





from typing import TYPE_CHECKING

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


SCREAMING_SNAKE_CASE_		     = {"""configuration_xlnet""": ["""XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XLNetConfig"""]}

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

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

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      SCREAMING_SNAKE_CASE_		     = [
          """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:
      SCREAMING_SNAKE_CASE_		     = [
          """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

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









def      __lowercase     (   _SCREAMING_SNAKE_CASE				,		_SCREAMING_SNAKE_CASE			)				->     float:


      '''simple docstring'''


      return price * (1 + tax_rate)


if __name__ == "__main__":
      print(F'''{price_plus_tax(1_0_0, 0.25) = }''')
      print(F'''{price_plus_tax(125.50, 0.05) = }''')


