code: 
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_sentencepiece_available,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  _lowercase		:     int 					={
      """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
  }
  
  try:
  		if not is_sentencepiece_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[int] 					=["""CamembertTokenizer"""]
  
  try:
  		if not is_tokenizers_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     Optional[Any] 					=["""CamembertTokenizerFast"""]
  
  try:
  		if not is_torch_available():
  				raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  		pass
  else:
  		_lowercase		:     int 					=[
  		    """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:
  		_lowercase		:     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
  
  		_lowercase		:     List[Any] 					=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
  
style_context: 
  
  
  
  
  
  from __future__ import annotations
  
  
  def 		_SCREAMING_SNAKE_CASE    (   lowerCAmelCase__	):
  		lowerCamelCase_  :    int
  		lowerCamelCase_  :    list[str]   	=			list(lowerCAmelCase__	)
  		lowerCamelCase_	,			lowerCamelCase_  :    List[str]   	=			_vector[:-1], int(_vector[-1]	)
  		lowerCamelCase_  :    list[int]   	=			[int(lowerCAmelCase__	) for digit in __vector]
  
  		vector.reverse()
  		for i, digit in enumerate(lowerCAmelCase__	):
  				if i & 1 == 0:
  						lowerCamelCase_  :    int   	=			digit * 2
  						if doubled > 9:
  								doubled -= 9
  						check_digit += doubled
  				else:
  						check_digit += digit
  
  		return check_digit % 10 == 0
  
  
  if __name__ == "__main__":
  		import doctest
  
  		doctest.testmod()
  		assert is_luhn("""79927398713""")
  		assert not is_luhn("""79927398714""")
  