




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :				Any				=				{
    "configuration_mobilevitv2": [
        "MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileViTV2Config",
        "MobileViTV2OnnxConfig",
    ],
}


try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				Any				=				[
      "MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST",
      "MobileViTV2ForImageClassification",
      "MobileViTV2ForSemanticSegmentation",
      "MobileViTV2Model",
      "MobileViTV2PreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_mobilevitva import (
      MOBILEVITV2_PRETRAINED_CONFIG_ARCHIVE_MAP,
      MobileViTVaConfig,
      MobileViTVaOnnxConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_mobilevitva import (
        MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
        MobileViTVaForImageClassification,
        MobileViTVaForSemanticSegmentation,
        MobileViTVaModel,
        MobileViTVaPreTrainedModel,
    )

else:
  import sys

  _lowerCAmelCase  :				Dict				=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)











from typing import TYPE_CHECKING

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


_lowerCAmelCase  :				str						=				{
    "configuration_esm": ["ESM_PRETRAINED_CONFIG_ARCHIVE_MAP", "EsmConfig"],
    "tokenization_esm": ["EsmTokenizer"],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				Union[str, Any]						=				[
      "ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "EsmForMaskedLM",
      "EsmForSequenceClassification",
      "EsmForTokenClassification",
      "EsmModel",
      "EsmPreTrainedModel",
  ]
  _lowerCAmelCase  :				Tuple						=				["EsmForProteinFolding", "EsmFoldPreTrainedModel"]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				str						=				[
      "TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFEsmForMaskedLM",
      "TFEsmForSequenceClassification",
      "TFEsmForTokenClassification",
      "TFEsmModel",
      "TFEsmPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_esm import ESM_PRETRAINED_CONFIG_ARCHIVE_MAP, EsmConfig
  from .tokenization_esm import EsmTokenizer

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_esm import (
        ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        EsmForMaskedLM,
        EsmForSequenceClassification,
        EsmForTokenClassification,
        EsmModel,
        EsmPreTrainedModel,
    )
    from .modeling_esmfold import EsmFoldPreTrainedModel, EsmForProteinFolding

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_esm import (
        TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFEsmForMaskedLM,
        TFEsmForSequenceClassification,
        TFEsmForTokenClassification,
        TFEsmModel,
        TFEsmPreTrainedModel,
    )


else:
  import sys

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





