





'''simple docstring'''







# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowerCAmelCase_					:    Optional[int]      	=			{
    'configuration_efficientnet': [
        'EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'EfficientNetConfig',
        'EfficientNetOnnxConfig',
    ]
}

try:
					if not is_vision_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Any      	=			['EfficientNetImageProcessor']

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase_					:    Optional[Any]      	=			[
					    'EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'EfficientNetForImageClassification',
					    'EfficientNetModel',
					    'EfficientNetPreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_efficientnet import (
					    EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    EfficientNetConfig,
					    EfficientNetOnnxConfig,
					)

					try:
										if not is_vision_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .image_processing_efficientnet import EfficientNetImageProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_efficientnet import (
										    EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST,
										    EfficientNetForImageClassification,
										    EfficientNetModel,
										    EfficientNetPreTrainedModel,
										)

else:
					import sys

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








"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       List[Any]  				=		{
    '''configuration_mega''': ['''MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegaConfig''', '''MegaOnnxConfig'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       List[str]  				=		[
						    '''MEGA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MegaForCausalLM''',
						    '''MegaForMaskedLM''',
						    '''MegaForMultipleChoice''',
						    '''MegaForQuestionAnswering''',
						    '''MegaForSequenceClassification''',
						    '''MegaForTokenClassification''',
						    '''MegaModel''',
						    '''MegaPreTrainedModel''',
						]

if TYPE_CHECKING:
						from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mega import (
												    MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MegaForCausalLM,
												    MegaForMaskedLM,
												    MegaForMultipleChoice,
												    MegaForQuestionAnswering,
												    MegaForSequenceClassification,
												    MegaForTokenClassification,
												    MegaModel,
												    MegaPreTrainedModel,
												)

else:
						import sys

						__A		:       Any  				=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



