





from typing import TYPE_CHECKING

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


a__: str							   =						{
    'configuration_lxmert': ['LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LxmertConfig'],
    'tokenization_lxmert': ['LxmertTokenizer'],
}

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

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Dict							   =						[
           'LxmertEncoder',
           'LxmertForPreTraining',
           'LxmertForQuestionAnswering',
           'LxmertModel',
           'LxmertPreTrainedModel',
           'LxmertVisualFeatureEncoder',
           'LxmertXLayer',
       ]

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: 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

       a__: Tuple							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)












from typing import TYPE_CHECKING

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


a__: str							   =						{
    'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       a__: Union[str, Any]							   =						[
           'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
           'MegaForCausalLM',
           'MegaForMaskedLM',
           'MegaForMultipleChoice',
           'MegaForQuestionAnswering',
           'MegaForSequenceClassification',
           'MegaForTokenClassification',
           'MegaModel',
           'MegaPreTrainedModel',
       ]

if TYPE_CHECKING:
       from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mega import (
                  MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
                  MegaForCausalLM,
                  MegaForMaskedLM,
                  MegaForMultipleChoice,
                  MegaForQuestionAnswering,
                  MegaForSequenceClassification,
                  MegaForTokenClassification,
                  MegaModel,
                  MegaPreTrainedModel,
              )

else:
       import sys

       a__: Union[str, Any]							   =						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





