This function will apply normalizers to your data, in the
    context of the relevant types. Each normalizer is in the format:

    def normalizer(datatype, data):
        # Conditionally modify data
        return (datatype, data)

    Where datatype is a valid ABI type string, like "uint".

    In case of an array, like "bool[2]", normalizer will receive `data`
    as an iterable of typed data, like `[("bool", True), ("bool", False)]`.

    Internals
    ---

    This is accomplished by:

    1. Decorating the data tree with types
    2. Recursively mapping each of the normalizers to the data
    3. Stripping the types back out of the tree

def map_abi_data(normalizers, types, data):
    """
    This function will apply normalizers to your data, in the
    context of the relevant types. Each normalizer is in the format:

    def normalizer(datatype, data):
        # Conditionally modify data
        return (datatype, data)

    Where datatype is a valid ABI type string, like "uint".

    In case of an array, like "bool[2]", normalizer will receive `data`
    as an iterable of typed data, like `[("bool", True), ("bool", False)]`.

    Internals
    ---

    This is accomplished by:

    1. Decorating the data tree with types
    2. Recursively mapping each of the normalizers to the data
    3. Stripping the types back out of the tree
    """
    pipeline = itertools.chain(
        [abi_data_tree(types)],
        map(data_tree_map, normalizers),
        [partial(recursive_map, strip_abi_type)],
    )

    return pipe(data, *pipeline)