



'''simple docstring'''





import os
import unicodedata
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 SPIECE_UNDERLINE, logging


__a          =		logging.get_logger(__name__)

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

__a          =		{
    "vocab_file": {
        "xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
        "xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
    }
}

__a          =		{
    "xlnet-base-cased": None,
    "xlnet-large-cased": None,
}

# Segments (not really needed)
__a          =		0
__a          =		1
__a          =		2
__a          =		3
__a          =		4







class     UpperCAmelCase_       (       _a	):
    """simple docstring"""







    lowercase					=		VOCAB_FILES_NAMES
    lowercase					=		PRETRAINED_VOCAB_FILES_MAP
    lowercase					=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowercase					=		"left"




    def __init__(    self					:     Tuple ,     snake_case_					:     List[Any] ,     snake_case_					:     List[Any]=False ,     snake_case_					:     Union[str, Any]=True ,     snake_case_					:     Optional[Any]=False ,     snake_case_					:     str="<s>" ,     snake_case_					:     Dict="</s>" ,     snake_case_					:     Optional[int]="<unk>" ,     snake_case_					:     str="<sep>" ,     snake_case_					:     List[Any]="<pad>" ,     snake_case_					:     Optional[int]="<cls>" ,     snake_case_					:     int="<mask>" ,     snake_case_					:     Optional[Any]=["<eop>", "<eod>"] ,     snake_case_					:     Optional[Dict[str, Any]] = None ,     **snake_case_					:     str ,     ):
          # Mask token behave like a normal word, i.e. include the space before it
          snake_case__  :    Dict    						=      AddedToken(snake_case_ ,     lstrip=snake_case_ ,     rstrip=snake_case_						) if isinstance(snake_case_ ,     snake_case_						) else mask_token

          snake_case__  :    List[Any]    						=      {} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              do_lower_case=snake_case_ ,     remove_space=snake_case_ ,     keep_accents=snake_case_ ,     bos_token=snake_case_ ,     eos_token=snake_case_ ,     unk_token=snake_case_ ,     sep_token=snake_case_ ,     pad_token=snake_case_ ,     cls_token=snake_case_ ,     mask_token=snake_case_ ,     additional_special_tokens=snake_case_ ,     sp_model_kwargs=self.sp_model_kwargs ,     **snake_case_ ,     )

          snake_case__  :    List[str]    						=      3

          snake_case__  :    List[str]    						=      do_lower_case
          snake_case__  :    str    						=      remove_space
          snake_case__  :    Tuple    						=      keep_accents
          snake_case__  :    str    						=      vocab_file

          snake_case__  :    List[str]    						=      spm.SentencePieceProcessor(**self.sp_model_kwargs						)
          self.sp_model.Load(snake_case_						)




    @property
    def lowerCamelCase       (    self					:     Union[str, Any]						):
          return len(self.sp_model						)




    def lowerCamelCase       (    self					:     Union[str, Any]						):
          snake_case__  :    Union[str, Any]    						=      {self.convert_ids_to_tokens(snake_case_						): i for i in range(self.vocab_size						)}
          vocab.update(self.added_tokens_encoder						)
          return vocab




    def __getstate__(    self					:     Any						):
          snake_case__  :    str    						=      self.__dict__.copy()
          snake_case__  :    Tuple    						=      None
          return state




    def __setstate__(    self					:     List[Any] ,     snake_case_					:     Tuple						):
          snake_case__  :    Optional[int]    						=      d

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

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




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     Optional[Any]						):
          if self.remove_space:
                snake_case__  :    Dict    						=      """ """.join(inputs.strip().split()						)
          else:
                snake_case__  :    Any    						=      inputs
          snake_case__  :    List[str]    						=      outputs.replace("""``""" ,     """\""""						).replace("""''""" ,     """\""""						)

          if not self.keep_accents:
                snake_case__  :    Union[str, Any]    						=      unicodedata.normalize("""NFKD""" ,     snake_case_						)
                snake_case__  :    str    						=      """""".join([c for c in outputs if not unicodedata.combining(snake_case_						)]						)
          if self.do_lower_case:
                snake_case__  :    Optional[Any]    						=      outputs.lower()

          return outputs




    def lowerCamelCase       (    self					:     Tuple ,     snake_case_					:     str						):
          snake_case__  :    int    						=      self.preprocess_text(snake_case_						)
          snake_case__  :    int    						=      self.sp_model.encode(snake_case_ ,     out_type=snake_case_						)
          snake_case__  :    Optional[int]    						=      []
          for piece in pieces:
                if len(snake_case_						) > 1 and piece[-1] == str(""","""						) and piece[-2].isdigit():
                      snake_case__  :    Union[str, Any]    						=      self.sp_model.EncodeAsPieces(piece[:-1].replace(snake_case_ ,     """"""						)						)
                      if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                            if len(cur_pieces[0]						) == 1:
                                  snake_case__  :    str    						=      cur_pieces[1:]
                            else:
                                  snake_case__  :    Union[str, Any]    						=      cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]						)
                      new_pieces.extend(snake_case_						)
                else:
                      new_pieces.append(snake_case_						)

