code: 
  
  
  
  
  import json
  import os
  import re
  import unicodedata
  from typing import Dict, List, Optional, Tuple
  
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
  
  
  SCREAMING_SNAKE_CASE : List[Any]          =    logging.get_logger(__name__)
  
  SCREAMING_SNAKE_CASE : List[str]          =    {
      'src_vocab_file': 'vocab-src.json',
      'tgt_vocab_file': 'vocab-tgt.json',
      'merges_file': 'merges.txt',
  }
  
  SCREAMING_SNAKE_CASE : Tuple          =    {
      'src_vocab_file': {
          'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-src.json'
      },
      'tgt_vocab_file': {
          'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/vocab-tgt.json'
      },
      'merges_file': {'stas/tiny-wmt19-en-de': 'https://huggingface.co/stas/tiny-wmt19-en-de/resolve/main/merges.txt'},
  }
  
  SCREAMING_SNAKE_CASE : Optional[int]          =    {'stas/tiny-wmt19-en-de': 10_24}
  SCREAMING_SNAKE_CASE : Union[str, Any]          =    {
      'stas/tiny-wmt19-en-de': {
          'langs': ['en', 'de'],
          'model_max_length': 10_24,
          'special_tokens_map_file': None,
          'full_tokenizer_file': None,
      }
  }
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Optional[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   str			=	set()
  							a_       :   Optional[Any]			=	word[0]
  							for char in word[1:]:
  														pairs.add((prev_char, char)      )
  														a_       :   Any			=	char
  							return pairs
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Any      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   Optional[int]			=	text.replace("""，"""	,	""","""      )
  							a_       :   int			=	re.sub(r"""。\s*"""	,	""". """	,	__A      )
  							a_       :   Tuple			=	text.replace("""、"""	,	""","""      )
  							a_       :   Optional[Any]			=	text.replace("""”"""	,	"""\""""      )
  							a_       :   Any			=	text.replace("""“"""	,	"""\""""      )
  							a_       :   List[Any]			=	text.replace("""∶"""	,	""":"""      )
  							a_       :   Optional[Any]			=	text.replace("""："""	,	""":"""      )
  							a_       :   List[Any]			=	text.replace("""？"""	,	"""?"""      )
  							a_       :   Optional[int]			=	text.replace("""《"""	,	"""\""""      )
  							a_       :   List[Any]			=	text.replace("""》"""	,	"""\""""      )
  							a_       :   Optional[int]			=	text.replace("""）"""	,	""")"""      )
  							a_       :   Union[str, Any]			=	text.replace("""！"""	,	"""!"""      )
  							a_       :   Optional[int]			=	text.replace("""（"""	,	"""("""      )
  							a_       :   int			=	text.replace("""；"""	,	""";"""      )
  							a_       :   int			=	text.replace("""１"""	,	"""1"""      )
  							a_       :   Optional[Any]			=	text.replace("""」"""	,	"""\""""      )
  							a_       :   List[str]			=	text.replace("""「"""	,	"""\""""      )
  							a_       :   List[str]			=	text.replace("""０"""	,	"""0"""      )
  							a_       :   Optional[int]			=	text.replace("""３"""	,	"""3"""      )
  							a_       :   Union[str, Any]			=	text.replace("""２"""	,	"""2"""      )
  							a_       :   int			=	text.replace("""５"""	,	"""5"""      )
  							a_       :   Union[str, Any]			=	text.replace("""６"""	,	"""6"""      )
  							a_       :   List[str]			=	text.replace("""９"""	,	"""9"""      )
  							a_       :   List[str]			=	text.replace("""７"""	,	"""7"""      )
  							a_       :   Optional[Any]			=	text.replace("""８"""	,	"""8"""      )
  							a_       :   List[str]			=	text.replace("""４"""	,	"""4"""      )
  							a_       :   List[Any]			=	re.sub(r"""．\s*"""	,	""". """	,	__A      )
  							a_       :   str			=	text.replace("""～"""	,	"""~"""      )
  							a_       :   Optional[Any]			=	text.replace("""’"""	,	"""'"""      )
  							a_       :   int			=	text.replace("""…"""	,	"""..."""      )
  							a_       :   List[Any]			=	text.replace("""━"""	,	"""-"""      )
  							a_       :   List[str]			=	text.replace("""〈"""	,	"""<"""      )
  							a_       :   Any			=	text.replace("""〉"""	,	""">"""      )
  							a_       :   Any			=	text.replace("""【"""	,	"""["""      )
  							a_       :   List[str]			=	text.replace("""】"""	,	"""]"""      )
  							a_       :   Optional[Any]			=	text.replace("""％"""	,	"""%"""      )
  							return text
  
