






'''simple docstring'''






import os
import re
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


__A    	=   logging.get_logger(__name__)

__A    	=   {'''vocab_file''': '''spiece.model'''}

__A    	=   {
    '''vocab_file''': {
        '''google/bigbird-roberta-base''': '''https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model''',
        '''google/bigbird-roberta-large''': (
            '''https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model'''
        ),
        '''google/bigbird-base-trivia-itc''': (
            '''https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model'''
        ),
    }
}

__A    	=   {
    '''google/bigbird-roberta-base''': 4_0_9_6,
    '''google/bigbird-roberta-large''': 4_0_9_6,
    '''google/bigbird-base-trivia-itc''': 4_0_9_6,
}




class a_    (    UpperCamelCase_							):
   _snake_case        =							VOCAB_FILES_NAMES
   _snake_case        =							PRETRAINED_VOCAB_FILES_MAP
   _snake_case        =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   _snake_case        =							["""input_ids""", """attention_mask"""]
   _snake_case        =							[]







   def __init__(self					,   __a					,   __a="<unk>"					,   __a="<s>"					,   __a="</s>"					,   __a="<pad>"					,   __a="[SEP]"					,   __a="[MASK]"					,   __a="[CLS]"					,   __a = None					,   **__a					,   )			->      None:
      """simple docstring"""






      __snake_case				:      Tuple     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else bos_token
      __snake_case				:      List[str]     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else eos_token
      __snake_case				:      Optional[int]     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else unk_token
      __snake_case				:      List[str]     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else pad_token
      __snake_case				:      Tuple     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else cls_token
      __snake_case				:      Tuple     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else sep_token

      # Mask token behave like a normal word, i.e. include the space before it
      __snake_case				:      Tuple     							= AddedToken(__a					,   lstrip=__a					,   rstrip=__a) if isinstance(__a					,   __a) else mask_token

      __snake_case				:      str     							= {} if sp_model_kwargs is None else sp_model_kwargs

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

      __snake_case				:      Optional[int]     							= vocab_file

      __snake_case				:      Dict     							= spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(__a)







   @property
   def     SCREAMING_SNAKE_CASE__					(self)			->      List[str]:
      """simple docstring"""






      return self.sp_model.get_piece_size()







   def     SCREAMING_SNAKE_CASE__					(self)			->      int:
      """simple docstring"""






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







   def __getstate__(self)			->      Tuple:
      """simple docstring"""






      __snake_case				:      str     							= self.__dict__.copy()
      __snake_case				:      Optional[Any]     							= None
      return state







   def __setstate__(self					,   __a)			->      Tuple:
      """simple docstring"""






      __snake_case				:      int     							= d

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

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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      return self.sp_model.encode(__a					,   out_type=__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[str]:
      """simple docstring"""






      return self.sp_model.piece_to_id(__a)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      List[Any]:
      """simple docstring"""






      __snake_case				:      Optional[int]     							= self.sp_model.IdToPiece(__a)
      return token







   def     SCREAMING_SNAKE_CASE__					(self					,   __a)			->      Optional[int]:
      """simple docstring"""






      __snake_case				:      str     							= []
      __snake_case				:      Dict     							= ''
      __snake_case				:      Union[str, 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(__a) + token
            __snake_case				:      int     							= True
            __snake_case				:      str     							= []
         else:
            current_sub_tokens.append(__a)
            __snake_case				:      Any     							= False
      out_string += self.sp_model.decode(__a)
      return out_string.strip()







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = False					,   __a = None					,   __a = True					,   **__a					,   )			->      str:
      """simple docstring"""






      __snake_case				:      Optional[Any]     							= kwargs.pop('use_source_tokenizer'					,   __a)

      __snake_case				:      List[Any]     							= self.convert_ids_to_tokens(__a					,   skip_special_tokens=__a)

      # 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				:      Tuple     							= []
      __snake_case				:      str     							= []
      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(__a))
               __snake_case				:      Optional[int]     							= []
            sub_texts.append(__a)
         else:
            current_sub_text.append(__a)
      if current_sub_text:
         sub_texts.append(self.convert_tokens_to_string(__a))

      # Mimic the behavior of the Rust tokenizer:
      # No space before [MASK] and [SEP]
      if spaces_between_special_tokens:
         __snake_case				:      List[str]     							= re.sub(R' (\[(MASK|SEP)\])'					,   R'\1'					,   ' '.join(__a))
      else:
         __snake_case				:      Optional[Any]     							= ''.join(__a)

      __snake_case				:      Optional[int]     							= (
          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(__a)
         return clean_text
      else:
         return text







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None)			->      Tuple[str]:
      """simple docstring"""






      if not os.path.isdir(__a):
         logger.error(F"""Vocabulary path ({save_directory}) should be a directory""")
         return
      __snake_case				:      Any     							= 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:
            __snake_case				:      int     							= self.sp_model.serialized_model_proto()
            fi.write(__a)

      return (out_vocab_file,)







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None)			->      List[int]:
      """simple docstring"""






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







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None					,   __a = False)			->      List[int]:
      """simple docstring"""






      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] + ([0] * len(__a)) + [1]







   def     SCREAMING_SNAKE_CASE__					(self					,   __a					,   __a = None)			->      List[int]:
      """simple docstring"""






      __snake_case				:      Dict     							= [self.sep_token_id]
      __snake_case				:      Union[str, 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) * [0] + len(token_ids_a + sep) * [1]







'''simple docstring'''






from typing import TYPE_CHECKING

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


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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A    	=   [
          '''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

      __A    	=   _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)