




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


a_      						=   {
    "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertOnnxConfig"],
    "tokenization_flaubert": ["FlaubertTokenizer"],
}

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_      						=   [
     "FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "FlaubertForMultipleChoice",
     "FlaubertForQuestionAnswering",
     "FlaubertForQuestionAnsweringSimple",
     "FlaubertForSequenceClassification",
     "FlaubertForTokenClassification",
     "FlaubertModel",
     "FlaubertWithLMHeadModel",
     "FlaubertPreTrainedModel",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a_      						=   [
     "TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
     "TFFlaubertForMultipleChoice",
     "TFFlaubertForQuestionAnsweringSimple",
     "TFFlaubertForSequenceClassification",
     "TFFlaubertForTokenClassification",
     "TFFlaubertModel",
     "TFFlaubertPreTrainedModel",
     "TFFlaubertWithLMHeadModel",
 ]


if TYPE_CHECKING:
 from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
 from .tokenization_flaubert import FlaubertTokenizer

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flaubert import (
      FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      FlaubertForMultipleChoice,
      FlaubertForQuestionAnswering,
      FlaubertForQuestionAnsweringSimple,
      FlaubertForSequenceClassification,
      FlaubertForTokenClassification,
      FlaubertModel,
      FlaubertPreTrainedModel,
      FlaubertWithLMHeadModel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_flaubert import (
      TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFlaubertForMultipleChoice,
      TFFlaubertForQuestionAnsweringSimple,
      TFFlaubertForSequenceClassification,
      TFFlaubertForTokenClassification,
      TFFlaubertModel,
      TFFlaubertPreTrainedModel,
      TFFlaubertWithLMHeadModel,
  )

else:
 import sys

 a_      						=   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)












def  lowerCAmelCase  (	UpperCAmelCase       )  ->str:






					"""simple docstring"""






					return " ".join(sorted(set(sentence.split()       )       )       )


if __name__ == "__main__":
				import doctest

				doctest.testmod()



