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
  
  
  _A						=     logging.get_logger(__name__)
  
  _A						=     {
      """source_spm""": """source.spm""",
      """target_spm""": """target.spm""",
      """vocab""": """vocab.json""",
      """target_vocab_file""": """target_vocab.json""",
      """tokenizer_config_file""": """tokenizer_config.json""",
  }
  
  _A						=     {
      """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						=     {"""Helsinki-NLP/opus-mt-en-de""": 5_12}
  _A						=     {}
  
  class 			lowerCamelCase  (					lowerCAmelCase__      ):
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    SCREAMING_SNAKE_CASE							=     VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE							=     PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE							=     PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE							=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE							=     ['input_ids', 'attention_mask']
    SCREAMING_SNAKE_CASE							=     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       ,					):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       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}"""
  
          UpperCAmelCase__ :       Tuple						=						separate_vocabs
          UpperCAmelCase__ :       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:
                UpperCAmelCase__ :       str						=						load_json(_lowerCamelCase  )
                UpperCAmelCase__ :       List[Any]						=						{v: k for k, v in self.target_encoder.items()}
                UpperCAmelCase__ :       Optional[Any]						=						[]
          else:
                UpperCAmelCase__ :       Any						=						{v: k for k, v in self.encoder.items()}
                UpperCAmelCase__ :       list						=						[k for k in self.encoder if k.startswith(""">>"""  ) and k.endswith("""<<"""  )]
  
          UpperCAmelCase__ :       Any						=						source_lang
          UpperCAmelCase__ :       int						=						target_lang
          UpperCAmelCase__ :       List[Any]						=						[source_spm, target_spm]
  
          # load SentencePiece model for pre-processing
          UpperCAmelCase__ :       str						=						load_spm(_lowerCamelCase       ,					self.sp_model_kwargs  )
          UpperCAmelCase__ :       Optional[int]						=						load_spm(_lowerCamelCase       ,					self.sp_model_kwargs  )
          UpperCAmelCase__ :       Optional[int]						=						self.spm_source
          UpperCAmelCase__ :       int						=						self.encoder
  
          # Multilingual target side: default to using first supported language code.
  
          self._setup_normalizer()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          try:
                from sacremoses import MosesPunctNormalizer
  
                UpperCAmelCase__ :       Optional[int]						=						MosesPunctNormalizer(self.source_lang  ).normalize
          except (ImportError, FileNotFoundError):
                warnings.warn("""Recommended: pip install sacremoses."""  )
                UpperCAmelCase__ :       List[str]						=						lambda _lowerCamelCase  : x
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.punc_normalizer(_lowerCamelCase  ) if x else ""
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.current_encoder.get(_lowerCamelCase       ,					self.current_encoder[self.unk_token]  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						self.language_code_re.match(_lowerCamelCase  )
          UpperCAmelCase__ :       list						=						[match.group(0  )] if match else []
          return code, self.language_code_re.sub(""""""       ,					_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       List[Any]						=						self.remove_language_code(_lowerCamelCase  )
          UpperCAmelCase__ :       Any						=						self.current_spm.encode(_lowerCamelCase       ,					out_type=_lowerCamelCase  )
          return code + pieces
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return self.decoder.get(_lowerCamelCase       ,					self.unk_token  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return super().batch_decode(_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return super().decode(_lowerCamelCase       ,					**_lowerCamelCase  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						self.spm_source if self._decode_use_source_tokenizer else self.spm_target
          UpperCAmelCase__ :       List[str]						=						[]
          UpperCAmelCase__ :       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 + " "
                      UpperCAmelCase__ :       Any						=						[]
                else:
                      current_sub_tokens.append(_lowerCamelCase  )
          out_string += sp_model.decode_pieces(_lowerCamelCase  )
          return out_string.strip()
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase=None  ):
  
  
          """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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						self.spm_source
          UpperCAmelCase__ :       Union[str, Any]						=						self.encoder
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						self.spm_target
          if self.separate_vocabs:
                UpperCAmelCase__ :       Tuple						=						self.target_encoder
  
  
  
    @property
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return len(self.encoder  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          if not os.path.isdir(_lowerCamelCase  ):
                logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""  )
                return
          UpperCAmelCase__ :       Tuple						=						[]
  
          if self.separate_vocabs:
                UpperCAmelCase__ :       Any						=						os.path.join(
                    _lowerCamelCase       ,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]       ,					)
                UpperCAmelCase__ :       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:
                UpperCAmelCase__ :       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]       ,					):
                UpperCAmelCase__ :       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:
                            UpperCAmelCase__ :       List[Any]						=						spm_model.serialized_model_proto()
                            fi.write(_lowerCamelCase  )
                      saved_files.append(_lowerCamelCase  )
  
          return tuple(_lowerCamelCase  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return self.get_src_vocab()
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return dict(self.encoder       ,					**self.added_tokens_encoder  )
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          return dict(self.target_encoder       ,					**self.added_tokens_decoder  )
  
