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
  
  
  __lowerCamelCase       :       int				   =			logging.get_logger(__name__)
  
  __lowerCamelCase       :       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",
  }
  
  __lowerCamelCase       :       Any				   =			{
      "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"
          )
      },
  }
  
  __lowerCamelCase       :       int				   =			{"Helsinki-NLP/opus-mt-en-de": 512}
  __lowerCamelCase       :       Optional[Any]				   =			{}
  
  
  
  class 			a      (      lowerCAmelCase__   ):
  		__lowercase													=		VOCAB_FILES_NAMES
  		__lowercase													=		PRETRAINED_VOCAB_FILES_MAP
  		__lowercase													=		PRETRAINED_INIT_CONFIGURATION
  		__lowercase													=		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  		__lowercase													=		["""input_ids""", """attention_mask"""]
  		__lowercase													=		re.compile(""">>.+<<"""   )  # type: re.Pattern
  
  
  
  
  
  
  		def __init__(  self	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase=None	,     __UpperCamelCase="<unk>"	,     __UpperCamelCase="</s>"	,     __UpperCamelCase="<pad>"	,     __UpperCamelCase=5_12	,     __UpperCamelCase = None	,     __UpperCamelCase=False	,     **__UpperCamelCase	,     )->			int:
  						'''simple docstring'''
  
  
  						A__     :							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}'
  
  						A__     :							Tuple   				=separate_vocabs
  						A__     :							Optional[int]   				=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:
  										A__     :							str   				=load_json(_lowerCamelCase							)
  										A__     :							List[Any]   				={v: k for k, v in self.target_encoder.items()}
  										A__     :							Optional[Any]   				=[]
  						else:
  										A__     :							Any   				={v: k for k, v in self.encoder.items()}
  										A__     :							list   				=[k for k in self.encoder if k.startswith('''>>'''							) and k.endswith('''<<'''							)]
  
  						A__     :							Any   				=source_lang
  						A__     :							int   				=target_lang
  						A__     :							List[Any]   				=[source_spm, target_spm]
  
  						# load SentencePiece model for pre-processing
  						A__     :							str   				=load_spm(_lowerCamelCase	,     self.sp_model_kwargs							)
  						A__     :							Optional[int]   				=load_spm(_lowerCamelCase	,     self.sp_model_kwargs							)
  						A__     :							Optional[int]   				=self.spm_source
  						A__     :							int   				=self.encoder
  
  						# Multilingual target side: default to using first supported language code.
  
  						self._setup_normalizer()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			str:
  						'''simple docstring'''
  
  
  						try:
  										from sacremoses import MosesPunctNormalizer
  
  										A__     :							Optional[int]   				=MosesPunctNormalizer(self.source_lang							).normalize
  						except (ImportError, FileNotFoundError):
  										warnings.warn('''Recommended: pip install sacremoses.'''							)
  										A__     :							List[str]   				=lambda __UpperCamelCase							: x
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						return self.punc_normalizer(_lowerCamelCase							) if x else ""
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Tuple:
  						'''simple docstring'''
  
  
  						return self.current_encoder.get(_lowerCamelCase	,     self.current_encoder[self.unk_token]							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Dict   				=self.language_code_re.match(_lowerCamelCase							)
  						A__     :							list   				=[match.group(0							)] if match else []
  						return code, self.language_code_re.sub(''''''	,     _lowerCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.remove_language_code(_lowerCamelCase							)
  						A__     :							Any   				=self.current_spm.encode(_lowerCamelCase	,     out_type=_lowerCamelCase							)
  						return code + pieces
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[int]:
  						'''simple docstring'''
  
  
  						return self.decoder.get(_lowerCamelCase	,     self.unk_token							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     **__UpperCamelCase							)->			str:
  						'''simple docstring'''
  
  
  						return super().batch_decode(_lowerCamelCase	,     **_lowerCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     **__UpperCamelCase							)->			List[Any]:
  						'''simple docstring'''
  
  
  						return super().decode(_lowerCamelCase	,     **_lowerCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Optional[Any]:
  						'''simple docstring'''
  
  
  						A__     :							str   				=self.spm_source if self._decode_use_source_tokenizer else self.spm_target
  						A__     :							List[str]   				=[]
  						A__     :							Tuple   				=""""""
  						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 + " "
  														A__     :							Any   				=[]
  										else:
  														current_sub_tokens.append(_lowerCamelCase							)
  						out_string += sp_model.decode_pieces(_lowerCamelCase							)
  						return out_string.strip()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase=None							)->			Union[str, Any]:
  						'''simple docstring'''
  
  
  						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       lowerCAmelCase_  (  self							)->			int:
  						'''simple docstring'''
  
