





"""simple docstring"""






from typing import TYPE_CHECKING

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


A			      =	{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A			      =	['''XLNetTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A			      =	['''XLNetTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		A			      =	[
		    '''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:
		A			      =	[
		    '''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

		A			      =	_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)





'''simple docstring'''


def 						lowerCamelCase__   (				a							):
						if number < 0:
												raise ValueError('number must not be negative'							)
						return number & (number - 1) == 0


if __name__ == "__main__":
	import doctest

	doctest.testmod()

