
from typing import TYPE_CHECKING

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


_A  =	{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "RobertaPreLayerNormForCausalLM",
			    "RobertaPreLayerNormForMaskedLM",
			    "RobertaPreLayerNormForMultipleChoice",
			    "RobertaPreLayerNormForQuestionAnswering",
			    "RobertaPreLayerNormForSequenceClassification",
			    "RobertaPreLayerNormForTokenClassification",
			    "RobertaPreLayerNormModel",
			    "RobertaPreLayerNormPreTrainedModel",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFRobertaPreLayerNormForCausalLM",
			    "TFRobertaPreLayerNormForMaskedLM",
			    "TFRobertaPreLayerNormForMultipleChoice",
			    "TFRobertaPreLayerNormForQuestionAnswering",
			    "TFRobertaPreLayerNormForSequenceClassification",
			    "TFRobertaPreLayerNormForTokenClassification",
			    "TFRobertaPreLayerNormMainLayer",
			    "TFRobertaPreLayerNormModel",
			    "TFRobertaPreLayerNormPreTrainedModel",
			]

try:
			if not is_flax_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "FlaxRobertaPreLayerNormForCausalLM",
			    "FlaxRobertaPreLayerNormForMaskedLM",
			    "FlaxRobertaPreLayerNormForMultipleChoice",
			    "FlaxRobertaPreLayerNormForQuestionAnswering",
			    "FlaxRobertaPreLayerNormForSequenceClassification",
			    "FlaxRobertaPreLayerNormForTokenClassification",
			    "FlaxRobertaPreLayerNormModel",
			    "FlaxRobertaPreLayerNormPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_roberta_prelayernorm import (
			    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    RobertaPreLayerNormConfig,
			    RobertaPreLayerNormOnnxConfig,
			)

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_roberta_prelayernorm import (
						    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    RobertaPreLayerNormForCausalLM,
						    RobertaPreLayerNormForMaskedLM,
						    RobertaPreLayerNormForMultipleChoice,
						    RobertaPreLayerNormForQuestionAnswering,
						    RobertaPreLayerNormForSequenceClassification,
						    RobertaPreLayerNormForTokenClassification,
						    RobertaPreLayerNormModel,
						    RobertaPreLayerNormPreTrainedModel,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_roberta_prelayernorm import (
						    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFRobertaPreLayerNormForCausalLM,
						    TFRobertaPreLayerNormForMaskedLM,
						    TFRobertaPreLayerNormForMultipleChoice,
						    TFRobertaPreLayerNormForQuestionAnswering,
						    TFRobertaPreLayerNormForSequenceClassification,
						    TFRobertaPreLayerNormForTokenClassification,
						    TFRobertaPreLayerNormMainLayer,
						    TFRobertaPreLayerNormModel,
						    TFRobertaPreLayerNormPreTrainedModel,
						)

			try:
						if not is_flax_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_flax_roberta_prelayernorm import (
						    FlaxRobertaPreLayerNormForCausalLM,
						    FlaxRobertaPreLayerNormForMaskedLM,
						    FlaxRobertaPreLayerNormForMultipleChoice,
						    FlaxRobertaPreLayerNormForQuestionAnswering,
						    FlaxRobertaPreLayerNormForSequenceClassification,
						    FlaxRobertaPreLayerNormForTokenClassification,
						    FlaxRobertaPreLayerNormModel,
						    FlaxRobertaPreLayerNormPreTrainedModel,
						)

else:
			import sys

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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A  =	{"configuration_sew": ["SEW_PRETRAINED_CONFIG_ARCHIVE_MAP", "SEWConfig"]}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_A  =	[
			    "SEW_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "SEWForCTC",
			    "SEWForSequenceClassification",
			    "SEWModel",
			    "SEWPreTrainedModel",
			]

if TYPE_CHECKING:
			from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_sew import (
						    SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
						    SEWForCTC,
						    SEWForSequenceClassification,
						    SEWModel,
						    SEWPreTrainedModel,
						)


else:
			import sys

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