  
  
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: Dict      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   str			=	[]
  							for char in text:
  														a_       :   Optional[Any]			=	unicodedata.category(__A      )
  														if cat.startswith("""C"""      ):
  																					continue
  														output.append(__A      )
  							return "".join(__A      )
  
  
  
  
  
  class 					snake_case__  ( UpperCAmelCase_       ):
  	UpperCAmelCase   :							Union[str, Any]         = VOCAB_FILES_NAMES
  	UpperCAmelCase   :							Any         = PRETRAINED_VOCAB_FILES_MAP
  	UpperCAmelCase   :							str         = PRETRAINED_INIT_CONFIGURATION
  	UpperCAmelCase   :							Any         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	UpperCAmelCase   :							Tuple         = ["""input_ids""", """attention_mask"""]
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    UpperCamelCase_=None				,    UpperCamelCase_=False				,    UpperCamelCase_="<unk>"				,    UpperCamelCase_="<s>"				,    UpperCamelCase_="</s>"				,    UpperCamelCase_="<pad>"				,    **UpperCamelCase_				,    )		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								super().__init__(
  								    langs=UpperCamelCase_				,    src_vocab_file=UpperCamelCase_				,    tgt_vocab_file=UpperCamelCase_				,    merges_file=UpperCamelCase_				,    do_lower_case=UpperCamelCase_				,    unk_token=UpperCamelCase_				,    bos_token=UpperCamelCase_				,    sep_token=UpperCamelCase_				,    pad_token=UpperCamelCase_				,    **UpperCamelCase_				,    )
  
  								try:
  															import sacremoses
  								except ImportError:
  															raise ImportError(
  															    """You need to install sacremoses to use XLMTokenizer. """
  															    """See https://pypi.org/project/sacremoses/ for installation."""							)
  
  								a_       :   Tuple			=	sacremoses
  
  								a_       :   List[Any]			=	src_vocab_file
  								a_       :   Union[str, Any]			=	tgt_vocab_file
  								a_       :   Dict			=	merges_file
  								a_       :   Union[str, Any]			=	do_lower_case
  
  								# cache of sm.MosesPunctNormalizer instance
  								a_       :   Optional[Any]			=	{}
  								# cache of sm.MosesTokenizer instance
  								a_       :   Any			=	{}
  								a_       :   Optional[int]			=	{}
  
  								if langs and len(UpperCamelCase_							) == 2:
  															a_		,				a_       :   Optional[Any]			=	langs
  								else:
  															raise ValueError(
  															    f"""arg `langs` needs to be a list of 2 langs, e.g. ['en', 'ru'], but got {langs}. """
  															    """Usually that means that tokenizer can't find a mapping for the given model path """
  															    """in PRETRAINED_VOCAB_FILES_MAP, and other maps of this tokenizer."""							)
  
  								with open(UpperCamelCase_				,    encoding="""utf-8"""							) as src_vocab_handle:
  															a_       :   str			=	json.load(UpperCamelCase_							)
  								with open(UpperCamelCase_				,    encoding="""utf-8"""							) as tgt_vocab_handle:
  															a_       :   Any			=	json.load(UpperCamelCase_							)
  															a_       :   List[str]			=	{v: k for k, v in tgt_vocab.items()}
  								with open(UpperCamelCase_				,    encoding="""utf-8"""							) as merges_handle:
  															a_       :   Union[str, Any]			=	merges_handle.read().split("""\n"""							)[:-1]
  								a_       :   Optional[Any]			=	[tuple(merge.split()[:2]							) for merge in merges]
  								a_       :   str			=	dict(zip(UpperCamelCase_				,    range(len(UpperCamelCase_							)							)							)							)
  								a_       :   str			=	{}
  
