
"""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__          =	logging.get_logger(__name__)

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

UpperCamelCase__          =	{
    '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__          =	{
    'google/bigbird-roberta-base': 40_96,
    'google/bigbird-roberta-large': 40_96,
    'google/bigbird-base-trivia-itc': 40_96,
}





class 				a			(					lowercase      ):
      UpperCamelCase							:   Dict           =     VOCAB_FILES_NAMES
      UpperCamelCase							:   int           =     PRETRAINED_VOCAB_FILES_MAP
      UpperCamelCase							:   Optional[Any]           =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCamelCase							:   List[str]           =     ["""input_ids""", """attention_mask"""]
      UpperCamelCase							:   List[int]           =     []



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

          # Mask token behave like a normal word, i.e. include the space before it
          UpperCAmelCase__	:      List[str]     							=					AddedToken(UpperCamelCase_     ,	lstrip=UpperCamelCase_     ,	rstrip=UpperCamelCase_      ) if isinstance(UpperCamelCase_     ,	UpperCamelCase_      ) else mask_token

          UpperCAmelCase__	:      Optional[int]     							=					{} if sp_model_kwargs is None else sp_model_kwargs

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

          UpperCAmelCase__	:      Any     							=					vocab_file

          UpperCAmelCase__	:      str     							=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(UpperCamelCase_      )



      @property
      def  __snake_case							(   self      ):
          return self.sp_model.get_piece_size()



      def  __snake_case							(   self      ):
          UpperCAmelCase__	:      Optional[Any]     							=					{self.convert_ids_to_tokens(UpperCamelCase_      ): i for i in range(self.vocab_size      )}
          vocab.update(self.added_tokens_encoder      )
          return vocab



      def __getstate__(   self      ):
          UpperCAmelCase__	:      str     							=					self.__dict__.copy()
          UpperCAmelCase__	:      Dict     							=					None
          return state



      def __setstate__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[Any]     							=					d

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

          UpperCAmelCase__	:      Any     							=					spm.SentencePieceProcessor(**self.sp_model_kwargs      )
          self.sp_model.Load(self.vocab_file      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          return self.sp_model.encode(UpperCamelCase_     ,	out_type=UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          return self.sp_model.piece_to_id(UpperCamelCase_      )



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      int     							=					self.sp_model.IdToPiece(UpperCamelCase_      )
          return token



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      List[str]     							=					[]
          UpperCAmelCase__	:      Optional[Any]     							=					''
          UpperCAmelCase__	:      str     							=					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(UpperCamelCase_      ) + token
                  UpperCAmelCase__	:      Optional[Any]     							=					True
                  UpperCAmelCase__	:      str     							=					[]
              else:
                  current_sub_tokens.append(UpperCamelCase_      )
                  UpperCAmelCase__	:      Tuple     							=					False
          out_string += self.sp_model.decode(UpperCamelCase_      )
          return out_string.strip()



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = False     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = True     ,	**UpperCamelCase_     ,	):
          UpperCAmelCase__	:      Any     							=					kwargs.pop('use_source_tokenizer'     ,	UpperCamelCase_      )

          UpperCAmelCase__	:      List[Any]     							=					self.convert_ids_to_tokens(UpperCamelCase_     ,	skip_special_tokens=UpperCamelCase_      )

          # 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
          UpperCAmelCase__	:      List[str]     							=					[]
          UpperCAmelCase__	:      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(UpperCamelCase_      )      )
                      UpperCAmelCase__	:      Dict     							=					[]
                  sub_texts.append(UpperCamelCase_      )
              else:
                  current_sub_text.append(UpperCamelCase_      )
          if current_sub_text:
              sub_texts.append(self.convert_tokens_to_string(UpperCamelCase_      )      )

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

          UpperCAmelCase__	:      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:
              UpperCAmelCase__	:      str     							=					self.clean_up_tokenization(UpperCamelCase_      )
              return clean_text
          else:
              return text



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          if not os.path.isdir(UpperCamelCase_      ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''      )
              return
          UpperCAmelCase__	:      Tuple     							=					os.path.join(
              UpperCamelCase_     ,	(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

          if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase_      ) and os.path.isfile(self.vocab_file      ):
              copyfile(self.vocab_file     ,	UpperCamelCase_      )
          elif not os.path.isfile(self.vocab_file      ):
              with open(UpperCamelCase_     ,	'wb'      ) as fi:
                  UpperCAmelCase__	:      str     							=					self.sp_model.serialized_model_proto()
                  fi.write(UpperCamelCase_      )

          return (out_vocab_file,)



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          UpperCAmelCase__	:      int     							=					[self.cls_token_id]
          UpperCAmelCase__	:      Optional[Any]     							=					[self.sep_token_id]
          return cls + token_ids_a + sep + token_ids_a + sep



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None     ,	UpperCamelCase_ = False      ):
          if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=UpperCamelCase_     ,	token_ids_a=UpperCamelCase_     ,	already_has_special_tokens=UpperCamelCase_      )

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



      def  __snake_case							(   self     ,	UpperCamelCase_     ,	UpperCamelCase_ = None      ):
          UpperCAmelCase__	:      str     							=					[self.sep_token_id]
          UpperCAmelCase__	:      int     							=					[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 __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

