





'''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__)








def   lowerCAmelCase_				( __a			,					__a	)  ->	float:







  """simple docstring"""





  if density <= 0:
    raise ValueError("Impossible fluid density"	)
  if bulk_modulus <= 0:
    raise ValueError("Impossible bulk modulus"	)

  return (bulk_modulus / density) ** 0.5


if __name__ == "__main__":
     import doctest

     doctest.testmod()

