





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


_snake_case  :    Dict						    =  logging.get_logger(__name__)

_snake_case  :    List[Any]						    =  {'vocab_file': 'spiece.model'}

_snake_case  :    int						    =  {
    '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'
        ),
    }
}

_snake_case  :    Dict						    =  {
    'google/bigbird-roberta-base': 4096,
    'google/bigbird-roberta-large': 4096,
    'google/bigbird-base-trivia-itc': 4096,
}



class 				_UpperCAmelCase    (    _UpperCamelCase    ):

  """simple docstring"""


  a_        = VOCAB_FILES_NAMES
  a_        = PRETRAINED_VOCAB_FILES_MAP
  a_        = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_        = ["""input_ids""", """attention_mask"""]
  a_        = []






  def __init__(				self :		Dict					,  lowerCAmelCase_ :		Any					,  lowerCAmelCase_ :		List[Any]="<unk>"					,  lowerCAmelCase_ :		Optional[Any]="<s>"					,  lowerCAmelCase_ :		int="</s>"					,  lowerCAmelCase_ :		Optional[Any]="<pad>"					,  lowerCAmelCase_ :		Dict="[SEP]"					,  lowerCAmelCase_ :		Optional[int]="[MASK]"					,  lowerCAmelCase_ :		List[str]="[CLS]"					,  lowerCAmelCase_ :		Optional[Dict[str, Any]] = None					,  **lowerCAmelCase_ :		Optional[int]					,  )							->						None:
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else bos_token
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else eos_token
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else unk_token
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else pad_token
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else cls_token
     __lowerCAmelCase								=	AddedToken(lowerCAmelCase_					,  lstrip=lowerCAmelCase_					,  rstrip=lowerCAmelCase_     ) if isinstance(lowerCAmelCase_					,  lowerCAmelCase_     ) else sep_token

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

     __lowerCAmelCase								=	{} if sp_model_kwargs is None else sp_model_kwargs

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

     __lowerCAmelCase								=	vocab_file

     __lowerCAmelCase								=	spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.Load(lowerCAmelCase_     )






  @property
  def 				lowercase		(				self :		List[Any]     )							->						Optional[Any]:
     return self.sp_model.get_piece_size()






  def 				lowercase		(				self :		Optional[int]     )							->						Optional[Any]:
     __lowerCAmelCase								=	{self.convert_ids_to_tokens(lowerCAmelCase_     ): i for i in range(self.vocab_size     )}
     vocab.update(self.added_tokens_encoder     )
     return vocab






  def __getstate__(				self :		Dict     )							->						List[str]:
     __lowerCAmelCase								=	self.__dict__.copy()
     __lowerCAmelCase								=	None
     return state






  def __setstate__(				self :		str					,  lowerCAmelCase_ :		int     )							->						Optional[int]:
     __lowerCAmelCase								=	d

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

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






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		str     )							->						List[str]:
     return self.sp_model.encode(lowerCAmelCase_					,  out_type=lowerCAmelCase_     )






  def 				lowercase		(				self :		str					,  lowerCAmelCase_ :		Optional[int]     )							->						int:
     return self.sp_model.piece_to_id(lowerCAmelCase_     )






  def 				lowercase		(				self :		Optional[Any]					,  lowerCAmelCase_ :		Optional[int]     )							->						Any:
     __lowerCAmelCase								=	self.sp_model.IdToPiece(lowerCAmelCase_     )
     return token






  def 				lowercase		(				self :		Any					,  lowerCAmelCase_ :		List[Any]     )							->						Dict:
     __lowerCAmelCase								=	[]
     __lowerCAmelCase								=	''
     __lowerCAmelCase								=	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
           __lowerCAmelCase								=	True
           __lowerCAmelCase								=	[]
        else:
           current_sub_tokens.append(lowerCAmelCase_     )
           __lowerCAmelCase								=	False
     out_string += self.sp_model.decode(lowerCAmelCase_     )
     return out_string.strip()






  def 				lowercase		(				self :		Tuple					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		bool = False					,  lowerCAmelCase_ :		bool = None					,  lowerCAmelCase_ :		bool = True					,  **lowerCAmelCase_ :		Tuple					,  )							->						str:
     __lowerCAmelCase								=	kwargs.pop('use_source_tokenizer'					,  lowerCAmelCase_     )

