def _getSmartIndenter(indenterName, qpart, indenter):
    """Get indenter by name.
    Available indenters are none, normal, cstyle, haskell, lilypond, lisp, python, ruby, xml
    Indenter name is not case sensitive
    Raise KeyError if not found
    indentText is indentation, which shall be used. i.e. '\t' for tabs, '    ' for 4 space symbols
    """
    indenterName = indenterName.lower()

    if indenterName in ('haskell', 'lilypond'):  # not supported yet
        logger.warning('Smart indentation for %s not supported yet. But you could be a hero who implemented it' % indenterName)
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'none' == indenterName:
        from qutepart.indenter.base import IndentAlgBase as indenterClass
    elif 'normal' == indenterName:
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'cstyle' == indenterName:
        from qutepart.indenter.cstyle import IndentAlgCStyle as indenterClass
    elif 'python' == indenterName:
        from qutepart.indenter.python import IndentAlgPython as indenterClass
    elif 'ruby' == indenterName:
        from qutepart.indenter.ruby import IndentAlgRuby as indenterClass
    elif 'xml' == indenterName:
        from qutepart.indenter.xmlindent import IndentAlgXml as indenterClass
    elif 'haskell' == indenterName:
        from qutepart.indenter.haskell import IndenterHaskell as indenterClass
    elif 'lilypond' == indenterName:
        from qutepart.indenter.lilypond import IndenterLilypond as indenterClass
    elif 'lisp' == indenterName:
        from qutepart.indenter.lisp import IndentAlgLisp as indenterClass
    elif 'scheme' == indenterName:
        from qutepart.indenter.scheme import IndentAlgScheme as indenterClass
    else:
        raise KeyError("Indenter %s not found" % indenterName)

    return indenterClass(qpart, indenter)
def maxpool(module, grad_input, grad_output):
    pool_to_unpool = {
        'MaxPool1d': torch.nn.functional.max_unpool1d,
        'MaxPool2d': torch.nn.functional.max_unpool2d,
        'MaxPool3d': torch.nn.functional.max_unpool3d
    }
    pool_to_function = {
        'MaxPool1d': torch.nn.functional.max_pool1d,
        'MaxPool2d': torch.nn.functional.max_pool2d,
        'MaxPool3d': torch.nn.functional.max_pool3d
    }
    delta_in = module.x[: int(module.x.shape[0] / 2)] - module.x[int(module.x.shape[0] / 2):]
    dup0 = [2] + [1 for i in delta_in.shape[1:]]
    # we also need to check if the output is a tuple
    y, ref_output = torch.chunk(module.y, 2)
    cross_max = torch.max(y, ref_output)
    diffs = torch.cat([cross_max - ref_output, y - cross_max], 0)

    # all of this just to unpool the outputs
    with torch.no_grad():
        _, indices = pool_to_function[module.__class__.__name__](
            module.x, module.kernel_size, module.stride, module.padding,
            module.dilation, module.ceil_mode, True)
        xmax_pos, rmax_pos = torch.chunk(pool_to_unpool[module.__class__.__name__](
            grad_output[0] * diffs, indices, module.kernel_size, module.stride,
            module.padding, list(module.x.shape)), 2)
    org_input_shape = grad_input[0].shape  # for the maxpool 1d
    grad_input = [None for _ in grad_input]
    grad_input[0] = torch.where(torch.abs(delta_in) < 1e-7, torch.zeros_like(delta_in),
                           (xmax_pos + rmax_pos) / delta_in).repeat(dup0)
    if module.__class__.__name__ == 'MaxPool1d':
        complex_module_gradients.append(grad_input[0])
        # the grad input that is returned doesn't matter, since it will immediately be
        # be overridden by the grad in the complex_module_gradient
        grad_input[0] = torch.ones(org_input_shape)
    return tuple(grad_input)