  
  
  
  
  	def     A				(  self							)		->			Dict[str, int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.get_src_vocab()
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.src_vocab_size
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								if lang not in self.cache_moses_punct_normalizer:
  															a_       :   Dict			=	self.sm.MosesPunctNormalizer(lang=UpperCamelCase_							)
  															a_       :   Optional[Any]			=	punct_normalizer
  								return self.cache_moses_punct_normalizer[lang].normalize(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if lang not in self.cache_moses_tokenizer:
  															a_       :   Any			=	self.sm.MosesTokenizer(lang=UpperCamelCase_							)
  															a_       :   Tuple			=	moses_tokenizer
  								return self.cache_moses_tokenizer[lang].tokenize(
  								    UpperCamelCase_				,    aggressive_dash_splits=UpperCamelCase_				,    return_str=UpperCamelCase_				,    escape=UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if lang not in self.cache_moses_detokenizer:
  															a_       :   Optional[Any]			=	self.sm.MosesDetokenizer(lang=UpperCamelCase_							)
  															a_       :   List[Any]			=	moses_detokenizer
  								return self.cache_moses_detokenizer[lang].detokenize(UpperCamelCase_							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	replace_unicode_punct(UpperCamelCase_							)
  								a_       :   Optional[Any]			=	self.moses_punct_norm(UpperCamelCase_				,    UpperCamelCase_							)
  								a_       :   Dict			=	remove_non_printing_char(UpperCamelCase_							)
  								return text
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.encoder							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.decoder							)
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								return dict(self.encoder				,    **self.added_tokens_encoder							)
  
  
  
  
  
  	def     A				(  self							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								return dict(self.decoder				,    **self.added_tokens_decoder							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   List[str]			=	tuple(token[:-1]							) + (token[-1] + """</w>""",)
  								if token in self.cache:
  															return self.cache[token]
  								a_       :   Optional[int]			=	get_pairs(UpperCamelCase_							)
  
  								if not pairs:
  															return token + "</w>"
  
  								while True:
  															a_       :   Optional[int]			=	min(UpperCamelCase_				,    key=lambda UpperCamelCase_							: self.bpe_ranks.get(UpperCamelCase_				,    float("""inf"""							)							)							)
  															if bigram not in self.bpe_ranks:
  																						break
  															a_		,				a_       :   Tuple			=	bigram
  															a_       :   str			=	[]
  															a_       :   Optional[Any]			=	0
  															while i < len(UpperCamelCase_							):
  																						try:
  																													a_       :   List[str]			=	word.index(UpperCamelCase_				,    UpperCamelCase_							)
  																						except ValueError:
  																													new_word.extend(word[i:]							)
  																													break
  																						else:
  																													new_word.extend(word[i:j]							)
  																													a_       :   Tuple			=	j
  
  																						if word[i] == first and i < len(UpperCamelCase_							) - 1 and word[i + 1] == second:
  																													new_word.append(first + second							)
  																													i += 2
  																						else:
  																													new_word.append(word[i]							)
  																													i += 1
  															a_       :   Dict			=	tuple(UpperCamelCase_							)
  															a_       :   int			=	new_word
  															if len(UpperCamelCase_							) == 1:
  																						break
  															else:
  																						a_       :   Union[str, Any]			=	get_pairs(UpperCamelCase_							)
  								a_       :   Union[str, Any]			=	""" """.join(UpperCamelCase_							)
  								if word == "\n  </w>":
  															a_       :   Optional[Any]			=	"""\n</w>"""
  								a_       :   Union[str, Any]			=	word
  								return word
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_="en"				,    UpperCamelCase_=False							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	self.src_lang
  
  								if self.do_lower_case:
  															a_       :   int			=	text.lower()
  
  								if bypass_tokenizer:
  															a_       :   str			=	text.split()
  								else:
  															a_       :   Optional[int]			=	self.moses_pipeline(UpperCamelCase_				,    lang=UpperCamelCase_							)
  															a_       :   Any			=	self.moses_tokenize(UpperCamelCase_				,    lang=UpperCamelCase_							)
  
