






from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		int   				=    {"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    ["XLNetTokenizer"]

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		List[Any]   				=    ["XLNetTokenizerFast"]

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Union[str, Any]   				=    [
			    "XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "XLNetForMultipleChoice",
			    "XLNetForQuestionAnswering",
			    "XLNetForQuestionAnsweringSimple",
			    "XLNetForSequenceClassification",
			    "XLNetForTokenClassification",
			    "XLNetLMHeadModel",
			    "XLNetModel",
			    "XLNetPreTrainedModel",
			    "load_tf_weights_in_xlnet",
			]

try:
			if not is_tf_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "TFXLNetForMultipleChoice",
			    "TFXLNetForQuestionAnsweringSimple",
			    "TFXLNetForSequenceClassification",
			    "TFXLNetForTokenClassification",
			    "TFXLNetLMHeadModel",
			    "TFXLNetMainLayer",
			    "TFXLNetModel",
			    "TFXLNetPreTrainedModel",
			]


if TYPE_CHECKING:
			from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet import XLNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_xlnet_fast import XLNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_xlnet import (
						    XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    XLNetForMultipleChoice,
						    XLNetForQuestionAnswering,
						    XLNetForQuestionAnsweringSimple,
						    XLNetForSequenceClassification,
						    XLNetForTokenClassification,
						    XLNetLMHeadModel,
						    XLNetModel,
						    XLNetPreTrainedModel,
						    load_tf_weights_in_xlnet,
						)

			try:
						if not is_tf_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tf_xlnet import (
						    TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TFXLNetForMultipleChoice,
						    TFXLNetForQuestionAnsweringSimple,
						    TFXLNetForSequenceClassification,
						    TFXLNetForTokenClassification,
						    TFXLNetLMHeadModel,
						    TFXLNetMainLayer,
						    TFXLNetModel,
						    TFXLNetPreTrainedModel,
						)

else:
			import sys

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













from typing import TYPE_CHECKING

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


_UpperCAmelCase			:		List[Any]   				=    {
    "configuration_mobilenet_v2": [
        "MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileNetV2Config",
        "MobileNetV2OnnxConfig",
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		str   				=    ["MobileNetV2FeatureExtractor"]
			_UpperCAmelCase			:		Union[str, Any]   				=    ["MobileNetV2ImageProcessor"]


try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_UpperCAmelCase			:		Any   				=    [
			    "MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST",
			    "MobileNetV2ForImageClassification",
			    "MobileNetV2ForSemanticSegmentation",
			    "MobileNetV2Model",
			    "MobileNetV2PreTrainedModel",
			    "load_tf_weights_in_mobilenet_v2",
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaForSemanticSegmentation,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			_UpperCAmelCase			:		Dict   				=    _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





