




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowerCamelCase     :		str								=       {
    """configuration_xlm_roberta_xl""": [
        """XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaXLConfig""",
        """XLMRobertaXLOnnxConfig""",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		Dict								=       [
      """XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """XLMRobertaXLForCausalLM""",
      """XLMRobertaXLForMaskedLM""",
      """XLMRobertaXLForMultipleChoice""",
      """XLMRobertaXLForQuestionAnswering""",
      """XLMRobertaXLForSequenceClassification""",
      """XLMRobertaXLForTokenClassification""",
      """XLMRobertaXLModel""",
      """XLMRobertaXLPreTrainedModel""",
  ]

if TYPE_CHECKING:
  from .configuration_xlm_roberta_xl import (
      XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
      XLMRobertaXLConfig,
      XLMRobertaXLOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_xlm_roberta_xl import (
        XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
        XLMRobertaXLForCausalLM,
        XLMRobertaXLForMaskedLM,
        XLMRobertaXLForMultipleChoice,
        XLMRobertaXLForQuestionAnswering,
        XLMRobertaXLForSequenceClassification,
        XLMRobertaXLForTokenClassification,
        XLMRobertaXLModel,
        XLMRobertaXLPreTrainedModel,
    )

else:
  import sys

  _lowerCamelCase     :		Any								=       _LazyModule(__name__, globals()["""__file__"""], _import_structure)











from typing import TYPE_CHECKING

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


_lowerCamelCase     :		Any								=       {
    """configuration_nezha""": ["""NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NezhaConfig"""],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCamelCase     :		str								=       [
      """NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST""",
      """NezhaForNextSentencePrediction""",
      """NezhaForMaskedLM""",
      """NezhaForPreTraining""",
      """NezhaForMultipleChoice""",
      """NezhaForQuestionAnswering""",
      """NezhaForSequenceClassification""",
      """NezhaForTokenClassification""",
      """NezhaModel""",
      """NezhaPreTrainedModel""",
  ]


if TYPE_CHECKING:
  from .configuration_nezha import NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP, NezhaConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_nezha import (
        NEZHA_PRETRAINED_MODEL_ARCHIVE_LIST,
        NezhaForMaskedLM,
        NezhaForMultipleChoice,
        NezhaForNextSentencePrediction,
        NezhaForPreTraining,
        NezhaForQuestionAnswering,
        NezhaForSequenceClassification,
        NezhaForTokenClassification,
        NezhaModel,
        NezhaPreTrainedModel,
    )


else:
  import sys

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


