



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    _snake_case (       __snake_case					,  __snake_case    )     ->     int:






      '''simple docstring'''







      return int((input_a, input_a).count(0    ) != 0    )







def    _snake_case (       )     ->     None:






      '''simple docstring'''







      assert nand_gate(0					,  0    ) == 1
      assert nand_gate(0					,  1    ) == 1
      assert nand_gate(1					,  0    ) == 1
      assert nand_gate(1					,  1    ) == 0


if __name__ == "__main__":
   print(nand_gate(0, 0))
   print(nand_gate(0, 1))
   print(nand_gate(1, 0))
   print(nand_gate(1, 1))
