




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	:						List[str]			=     logging.get_logger(__name__)

_UpperCAmelCase	:						List[str]			=     {"vocab_file": "sentencepiece.bpe.model"}

_UpperCAmelCase	:						str			=     {
    "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	:						str			=     {
    "moussaKam/mbarthez": 1024,
    "moussaKam/barthez": 1024,
    "moussaKam/barthez-orangesum-title": 1024,
}

_UpperCAmelCase	:						str			=     "▁"




class    __lowerCAmelCase       (							UpperCamelCase__):
       _a 					=						VOCAB_FILES_NAMES
       _a 					=						PRETRAINED_VOCAB_FILES_MAP
       _a 					=						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
       _a 					=						['''input_ids''', '''attention_mask''']






       def __init__(   self:					List[Any]       ,					_lowerCAmelCase:					Tuple       ,					_lowerCAmelCase:					Any="<s>"       ,					_lowerCAmelCase:					List[Any]="</s>"       ,					_lowerCAmelCase:					Optional[Any]="</s>"       ,					_lowerCAmelCase:					Optional[int]="<s>"       ,					_lowerCAmelCase:					Tuple="<unk>"       ,					_lowerCAmelCase:					List[Any]="<pad>"       ,					_lowerCAmelCase:					Dict="<mask>"       ,					_lowerCAmelCase:					Optional[Dict[str, Any]] = None       ,					**_lowerCAmelCase:					Tuple       ,					):
         lowercase    :Optional[int]      	=						AddedToken(__A       ,					lstrip=__A       ,					rstrip=__A				) if isinstance(__A       ,					__A				) else mask_token

         lowercase    :List[str]      	=						{} if sp_model_kwargs is None else sp_model_kwargs

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

         lowercase    :Dict      	=						vocab_file
         lowercase    :List[Any]      	=						spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(str(__A				)				)

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

         lowercase    :List[str]      	=						len(self.sp_model				) - 1
         lowercase    :str      	=						{v: k for k, v in self.fairseq_tokens_to_ids.items()}






       def 					SCREAMING_SNAKE_CASE    (   self:					Union[str, Any]       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None				):



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






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None       ,					_lowerCAmelCase:					bool = False				):



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

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






       def 					SCREAMING_SNAKE_CASE    (   self:					Tuple       ,					_lowerCAmelCase:					List[int]       ,					_lowerCAmelCase:					Optional[List[int]] = None				):
         lowercase    :List[str]      	=						[self.sep_token_id]
         lowercase    :Dict      	=						[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 					SCREAMING_SNAKE_CASE    (   self:					List[str]				):
         return len(self.sp_model				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[Any]				):
         lowercase    :Tuple      	=						{self.convert_ids_to_tokens(__A				): i for i in range(self.vocab_size				)}
         vocab.update(self.added_tokens_encoder				)
         return vocab






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					str				):
         return self.sp_model.encode(__A       ,					out_type=__A				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Dict       ,					_lowerCAmelCase:					List[Any]				):



         if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
         lowercase    :Optional[Any]      	=						self.sp_model.PieceToId(__A				)

         return spm_id if spm_id else self.unk_token_id






       def 					SCREAMING_SNAKE_CASE    (   self:					str       ,					_lowerCAmelCase:					Optional[Any]				):



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






       def 					SCREAMING_SNAKE_CASE    (   self:					List[str]       ,					_lowerCAmelCase:					Union[str, Any]				):
         lowercase    :str      	=						[]
         lowercase    :List[Any]      	=						""
         lowercase    :Tuple      	=						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(__A				) + token
             lowercase    :Union[str, Any]      	=						True
             lowercase    :Optional[int]      	=						[]
           else:
             current_sub_tokens.append(__A				)
             lowercase    :Optional[Any]      	=						False
         out_string += self.sp_model.decode(__A				)
         return out_string.strip()






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






       def __setstate__(   self:					Optional[Any]       ,					_lowerCAmelCase:					List[Any]				):
         lowercase    :Tuple      	=						d

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

         lowercase    :Any      	=						spm.SentencePieceProcessor(**self.sp_model_kwargs				)
         self.sp_model.Load(self.vocab_file				)






       def 					SCREAMING_SNAKE_CASE    (   self:					Optional[int]       ,					_lowerCAmelCase:					str       ,					_lowerCAmelCase:					Optional[str] = None				):



         if not os.path.isdir(__A				):
           logger.error(F"Vocabulary path ({save_directory}) should be a directory"				)
           return
         lowercase    :Optional[int]      	=						os.path.join(
             __A       ,					(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

         if os.path.abspath(self.vocab_file				) != os.path.abspath(__A				) and os.path.isfile(self.vocab_file				):
           copyfile(self.vocab_file       ,					__A				)
         elif not os.path.isfile(self.vocab_file				):
           with open(__A       ,					"wb"				) as fi:
             lowercase    :Union[str, Any]      	=						self.sp_model.serialized_model_proto()
             fi.write(__A				)

         return (out_vocab_file,)









from typing import TYPE_CHECKING

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


_UpperCAmelCase	:						Optional[int]			=     {
    "configuration_biogpt": ["BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BioGptConfig"],
    "tokenization_biogpt": ["BioGptTokenizer"],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _UpperCAmelCase	:						int			=     [
         "BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST",
         "BioGptForCausalLM",
         "BioGptForTokenClassification",
         "BioGptForSequenceClassification",
         "BioGptModel",
         "BioGptPreTrainedModel",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     _UpperCAmelCase	:						Optional[Any]			=     _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



