





'''simple docstring'''






from __future__ import annotations

from collections import namedtuple
from dataclasses import dataclass




@dataclass
class  UpperCAmelCase     :
      _lowercase:			int
      _lowercase:			TreeNode | None						     =   None
      _lowercase:			TreeNode | None						     =   None




A__				:   Tuple      =namedtuple('''CoinsDistribResult''', '''moves excess''')
def    UpperCamelCase__      (		lowerCAmelCase  ):





      """simple docstring"""


      if root is None:
            return 0

      # Validation
      def count_nodes(lowerCAmelCase  ) -> int:
            if node is None:
                  return 0

            return count_nodes(node.left  ) + count_nodes(node.right  ) + 1

      def count_coins(lowerCAmelCase  ) -> int:
            if node is None:
                  return 0

            return count_coins(node.left  ) + count_coins(node.right  ) + node.data

      if count_nodes(lowerCAmelCase  ) != count_coins(lowerCAmelCase  ):
            raise ValueError("""The nodes number should be same as the number of coins"""  )

      # Main calculation
      def get_distrib(lowerCAmelCase  ) -> CoinsDistribResult:

            if node is None:
                  return CoinsDistribResult(0							,	1  )

            _lowerCAmelCase     ,							_lowerCAmelCase							     =							get_distrib(node.left  )
            _lowerCAmelCase     ,							_lowerCAmelCase							     =							get_distrib(node.right  )

            _lowerCAmelCase							     =							1 - left_distrib_excess
            _lowerCAmelCase							     =							1 - right_distrib_excess

            _lowerCAmelCase							     =							(
                left_distrib_moves
                + right_distrib_moves
                + abs(lowerCAmelCase  )
                + abs(lowerCAmelCase  )
            )
            _lowerCAmelCase							     =							node.data - coins_to_left - coins_to_right

            return CoinsDistribResult(lowerCAmelCase							,	lowerCAmelCase  )

      return get_distrib(lowerCAmelCase  )[0]


if __name__ == "__main__":
      import doctest

      doctest.testmod()









'''simple docstring'''






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


lowerCAmelCase__											=	logging.get_logger(__name__)

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

lowerCAmelCase__											=	{
    '''vocab_file''': {
        '''bert_for_seq_generation''': (
            '''https://huggingface.co/google/bert_for_seq_generation_L-24_bbc_encoder/resolve/main/spiece.model'''
        ),
    }
}

lowerCAmelCase__											=	{'''bert_for_seq_generation''': 512}






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  List[Any]							=			VOCAB_FILES_NAMES
  SCREAMING_SNAKE_CASE    :  Dict							=			PRETRAINED_VOCAB_FILES_MAP
  SCREAMING_SNAKE_CASE    :  Optional[int]							=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  SCREAMING_SNAKE_CASE    :  List[int]							=			[]
  SCREAMING_SNAKE_CASE    :  Any							=			['input_ids', 'attention_mask']






  def __init__(      self   :     Optional[Any]					,lowercase__   :     Tuple					,lowercase__   :     Tuple="<s>"					,lowercase__   :     Union[str, Any]="</s>"					,lowercase__   :     str="<unk>"					,lowercase__   :     Tuple="<pad>"					,lowercase__   :     Union[str, Any]="<::::>"					,lowercase__   :     Optional[Dict[str, Any]] = None					,**lowercase__   :     Any					,):
   __lowercase							  = {} if sp_model_kwargs is None else sp_model_kwargs

   # Add extra_ids to the special token list
   super().__init__(
       bos_token=lowercase__					,eos_token=lowercase__					,unk_token=lowercase__					,pad_token=lowercase__					,sep_token=lowercase__					,sp_model_kwargs=self.sp_model_kwargs					,**lowercase__					,)

   __lowercase							  = vocab_file

   __lowercase							  = spm.SentencePieceProcessor(**self.sp_model_kwargs			)
   self.sp_model.Load(lowercase__			)






  @property
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   return self.sp_model.get_piece_size()






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






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






  def __setstate__(      self   :     Optional[int]					,lowercase__   :     Optional[Any]			):
   __lowercase							  = d

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

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






  def 					SCREAMING_SNAKE_CASE      (      self   :     str					,lowercase__   :     str			):
   return self.sp_model.encode(lowercase__					,out_type=lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple					,lowercase__   :     Union[str, Any]			):
   return self.sp_model.piece_to_id(lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     Tuple			):
   __lowercase							  = self.sp_model.IdToPiece(lowercase__			)
   return token






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]					,lowercase__   :     int			):
   __lowercase							  = []
   __lowercase							  = ''''''
   for token in tokens:
    # make sure that special tokens are not decoded using sentencepiece model
    if token in self.all_special_tokens:
     out_string += self.sp_model.decode(lowercase__			) + token
     __lowercase							  = []
    else:
     current_sub_tokens.append(lowercase__			)
   out_string += self.sp_model.decode(lowercase__			)
   return out_string.strip()






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     str					,lowercase__   :     Optional[str] = None			):
   if not os.path.isdir(lowercase__			):
    logger.error(F"Vocabulary path ({save_directory}) should be a directory"			)
    return
   __lowercase							  = os.path.join(
       lowercase__					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

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

   return (out_vocab_file,)

