




'''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


UpperCamelCase_						:      Dict       =						logging.get_logger(__name__)

UpperCamelCase_						:      Tuple       =						{'''vocab_file''': '''spiece.model'''}

UpperCamelCase_						:      List[str]       =						{
    '''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'''
        ),
    }
}

UpperCamelCase_						:      Union[str, Any]       =						{
    '''google/bigbird-roberta-base''': 4096,
    '''google/bigbird-roberta-large''': 4096,
    '''google/bigbird-base-trivia-itc''': 4096,
}

class        _a    (						__lowerCAmelCase							):
     SCREAMING_SNAKE_CASE_							:   str       =						VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE_							:   int       =						PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE_							:   List[Any]       =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE_							:   Optional[int]       =						["""input_ids""", """attention_mask"""]
     SCREAMING_SNAKE_CASE_							:   List[int]       =						[]






     def __init__(			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE="<unk>" ,_SCREAMING_SNAKE_CASE="<s>" ,_SCREAMING_SNAKE_CASE="</s>" ,_SCREAMING_SNAKE_CASE="<pad>" ,_SCREAMING_SNAKE_CASE="[SEP]" ,_SCREAMING_SNAKE_CASE="[MASK]" ,_SCREAMING_SNAKE_CASE="[CLS]" ,_SCREAMING_SNAKE_CASE = None ,**_SCREAMING_SNAKE_CASE ,)							->      None:
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else bos_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else eos_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else unk_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else pad_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else cls_token
          _snake_case							=				AddedToken(_SCREAMING_SNAKE_CASE ,lstrip=_SCREAMING_SNAKE_CASE ,rstrip=_SCREAMING_SNAKE_CASE   ) if isinstance(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE   ) else sep_token

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

          _snake_case							=				{} if sp_model_kwargs is None else sp_model_kwargs

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

          _snake_case							=				vocab_file

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(_SCREAMING_SNAKE_CASE   )






     @property
     def     _lowercase     (			self   )							->      str:
          return self.sp_model.get_piece_size()






     def     _lowercase     (			self   )							->      Optional[Any]:
          _snake_case							=				{self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE   ): i for i in range(self.vocab_size   )}
          vocab.update(self.added_tokens_encoder   )
          return vocab






     def __getstate__(			self   )							->      List[Any]:
          _snake_case							=				self.__dict__.copy()
          _snake_case							=				None
          return state






     def __setstate__(			self ,_SCREAMING_SNAKE_CASE   )							->      str:
          _snake_case							=				d

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

          _snake_case							=				spm.SentencePieceProcessor(**self.sp_model_kwargs   )
          self.sp_model.Load(self.vocab_file   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      List[str]:
          return self.sp_model.encode(_SCREAMING_SNAKE_CASE ,out_type=_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          return self.sp_model.piece_to_id(_SCREAMING_SNAKE_CASE   )






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Union[str, Any]:
          _snake_case							=				self.sp_model.IdToPiece(_SCREAMING_SNAKE_CASE   )
          return token






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE   )							->      Dict:
          _snake_case							=				[]
          _snake_case							=				""
          _snake_case							=				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(_SCREAMING_SNAKE_CASE   ) + token
                    _snake_case							=				True
                    _snake_case							=				[]
               else:
                    current_sub_tokens.append(_SCREAMING_SNAKE_CASE   )
                    _snake_case							=				False
          out_string += self.sp_model.decode(_SCREAMING_SNAKE_CASE   )
          return out_string.strip()






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = True ,**_SCREAMING_SNAKE_CASE ,)							->      str:
          _snake_case							=				kwargs.pop("use_source_tokenizer" ,_SCREAMING_SNAKE_CASE   )

          _snake_case							=				self.convert_ids_to_tokens(_SCREAMING_SNAKE_CASE ,skip_special_tokens=_SCREAMING_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							=				[]
          _snake_case							=				[]
          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(_SCREAMING_SNAKE_CASE   )   )
                         _snake_case							=				[]
                    sub_texts.append(_SCREAMING_SNAKE_CASE   )
               else:
                    current_sub_text.append(_SCREAMING_SNAKE_CASE   )
          if current_sub_text:
               sub_texts.append(self.convert_tokens_to_string(_SCREAMING_SNAKE_CASE   )   )

          # Mimic the behavior of the Rust tokenizer:
          # No space before [MASK] and [SEP]
          if spaces_between_special_tokens:
               _snake_case							=				re.sub(r" (\[(MASK|SEP)\])" ,r"\1" ," ".join(_SCREAMING_SNAKE_CASE   )   )
          else:
               _snake_case							=				"".join(_SCREAMING_SNAKE_CASE   )

          _snake_case							=				(
              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							=				self.clean_up_tokenization(_SCREAMING_SNAKE_CASE   )
               return clean_text
          else:
               return text






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      Tuple[str]:
          if not os.path.isdir(_SCREAMING_SNAKE_CASE   ):
               logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""   )
               return
          _snake_case							=				os.path.join(
              _SCREAMING_SNAKE_CASE ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]   )

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

          return (out_vocab_file,)






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          _snake_case							=				[self.cls_token_id]
          _snake_case							=				[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None ,_SCREAMING_SNAKE_CASE = False   )							->      List[int]:
          if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=_SCREAMING_SNAKE_CASE ,token_ids_a=_SCREAMING_SNAKE_CASE ,already_has_special_tokens=_SCREAMING_SNAKE_CASE   )

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






     def     _lowercase     (			self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = None   )							->      List[int]:
          _snake_case							=				[self.sep_token_id]
          _snake_case							=				[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'''





def      __a     (       _UpperCamelCase:   int   )						->			str:

     """simple docstring"""




     if number > 0:
          raise ValueError("input must be a negative integer"   )
     _snake_case							=				len(bin(_UpperCamelCase   )[3:]   )
     _snake_case							=				bin(abs(_UpperCamelCase   ) - (1 << binary_number_length)   )[3:]
     _snake_case							=				(
         (
             "1"
             + "0" * (binary_number_length - len(_UpperCamelCase   ))
             + twos_complement_number
         )
         if number < 0
         else "0"
     )
     return "0b" + twos_complement_number


if __name__ == "__main__":
   import doctest

   doctest.testmod()


