
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


__magic_name__            =  logging.get_logger(__name__)


__magic_name__            =  "▁"

__magic_name__            =  {"vocab_file": "spiece.model"}

__magic_name__            =  {
    "vocab_file": {
        "google/reformer-crime-and-punishment": (
            "https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model"
        )
    }
}

__magic_name__            =  {
    "google/reformer-crime-and-punishment": 524_288,
}




class 					_SCREAMING_SNAKE_CASE  (      _lowerCamelCase ):
      _A						:		Union[str, Any]    	=     VOCAB_FILES_NAMES
      _A						:		List[str]    	=     PRETRAINED_VOCAB_FILES_MAP
      _A						:		List[str]    	=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _A						:		Dict    	=     ['input_ids', 'attention_mask']
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase="</s>"		,	lowerCamelCase="<unk>"		,	lowerCamelCase=[]		,	lowerCamelCase = None		,	**lowerCamelCase		,	):
          snake_case__						=				{} if sp_model_kwargs is None else sp_model_kwargs

          super().__init__(
              eos_token=A__		,	unk_token=A__		,	additional_special_tokens=A__		,	sp_model_kwargs=self.sp_model_kwargs		,	**A__		,	)

          snake_case__						=				vocab_file
          snake_case__						=				spm.SentencePieceProcessor(**self.sp_model_kwargs )
          self.sp_model.Load(A__ )
      @property
      def A_			(			self ):
          return self.sp_model.get_piece_size()
      def A_			(			self ):
          snake_case__						=				{self.convert_ids_to_tokens(A__ ): i for i in range(self.vocab_size )}
          vocab.update(self.added_tokens_encoder )
          return vocab
      def __getstate__(			self ):
          snake_case__						=				self.__dict__.copy()
          snake_case__						=				None
          return state
      def __setstate__(			self		,	lowerCamelCase ):
          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 A_			(			self		,	lowerCamelCase ):
          return self.sp_model.encode(A__		,	out_type=A__ )
      def A_			(			self		,	lowerCamelCase ):
          return self.sp_model.piece_to_id(A__ )
      def A_			(			self		,	lowerCamelCase ):
          if index < self.sp_model.get_piece_size():
              snake_case__						=				self.sp_model.IdToPiece(A__ )
          return token
      def A_			(			self		,	lowerCamelCase ):
          snake_case__						=				[]
          snake_case__						=				""
          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(A__ ) + token
                  snake_case__						=				[]
              else:
                  current_sub_tokens.append(A__ )
          out_string += self.sp_model.decode(A__ )
          return out_string.strip()



      def A_			(			self		,	lowerCamelCase		,	lowerCamelCase = None ):
          if not os.path.isdir(A__ ):
              logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
              return
          snake_case__						=				os.path.join(
              A__		,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )

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

          return (out_vocab_file,)





from __future__ import annotations





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase							):
    if len(__lowerCAmelCase							) == 0:
        return False
    snake_case__						=				len(__lowerCAmelCase							) // 2
    if a_list[midpoint] == item:
        return True
    if item < a_list[midpoint]:
        return binary_search(a_list[:midpoint]    ,		__lowerCAmelCase							)
    else:
        return binary_search(a_list[midpoint + 1 :]    ,		__lowerCAmelCase							)


if __name__ == "__main__":
     __magic_name__            =  input('''Enter numbers separated by comma:\n''').strip()
     __magic_name__            =  [int(item.strip()) for item in user_input.split(''',''')]
     __magic_name__            =  int(input('''Enter the number to be found in the list:\n''').strip())
     __magic_name__            =  '''''' if binary_search(sequence, target) else '''not '''
     print(F'''{target} was {not_str}found in {sequence}''')



