

from typing import TYPE_CHECKING

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


UpperCamelCase__					: Optional[int]								=      {
    "configuration_flaubert": ["FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "FlaubertConfig", "FlaubertOnnxConfig"],
    "tokenization_flaubert": ["FlaubertTokenizer"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       UpperCamelCase__					: List[Any]								=      [
           "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:
       UpperCamelCase__					: Union[str, Any]								=      [
           "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

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





def    _UpperCAmelCase    (					_SCREAMING_SNAKE_CASE   :						float	,				_SCREAMING_SNAKE_CASE   :						float    ):





    """simple docstring"""


    return a + b


if __name__ == "__main__":
       UpperCamelCase__					: Union[str, Any]								=      5
       UpperCamelCase__					: List[Any]								=      6
       print(F'The sum of {a} + {b} is {add(a, b)}')


