code: import json
  import os
  import re
  import warnings
  from pathlib import Path
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  
  import sentencepiece
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  a     :			Optional[Any]					    =						logging.get_logger(__name__)
  
  a     :			Union[str, Any]					    =						{
      """source_spm""": """source.spm""",
      """target_spm""": """target.spm""",
      """vocab""": """vocab.json""",
      """target_vocab_file""": """target_vocab.json""",
      """tokenizer_config_file""": """tokenizer_config.json""",
  }
  
  a     :			Optional[int]					    =						{
      """source_spm""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/source.spm"""
      },
      """target_spm""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/target.spm"""
      },
      """vocab""": {
          """Helsinki-NLP/opus-mt-en-de""": """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/vocab.json"""
      },
      """tokenizer_config_file""": {
          """Helsinki-NLP/opus-mt-en-de""": (
              """https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/tokenizer_config.json"""
          )
      },
  }
  
  a     :			int					    =						{"""Helsinki-NLP/opus-mt-en-de""": 5_1_2}
  a     :			Dict					    =						{}
  
  class 				_lowercase						(    UpperCAmelCase__ ):
  
  
  
  
  
  
      '''simple docstring'''
      SCREAMING_SNAKE_CASE:							List[str]        =						VOCAB_FILES_NAMES
      SCREAMING_SNAKE_CASE:							int        =						PRETRAINED_VOCAB_FILES_MAP
      SCREAMING_SNAKE_CASE:							Union[str, Any]        =						PRETRAINED_INIT_CONFIGURATION
      SCREAMING_SNAKE_CASE:							str        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      SCREAMING_SNAKE_CASE:							str        =						['input_ids', 'attention_mask']
      SCREAMING_SNAKE_CASE:							str        =						re.compile('>>.+<<' )  # type: re.Pattern
  
  
  
  
      def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	lowerCamelCase__=None					,	lowerCamelCase__="<unk>"					,	lowerCamelCase__="</s>"					,	lowerCamelCase__="<pad>"					,	lowerCamelCase__=512					,	lowerCamelCase__ = None					,	lowerCamelCase__=False					,	**lowerCamelCase__					,	):
         lowerCAmelCase_:       Union[str, Any]         =  {} if sp_model_kwargs is None else sp_model_kwargs
  
         super().__init__(
             # bos_token=bos_token,  unused. Start decoding with config.decoder_start_token_id
             source_lang=lowerCamelCase__					,	target_lang=lowerCamelCase__					,	unk_token=lowerCamelCase__					,	eos_token=lowerCamelCase__					,	pad_token=lowerCamelCase__					,	model_max_length=lowerCamelCase__					,	sp_model_kwargs=self.sp_model_kwargs					,	target_vocab_file=lowerCamelCase__					,	separate_vocabs=lowerCamelCase__					,	**lowerCamelCase__					,	)
         assert Path(lowerCamelCase__   ).exists(), F'''cannot find spm source {source_spm}'''
  
         lowerCAmelCase_:       Optional[int]         =  separate_vocabs
         lowerCAmelCase_:       Dict         =  load_json(lowerCamelCase__   )
         if self.unk_token not in self.encoder:
            raise KeyError("<unk> token must be in vocab"   )
         assert self.pad_token in self.encoder
  
         if separate_vocabs:
            lowerCAmelCase_:       Any         =  load_json(lowerCamelCase__   )
            lowerCAmelCase_:       Tuple         =  {v: k for k, v in self.target_encoder.items()}
            lowerCAmelCase_:       List[Any]         =  []
         else:
            lowerCAmelCase_:       int         =  {v: k for k, v in self.encoder.items()}
            lowerCAmelCase_:       list         =  [k for k in self.encoder if k.startswith(">>"   ) and k.endswith("<<"   )]
  
         lowerCAmelCase_:       List[Any]         =  source_lang
         lowerCAmelCase_:       int         =  target_lang
         lowerCAmelCase_:       List[str]         =  [source_spm, target_spm]
  
