
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,
)


UpperCamelCase_						:     List[Any]	       =							{
    """configuration_albert""": ["""ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """AlbertConfig""", """AlbertOnnxConfig"""],
}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Union[str, Any]	       =							["""AlbertTokenizer"""]

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

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

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






from typing import TYPE_CHECKING

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


UpperCamelCase_						:     int	       =							{
    """configuration_transfo_xl""": ["""TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TransfoXLConfig"""],
    """tokenization_transfo_xl""": ["""TransfoXLCorpus""", """TransfoXLTokenizer"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Any	       =							[
							    """TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """AdaptiveEmbedding""",
							    """TransfoXLForSequenceClassification""",
							    """TransfoXLLMHeadModel""",
							    """TransfoXLModel""",
							    """TransfoXLPreTrainedModel""",
							    """load_tf_weights_in_transfo_xl""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCamelCase_						:     Tuple	       =							[
							    """TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFAdaptiveEmbedding""",
							    """TFTransfoXLForSequenceClassification""",
							    """TFTransfoXLLMHeadModel""",
							    """TFTransfoXLMainLayer""",
							    """TFTransfoXLModel""",
							    """TFTransfoXLPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
							from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_transfo_xl import (
														    TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    AdaptiveEmbedding,
														    TransfoXLForSequenceClassification,
														    TransfoXLLMHeadModel,
														    TransfoXLModel,
														    TransfoXLPreTrainedModel,
														    load_tf_weights_in_transfo_xl,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_transfo_xl import (
														    TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFAdaptiveEmbedding,
														    TFTransfoXLForSequenceClassification,
														    TFTransfoXLLMHeadModel,
														    TFTransfoXLMainLayer,
														    TFTransfoXLModel,
														    TFTransfoXLPreTrainedModel,
														)

else:
							import sys

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




