

"""simple docstring"""
from typing import TYPE_CHECKING

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


_A							=  {
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  ["""AlbertTokenizer"""]

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  ["""AlbertTokenizerFast"""]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """AlbertForMaskedLM""",
        """AlbertForMultipleChoice""",
        """AlbertForPreTraining""",
        """AlbertForQuestionAnswering""",
        """AlbertForSequenceClassification""",
        """AlbertForTokenClassification""",
        """AlbertModel""",
        """AlbertPreTrainedModel""",
        """load_tf_weights_in_albert""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
        """TFAlbertForMaskedLM""",
        """TFAlbertForMultipleChoice""",
        """TFAlbertForPreTraining""",
        """TFAlbertForQuestionAnswering""",
        """TFAlbertForSequenceClassification""",
        """TFAlbertForTokenClassification""",
        """TFAlbertMainLayer""",
        """TFAlbertModel""",
        """TFAlbertPreTrainedModel""",
    ]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  [
        """FlaxAlbertForMaskedLM""",
        """FlaxAlbertForMultipleChoice""",
        """FlaxAlbertForPreTraining""",
        """FlaxAlbertForQuestionAnswering""",
        """FlaxAlbertForSequenceClassification""",
        """FlaxAlbertForTokenClassification""",
        """FlaxAlbertModel""",
        """FlaxAlbertPreTrainedModel""",
    ]

if TYPE_CHECKING:
    from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig

    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_albert import AlbertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_albert_fast import AlbertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_albert import (
            ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            AlbertForMaskedLM,
            AlbertForMultipleChoice,
            AlbertForPreTraining,
            AlbertForQuestionAnswering,
            AlbertForSequenceClassification,
            AlbertForTokenClassification,
            AlbertModel,
            AlbertPreTrainedModel,
            load_tf_weights_in_albert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_albert import (
            TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFAlbertForMaskedLM,
            TFAlbertForMultipleChoice,
            TFAlbertForPreTraining,
            TFAlbertForQuestionAnswering,
            TFAlbertForSequenceClassification,
            TFAlbertForTokenClassification,
            TFAlbertMainLayer,
            TFAlbertModel,
            TFAlbertPreTrainedModel,
        )

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_albert import (
            FlaxAlbertForMaskedLM,
            FlaxAlbertForMultipleChoice,
            FlaxAlbertForPreTraining,
            FlaxAlbertForQuestionAnswering,
            FlaxAlbertForSequenceClassification,
            FlaxAlbertForTokenClassification,
            FlaxAlbertModel,
            FlaxAlbertPreTrainedModel,
        )
else:
    import sys

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




"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available


_A							=  {}

try:
    if not is_sentencepiece_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _A							=  ["""BartphoTokenizer"""]

if TYPE_CHECKING:
    try:
        if not is_sentencepiece_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_bartpho import BartphoTokenizer

else:
    import sys

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

