






"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       Union[str, Any]  				=		{
    '''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RobertaConfig''', '''RobertaOnnxConfig'''],
    '''tokenization_roberta''': ['''RobertaTokenizer'''],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       str  				=		['''RobertaTokenizerFast''']

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Tuple  				=		[
						    '''ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''RobertaForCausalLM''',
						    '''RobertaForMaskedLM''',
						    '''RobertaForMultipleChoice''',
						    '''RobertaForQuestionAnswering''',
						    '''RobertaForSequenceClassification''',
						    '''RobertaForTokenClassification''',
						    '''RobertaModel''',
						    '''RobertaPreTrainedModel''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[int]  				=		[
						    '''TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''TFRobertaForCausalLM''',
						    '''TFRobertaForMaskedLM''',
						    '''TFRobertaForMultipleChoice''',
						    '''TFRobertaForQuestionAnswering''',
						    '''TFRobertaForSequenceClassification''',
						    '''TFRobertaForTokenClassification''',
						    '''TFRobertaMainLayer''',
						    '''TFRobertaModel''',
						    '''TFRobertaPreTrainedModel''',
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Any  				=		[
						    '''FlaxRobertaForCausalLM''',
						    '''FlaxRobertaForMaskedLM''',
						    '''FlaxRobertaForMultipleChoice''',
						    '''FlaxRobertaForQuestionAnswering''',
						    '''FlaxRobertaForSequenceClassification''',
						    '''FlaxRobertaForTokenClassification''',
						    '''FlaxRobertaModel''',
						    '''FlaxRobertaPreTrainedModel''',
						]


if TYPE_CHECKING:
						from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
						from .tokenization_roberta import RobertaTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_roberta_fast import RobertaTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roberta import (
												    ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RobertaForCausalLM,
												    RobertaForMaskedLM,
												    RobertaForMultipleChoice,
												    RobertaForQuestionAnswering,
												    RobertaForSequenceClassification,
												    RobertaForTokenClassification,
												    RobertaModel,
												    RobertaPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roberta import (
												    TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRobertaForCausalLM,
												    TFRobertaForMaskedLM,
												    TFRobertaForMultipleChoice,
												    TFRobertaForQuestionAnswering,
												    TFRobertaForSequenceClassification,
												    TFRobertaForTokenClassification,
												    TFRobertaMainLayer,
												    TFRobertaModel,
												    TFRobertaPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roberta import (
												    FlaxRobertaForCausalLM,
												    FlaxRobertaForMaskedLM,
												    FlaxRobertaForMultipleChoice,
												    FlaxRobertaForQuestionAnswering,
												    FlaxRobertaForSequenceClassification,
												    FlaxRobertaForTokenClassification,
												    FlaxRobertaModel,
												    FlaxRobertaPreTrainedModel,
												)

else:
						import sys

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











"""simple docstring"""



from typing import TYPE_CHECKING

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


__A		:       List[str]  				=		{
    '''configuration_mobilenet_v2''': [
        '''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''MobileNetV2Config''',
        '''MobileNetV2OnnxConfig''',
    ],
}

try:
						if not is_vision_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Dict  				=		['''MobileNetV2FeatureExtractor''']
						__A		:       Optional[int]  				=		['''MobileNetV2ImageProcessor''']


try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__A		:       Optional[Any]  				=		[
						    '''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
						    '''MobileNetV2ForImageClassification''',
						    '''MobileNetV2ForSemanticSegmentation''',
						    '''MobileNetV2Model''',
						    '''MobileNetV2PreTrainedModel''',
						    '''load_tf_weights_in_mobilenet_v2''',
						]


if TYPE_CHECKING:
						from .configuration_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    MobileNetVaConfig,
						    MobileNetVaOnnxConfig,
						)

						try:
												if not is_vision_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
												from .image_processing_mobilenet_va import MobileNetVaImageProcessor

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mobilenet_va import (
												    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MobileNetVaForImageClassification,
												    MobileNetVaForSemanticSegmentation,
												    MobileNetVaModel,
												    MobileNetVaPreTrainedModel,
												    load_tf_weights_in_mobilenet_va,
												)


else:
						import sys

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



