

'''simple docstring'''

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


lowercase__			    =			"▁"

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

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

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


lowercase__			    =			logging.get_logger(__name__)




class        A_ (				_UpperCAmelCase							):






     '''simple docstring'''



     UpperCAmelCase_						:	Optional[int]  		=  VOCAB_FILES_NAMES

     UpperCAmelCase_						:	Optional[int]  		=  VOCAB_FILES_NAMES
     UpperCAmelCase_						:	Any  		=  PRETRAINED_VOCAB_FILES_MAP
     UpperCAmelCase_						:	List[Any]  		=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     UpperCAmelCase_						:	Tuple  		=  ["""input_ids""", """attention_mask"""]


     def __init__(		self :     Dict     ,			lowercase_ :     Dict     ,			lowercase_ :     Optional[Any]="<pad>"     ,			lowercase_ :     Dict="</s>"     ,			lowercase_ :     str="<unk>"     ,			lowercase_ :     List[Any]="<mask_2>"     ,			lowercase_ :     List[str]="<mask_1>"     ,			lowercase_ :     int=None     ,			lowercase_ :     Optional[int]=103     ,			lowercase_ :     int = None     ,			**lowercase_ :     List[Any]     ,			)						->	None:
      UpperCAmelCase       :	Dict											= offset
      if additional_special_tokens is not None:
       if not isinstance(lowercase_     ,			lowercase_       ):
        raise TypeError(
            f"""additional_special_tokens should be of type {type(lowercase_       )}, but is"""
            f""" {type(lowercase_       )}"""       )

       UpperCAmelCase       :	int											= (
           ([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(lowercase_       )     ,			self.offset - 1       )
       ]

       if len(set(lowercase_       )       ) != len(lowercase_       ):
        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       :	Tuple											= additional_special_tokens_extended
      else:
       UpperCAmelCase       :	List[Any]											= [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       :	Any											= {} if sp_model_kwargs is None else sp_model_kwargs

      super().__init__(
          eos_token=lowercase_     ,			unk_token=lowercase_     ,			mask_token=lowercase_     ,			pad_token=lowercase_     ,			mask_token_sent=lowercase_     ,			offset=lowercase_     ,			additional_special_tokens=lowercase_     ,			sp_model_kwargs=self.sp_model_kwargs     ,			**lowercase_     ,			)
      UpperCAmelCase       :	Union[str, Any]											= mask_token_sent
      UpperCAmelCase       :	List[Any]											= vocab_file
      UpperCAmelCase       :	List[str]											= spm.SentencePieceProcessor(**self.sp_model_kwargs       )
      self.sp_model.Load(lowercase_       )

      # add special tokens to encoder dict
      UpperCAmelCase       :	List[str]											= {
          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       :	Optional[Any]											= {v: k for k, v in self.encoder.items()}


     @property
     def    UpperCAmelCase_       (		self :     int       )						->	int:
      return len(self.sp_model       ) + self.offset


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


     def __getstate__(		self :     Dict       )						->	Optional[int]:
      UpperCAmelCase       :	Optional[int]											= self.__dict__.copy()
      UpperCAmelCase       :	int											= None
      return state


     def __setstate__(		self :     Union[str, Any]     ,			lowercase_ :     Dict       )						->	List[Any]:
      UpperCAmelCase       :	Tuple											= d

      # for backward compatibility
      if not hasattr(self     ,			'sp_model_kwargs'       ):
       UpperCAmelCase       :	List[Any]											= {}

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


     def    UpperCAmelCase_       (		self :     int     ,			lowercase_ :     Dict       )						->	List[str]:
      return self.sp_model.encode(lowercase_     ,			out_type=lowercase_       )


     def    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     List[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       :	Any											= self.sp_model.piece_to_id(lowercase_       )
      return sp_id + self.offset


     def    UpperCAmelCase_       (		self :     Any     ,			lowercase_ :     Any       )						->	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       :	Union[str, Any]											= self.sp_model.IdToPiece(index - self.offset       )
      return token


     def    UpperCAmelCase_       (		self :     Tuple     ,			lowercase_ :     List[str]       )						->	Optional[int]:
      UpperCAmelCase       :	Union[str, Any]											= []
      UpperCAmelCase       :	List[str]											= ''
      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(lowercase_       ) + token
        UpperCAmelCase       :	Dict											= []
       else:
        current_sub_tokens.append(lowercase_       )
      out_string += self.sp_model.decode(lowercase_       )
      return out_string.strip()


     def    UpperCAmelCase_       (		self :     Optional[Any]     ,			lowercase_ :     List[Any]=False       )						->	Tuple:
      return 1


     def    UpperCAmelCase_       (		self :     List[Any]     ,			lowercase_ :     Tuple       )						->	Tuple:
      UpperCAmelCase       :	Tuple											= 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     ,			lowercase_ :     Optional[Any]     ,			lowercase_ :     List[str] = None     ,			lowercase_ :     Union[str, Any] = False       )						->	List[int]:
      if already_has_special_tokens:
       return self._special_token_mask(lowercase_       )
      elif token_ids_a is None:
       return self._special_token_mask(lowercase_       ) + [1]
      else:
       return self._special_token_mask(token_ids_a + token_ids_a       ) + [1]


     def    UpperCAmelCase_       (		self :     List[str]     ,			lowercase_ :     str     ,			lowercase_ :     List[Any]=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     ,			lowercase_ :     Dict     ,			lowercase_ :     Any = None       )						->	Tuple[str]:
      if not os.path.isdir(lowercase_       ):
       logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""       )
       return
      UpperCAmelCase       :	Optional[Any]											= os.path.join(
          lowercase_     ,			(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']       )

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

      return (out_vocab_file,)











"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


SCREAMING_SNAKE_CASE__					=   {}

try:
       if not is_sentencepiece_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       SCREAMING_SNAKE_CASE__					=   ["BartphoTokenizer"]

if TYPE_CHECKING:
       try:
              if not is_sentencepiece_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .tokenization_bartpho import BartphoTokenizer

else:
       import sys

       SCREAMING_SNAKE_CASE__					=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)






