


from typing import TYPE_CHECKING

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


_lowercase							:   List[Any]						=						{
    "configuration_resnet": ["RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "ResNetConfig", "ResNetOnnxConfig"]
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   str						=						[
				    "RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "ResNetForImageClassification",
				    "ResNetModel",
				    "ResNetPreTrainedModel",
				    "ResNetBackbone",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Dict						=						[
				    "TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFResNetForImageClassification",
				    "TFResNetModel",
				    "TFResNetPreTrainedModel",
				]

try:
				if not is_flax_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_lowercase							:   Dict						=						[
				    "FlaxResNetForImageClassification",
				    "FlaxResNetModel",
				    "FlaxResNetPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_resnet import (
								    RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    ResNetBackbone,
								    ResNetForImageClassification,
								    ResNetModel,
								    ResNetPreTrainedModel,
								)

				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_resnet import (
								    TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFResNetForImageClassification,
								    TFResNetModel,
								    TFResNetPreTrainedModel,
								)

				try:
								if not is_flax_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel


else:
				import sys

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







from ..utils import DummyObject, requires_backends






class    _UpperCamelCase     (       metaclass=__snake_case					):

				"""simple docstring"""







				lowerCAmelCase					=			['note_seq']

				def __init__(    self					, *a__					, **a__ )						->					Optional[int]:
					requires_backends(self					, ["""note_seq"""] )

				@classmethod
				def   _UpperCAmelCase       (    cls					, *a__					, **a__ )						->					Dict:
					requires_backends(cls					, ["""note_seq"""] )

				@classmethod
				def   _UpperCAmelCase       (    cls					, *a__					, **a__ )						->					Optional[int]:
					requires_backends(cls					, ["""note_seq"""] )



