
'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase    :							Union[str, Any]      =					{"configuration_xlnet": ["XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLNetConfig"]}

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

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

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

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



'''simple docstring'''







from typing import TYPE_CHECKING

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


lowercase    :							Tuple      =					{
    "configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
    "tokenization_xlm": ["XLMTokenizer"],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							int      =					[
					    "XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "XLMForMultipleChoice",
					    "XLMForQuestionAnswering",
					    "XLMForQuestionAnsweringSimple",
					    "XLMForSequenceClassification",
					    "XLMForTokenClassification",
					    "XLMModel",
					    "XLMPreTrainedModel",
					    "XLMWithLMHeadModel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase    :							Tuple      =					[
					    "TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFXLMForMultipleChoice",
					    "TFXLMForQuestionAnsweringSimple",
					    "TFXLMForSequenceClassification",
					    "TFXLMForTokenClassification",
					    "TFXLMMainLayer",
					    "TFXLMModel",
					    "TFXLMPreTrainedModel",
					    "TFXLMWithLMHeadModel",
					]


if TYPE_CHECKING:
					from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
					from .tokenization_xlm import XLMTokenizer

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_xlm import (
										    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    XLMForMultipleChoice,
										    XLMForQuestionAnswering,
										    XLMForQuestionAnsweringSimple,
										    XLMForSequenceClassification,
										    XLMForTokenClassification,
										    XLMModel,
										    XLMPreTrainedModel,
										    XLMWithLMHeadModel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_xlm import (
										    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFXLMForMultipleChoice,
										    TFXLMForQuestionAnsweringSimple,
										    TFXLMForSequenceClassification,
										    TFXLMForTokenClassification,
										    TFXLMMainLayer,
										    TFXLMModel,
										    TFXLMPreTrainedModel,
										    TFXLMWithLMHeadModel,
										)

else:
					import sys

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

