






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


SCREAMING_SNAKE_CASE__		:							str 				=				logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__		:							Tuple 				=				{"""vocab_file""": """sentencepiece.bpe.model"""}

SCREAMING_SNAKE_CASE__		:							Any 				=				{
    """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"""
        ),
    },
}

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

SCREAMING_SNAKE_CASE__		:							Optional[Any] 				=				"""▁"""







class 				UpperCAmelCase_  (		__lowerCamelCase		):
     __lowerCamelCase      				=  VOCAB_FILES_NAMES
     __lowerCamelCase      				=  PRETRAINED_VOCAB_FILES_MAP
     __lowerCamelCase      				=  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __lowerCamelCase      				=  ['input_ids', 'attention_mask']




     def __init__(				self	,						_lowerCAmelCase	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="</s>"	,						_lowerCAmelCase="<s>"	,						_lowerCAmelCase="<unk>"	,						_lowerCAmelCase="<pad>"	,						_lowerCAmelCase="<mask>"	,						_lowerCAmelCase = None	,						**_lowerCAmelCase	,						):
          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__      :  str				      =			AddedToken(_lowerCAmelCase	,						lstrip=_lowerCAmelCase	,						rstrip=_lowerCAmelCase		) if isinstance(_lowerCAmelCase	,						_lowerCAmelCase		) else mask_token

          UpperCAmelCase__      :  Union[str, Any]				      =			{} 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	,						)

          UpperCAmelCase__      :  int				      =			vocab_file
          UpperCAmelCase__      :  Union[str, Any]				      =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(str(_lowerCAmelCase		)		)

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

          UpperCAmelCase__      :  Union[str, Any]				      =			len(self.sp_model		) - 1
          UpperCAmelCase__      :  Optional[int]				      =			{v: k for k, v in self.fairseq_tokens_to_ids.items()}




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):

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




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None	,						_lowerCAmelCase = False		):
          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	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          UpperCAmelCase__      :  int				      =			[self.sep_token_id]
          UpperCAmelCase__      :  Optional[Any]				      =			[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		):
          return len(self.sp_model		)




     def   __UpperCAmelCase  (				self		):
          UpperCAmelCase__      :  List[str]				      =			{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	,						_lowerCAmelCase		):
          return self.sp_model.encode(_lowerCAmelCase	,						out_type=_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          UpperCAmelCase__      :  int				      =			self.sp_model.PieceToId(_lowerCAmelCase		)

          return spm_id if spm_id else self.unk_token_id




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          if index in self.fairseq_ids_to_tokens:
               return self.fairseq_ids_to_tokens[index]
          return self.sp_model.IdToPiece(_lowerCAmelCase		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  int				      =			[]
          UpperCAmelCase__      :  List[str]				      =			""""""
          UpperCAmelCase__      :  Optional[Any]				      =			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
                    UpperCAmelCase__      :  Dict				      =			True
                    UpperCAmelCase__      :  Tuple				      =			[]
               else:
                    current_sub_tokens.append(_lowerCAmelCase		)
                    UpperCAmelCase__      :  Optional[int]				      =			False
          out_string += self.sp_model.decode(_lowerCAmelCase		)
          return out_string.strip()




     def __getstate__(				self		):
          UpperCAmelCase__      :  str				      =			self.__dict__.copy()
          UpperCAmelCase__      :  List[Any]				      =			None
          return state




     def __setstate__(				self	,						_lowerCAmelCase		):
          UpperCAmelCase__      :  Union[str, Any]				      =			d

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

          UpperCAmelCase__      :  Optional[Any]				      =			spm.SentencePieceProcessor(**self.sp_model_kwargs		)
          self.sp_model.Load(self.vocab_file		)




     def   __UpperCAmelCase  (				self	,						_lowerCAmelCase	,						_lowerCAmelCase = None		):
          if not os.path.isdir(_lowerCAmelCase		):
               logger.error(f"Vocabulary path ({save_directory}) should be a directory"		)
               return
          UpperCAmelCase__      :  Tuple				      =			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__      :  str				      =			self.sp_model.serialized_model_proto()
                    fi.write(_lowerCAmelCase		)

          return (out_vocab_file,)







'''simple docstring'''


from typing import TYPE_CHECKING

from ...file_utils import _LazyModule, is_torch_available
from ...utils import OptionalDependencyNotAvailable


__snake_case						  =		{
    """configuration_gpt_neox_japanese""": ["""GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTNeoXJapaneseConfig"""],
    """tokenization_gpt_neox_japanese""": ["""GPTNeoXJapaneseTokenizer"""],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __snake_case						  =		[
           """GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """GPTNeoXJapaneseForCausalLM""",
           """GPTNeoXJapaneseLayer""",
           """GPTNeoXJapaneseModel""",
           """GPTNeoXJapanesePreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neox_japanese import GPT_NEOX_JAPANESE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXJapaneseConfig
       from .tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neox_japanese import (
                  GPT_NEOX_JAPANESE_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoXJapaneseForCausalLM,
                  GPTNeoXJapaneseLayer,
                  GPTNeoXJapaneseModel,
                  GPTNeoXJapanesePreTrainedModel,
              )


else:
       import sys

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

