import numpy

from chainer.backends import cuda
from chainer import function_node
from chainer import utils
from chainer.utils import type_check


class HardSigmoid(function_node.FunctionNode):

    """Hard-sigmoid function."""

    def check_type_forward(self, in_types):
        type_check._argname(in_types, ('x',))
        x_type, = in_types

        type_check.expect(x_type.dtype.kind == 'f')

    def forward_cpu(self, inputs):
        x = inputs[0]
        y = numpy.clip(x * 0.2 + 0.5, 0.0, 1.0)
        self.retain_inputs((0,))
        return utils.force_array(y, x.dtype),

    def forward_gpu(self, inputs):
        x = inputs[0]
        self.retain_inputs((0,))
        return cuda.elementwise(
            'T x', 'T y',
            'y = min(1.0, max(0.0, x * 0.2 + 0.5))',
            'hard_sigmoid_fwd'
        )(x),

    def backward(self, indexes, grad_outputs):
        x, = self.get_retained_inputs()
        return HardSigmoidGrad(x.data).apply(grad_outputs)


class HardSigmoidGrad(function_node.FunctionNode):

    """Hard-sigmoid gradient function."""

    def __init__(self, x):
        self.x = x

    def check_type_forward(self, in_types):
        type_check._argname(in_types, ('gy',))

        type_check.expect(
            in_types[0].dtype.kind == 'f',
            in_types[0].dtype == self.x.dtype
        )

    def forward_cpu(self, inputs):
        gy, = inputs
        gx = ((-2.5 < self.x) & (self.x < 2.5)) * gy * 0.2
        return utils.force_array(gx, self.x.dtype),

    def forward_gpu(self, inputs):
        gy, = inputs
        return cuda.elementwise(
            'T x, T g', 'T gx',
            'gx = fabs(x) < 2.5 ? 0.2 * g : 0',
            'hard_sigmoid_bwd'
        )(self.x, gy),

    def backward(self, indexes, grad_outputs):
        return HardSigmoidGrad(self.x).apply(grad_outputs)


def hard_sigmoid(x):
    """Element-wise hard-sigmoid function.

    This function is defined as

    .. math::

        f(x) = \\left \\{ \\begin{array}{ll}
        0 & {\\rm if}~ x < -2.5 \\\\
        0.2 x + 0.5 & {\\rm if}~ -2.5 < x < 2.5 \\\\
        1 & {\\rm if}~ 2.5 < x.
        \\end{array} \\right.

    Args:
        x (:class:`~chainer.Variable` or :ref:`ndarray`):
            Input variable. A :math:`(s_1, s_2, ..., s_N)`-shaped float array.

    Returns:
        ~chainer.Variable: Output variable. A
        :math:`(s_1, s_2, ..., s_N)`-shaped float array.

    .. admonition:: Example

        It maps the input values into the range of :math:`[0, 1]`.

        >>> x = np.array([-2.6, -1, 0, 1, 2.6])
        >>> x
        array([-2.6, -1. ,  0. ,  1. ,  2.6])
        >>> F.hard_sigmoid(x).data
        array([0. , 0.3, 0.5, 0.7, 1. ])

    """
    return HardSigmoid().apply((x,))[0]
