import chainer
from chainer import backend
from chainer import function_node
from chainer.utils import type_check


class CReLU(function_node.FunctionNode):

    """Concatenated Rectified Linear Unit."""

    def __init__(self, axis=1):
        if not isinstance(axis, int):
            raise TypeError('axis must be an integer value')
        self.axis = axis

    def check_type_forward(self, in_types):
        type_check._argname(in_types, ('x',))
        type_check.expect(
            in_types[0].dtype.kind == 'f',
            in_types[0].ndim > self.axis,
            in_types[0].ndim >= -self.axis
        )

    def get_output_shape(self, input_shape):
        output_shape = list(input_shape)
        output_shape[self.axis] *= 2
        return tuple(output_shape)

    def forward(self, inputs):
        x, = inputs
        xp = backend.get_array_module(x)
        y = xp.empty(self.get_output_shape(x.shape), dtype=x.dtype)
        y_former, y_latter = xp.split(y, 2, axis=self.axis)
        zero = x.dtype.type(0)
        xp.maximum(zero, x, out=y_former)
        xp.maximum(zero, -x, out=y_latter)
        self.retain_inputs((0,))
        return y,

    def backward(self, indexes, grad_outputs):
        x, = self.get_retained_inputs()
        gy, = grad_outputs
        gy_former, gy_latter = chainer.functions.split_axis(
            gy, 2, axis=self.axis)
        return gy_former * (x.data > 0) - gy_latter * (x.data < 0),


def crelu(x, axis=1):
    """Concatenated Rectified Linear Unit function.

    This function is expressed as follows

     .. math:: f(x) = (\\max(0, x), \\max(0, -x)).

    Here, two output values are concatenated along an axis.

    See: https://arxiv.org/abs/1603.05201

    Args:
        x (:class:`~chainer.Variable` or :ref:`ndarray`):
            Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.
        axis (int): Axis that the output values are concatenated along.
            Default is 1.

    Returns:
        ~chainer.Variable: Output variable of concatenated array.
        If the axis is 1, A :math:`(s_1, s_2 \\times 2, ..., s_N)`-shaped float
        array.

    .. admonition:: Example

        >>> x = np.array([[-1, 0], [2, -3]], np.float32)
        >>> x
        array([[-1.,  0.],
               [ 2., -3.]], dtype=float32)
        >>> y = F.crelu(x, axis=1)
        >>> y.data
        array([[0., 0., 1., 0.],
               [2., 0., 0., 3.]], dtype=float32)

    """
    return CReLU(axis=axis).apply((x,))[0]
