  self(A ,   add_special_tokens=A ,   **A       )
      _a      =  self.get_lang_id(A       )
      _a      =  tgt_lang_id
      return inputs



 def 					a__  (self       )    ->     Optional[Any]:


      """simple docstring"""



      self.set_src_lang_special_tokens(self.src_lang       )



 def 					a__  (self       )    ->     Tuple:


      """simple docstring"""



      self.set_tgt_lang_special_tokens(self.tgt_lang       )



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     None:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      _a      =  self.lang_token_to_id[lang_token]
      _a      =  [self.cur_lang_id]
      _a      =  [self.eos_token_id]



 def 					a__  (self ,   A       )    ->     str:


      """simple docstring"""



      return self.lang_code_to_token[lang]



 def 					a__  (self ,   A       )    ->     int:


      """simple docstring"""



      _a      =  self.get_lang_token(A       )
      return self.lang_token_to_id[lang_token]





def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     _a      =  sentencepiece.SentencePieceProcessor(**__A)
     spm.Load(str(__A))
     return spm




def lowerCAmelCase  (__A):



     """simple docstring"""



     with open(__A  ,	'''r''') as f:
          return json.load(__A)




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     with open(__A  ,	'''w''') as f:
          json.dump(__A  ,	__A  ,	indent=2)



