




from typing import TYPE_CHECKING

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


_lowerCAmelCase  :				List[Any]						=				{"configuration_hubert": ["HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "HubertConfig"]}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				Dict						=				[
      "HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "HubertForCTC",
      "HubertForSequenceClassification",
      "HubertModel",
      "HubertPreTrainedModel",
  ]


try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  _lowerCAmelCase  :				List[str]						=				[
      "TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TFHubertForCTC",
      "TFHubertModel",
      "TFHubertPreTrainedModel",
  ]

if TYPE_CHECKING:
  from .configuration_hubert import HUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, HubertConfig

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_hubert import (
        HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        HubertForCTC,
        HubertForSequenceClassification,
        HubertModel,
        HubertPreTrainedModel,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_hubert import (
        TF_HUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFHubertForCTC,
        TFHubertModel,
        TFHubertPreTrainedModel,
    )


else:
  import sys

  _lowerCAmelCase  :				str						=				_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)