          return new_pieces




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Optional[int]						):
          return self.sp_model.PieceToId(snake_case_						)




    def lowerCamelCase       (    self					:     List[str] ,     snake_case_					:     List[Any]						):
          return self.sp_model.IdToPiece(snake_case_						)




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     Union[str, Any]						):
          snake_case__  :    str    						=      """""".join(snake_case_						).replace(snake_case_ ,     """ """						).strip()
          return out_string




    def lowerCamelCase       (    self					:     Union[str, Any] ,     snake_case_					:     List[int] ,     snake_case_					:     bool = False ,     snake_case_					:     bool = None ,     snake_case_					:     bool = True ,     **snake_case_					:     List[Any] ,     ):
          snake_case__  :    Optional[int]    						=      kwargs.pop("""use_source_tokenizer""" ,     snake_case_						)

          snake_case__  :    Dict    						=      self.convert_ids_to_tokens(snake_case_ ,     skip_special_tokens=snake_case_						)

          # To avoid mixing byte-level and unicode for byte-level BPT
          # we need to build string separately for added tokens and byte-level tokens
          # cf. https://github.com/huggingface/transformers/issues/1133
          snake_case__  :    List[str]    						=      []
          snake_case__  :    List[Any]    						=      []
          for token in filtered_tokens:
                if skip_special_tokens and token in self.all_special_ids:
                      continue
                if token in self.added_tokens_encoder:
                      if current_sub_text:
                            sub_texts.append(self.convert_tokens_to_string(snake_case_						)						)
                            snake_case__  :    List[Any]    						=      []
                      sub_texts.append(snake_case_						)
                else:
                      current_sub_text.append(snake_case_						)
          if current_sub_text:
                sub_texts.append(self.convert_tokens_to_string(snake_case_						)						)

          # Mimic the behavior of the Rust tokenizer:
          # By default, there are no spaces between special tokens
          snake_case__  :    Union[str, Any]    						=      """""".join(snake_case_						)

          snake_case__  :    List[str]    						=      (
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                snake_case__  :    Union[str, Any]    						=      self.clean_up_tokenization(snake_case_						)
                return clean_text
          else:
                return text




    def lowerCamelCase       (    self					:     Optional[int] ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None						):
          snake_case__  :    int    						=      [self.sep_token_id]
          snake_case__  :    Optional[Any]    						=      [self.cls_token_id]
          if token_ids_a is None:
                return token_ids_a + sep + cls
          return token_ids_a + sep + token_ids_a + sep + cls




    def lowerCamelCase       (    self					:     int ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None ,     snake_case_					:     bool = False						):

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

          if token_ids_a is not None:
                return ([0] * len(snake_case_						)) + [1] + ([0] * len(snake_case_						)) + [1, 1]
          return ([0] * len(snake_case_						)) + [1, 1]




    def lowerCamelCase       (    self					:     Any ,     snake_case_					:     List[int] ,     snake_case_					:     Optional[List[int]] = None						):
          snake_case__  :    Dict    						=      [self.sep_token_id]
          snake_case__  :    Tuple    						=      [2]

          if token_ids_a is None:
                return len(token_ids_a + sep						) * [0] + cls_segment_id
          return len(token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1] + cls_segment_id




    def lowerCamelCase       (    self					:     str ,     snake_case_					:     str ,     snake_case_					:     Optional[str] = None						):
          if not os.path.isdir(snake_case_						):
                logger.error(f"Vocabulary path ({save_directory}) should be a directory"						)
                return
          snake_case__  :    int    						=      os.path.join(
              snake_case_ ,     (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]						)

          if os.path.abspath(self.vocab_file						) != os.path.abspath(snake_case_						) and os.path.isfile(self.vocab_file						):
                copyfile(self.vocab_file ,     snake_case_						)
          elif not os.path.isfile(self.vocab_file						):
                with open(snake_case_ ,     """wb"""						) as fi:
                      snake_case__  :    int    						=      self.sp_model.serialized_model_proto()
                      fi.write(snake_case_						)

          return (out_vocab_file,)





'''simple docstring'''





from typing import TYPE_CHECKING

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


_UpperCamelCase											=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCamelCase											=						[
       '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											=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






