


'''simple docstring'''



from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatures, SingleSentenceClassificationProcessor
from .xnli import xnli_output_modes, xnli_processors, xnli_tasks_num_labels



'''simple docstring'''



from typing import TYPE_CHECKING

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


__UpperCAmelCase      :List[Any]			    =       {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       ["LxmertTokenizerFast"]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[Any]			    =       [
          "LxmertEncoder",
          "LxmertForPreTraining",
          "LxmertForQuestionAnswering",
          "LxmertModel",
          "LxmertPreTrainedModel",
          "LxmertVisualFeatureEncoder",
          "LxmertXLayer",
      ]

try:
      if not is_tf_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __UpperCAmelCase      :List[str]			    =       [
          "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
          "TFLxmertForPreTraining",
          "TFLxmertMainLayer",
          "TFLxmertModel",
          "TFLxmertPreTrainedModel",
          "TFLxmertVisualFeatureEncoder",
      ]


if TYPE_CHECKING:
      from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
      from .tokenization_lxmert import LxmertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_lxmert_fast import LxmertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_lxmert import (
                LxmertEncoder,
                LxmertForPreTraining,
                LxmertForQuestionAnswering,
                LxmertModel,
                LxmertPreTrainedModel,
                LxmertVisualFeatureEncoder,
                LxmertXLayer,
            )

      try:
            if not is_tf_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_tf_lxmert import (
                TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                TFLxmertForPreTraining,
                TFLxmertMainLayer,
                TFLxmertModel,
                TFLxmertPreTrainedModel,
                TFLxmertVisualFeatureEncoder,
            )

else:
      import sys

      __UpperCAmelCase      :int			    =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)