  								a_       :   Any			=	[]
  								for token in text:
  															if token:
  																						split_tokens.extend(list(self.bpe(UpperCamelCase_							).split(""" """							)							)							)
  
  								return split_tokens
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.encoder.get(UpperCamelCase_				,    self.encoder.get(self.unk_token							)							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.decoder.get(UpperCamelCase_				,    self.unk_token							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Tuple:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	[t.replace(""" """				,    """"""							).replace("""</w>"""				,    """ """							) for t in tokens]
  								a_       :   Optional[int]			=	"""""".join(UpperCamelCase_							).split()
  								# detokenize
  								a_       :   List[str]			=	self.moses_detokenize(UpperCamelCase_				,    self.tgt_lang							)
  								return text
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	[self.sep_token_id]
  
  								# no bos used in fairseq
  								if token_ids_a is None:
  															return token_ids_a + sep
  								return token_ids_a + sep + token_ids_a + sep
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if already_has_special_tokens:
  															return super().get_special_tokens_mask(
  															    token_ids_a=UpperCamelCase_				,    token_ids_a=UpperCamelCase_				,    already_has_special_tokens=UpperCamelCase_							)
  								# no bos used in fairseq
  								if token_ids_a is not None:
  															return ([0] * len(UpperCamelCase_							)) + [1] + ([0] * len(UpperCamelCase_							)) + [1]
  								return ([0] * len(UpperCamelCase_							)) + [1]
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	[self.sep_token_id]
  
  								# no bos used in fairseq
  								if token_ids_a is None:
  															return len(token_ids_a + sep							) * [0]
  								return len(token_ids_a + sep							) * [0] + len(token_ids_a + sep							) * [1]
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			Tuple[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not os.path.isdir(UpperCamelCase_							):
  															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
  															return
  
  								a_       :   Any			=	os.path.join(
  								    UpperCamelCase_				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""src_vocab_file"""]							)
  								a_       :   Union[str, Any]			=	os.path.join(
  								    UpperCamelCase_				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""tgt_vocab_file"""]							)
  								a_       :   List[str]			=	os.path.join(
  								    UpperCamelCase_				,    (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""]							)
  
  								with open(UpperCamelCase_				,    """w"""				,    encoding="""utf-8"""							) as f:
  															f.write(json.dumps(self.encoder				,    indent=2				,    sort_keys=UpperCamelCase_				,    ensure_ascii=UpperCamelCase_							) + """\n"""							)
  
  								with open(UpperCamelCase_				,    """w"""				,    encoding="""utf-8"""							) as f:
  															a_       :   Any			=	{v: k for k, v in self.decoder.items()}
  															f.write(json.dumps(UpperCamelCase_				,    indent=2				,    sort_keys=UpperCamelCase_				,    ensure_ascii=UpperCamelCase_							) + """\n"""							)
  
  								a_       :   List[Any]			=	0
  								with open(UpperCamelCase_				,    """w"""				,    encoding="""utf-8"""							) as writer:
  															for bpe_tokens, token_index in sorted(self.bpe_ranks.items()				,    key=lambda UpperCamelCase_							: kv[1]							):
  																						if index != token_index:
  																													logger.warning(
  																													    f"""Saving vocabulary to {merges_file}: BPE merge indices are not consecutive."""
  																													    """ Please check that the tokenizer is not corrupted!"""							)
  																													a_       :   Union[str, Any]			=	token_index
  																						writer.write(""" """.join(UpperCamelCase_							) + """\n"""							)
  																						index += 1
  
  								return src_vocab_file, tgt_vocab_file, merges_file
  
  
  
  
  
  	def __getstate__(  self							)		->			Union[str, Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	self.__dict__.copy()
  								a_       :   str			=	None
  								return state
  
  
  
  
  
  	def __setstate__(  self				,    UpperCamelCase_							)		->			List[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Any			=	d
  
  								try:
  															import sacremoses
  								except ImportError:
  															raise ImportError(
  															    """You need to install sacremoses to use XLMTokenizer. """
  															    """See https://pypi.org/project/sacremoses/ for installation."""							)
  
  								a_       :   Optional[int]			=	sacremoses
  
  
  
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__)
  
  
  