code: 
  '''simple docstring'''
  
  
  
  
  
  
  
  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							:		Optional[Any]										=						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							:		List[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							:		Tuple										=						{'Helsinki-NLP/opus-mt-en-de': 512}
  _lowerCamelCase							:		str										=						{}
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					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=512,						__UpperCamelCase = None,						__UpperCamelCase=False,						**__UpperCamelCase,						):
  							_snake_case			 = {} 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=__UpperCamelCase,						target_lang=__UpperCamelCase,						unk_token=__UpperCamelCase,						eos_token=__UpperCamelCase,						pad_token=__UpperCamelCase,						model_max_length=__UpperCamelCase,						sp_model_kwargs=self.sp_model_kwargs,						target_vocab_file=__UpperCamelCase,						separate_vocabs=__UpperCamelCase,						**__UpperCamelCase,						)
  							assert Path(__UpperCamelCase				).exists(), f'''cannot find spm source {source_spm}'''
  
  							_snake_case			 = separate_vocabs
  							_snake_case			 = load_json(__UpperCamelCase				)
  							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:
  									_snake_case			 = load_json(__UpperCamelCase				)
  									_snake_case			 = {v: k for k, v in self.target_encoder.items()}
  									_snake_case			 = []
  							else:
  									_snake_case			 = {v: k for k, v in self.encoder.items()}
  									_snake_case			 = [k for k in self.encoder if k.startswith(">>"				) and k.endswith("<<"				)]
  
  							_snake_case			 = source_lang
  							_snake_case			 = target_lang
  							_snake_case			 = [source_spm, target_spm]
  
  							# load SentencePiece model for pre-processing
  							_snake_case			 = load_spm(__UpperCamelCase,						self.sp_model_kwargs				)
  							_snake_case			 = load_spm(__UpperCamelCase,						self.sp_model_kwargs				)
  							_snake_case			 = self.spm_source
  							_snake_case			 = self.encoder
  
  							# Multilingual target side: default to using first supported language code.
  
  							self._setup_normalizer()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  
  
  
  
  
  
  
  							try:
  									from sacremoses import MosesPunctNormalizer
  
  									_snake_case			 = MosesPunctNormalizer(self.source_lang				).normalize
  							except (ImportError, FileNotFoundError):
  									warnings.warn("Recommended: pip install sacremoses."				)
  									_snake_case			 = lambda __UpperCamelCase				: x
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							return self.punc_normalizer(__UpperCamelCase				) if x else ""
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							return self.current_encoder.get(__UpperCamelCase,						self.current_encoder[self.unk_token]				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = self.language_code_re.match(__UpperCamelCase				)
  							_snake_case			 = [match.group(0				)] if match else []
  							return code, self.language_code_re.sub("",						__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case ,					_snake_case			 = self.remove_language_code(__UpperCamelCase				)
  							_snake_case			 = self.current_spm.encode(__UpperCamelCase,						out_type=__UpperCamelCase				)
  							return code + pieces
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							return self.decoder.get(__UpperCamelCase,						self.unk_token				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						**__UpperCamelCase				):
  							return super().batch_decode(__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						**__UpperCamelCase				):
  							return super().decode(__UpperCamelCase,						**__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = self.spm_source if self._decode_use_source_tokenizer else self.spm_target
  							_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 += sp_model.decode_pieces(__UpperCamelCase				) + token + " "
  											_snake_case			 = []
  									else:
  											current_sub_tokens.append(__UpperCamelCase				)
  							out_string += sp_model.decode_pieces(__UpperCamelCase				)
  							return out_string.strip()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase=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        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.spm_source
  							_snake_case			 = self.encoder
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							_snake_case			 = self.spm_target
  							if self.separate_vocabs:
  									_snake_case			 = self.target_encoder
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return len(self.encoder				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
  
  
  
  
  
  
  
  							if not os.path.isdir(__UpperCamelCase				):
  									logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''				)
  									return
  							_snake_case			 = []
  
  							if self.separate_vocabs:
  									_snake_case			 = os.path.join(
  									    __UpperCamelCase,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"],						)
  									_snake_case			 = os.path.join(
  									    __UpperCamelCase,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["target_vocab_file"],						)
  									save_json(self.encoder,						__UpperCamelCase				)
  									save_json(self.target_encoder,						__UpperCamelCase				)
  									saved_files.append(__UpperCamelCase				)
  									saved_files.append(__UpperCamelCase				)
  							else:
  									_snake_case			 = os.path.join(
  									    __UpperCamelCase,						(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab"]				)
  									save_json(self.encoder,						__UpperCamelCase				)
  									saved_files.append(__UpperCamelCase				)
  
  							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],						):
  									_snake_case			 = os.path.join(
  									    __UpperCamelCase,						(filename_prefix + "-" if filename_prefix else "") + spm_save_filename				)
  									if os.path.abspath(__UpperCamelCase				) != os.path.abspath(__UpperCamelCase				) and os.path.isfile(__UpperCamelCase				):
  											copyfile(__UpperCamelCase,						__UpperCamelCase				)
  											saved_files.append(__UpperCamelCase				)
  									elif not os.path.isfile(__UpperCamelCase				):
  											with open(__UpperCamelCase,						"wb"				) as fi:
  													_snake_case			 = spm_model.serialized_model_proto()
  													fi.write(__UpperCamelCase				)
  											saved_files.append(__UpperCamelCase				)
  
  							return tuple(__UpperCamelCase				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return self.get_src_vocab()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return dict(self.encoder,						**self.added_tokens_encoder				)
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							return dict(self.target_encoder,						**self.added_tokens_decoder				)
  
  
  
  
  
  
  					def __getstate__(					self				):
  							_snake_case			 = 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				):
  							_snake_case			 = d
  
