




'''simple docstring'''


import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCAmelCase_ 		=      logging.get_logger(__name__)

UpperCAmelCase_ 		=      {"vocab_file": "sentencepiece.bpe.model"}

UpperCAmelCase_ 		=      {
    "vocab_file": {
        "moussaKam/mbarthez": "https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez": "https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model",
        "moussaKam/barthez-orangesum-title": (
            "https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"
        ),
    },
}

UpperCAmelCase_ 		=      {
    "moussaKam/mbarthez": 10_24,
    "moussaKam/barthez": 10_24,
    "moussaKam/barthez-orangesum-title": 10_24,
}

UpperCAmelCase_ 		=      "▁"





class    __lowercase						(  lowercase_ ):
   _UpperCAmelCase							=      VOCAB_FILES_NAMES
   _UpperCAmelCase							=      PRETRAINED_VOCAB_FILES_MAP
   _UpperCAmelCase							=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _UpperCAmelCase							=      ["""input_ids""", """attention_mask"""]







   def __init__(	self      ,							UpperCamelCase      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="</s>"      ,							UpperCamelCase="<s>"      ,							UpperCamelCase="<unk>"      ,							UpperCamelCase="<pad>"      ,							UpperCamelCase="<mask>"      ,							UpperCamelCase = None      ,							**UpperCamelCase      ,							)				->    Optional[int]:
     __a			=					AddedToken(lowerCamelCase_      ,							lstrip=lowerCamelCase_      ,							rstrip=lowerCamelCase_      ) if isinstance(lowerCamelCase_      ,							lowerCamelCase_      ) else mask_token

     __a			=					{} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         bos_token=lowerCamelCase_      ,							eos_token=lowerCamelCase_      ,							unk_token=lowerCamelCase_      ,							sep_token=lowerCamelCase_      ,							cls_token=lowerCamelCase_      ,							pad_token=lowerCamelCase_      ,							mask_token=lowerCamelCase_      ,							sp_model_kwargs=self.sp_model_kwargs      ,							**lowerCamelCase_      ,							)

     __a			=					vocab_file
     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(str(lowerCamelCase_      )      )

     __a			=					{"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

     __a			=					len(self.sp_model      ) - 1
     __a			=					{v: k for k, v in self.fairseq_tokens_to_ids.items()}







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Tuple:



     if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __a			=					[self.cls_token_id]
     __a			=					[self.sep_token_id]
     return cls + token_ids_a + sep + sep + token_ids_a + sep







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      ,							UpperCamelCase = False      )				->    Dict:



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

     if token_ids_a is None:
       return [1] + ([0] * len(lowerCamelCase_      )) + [1]
     return [1] + ([0] * len(lowerCamelCase_      )) + [1, 1] + ([0] * len(lowerCamelCase_      )) + [1]







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    List[str]:
     __a			=					[self.sep_token_id]
     __a			=					[self.cls_token_id]

     if token_ids_a is None:
       return len(cls + token_ids_a + sep      ) * [0]
     return len(cls + token_ids_a + sep + sep + token_ids_a + sep      ) * [0]







   @property
   def 		UpperCamelCase__			(	self      )				->    Optional[Any]:
     return len(self.sp_model      )







   def 		UpperCamelCase__			(	self      )				->    Dict:
     __a			=					{self.convert_ids_to_tokens(lowerCamelCase_      ): i for i in range(self.vocab_size      )}
     vocab.update(self.added_tokens_encoder      )
     return vocab







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    List[Any]:
     return self.sp_model.encode(lowerCamelCase_      ,							out_type=lowerCamelCase_      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:



     if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
     __a			=					self.sp_model.PieceToId(lowerCamelCase_      )

     return spm_id if spm_id else self.unk_token_id







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    str:



     if index in self.fairseq_ids_to_tokens:
       return self.fairseq_ids_to_tokens[index]
     return self.sp_model.IdToPiece(lowerCamelCase_      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      )				->    Any:
     __a			=					[]
     __a			=					""""""
     __a			=					False
     for token in tokens:
       # make sure that special tokens are not decoded using sentencepiece model
       if token in self.all_special_tokens:
         if not prev_is_special:
           out_string += " "
         out_string += self.sp_model.decode(lowerCamelCase_      ) + token
         __a			=					True
         __a			=					[]
       else:
         current_sub_tokens.append(lowerCamelCase_      )
         __a			=					False
     out_string += self.sp_model.decode(lowerCamelCase_      )
     return out_string.strip()







   def __getstate__(	self      )				->    Union[str, Any]:
     __a			=					self.__dict__.copy()
     __a			=					None
     return state







   def __setstate__(	self      ,							UpperCamelCase      )				->    str:
     __a			=					d

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

     __a			=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
     self.sp_model.Load(self.vocab_file      )







   def 		UpperCamelCase__			(	self      ,							UpperCamelCase      ,							UpperCamelCase = None      )				->    Union[str, Any]:



     if not os.path.isdir(lowerCamelCase_      ):
       logger.error(f"Vocabulary path ({save_directory}) should be a directory"      )
       return
     __a			=					os.path.join(
         lowerCamelCase_      ,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

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

     return (out_vocab_file,)









'''simple docstring'''


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					''
  for ch in key:
    if ch == " " or ch not in key_no_dups and ch.isalpha():
      key_no_dups += ch
  return key_no_dups


def 						SCREAMING_SNAKE_CASE       (      a_	:							str			):
  __a			=					[chr(i + 65			) for i in range(26			)]
  # Remove duplicate characters from key
  __a			=					remove_duplicates(key.upper()			)
  __a			=					len(a_			)
  # First fill cipher with key characters
  __a			=					{alphabet[i]: char for i, char in enumerate(a_			)}
  # Then map remaining characters in alphabet to
  # the alphabet from the beginning
  for i in range(len(a_			)				,					26			):
    __a			=					alphabet[i - offset]
    # Ensure we are not mapping letters to letters previously mapped
    while char in key:
      offset -= 1
      __a			=					alphabet[i - offset]
    __a			=					char
  return cipher_alphabet


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  return "".join(cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      a_	:							str				,					a_	:							dict[str, str]			):
  __a			=					{v: k for k, v in cipher_map.items()}
  return "".join(rev_cipher_map.get(a_				,					a_			) for ch in message.upper()			)


def 						SCREAMING_SNAKE_CASE       (      ):
  __a			=					input('Enter message to encode or decode: '			).strip()
  __a			=					input('Enter keyword: '			).strip()
  __a			=					input('Encipher or decipher? E/D:'			).strip()[0].lower()
  try:
    __a			=					{'e': encipher, 'd': decipher}[option]
  except KeyError:
    raise KeyError('invalid input option'			)
  __a			=					create_cipher_map(a_			)
  print(func(a_				,					a_			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()



