




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__magic_name__     :     Any  							=					{
    """configuration_camembert""": ["""CAMEMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """CamembertConfig""", """CamembertOnnxConfig"""],
}

try:
      if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__     :     List[Any]  							=					["""CamembertTokenizer"""]

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__     :     Optional[Any]  							=					["""CamembertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __magic_name__     :     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:
      __magic_name__     :     Dict  							=					[
          """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

      __magic_name__     :     List[Any]  							=					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)







from __future__ import annotations

from collections.abc import Iterator
from typing import Any





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		int					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			data
       lowerCAmelCase__								=			None



     def __str__(	self  :		int	):



       """simple docstring"""
       return F"""{self.data}"""





class 						SCREAMING_SNAKE_CASE__		:
     def __init__(	self  :		Union[str, Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			None
       lowerCAmelCase__								=			None
     def __iter__(	self  :		List[Any]	):



       """simple docstring"""
       lowerCAmelCase__								=			self.front
       while node:
         yield node.data
         lowerCAmelCase__								=			node.next
     def __len__(	self  :		int	):



       """simple docstring"""
       return len(tuple(iter(self	)	)	)
     def __str__(	self  :		Optional[int]	):



       """simple docstring"""
       return " <- ".join(str(__lowerCamelCase	) for item in self	)
     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       return len(self	) == 0
     def 					A__			(	self  :		str					,	__lowerCamelCase  :		Any	):



       """simple docstring"""
       lowerCAmelCase__								=			Node(__lowerCamelCase	)
       if self.is_empty():
         lowerCAmelCase__								=			lowerCAmelCase__								=			node
       else:
         assert isinstance(self.rear					,	__lowerCamelCase	)
         lowerCAmelCase__								=			node
         lowerCAmelCase__								=			node
     def 					A__			(	self  :		Optional[Any]	):



       """simple docstring"""
       if self.is_empty():
         raise IndexError('''dequeue from empty queue'''	)
       assert isinstance(self.front					,	__lowerCamelCase	)
       lowerCAmelCase__								=			self.front
       lowerCAmelCase__								=			self.front.next
       if self.front is None:
         lowerCAmelCase__								=			None
       return node.data



     def 					A__			(	self  :		Tuple	):



       """simple docstring"""
       lowerCAmelCase__								=			lowerCAmelCase__								=			None


if __name__ == "__main__":
      from doctest import testmod

      testmod()

