
"""simple docstring"""





from typing import TYPE_CHECKING

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


_a		       =							{
    'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
    'tokenization_convbert': ['ConvBertTokenizer'],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a		       =							['ConvBertTokenizerFast']

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a		       =							[
        'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'ConvBertForMaskedLM',
        'ConvBertForMultipleChoice',
        'ConvBertForQuestionAnswering',
        'ConvBertForSequenceClassification',
        'ConvBertForTokenClassification',
        'ConvBertLayer',
        'ConvBertModel',
        'ConvBertPreTrainedModel',
        'load_tf_weights_in_convbert',
    ]


try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _a		       =							[
        'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
        'TFConvBertForMaskedLM',
        'TFConvBertForMultipleChoice',
        'TFConvBertForQuestionAnswering',
        'TFConvBertForSequenceClassification',
        'TFConvBertForTokenClassification',
        'TFConvBertLayer',
        'TFConvBertModel',
        'TFConvBertPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
    from .tokenization_convbert import ConvBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_convbert_fast import ConvBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_convbert import (
            CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            ConvBertForMaskedLM,
            ConvBertForMultipleChoice,
            ConvBertForQuestionAnswering,
            ConvBertForSequenceClassification,
            ConvBertForTokenClassification,
            ConvBertLayer,
            ConvBertModel,
            ConvBertPreTrainedModel,
            load_tf_weights_in_convbert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_convbert import (
            TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFConvBertForMaskedLM,
            TFConvBertForMultipleChoice,
            TFConvBertForQuestionAnswering,
            TFConvBertForSequenceClassification,
            TFConvBertForTokenClassification,
            TFConvBertLayer,
            TFConvBertModel,
            TFConvBertPreTrainedModel,
        )


else:
    import sys

    _a		       =							_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




"""simple docstring"""





from typing import TYPE_CHECKING

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


_a														=		{
    """configuration_autoformer""": [
        """AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """AutoformerConfig""",
    ],
}

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				_a														=		[
				    """AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
				    """AutoformerForPrediction""",
				    """AutoformerModel""",
				    """AutoformerPreTrainedModel""",
				]


if TYPE_CHECKING:
				from .configuration_autoformer import (
				    AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
				    AutoformerConfig,
				)

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_autoformer import (
								    AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    AutoformerForPrediction,
								    AutoformerModel,
								    AutoformerPreTrainedModel,
								)

else:
				import sys

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