  
  
    def __getstate__(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       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       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[Any]						=						d
  
          # for backward compatibility
          if not hasattr(self       ,					"""sp_model_kwargs"""  ):
                UpperCAmelCase__ :       str						=						{}
  
          UpperCAmelCase__				,		UpperCAmelCase__ :       Dict						=						(load_spm(_lowerCamelCase       ,					self.sp_model_kwargs  ) for f in self.spm_files)
          UpperCAmelCase__ :       Tuple						=						self.spm_source
          self._setup_normalizer()
  
  
  
    def    _a			(self       ,					*_lowerCamelCase       ,					**_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          return 1
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       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    _a			(self       ,					_lowerCamelCase       ,					_lowerCamelCase = None       ,					_lowerCamelCase = False  ):
  
  
          """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 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   sentencepiece.SentencePieceProcessor:
        UpperCAmelCase__ :       List[Any]						=						sentencepiece.SentencePieceProcessor(**lowerCAmelCase						)
        spm.Load(lowerCAmelCase						)
        return spm
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase						)  ->   None:
        with open(lowerCAmelCase ,  """w"""						) as f:
              json.dump(lowerCAmelCase ,  lowerCAmelCase ,  indent=2						)
  
  
  
  
  
  def 				a__	(	lowerCAmelCase						)  ->   Union[Dict, List]:
        with open(lowerCAmelCase ,  """r"""						) as f:
              return json.load(lowerCAmelCase						)
  
  
style_context: 
  
  """simple docstring"""
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_flax_available,
      is_keras_nlp_available,
      is_tensorflow_text_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _A						=     {
      """configuration_gpt2""": ["""GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPT2Config""", """GPT2OnnxConfig"""],
      """tokenization_gpt2""": ["""GPT2Tokenizer"""],
  }
  
  try:
      if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""GPT2TokenizerFast"""]
  
  try:
      if not is_torch_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """GPT2DoubleHeadsModel""",
          """GPT2ForQuestionAnswering""",
          """GPT2ForSequenceClassification""",
          """GPT2ForTokenClassification""",
          """GPT2LMHeadModel""",
          """GPT2Model""",
          """GPT2PreTrainedModel""",
          """load_tf_weights_in_gpt2""",
      ]
  
  try:
      if not is_tf_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     [
          """TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """TFGPT2DoubleHeadsModel""",
          """TFGPT2ForSequenceClassification""",
          """TFGPT2LMHeadModel""",
          """TFGPT2MainLayer""",
          """TFGPT2Model""",
          """TFGPT2PreTrainedModel""",
      ]
  
  try:
      if not is_keras_nlp_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""TFGPT2Tokenizer"""]
  
  try:
      if not is_flax_available():
          raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
      pass
  else:
      _A						=     ["""FlaxGPT2LMHeadModel""", """FlaxGPT2Model""", """FlaxGPT2PreTrainedModel"""]
  
  if TYPE_CHECKING:
      from .configuration_gpta import GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTaConfig, GPTaOnnxConfig
      from .tokenization_gpta import GPTaTokenizer
  
      try:
          if not is_tokenizers_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_fast import GPTaTokenizerFast
  
      try:
          if not is_torch_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_gpta import (
              GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              GPTaDoubleHeadsModel,
              GPTaForQuestionAnswering,
              GPTaForSequenceClassification,
              GPTaForTokenClassification,
              GPTaLMHeadModel,
              GPTaModel,
              GPTaPreTrainedModel,
              load_tf_weights_in_gpta,
          )
  
      try:
          if not is_tf_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_tf_gpta import (
              TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST,
              TFGPTaDoubleHeadsModel,
              TFGPTaForSequenceClassification,
              TFGPTaLMHeadModel,
              TFGPTaMainLayer,
              TFGPTaModel,
              TFGPTaPreTrainedModel,
          )
  
      try:
          if not is_keras_nlp_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .tokenization_gpta_tf import TFGPTaTokenizer
  
      try:
          if not is_flax_available():
              raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
          pass
      else:
          from .modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel, FlaxGPTaPreTrainedModel
  
  else:
      import sys
  
      _A						=     _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
  