         # load SentencePiece model for pre-processing
         lowerCAmelCase_:       int         =  load_spm(lowerCamelCase__					,	self.sp_model_kwargs   )
         lowerCAmelCase_:       str         =  load_spm(lowerCamelCase__					,	self.sp_model_kwargs   )
         lowerCAmelCase_:       Optional[Any]         =  self.spm_source
         lowerCAmelCase_:       Tuple         =  self.encoder
  
         # Multilingual target side: default to using first supported language code.
  
         self._setup_normalizer()
  
  
  
  
      def     _a						(						self   ):
         try:
            from sacremoses import MosesPunctNormalizer
  
            lowerCAmelCase_:       str         =  MosesPunctNormalizer(self.source_lang   ).normalize
         except (ImportError, FileNotFoundError):
            warnings.warn("Recommended: pip install sacremoses."   )
            lowerCAmelCase_:       Any         =  lambda lowerCamelCase__   : x
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return self.punc_normalizer(lowerCamelCase__   ) if x else ""
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return self.current_encoder.get(lowerCamelCase__					,	self.current_encoder[self.unk_token]   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Dict         =  self.language_code_re.match(lowerCamelCase__   )
         lowerCAmelCase_:       list         =  [match.group(0   )] if match else []
         return code, self.language_code_re.sub(""					,	lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_     ,  lowerCAmelCase_:       List[Any]         =  self.remove_language_code(lowerCamelCase__   )
         lowerCAmelCase_:       Any         =  self.current_spm.encode(lowerCamelCase__					,	out_type=lowerCamelCase__   )
         return code + pieces
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         return self.decoder.get(lowerCamelCase__					,	self.unk_token   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	**lowerCamelCase__   ):
         return super().batch_decode(lowerCamelCase__					,	**lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	**lowerCamelCase__   ):
         return super().decode(lowerCamelCase__					,	**lowerCamelCase__   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       List[str]         =  self.spm_source if self._decode_use_source_tokenizer else self.spm_target
         lowerCAmelCase_:       List[str]         =  []
         lowerCAmelCase_:       List[Any]         =  ""
         for token in tokens:
            # make sure that special tokens are not decoded using sentencepiece model
            if token in self.all_special_tokens:
               out_string += sp_model.decode_pieces(lowerCamelCase__   ) + token + " "
               lowerCAmelCase_:       Union[str, Any]         =  []
            else:
               current_sub_tokens.append(lowerCamelCase__   )
         out_string += sp_model.decode_pieces(lowerCamelCase__   )
         return out_string.strip()
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__=None   ):
         if token_ids_a is None:
            return token_ids_a + [self.eos_token_id]
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return token_ids_a + token_ids_a + [self.eos_token_id]
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       Union[str, Any]         =  self.spm_source
         lowerCAmelCase_:       Tuple         =  self.encoder
  
  
  
  
      def     _a						(						self   ):
         lowerCAmelCase_:       List[Any]         =  self.spm_target
         if self.separate_vocabs:
            lowerCAmelCase_:       Tuple         =  self.target_encoder
  
  
  
  
      @property
      def     _a						(						self   ):
         return len(self.encoder   )
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None   ):
         if not os.path.isdir(lowerCamelCase__   ):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''   )
            return
         lowerCAmelCase_:       Tuple         =  []
  
         if self.separate_vocabs:
            lowerCAmelCase_:       int         =  os.path.join(
                lowerCamelCase__					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]					,	)
            lowerCAmelCase_:       Dict         =  os.path.join(
                lowerCamelCase__					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"]					,	)
            save_json(self.encoder					,	lowerCamelCase__   )
            save_json(self.target_encoder					,	lowerCamelCase__   )
            saved_files.append(lowerCamelCase__   )
            saved_files.append(lowerCamelCase__   )
         else:
            lowerCAmelCase_:       int         =  os.path.join(
                lowerCamelCase__					,	(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]   )
            save_json(self.encoder					,	lowerCamelCase__   )
            saved_files.append(lowerCamelCase__   )
  
