



def      __lowercase       (			a__							,       a__       )    ->			float:

							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()











from typing import TYPE_CHECKING

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


lowerCAmelCase__		:					Optional[int]							={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}

try:
			if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[int]							=['''FNetTokenizer''']

try:
			if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Optional[Any]							=['''FNetTokenizerFast''']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowerCAmelCase__		:					Tuple							=[
			    '''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''FNetForMaskedLM''',
			    '''FNetForMultipleChoice''',
			    '''FNetForNextSentencePrediction''',
			    '''FNetForPreTraining''',
			    '''FNetForQuestionAnswering''',
			    '''FNetForSequenceClassification''',
			    '''FNetForTokenClassification''',
			    '''FNetLayer''',
			    '''FNetModel''',
			    '''FNetPreTrainedModel''',
			]


if TYPE_CHECKING:
			from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig

			try:
						if not is_sentencepiece_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet import FNetTokenizer

			try:
						if not is_tokenizers_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .tokenization_fnet_fast import FNetTokenizerFast

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_fnet import (
						    FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
						    FNetForMaskedLM,
						    FNetForMultipleChoice,
						    FNetForNextSentencePrediction,
						    FNetForPreTraining,
						    FNetForQuestionAnswering,
						    FNetForSequenceClassification,
						    FNetForTokenClassification,
						    FNetLayer,
						    FNetModel,
						    FNetPreTrainedModel,
						)


else:
			import sys

			lowerCAmelCase__		:					Any							=_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






