




'''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


lowercase      :   Optional[int]      =       logging.get_logger(__name__)

lowercase      :   Tuple      =       {"""vocab_file""": """spiece.model"""}

lowercase      :   Dict      =       {
    """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"""
        ),
    }
}

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







class 					_a (__UpperCAmelCase	):





     '''simple docstring'''



     lowerCAmelCase_  :     Optional[int]          =					VOCAB_FILES_NAMES
     lowerCAmelCase_  :     Union[str, Any]          =					PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase_  :     Dict          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase_  :     Tuple          =					['''input_ids''', '''attention_mask''']
     lowerCAmelCase_  :     List[int]          =					[]







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

          snake_case			:      int        =			{} 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__							,)

          snake_case			:      Any        =			vocab_file

          snake_case			:      Any        =			spm.SentencePieceProcessor(**self.sp_model_kwargs							)
          self.sp_model.Load(lowerCamelCase__							)







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







     def 			snake_case_      (				self							)      ->				Optional[Any]:
          snake_case			:      Dict        =			{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							)      ->				Any:
          snake_case			:      List[Any]        =			self.__dict__.copy()
          snake_case			:      List[str]        =			None
          return state







     def __setstate__(				self							,__a							)      ->				Optional[int]:
          snake_case			:      Optional[Any]        =			d

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

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







     def 			snake_case_      (				self							,__a							)      ->				List[str]:
          return self.sp_model.encode(lowerCamelCase__							,out_type=lowerCamelCase__							)







     def 			snake_case_      (				self							,__a							)      ->				List[str]:
          return self.sp_model.piece_to_id(lowerCamelCase__							)







     def 			snake_case_      (				self							,__a							)      ->				Union[str, Any]:
          snake_case			:      Any        =			self.sp_model.IdToPiece(lowerCamelCase__							)
          return token







     def 			snake_case_      (				self							,__a							)      ->				Optional[Any]:
          snake_case			:      Tuple        =			[]
          snake_case			:      Any        =			""""""
          snake_case			:      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(lowerCamelCase__							) + token
                    snake_case			:      Optional[Any]        =			True
                    snake_case			:      Tuple        =			[]
               else:
                    current_sub_tokens.append(lowerCamelCase__							)
                    snake_case			:      Optional[Any]        =			False
          out_string += self.sp_model.decode(lowerCamelCase__							)
          return out_string.strip()







     def 			snake_case_      (				self							,__a							,__a = False							,__a = None							,__a = True							,**__a							,)      ->				str:
          snake_case			:      Optional[Any]        =			kwargs.pop("""use_source_tokenizer"""							,lowerCamelCase__							)

          snake_case			:      Tuple        =			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
          snake_case			:      str        =			[]
          snake_case			:      Union[str, 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(lowerCamelCase__							)							)
                         snake_case			:      Tuple        =			[]
                    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:
               snake_case			:      Tuple        =			re.sub(r""" (\[(MASK|SEP)\])"""							,r"""\1"""							,""" """.join(lowerCamelCase__							)							)
          else:
               snake_case			:      List[str]        =			"""""".join(lowerCamelCase__							)

          snake_case			:      Union[str, Any]        =			(
              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			:      Optional[int]        =			self.clean_up_tokenization(lowerCamelCase__							)
               return clean_text
          else:
               return text







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				Tuple[str]:
          if not os.path.isdir(lowerCamelCase__							):
               logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
               return
          snake_case			:      Optional[int]        =			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:
                    snake_case			:      Tuple        =			self.sp_model.serialized_model_proto()
                    fi.write(lowerCamelCase__							)

          return (out_vocab_file,)







     def 			snake_case_      (				self							,__a							,__a = None							)      ->				List[int]:
          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			:      Tuple        =			[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep







     def 			snake_case_      (				self							,__a							,__a = None							,__a = 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 			snake_case_      (				self							,__a							,__a = None							)      ->				List[int]:
          snake_case			:      Tuple        =			[self.sep_token_id]
          snake_case			:      List[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"""


def    _lowerCamelCase   (     lowerCamelCase__	:							Optional[Any]  ):
 lowercase__       :       List[str]				=						len(lowerCamelCase__  )
 lowercase__       :       Optional[int]				=						sum(lowerCamelCase__  )

 lowercase__       :       Optional[int]				=						[[False for x in range(s + 1  )] for y in range(n + 1  )]

 for i in range(1       ,		n + 1  ):
  lowercase__       :       int				=						True

 for i in range(1       ,		s + 1  ):
  lowercase__       :       int				=						False

 for i in range(1       ,		n + 1  ):
  for j in range(1       ,		s + 1  ):
   lowercase__       :       Optional[Any]				=						dp[i][j - 1]

   if arr[i - 1] <= j:
    lowercase__       :       int				=						dp[i][j] or dp[i - 1][j - arr[i - 1]]

 for j in range(int(s / 2  )       ,		-1       ,		-1  ):
  if dp[n][j] is True:
   lowercase__       :       List[Any]				=						s - 2 * j
   break

 return diff