     __lowerCAmelCase								=	self.convert_ids_to_tokens(lowerCAmelCase_					,  skip_special_tokens=lowerCAmelCase_     )

     # 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
     __lowerCAmelCase								=	[]
     __lowerCAmelCase								=	[]
     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(lowerCAmelCase_     )     )
              __lowerCAmelCase								=	[]
           sub_texts.append(lowerCAmelCase_     )
        else:
           current_sub_text.append(lowerCAmelCase_     )
     if current_sub_text:
        sub_texts.append(self.convert_tokens_to_string(lowerCAmelCase_     )     )

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

     __lowerCAmelCase								=	(
         clean_up_tokenization_spaces
         if clean_up_tokenization_spaces is not None
         else self.clean_up_tokenization_spaces
     )
     if clean_up_tokenization_spaces:
        __lowerCAmelCase								=	self.clean_up_tokenization(lowerCAmelCase_     )
        return clean_text
     else:
        return text






  def 				lowercase		(				self :		List[str]					,  lowerCAmelCase_ :		str					,  lowerCAmelCase_ :		Optional[str] = None     )							->						Tuple[str]:
     if not os.path.isdir(lowerCAmelCase_     ):
        logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""     )
        return
     __lowerCAmelCase								=	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:
           __lowerCAmelCase								=	self.sp_model.serialized_model_proto()
           fi.write(lowerCAmelCase_     )

     return (out_vocab_file,)






  def 				lowercase		(				self :		List[Any]					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None     )							->						List[int]:
     if token_ids_a is None:
        return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
     __lowerCAmelCase								=	[self.cls_token_id]
     __lowerCAmelCase								=	[self.sep_token_id]
     return cls + token_ids_a + sep + token_ids_a + sep






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None					,  lowerCAmelCase_ :		bool = False     )							->						List[int]:
     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] + ([0] * len(lowerCAmelCase_     )) + [1]






  def 				lowercase		(				self :		Dict					,  lowerCAmelCase_ :		List[int]					,  lowerCAmelCase_ :		Optional[List[int]] = None     )							->						List[int]:
     __lowerCAmelCase								=	[self.sep_token_id]
     __lowerCAmelCase								=	[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]








from timeit import timeit

def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('the value of input must not be negative'	)
   __lowerCAmelCase								=	0
   while number:
      number &= number - 1
      result += 1
   return result

def   a_    ( lowerCAmelCase_      :			int	):
   if number < 0:
      raise ValueError('the value of input must not be negative'	)
   __lowerCAmelCase								=	0
   while number:
      if number % 2 == 1:
         result += 1
      number >>= 1
   return result

def   a_    ( ):

   def do_benchmark(lowerCAmelCase_      :			int	) -> None:
      __lowerCAmelCase								=	'import __main__ as z'
      print(F"""Benchmark when {number = }:"""	)
      print(F"""{get_set_bits_count_using_modulo_operator(lowerCAmelCase_	) = }"""	)
      __lowerCAmelCase								=	timeit('z.get_set_bits_count_using_modulo_operator(25)',	setup=lowerCAmelCase_	)
      print(F"""timeit() runs in {timing} seconds"""	)
      print(F"""{get_set_bits_count_using_brian_kernighans_algorithm(lowerCAmelCase_	) = }"""	)
      __lowerCAmelCase								=	timeit(
          'z.get_set_bits_count_using_brian_kernighans_algorithm(25)',	setup=lowerCAmelCase_,	)
      print(F"""timeit() runs in {timing} seconds"""	)

   for number in (25, 37, 58, 0):
      do_benchmark(lowerCAmelCase_	)
      print()


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     benchmark()