  							# for backward compatibility
  							if not hasattr(self,						"sp_model_kwargs"				):
  									_snake_case			 = {}
  
  							_snake_case ,					_snake_case			 = (load_spm(__UpperCamelCase,						self.sp_model_kwargs				) for f in self.spm_files)
  							_snake_case			 = self.spm_source
  							self._setup_normalizer()
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						*__UpperCamelCase,						**__UpperCamelCase				):
  							return 1
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = 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        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None,						__UpperCamelCase = False				):
  
  
  
  
  
  
  
  							if already_has_special_tokens:
  									return self._special_token_mask(__UpperCamelCase				)
  							elif token_ids_a is None:
  									return self._special_token_mask(__UpperCamelCase				) + [1]
  							else:
  									return self._special_token_mask(token_ids_a + token_ids_a				) + [1]
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		_snake_case			 = sentencepiece.SentencePieceProcessor(**__snake_case						)
  		spm.Load(__snake_case						)
  		return spm
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_		,							lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		with open(__snake_case		,							"w"						) as f:
  				json.dump(__snake_case		,							__snake_case		,							indent=2						)
  
  
  
  
  
  
  def 	_lowercase	(			lowercase_						):
  
  
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		with open(__snake_case		,							"r"						) as f:
  				return json.load(__snake_case						)
  
  
  
  
  
  
style_context: 
  
  
  
  
  '''simple docstring'''
  
  
  
  
  
  
  
  import json
  from typing import List, Optional, Tuple
  
  from tokenizers import normalizers
  
  from ...tokenization_utils import AddedToken
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_mpnet import MPNetTokenizer
  
  
  _lowerCamelCase							:		int										=						logging.get_logger(__name__)
  
  _lowerCamelCase							:		Any										=						{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
  
  _lowerCamelCase							:		Dict										=						{
      'vocab_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/vocab.txt',
      },
      'tokenizer_file': {
          'microsoft/mpnet-base': 'https://huggingface.co/microsoft/mpnet-base/resolve/main/tokenizer.json',
      },
  }
  
  _lowerCamelCase							:		List[str]										=						{
      'microsoft/mpnet-base': 512,
  }
  
  _lowerCamelCase							:		Tuple										=						{
      'microsoft/mpnet-base': {'do_lower_case': True},
  }
  
  
  
  
  
  class   lowerCamelCase_							( UpperCAmelCase_							):
  
  
  
  
  					'''simple docstring'''
  
  
  
  
  
  
  					lowercase__					=				VOCAB_FILES_NAMES
  					lowercase__					=				PRETRAINED_VOCAB_FILES_MAP
  					lowercase__					=				PRETRAINED_INIT_CONFIGURATION
  					lowercase__					=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  					lowercase__					=				MPNetTokenizer
  					lowercase__					=				["""input_ids""", """attention_mask"""]
  
  
  
  
  
  
  					def __init__(					self,						__UpperCamelCase=None,						__UpperCamelCase=None,						__UpperCamelCase=True,						__UpperCamelCase="<s>",						__UpperCamelCase="</s>",						__UpperCamelCase="</s>",						__UpperCamelCase="<s>",						__UpperCamelCase="[UNK]",						__UpperCamelCase="<pad>",						__UpperCamelCase="<mask>",						__UpperCamelCase=True,						__UpperCamelCase=None,						**__UpperCamelCase,						):
  							super().__init__(
  							    __UpperCamelCase,						tokenizer_file=__UpperCamelCase,						do_lower_case=__UpperCamelCase,						bos_token=__UpperCamelCase,						eos_token=__UpperCamelCase,						sep_token=__UpperCamelCase,						cls_token=__UpperCamelCase,						unk_token=__UpperCamelCase,						pad_token=__UpperCamelCase,						mask_token=__UpperCamelCase,						tokenize_chinese_chars=__UpperCamelCase,						strip_accents=__UpperCamelCase,						**__UpperCamelCase,						)
  
  							_snake_case			 = json.loads(self.backend_tokenizer.normalizer.__getstate__()				)
  							if (
  							    pre_tok_state.get("lowercase",						__UpperCamelCase				) != do_lower_case
  							    or pre_tok_state.get("strip_accents",						__UpperCamelCase				) != strip_accents
  							):
  									_snake_case			 = getattr(__UpperCamelCase,						pre_tok_state.pop("type"				)				)
  									_snake_case			 = do_lower_case
  									_snake_case			 = strip_accents
  									_snake_case			 = pre_tok_class(**__UpperCamelCase				)
  
  							_snake_case			 = do_lower_case
  
  
  
  
  
  
  					@property
  					def        __SCREAMING_SNAKE_CASE			(					self				):
  							if self._mask_token is None:
  									if self.verbose:
  											logger.error("Using mask_token, but it is not set yet."				)
  									return None
  							return str(self._mask_token				)
  
  
  
  
  
  
  					@mask_token.setter
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase				):
  							_snake_case			 = AddedToken(__UpperCamelCase,						lstrip=__UpperCamelCase,						rstrip=__UpperCamelCase				) if isinstance(__UpperCamelCase,						__UpperCamelCase				) else value
  							_snake_case			 = value
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase=None				):
  							_snake_case			 = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
  							if token_ids_a is None:
  									return output
  
  							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
  							_snake_case			 = [self.sep_token_id]
  							_snake_case			 = [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 + sep + token_ids_a + sep				) * [0]
  
  
  
  
  
  
  					def        __SCREAMING_SNAKE_CASE			(					self,						__UpperCamelCase,						__UpperCamelCase = None				):
  							_snake_case			 = self._tokenizer.model.save(__UpperCamelCase,						name=__UpperCamelCase				)
  							return tuple(__UpperCamelCase				)
  
  
  
  
  
  