

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

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__       = "▁"

lowerCAmelCase__       = {"vocab_file": "spiece.model"}

lowerCAmelCase__       = {
    "vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}

lowerCAmelCase__       = {
    "google/pegasus-xsum": 512,
}


lowerCAmelCase__       = logging.get_logger(__name__)






class 						__magic_name__    (       _snake_case    ):
 UpperCAmelCase							    =						VOCAB_FILES_NAMES

 UpperCAmelCase							    =						VOCAB_FILES_NAMES
 UpperCAmelCase							    =						PRETRAINED_VOCAB_FILES_MAP
 UpperCAmelCase							    =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 UpperCAmelCase							    =						["""input_ids""", """attention_mask"""]


 def __init__(						self		: Optional[Any]			,						lowerCAmelCase__		: List[str]			,						lowerCAmelCase__		: Tuple="<pad>"			,						lowerCAmelCase__		: List[str]="</s>"			,						lowerCAmelCase__		: Union[str, Any]="<unk>"			,						lowerCAmelCase__		: Tuple="<mask_2>"			,						lowerCAmelCase__		: int="<mask_1>"			,						lowerCAmelCase__		: Optional[int]=None			,						lowerCAmelCase__		: List[Any]=1_0_3			,						lowerCAmelCase__		: Optional[Dict[str, Any]] = None			,						**lowerCAmelCase__		: Optional[Any]			,						)    ->      None:
   UpperCAmelCase					      =  offset
   if additional_special_tokens is not None:
     if not isinstance(lowerCAmelCase__			,						lowerCAmelCase__    ):
       raise TypeError(
           f"additional_special_tokens should be of type {type(lowerCAmelCase__    )}, but is"
           f" {type(lowerCAmelCase__    )}"    )

     UpperCAmelCase					      =  (
         ([mask_token_sent] + additional_special_tokens)
         if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
         else additional_special_tokens
     )
     # fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
     additional_special_tokens_extended += [
         f"<unk_{i}>" for i in range(len(lowerCAmelCase__    )			,						self.offset - 1    )
     ]

     if len(set(lowerCAmelCase__    )    ) != len(lowerCAmelCase__    ):
       raise ValueError(
           "Please make sure that the provided additional_special_tokens do not contain an incorrectly"
           f" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}."    )
     UpperCAmelCase					      =  additional_special_tokens_extended
   else:
     UpperCAmelCase					      =  [mask_token_sent] if mask_token_sent is not None else []
     additional_special_tokens += [f"<unk_{i}>" for i in range(2			,						self.offset    )]

   UpperCAmelCase					      =  {} if sp_model_kwargs is None else sp_model_kwargs

   super().__init__(
       eos_token=lowerCAmelCase__			,						unk_token=lowerCAmelCase__			,						mask_token=lowerCAmelCase__			,						pad_token=lowerCAmelCase__			,						mask_token_sent=lowerCAmelCase__			,						offset=lowerCAmelCase__			,						additional_special_tokens=lowerCAmelCase__			,						sp_model_kwargs=self.sp_model_kwargs			,						**lowerCAmelCase__			,						)
   UpperCAmelCase					      =  mask_token_sent
   UpperCAmelCase					      =  vocab_file
   UpperCAmelCase					      =  spm.SentencePieceProcessor(**self.sp_model_kwargs    )
   self.sp_model.Load(lowerCAmelCase__    )

   # add special tokens to encoder dict
   UpperCAmelCase					      =  {
       0: self.pad_token,
       1: self.eos_token,
   }

   if self.mask_token_sent is not None:
     self.encoder.update(
         {
             2: self.mask_token_sent,
             3: self.mask_token,
         }    )

   if self.offset > 0:
     # entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
     # mask_token_sent is already added to list -> so start at 1
     self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1			,						self.offset - 1    )}    )

   UpperCAmelCase					      =  {v: k for k, v in self.encoder.items()}


 @property
 def _UpperCamelCase	(						self		: List[Any]    )    ->      int:
   return len(self.sp_model    ) + self.offset


 def _UpperCamelCase	(						self		: str    )    ->      Dict[str, int]:
   UpperCAmelCase					      =  {self.convert_ids_to_tokens(lowerCAmelCase__    ): i for i in range(self.vocab_size    )}
   vocab.update(self.added_tokens_encoder    )
   return vocab


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


 def __setstate__(						self		: List[Any]			,						lowerCAmelCase__		: List[Any]    )    ->      List[Any]:
   UpperCAmelCase					      =  d

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

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


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: str    )    ->      List[str]:
   return self.sp_model.encode(lowerCAmelCase__			,						out_type=lowerCAmelCase__    )


