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
  
  
  SCREAMING_SNAKE_CASE : List[Any]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[str]          =    {
      """source_spm""": """source.spm""",
      """target_spm""": """target.spm""",
      """vocab""": """vocab.json""",
      """target_vocab_file""": """target_vocab.json""",
      """tokenizer_config_file""": """tokenizer_config.json""",
  }
  
  SCREAMING_SNAKE_CASE : 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"""
          )
      },
  }
  
  SCREAMING_SNAKE_CASE : Optional[int]          =    {"""Helsinki-NLP/opus-mt-en-de""": 5_12}
  SCREAMING_SNAKE_CASE : Any          =    {}
  
  
  
  
  
  class 					snake_case__  ( a__       ):
  	UpperCAmelCase   :							int         = VOCAB_FILES_NAMES
  	UpperCAmelCase   :							Any         = PRETRAINED_VOCAB_FILES_MAP
  	UpperCAmelCase   :							Optional[int]         = PRETRAINED_INIT_CONFIGURATION
  	UpperCAmelCase   :							Optional[Any]         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	UpperCAmelCase   :							Any         = ["""input_ids""", """attention_mask"""]
  	UpperCAmelCase   :							Dict         = 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_				,    )		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[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=__A				,    target_lang=__A				,    unk_token=__A				,    eos_token=__A				,    pad_token=__A				,    model_max_length=__A				,    sp_model_kwargs=self.sp_model_kwargs				,    target_vocab_file=__A				,    separate_vocabs=__A				,    **__A				,    )
  								assert Path(__A							).exists(), f"""cannot find spm source {source_spm}"""
  
  								a_       :   Dict			=	separate_vocabs
  								a_       :   Any			=	load_json(__A							)
  								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_       :   Union[str, Any]			=	load_json(__A							)
  															a_       :   Dict			=	{v: k for k, v in self.target_encoder.items()}
  															a_       :   Optional[int]			=	[]
  								else:
  															a_       :   Dict			=	{v: k for k, v in self.encoder.items()}
  															a_       :   list			=	[k for k in self.encoder if k.startswith(""">>"""							) and k.endswith("""<<"""							)]
  
  								a_       :   List[Any]			=	source_lang
  								a_       :   Tuple			=	target_lang
  								a_       :   Union[str, Any]			=	[source_spm, target_spm]
  
  								# load SentencePiece model for pre-processing
  								a_       :   List[str]			=	load_spm(__A				,    self.sp_model_kwargs							)
  								a_       :   List[Any]			=	load_spm(__A				,    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     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								try:
  															from sacremoses import MosesPunctNormalizer
  
  															a_       :   Tuple			=	MosesPunctNormalizer(self.source_lang							).normalize
  								except (ImportError, FileNotFoundError):
  															warnings.warn("""Recommended: pip install sacremoses."""							)
  															a_       :   Tuple			=	lambda UpperCamelCase_							: x
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.punc_normalizer(__A							) if x else ""
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.current_encoder.get(__A				,    self.current_encoder[self.unk_token]							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[Any]			=	self.language_code_re.match(__A							)
  								a_       :   list			=	[match.group(0							)] if match else []
  								return code, self.language_code_re.sub(""""""				,    __A							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	self.remove_language_code(__A							)
  								a_       :   List[str]			=	self.current_spm.encode(__A				,    out_type=__A							)
  								return code + pieces
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.decoder.get(__A				,    self.unk_token							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    **UpperCamelCase_							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return super().batch_decode(__A				,    **__A							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    **UpperCamelCase_							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								return super().decode(__A				,    **__A							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	self.spm_source if self._decode_use_source_tokenizer else self.spm_target
  								a_       :   List[str]			=	[]
  								a_       :   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(__A							) + token + " "
  																						a_       :   Tuple			=	[]
  															else:
  																						current_sub_tokens.append(__A							)
  								out_string += sp_model.decode_pieces(__A							)
  								return out_string.strip()
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_=None							)		->			List[int]:
  								"""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							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	self.spm_source
  								a_       :   Optional[Any]			=	self.encoder
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.spm_target
  								if self.separate_vocabs:
  															a_       :   Optional[Any]			=	self.target_encoder
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.encoder							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			Tuple[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not os.path.isdir(__A							):
  															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
  															return
  								a_       :   Optional[Any]			=	[]
  
  								if self.separate_vocabs:
  															a_       :   int			=	os.path.join(
  															    __A				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]				,    )
  															a_       :   Union[str, Any]			=	os.path.join(
  															    __A				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""target_vocab_file"""]				,    )
  															save_json(self.encoder				,    __A							)
  															save_json(self.target_encoder				,    __A							)
  															saved_files.append(__A							)
  															saved_files.append(__A							)
  								else:
  															a_       :   int			=	os.path.join(
  															    __A				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab"""]							)
  															save_json(self.encoder				,    __A							)
  															saved_files.append(__A							)
  