  
  						A__     :							List[str]   				=self.spm_source
  						A__     :							Union[str, Any]   				=self.encoder
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=self.spm_target
  						if self.separate_vocabs:
  										A__     :							Tuple   				=self.target_encoder
  
  
  
  
  
  
  		@property
  		def       lowerCAmelCase_  (  self							)->			Dict:
  						'''simple docstring'''
  
  
  						return len(self.encoder							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None							)->			int:
  						'''simple docstring'''
  
  
  						if not os.path.isdir(_lowerCamelCase							):
  										logger.error(F'Vocabulary path ({save_directory}) should be a directory'							)
  										return
  						A__     :							Tuple   				=[]
  
  						if self.separate_vocabs:
  										A__     :							Any   				=os.path.join(
  										    _lowerCamelCase	,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab''']	,     )
  										A__     :							List[Any]   				=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:
  										A__     :							Optional[Any]   				=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]	,     ):
  										A__     :							Tuple   				=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:
  																		A__     :							List[Any]   				=spm_model.serialized_model_proto()
  																		fi.write(_lowerCamelCase							)
  														saved_files.append(_lowerCamelCase							)
  
  						return tuple(_lowerCamelCase							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						return self.get_src_vocab()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			Any:
  						'''simple docstring'''
  
  
  						return dict(self.encoder	,     **self.added_tokens_encoder							)
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self							)->			List[str]:
  						'''simple docstring'''
  
  
  						return dict(self.target_encoder	,     **self.added_tokens_decoder							)
  
  
  
  
  
  
  		def __getstate__(  self							)->			List[Any]:
  						'''simple docstring'''
  
  
  						A__     :							Union[str, 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	,     __UpperCamelCase							)->			List[str]:
  						'''simple docstring'''
  
  
  						A__     :							List[Any]   				=d
  
  						# for backward compatibility
  						if not hasattr(self	,     '''sp_model_kwargs'''							):
  										A__     :							str   				={}
  
  						A__     :							Dict   				=(load_spm(_lowerCamelCase	,     self.sp_model_kwargs							) for f in self.spm_files)
  						A__     :							Tuple   				=self.spm_source
  						self._setup_normalizer()
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     *__UpperCamelCase	,     **__UpperCamelCase							)->			str:
  						'''simple docstring'''
  
  
  						return 1
  
  
  
  
  
  
  		def       lowerCAmelCase_  (  self	,     __UpperCamelCase							)->			Tuple:
  						'''simple docstring'''
  
  
  						A__     :							Optional[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       lowerCAmelCase_  (  self	,     __UpperCamelCase	,     __UpperCamelCase = None	,     __UpperCamelCase = False							)->			str:
  						'''simple docstring'''
  
  
  						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   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							sentencepiece.SentencePieceProcessor:
  				A__     :							List[Any]   				=sentencepiece.SentencePieceProcessor(**snake_case_	)
  				spm.Load(snake_case_	)
  				return spm
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_,	snake_case_	)			->							None:
  				with open(snake_case_,	'''w'''	) as f:
  								json.dump(snake_case_,	snake_case_,	indent=2	)
  
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							Union[Dict, List]:
  				with open(snake_case_,	'''r'''	) as f:
  								return json.load(snake_case_	)
  
  
  
  
style_context: 
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				barcode //= 1_0  # exclude the last digit
  				A__     :							List[str]   				=False
  				A__     :							Dict   				=0
  
  				# extract and check each digit
  				while barcode != 0:
  								A__     :							Union[str, Any]   				=1 if checker else 3
  								s += mult * (barcode % 1_0)
  								barcode //= 1_0
  								A__     :							Union[str, Any]   				=not checker
  
  				return (1_0 - (s % 1_0)) % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							bool:
  				return len(str(snake_case_	)	) == 1_3 and get_check_digit(snake_case_	) == barcode % 1_0
  
  
  
  def   SCREAMING_SNAKE_CASE__						(							snake_case_	)			->							int:
  				if str(snake_case_	).isalpha():
  								A__     :							Tuple   				=f'Barcode \'{barcode}\' has alphabetic characters.'
  								raise ValueError(snake_case_	)
  				elif int(snake_case_	) < 0:
  								raise ValueError('''The entered barcode has a negative value. Try again.'''	)
  				else:
  								return int(snake_case_	)
  
  
  if __name__ == "__main__":
  						import doctest
  
  						doctest.testmod()
  						__lowerCamelCase       :       List[Any]				   =			get_barcode(input("Barcode: ").strip())
  
  						if is_valid(barcode):
  												print(F"'{barcode}' is a valid barcode.")
  						else:
  												print(F"'{barcode}' is NOT a valid barcode.")
  
  
  
  