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# coding: utf-8
"""import function"""
# pylint: disable=no-member

from .import_onnx import GraphProto

def import_model(model_file):
    """Imports the ONNX model file, passed as a parameter, into MXNet symbol and parameters.
    Operator support and coverage - https://cwiki.apache.org/confluence/display/MXNET/ONNX

    Parameters
    ----------
    model_file : str
        ONNX model file name

    Returns
    -------
    sym : :class:`~mxnet.symbol.Symbol`
        MXNet symbol object

    arg_params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
        Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format

    aux_params : dict of ``str`` to :class:`~mxnet.ndarray.NDArray`
        Dict of converted parameters stored in ``mxnet.ndarray.NDArray`` format
    """
    graph = GraphProto()

    try:
        import onnx
    except ImportError:
        raise ImportError("Onnx and protobuf need to be installed. "
                          + "Instructions to install - https://github.com/onnx/onnx")
    # loads model file and returns ONNX protobuf object
    model_proto = onnx.load(model_file)
    sym, arg_params, aux_params = graph.from_onnx(model_proto.graph)
    return sym, arg_params, aux_params

def get_model_metadata(model_file):
    """
    Returns the name and shape information of input and output tensors of the given ONNX model file.

    Parameters
    ----------
    model_file : str
        ONNX model file name

    Returns
    -------
    model_metadata : dict
        A dictionary object mapping various metadata to its corresponding value.
        The dictionary will have the following template.
        {
            'input_tensor_data' : <list of tuples representing the shape of the input paramters>,
            'output_tensor_data' : <list of tuples representing the shape of the output
                                    of the model>
        }
    """
    graph = GraphProto()
    try:
        import onnx
    except ImportError:
        raise ImportError("Onnx and protobuf need to be installed. "
                          + "Instructions to install - https://github.com/onnx/onnx")
    model_proto = onnx.load(model_file)
    metadata = graph.get_graph_metadata(model_proto.graph)
    return metadata