  								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_       :   List[str]			=	os.path.join(
  															    __A				,    (filename_prefix + """-""" if filename_prefix else """""") + spm_save_filename							)
  															if os.path.abspath(__A							) != os.path.abspath(__A							) and os.path.isfile(__A							):
  																						copyfile(__A				,    __A							)
  																						saved_files.append(__A							)
  															elif not os.path.isfile(__A							):
  																						with open(__A				,    """wb"""							) as fi:
  																													a_       :   List[Any]			=	spm_model.serialized_model_proto()
  																													fi.write(__A							)
  																						saved_files.append(__A							)
  
  								return tuple(__A							)
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.get_src_vocab()
  
  
  
  
  
  	def     A				(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								return dict(self.encoder				,    **self.added_tokens_encoder							)
  
  
  
  
  
  	def     A				(  self							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return dict(self.target_encoder				,    **self.added_tokens_decoder							)
  
  
  
  
  
  	def __getstate__(  self							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[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_							)		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	d
  
  								# for backward compatibility
  								if not hasattr(self				,    """sp_model_kwargs"""							):
  															a_       :   Dict			=	{}
  
  								a_       :   Union[str, Any]			=	(load_spm(__A				,    self.sp_model_kwargs							) for f in self.spm_files)
  								a_       :   int			=	self.spm_source
  								self._setup_normalizer()
  
  
  
  
  
  	def     A				(  self				,    *UpperCamelCase_				,    **UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return 1
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	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				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if already_has_special_tokens:
  															return self._special_token_mask(__A							)
  								elif token_ids_a is None:
  															return self._special_token_mask(__A							) + [1]
  								else:
  															return self._special_token_mask(token_ids_a + token_ids_a							) + [1]
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict	,	SCREAMING_SNAKE_CASE_				: Tuple      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[Any]			=	sentencepiece.SentencePieceProcessor(**SCREAMING_SNAKE_CASE_      )
  							spm.Load(SCREAMING_SNAKE_CASE_      )
  							return spm
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[Any]	,	SCREAMING_SNAKE_CASE_				: Optional[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							with open(SCREAMING_SNAKE_CASE_	,	"""w"""      ) as f:
  														json.dump(SCREAMING_SNAKE_CASE_	,	SCREAMING_SNAKE_CASE_	,	indent=2      )
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: List[str]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							with open(SCREAMING_SNAKE_CASE_	,	"""r"""      ) as f:
  														return json.load(SCREAMING_SNAKE_CASE_      )
  
  
  
style_context: 
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  SCREAMING_SNAKE_CASE : Optional[Any]          =    {
      "configuration_camembert": ["CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CamembertConfig", "CamembertOnnxConfig"],
  }
  
  try:
  	if not is_sentencepiece_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Optional[int]          =    ["CamembertTokenizer"]
  
  try:
  	if not is_tokenizers_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Any          =    ["CamembertTokenizerFast"]
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : Union[str, Any]          =    [
  	    "CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "CamembertForCausalLM",
  	    "CamembertForMaskedLM",
  	    "CamembertForMultipleChoice",
  	    "CamembertForQuestionAnswering",
  	    "CamembertForSequenceClassification",
  	    "CamembertForTokenClassification",
  	    "CamembertModel",
  	    "CamembertPreTrainedModel",
  	]
  
  try:
  	if not is_tf_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	SCREAMING_SNAKE_CASE : List[Any]          =    [
  	    "TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
  	    "TFCamembertForCausalLM",
  	    "TFCamembertForMaskedLM",
  	    "TFCamembertForMultipleChoice",
  	    "TFCamembertForQuestionAnswering",
  	    "TFCamembertForSequenceClassification",
  	    "TFCamembertForTokenClassification",
  	    "TFCamembertModel",
  	    "TFCamembertPreTrainedModel",
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_camembert import CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, CamembertConfig, CamembertOnnxConfig
  
  	try:
  		if not is_sentencepiece_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert import CamembertTokenizer
  
  	try:
  		if not is_tokenizers_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .tokenization_camembert_fast import CamembertTokenizerFast
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_camembert import (
  		    CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    CamembertForCausalLM,
  		    CamembertForMaskedLM,
  		    CamembertForMultipleChoice,
  		    CamembertForQuestionAnswering,
  		    CamembertForSequenceClassification,
  		    CamembertForTokenClassification,
  		    CamembertModel,
  		    CamembertPreTrainedModel,
  		)
  
  	try:
  		if not is_tf_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_tf_camembert import (
  		    TF_CAMEMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    TFCamembertForCausalLM,
  		    TFCamembertForMaskedLM,
  		    TFCamembertForMultipleChoice,
  		    TFCamembertForQuestionAnswering,
  		    TFCamembertForSequenceClassification,
  		    TFCamembertForTokenClassification,
  		    TFCamembertModel,
  		    TFCamembertPreTrainedModel,
  		)
  
  else:
  	import sys
  
  	SCREAMING_SNAKE_CASE : Any          =    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  
  