





from typing import TYPE_CHECKING

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


A       :   str					= {
    '''configuration_resnet''': ['''RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ResNetConfig''', '''ResNetOnnxConfig''']
}

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

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Tuple					= [
					    '''TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
					    '''TFResNetForImageClassification''',
					    '''TFResNetModel''',
					    '''TFResNetPreTrainedModel''',
					]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Union[str, Any]					= [
					    '''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

					A       :   List[str]					= _LazyModule(__name__, globals()['''__file__'''], _import_structure)










from typing import TYPE_CHECKING

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


A       :   Dict					= {'''processing_layoutxlm''': ['''LayoutXLMProcessor''']}

try:
					if not is_sentencepiece_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizer''']

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					A       :   Dict					= ['''LayoutXLMTokenizerFast''']

if TYPE_CHECKING:
					from .processing_layoutxlm import LayoutXLMProcessor

					try:
										if not is_sentencepiece_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm import LayoutXLMTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_layoutxlm_fast import LayoutXLMTokenizerFast

else:
					import sys

					A       :   int					= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



