





'''simple docstring'''

from typing import TYPE_CHECKING

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


__snake_case:  Union[str, Any]								=			{
    "configuration_convbert": ["CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "ConvBertConfig", "ConvBertOnnxConfig"],
    "tokenization_convbert": ["ConvBertTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  int								=			["ConvBertTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			[
        "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:
    __snake_case:  Union[str, Any]								=			[
        "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

    __snake_case:  Any								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




'''simple docstring'''

from typing import TYPE_CHECKING

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


__snake_case:  Dict								=			{"configuration_encoder_decoder": ["EncoderDecoderConfig"]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["EncoderDecoderModel"]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  Any								=			["TFEncoderDecoderModel"]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    __snake_case:  List[str]								=			["FlaxEncoderDecoderModel"]

if TYPE_CHECKING:
    from .configuration_encoder_decoder import EncoderDecoderConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_encoder_decoder import EncoderDecoderModel

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
    import sys

    __snake_case:  Tuple								=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