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: str    )    ->      int:
   if token in self.decoder:
     return self.decoder[token]
   elif token in self.added_tokens_decoder:
     return self.added_tokens_decoder[token]
   UpperCAmelCase					      =  self.sp_model.piece_to_id(lowerCAmelCase__    )
   return sp_id + self.offset


 def _UpperCamelCase	(						self		: Union[str, Any]			,						lowerCAmelCase__		: int    )    ->      str:
   if index in self.encoder:
     return self.encoder[index]
   elif index in self.added_tokens_encoder:
     return self.added_tokens_encoder[index]
   else:
     UpperCAmelCase					      =  self.sp_model.IdToPiece(index - self.offset    )
   return token


 def _UpperCamelCase	(						self		: str			,						lowerCAmelCase__		: Optional[int]    )    ->      str:
   UpperCAmelCase					      =  []
   UpperCAmelCase					      =  ""
   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(lowerCAmelCase__    ) + token
       UpperCAmelCase					      =  []
     else:
       current_sub_tokens.append(lowerCAmelCase__    )
   out_string += self.sp_model.decode(lowerCAmelCase__    )
   return out_string.strip()


 def _UpperCamelCase	(						self		: Optional[Any]			,						lowerCAmelCase__		: int=False    )    ->      int:
   return 1


 def _UpperCamelCase	(						self		: Tuple			,						lowerCAmelCase__		: Any    )    ->      str:
   UpperCAmelCase					      =  set(self.all_special_ids    )  # call it once instead of inside list comp
   all_special_ids.remove(self.unk_token_id    )  # <unk> is only sometimes special

   return [1 if x in all_special_ids else 0 for x in seq]


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: List			,						lowerCAmelCase__		: Optional[List] = None			,						lowerCAmelCase__		: bool = False    )    ->      List[int]:
   if already_has_special_tokens:
     return self._special_token_mask(lowerCAmelCase__    )
   elif token_ids_a is None:
     return self._special_token_mask(lowerCAmelCase__    ) + [1]
   else:
     return self._special_token_mask(token_ids_a + token_ids_a    ) + [1]


 def _UpperCamelCase	(						self		: Optional[int]			,						lowerCAmelCase__		: Optional[int]			,						lowerCAmelCase__		: int=None    )    ->      List[int]:
   if token_ids_a is None:
     return token_ids_a + [self.eos_token_id]
   # We don't expect to process pairs, but leave the pair logic for API consistency
   return token_ids_a + token_ids_a + [self.eos_token_id]


 def _UpperCamelCase	(						self		: int			,						lowerCAmelCase__		: str			,						lowerCAmelCase__		: Optional[str] = None    )    ->      Tuple[str]:
   if not os.path.isdir(lowerCAmelCase__    ):
     logger.error(f"Vocabulary path ({save_directory}) should be a directory"    )
     return
   UpperCAmelCase					      =  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:
       UpperCAmelCase					      =  self.sp_model.serialized_model_proto()
       fi.write(lowerCAmelCase__    )

   return (out_vocab_file,)






lowerCAmelCase__       = {
    "a": "AAAAA",
    "b": "AAAAB",
    "c": "AAABA",
    "d": "AAABB",
    "e": "AABAA",
    "f": "AABAB",
    "g": "AABBA",
    "h": "AABBB",
    "i": "ABAAA",
    "j": "BBBAA",
    "k": "ABAAB",
    "l": "ABABA",
    "m": "ABABB",
    "n": "ABBAA",
    "o": "ABBAB",
    "p": "ABBBA",
    "q": "ABBBB",
    "r": "BAAAA",
    "s": "BAAAB",
    "t": "BAABA",
    "u": "BAABB",
    "v": "BBBAB",
    "w": "BABAA",
    "x": "BABAB",
    "y": "BABBA",
    "z": "BABBB",
    " ": " ",
}


lowerCAmelCase__       = {value: key for key, value in encode_dict.items()}
def      _lowerCAmelCase(	__A			):
  UpperCAmelCase					      =  ""
  for letter in word.lower():
    if letter.isalpha() or letter == " ":
      encoded += encode_dict[letter]
    else:
      raise Exception("encode() accepts only letters of the alphabet and spaces"			)
  return encoded
def      _lowerCAmelCase(	__A			):
  if set(__A			) - {"A", "B", " "} != set():
    raise Exception("decode() accepts only 'A', 'B' and spaces"			)
  UpperCAmelCase					      =  ""
  for word in coded.split():
    while len(__A			) != 0:
      decoded += decode_dict[word[:5]]
      UpperCAmelCase					      =  word[5:]
    decoded += " "
  return decoded.strip()


if __name__ == "__main__":
      from doctest import testmod

      testmod()



