


from typing import TYPE_CHECKING

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


a : Optional[int]		=				{'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}

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

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

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	a : Optional[int]		=				[
	    '''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 : List[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

	a : Dict		=				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			return price * (1 + tax_rate)


if __name__ == "__main__":
	print(f'''{price_plus_tax(100, 0.25) = }''')
	print(f'''{price_plus_tax(125.50, 0.05) = }''')


