
from ...utils import is_torch_available, is_transformers_available


if is_transformers_available() and is_torch_available():
					from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline



def 						lowerCamelCase__      ( __A    :int	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float(moles / volume		) * nfactor		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((moles * 0.0_821 * temperature) / (volume)		)		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((moles * 0.0_821 * temperature) / (pressure)		)		)







def 						lowerCamelCase__      ( __A    :float	,__A    :float	,__A    :float		):


       """simple docstring"""




       return round(float((pressure * volume) / (0.0_821 * moles)		)		)


if __name__ == "__main__":
    import doctest

    doctest.testmod()