         for spm_save_filename, spm_orig_path, spm_model in zip(
             [VOCAB_FILES_NAMES["source_spm"], VOCAB_FILES_NAMES["target_spm"]]					,	self.spm_files					,	[self.spm_source, self.spm_target]					,	):
            lowerCAmelCase_:       List[str]         =  os.path.join(
                lowerCamelCase__					,	(filename_prefix + "-" if filename_prefix else "") + spm_save_filename   )
            if os.path.abspath(lowerCamelCase__   ) != os.path.abspath(lowerCamelCase__   ) and os.path.isfile(lowerCamelCase__   ):
               copyfile(lowerCamelCase__					,	lowerCamelCase__   )
               saved_files.append(lowerCamelCase__   )
            elif not os.path.isfile(lowerCamelCase__   ):
               with open(lowerCamelCase__					,	"wb"   ) as fi:
                  lowerCAmelCase_:       str         =  spm_model.serialized_model_proto()
                  fi.write(lowerCamelCase__   )
               saved_files.append(lowerCamelCase__   )
  
         return tuple(lowerCamelCase__   )
  
  
  
  
      def     _a						(						self   ):
         return self.get_src_vocab()
  
  
  
  
      def     _a						(						self   ):
         return dict(self.encoder					,	**self.added_tokens_encoder   )
  
  
  
  
      def     _a						(						self   ):
         return dict(self.target_encoder					,	**self.added_tokens_decoder   )
  
  
  
  
      def __getstate__(						self   ):
         lowerCAmelCase_:       List[Any]         =  self.__dict__.copy()
         state.update(
             {k: None for k in ["spm_source", "spm_target", "current_spm", "punc_normalizer", "target_vocab_file"]}   )
         return state
  
  
  
  
      def __setstate__(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  d
  
         # for backward compatibility
         if not hasattr(self					,	"sp_model_kwargs"   ):
            lowerCAmelCase_:       Optional[int]         =  {}
  
         lowerCAmelCase_     ,  lowerCAmelCase_:       Tuple         =  (load_spm(lowerCamelCase__					,	self.sp_model_kwargs   ) for f in self.spm_files)
         lowerCAmelCase_:       Any         =  self.spm_source
         self._setup_normalizer()
  
  
  
  
      def     _a						(						self					,	*lowerCamelCase__					,	**lowerCamelCase__   ):
         return 1
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__   ):
         lowerCAmelCase_:       Union[str, Any]         =  set(self.all_special_ids   )  # call it once instead of inside list comp
         all_special_ids.remove(self.unk_token_id   )  # <unk> is only sometimes special
         return [1 if x in all_special_ids else 0 for x in seq]
  
  
  
  
      def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__ = None					,	lowerCamelCase__ = False   ):
         if already_has_special_tokens:
            return self._special_token_mask(lowerCamelCase__   )
         elif token_ids_a is None:
            return self._special_token_mask(lowerCamelCase__   ) + [1]
         else:
            return self._special_token_mask(token_ids_a + token_ids_a   ) + [1]
  
  
  
  
  
  def 		snake_case__  (		lowercase       ,							lowercase				):
     lowerCAmelCase_:       Dict         =  sentencepiece.SentencePieceProcessor(**lowercase				)
     spm.Load(lowercase				)
     return spm
  def 		snake_case__  (		lowercase       ,							lowercase				):
     with open(lowercase       ,							"w"				) as f:
        json.dump(lowercase       ,							lowercase       ,							indent=2				)
  
  
  
  
  
  
  
  def 		snake_case__  (		lowercase				):
     with open(lowercase       ,							"r"				) as f:
        return json.load(lowercase				)
style_context: import itertools
  def 		snake_case__  (		lowercase				):
     return (
         int("".join(combination[0:2]				)				) * int("".join(combination[2:5]				)				)
         == int("".join(combination[5:9]				)				)
     ) or (
         int("".join(combination[0]				)				) * int("".join(combination[1:5]				)				)
         == int("".join(combination[5:9]				)				)
     )
  def 		snake_case__  (		):
     return sum(
         {
             int("".join(pandigital[5:9]				)				)
             for pandigital in itertools.permutations("123456789"				)
             if is_combination_valid(lowercase				)
         }				)
  
  
  if __name__ == "__main__":
   print(solution())