




from typing import TYPE_CHECKING

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


_A							    =	{
    """configuration_nystromformer""": ["""NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NystromformerConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NystromformerForMaskedLM""",
							    """NystromformerForMultipleChoice""",
							    """NystromformerForQuestionAnswering""",
							    """NystromformerForSequenceClassification""",
							    """NystromformerForTokenClassification""",
							    """NystromformerLayer""",
							    """NystromformerModel""",
							    """NystromformerPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nystromformer import (
														    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NystromformerForMaskedLM,
														    NystromformerForMultipleChoice,
														    NystromformerForQuestionAnswering,
														    NystromformerForSequenceClassification,
														    NystromformerForTokenClassification,
														    NystromformerLayer,
														    NystromformerModel,
														    NystromformerPreTrainedModel,
														)


else:
							import sys

							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)












from typing import TYPE_CHECKING

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


_A							    =	{"""configuration_regnet""": ["""REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RegNetConfig"""]}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """RegNetForImageClassification""",
							    """RegNetModel""",
							    """RegNetPreTrainedModel""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFRegNetForImageClassification""",
							    """TFRegNetModel""",
							    """TFRegNetPreTrainedModel""",
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							_A							    =	[
							    """FlaxRegNetForImageClassification""",
							    """FlaxRegNetModel""",
							    """FlaxRegNetPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_regnet import REGNET_PRETRAINED_CONFIG_ARCHIVE_MAP, RegNetConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_regnet import (
														    REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    RegNetForImageClassification,
														    RegNetModel,
														    RegNetPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_regnet import (
														    TF_REGNET_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFRegNetForImageClassification,
														    TFRegNetModel,
														    TFRegNetPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_regnet import (
														    FlaxRegNetForImageClassification,
														    FlaxRegNetModel,
														    FlaxRegNetPreTrainedModel,
														)


else:
							import sys

							_A							    =	_LazyModule(__name__, globals()["""__file__"""], _import_structure)






