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class MXNetOperatorBenchmarkBase(ABC):
    """Abstract Base class for all MXNet operator benchmarks.
    """

    def __init__(self, ctx=mx.cpu(), warmup=10, runs=10, default_parameters={}, custom_parameters=None):
        self.ctx = ctx
        self.runs = runs
        self.warmup = warmup
        self.results = {}
        self.inputs = prepare_input_parameters(caller=self.__class__.__name__,
                                               default_parameters=default_parameters,
                                               custom_parameters=custom_parameters)

    @abstractmethod
    def run_benchmark(self):
        pass

    def print_benchmark_results(self):
        if not len(self.results):
            print("No benchmark results found. Run the benchmark before printing results!")
            return

        for key, val in self.results.items():
            print("{} - {:.6f} seconds".format(key, val))

    def get_benchmark_results(self):
        return self.results

class Add(MXNetOperatorBenchmarkBase):
    """Helps to Benchmark Tensor Add operation.

    By default benchmark both forward and backward element_wise tensor addition
    of 1024*1024 tensor of precision - 'float32'.

    """

    def __init__(self, ctx=mx.cpu(), warmup=10, runs=50, inputs=None):
        # Set the default Inputs
        default_parameters = {"lhs": (1024, 1024),
                              "rhs": (1024, 1024),
                              "initializer": nd.normal,
                              "run_backward": True,
                              "dtype": "float32"}

        super().__init__(ctx=ctx, warmup=warmup, runs=runs, default_parameters=default_parameters,
                         custom_parameters=inputs)

        self.lhs = get_mx_ndarray(ctx=self.ctx, in_tensor=self.inputs["lhs"],
                                  dtype=self.inputs["dtype"],
                                  initializer=self.inputs["initializer"],
                                  attach_grad=self.inputs["run_backward"])
        self.rhs = get_mx_ndarray(ctx=self.ctx, in_tensor=self.inputs["rhs"],
                                  dtype=self.inputs["dtype"],
                                  initializer=self.inputs["initializer"],
                                  attach_grad=self.inputs["run_backward"])

    def run_benchmark(self):
        # Warm up, ignore execution time value
        _, _ = nd_forward_backward_and_time(F=nd.add, runs=self.warmup, lhs=self.lhs, rhs=self.rhs)
        # Run Benchmarks
        exe_time, _ = nd_forward_backward_and_time(F=nd.add, runs=self.runs, lhs=self.lhs, rhs=self.rhs)

        self.results["MX_Add_Forward_Backward_Time"] = exe_time / self.runs