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#pylint: disable=invalid-name, unused-argument
"""Backend compiler related feature registration"""
from __future__ import absolute_import
from ..expr import const
from .op import register_gradient
from .transform import collapse_sum_like, broadcast_to_like, where
from .tensor import exp, negative, power, less
from .tensor import zeros_like, ones_like


@register_gradient("log")
def log_grad(orig, grad):
    """Returns [grad * (1 / x)]"""
    x = orig.args[0]
    return [grad * ones_like(x) / x]


@register_gradient("exp")
def exp_grad(orig, grad):
    """Returns [grad * exp(x)]"""
    return [grad * exp(orig.args[0])]


@register_gradient("sqrt")
def sqrt_grad(orig, grad):
    """Returns [grad * 0.5 * (x ^ -0.5)]"""
    a = const(0.5)  # (TODO) type?
    return [grad * a * power(orig.args[0], negative(a))]


@register_gradient("sigmoid")
def sigmoid_grad(orig, grad):
    """Returns [grad * sigmoid(x) * (1 - sigmoid(x))]."""
    return [grad * orig * (ones_like(orig) - orig)]


@register_gradient("tanh")
def tanh_grad(orig, grad):
    """Returns grad * (1 - tanh(x) * tanh(x))."""
    return [grad * ones_like(orig) - orig * orig]


@register_gradient("nn.relu")
def relu_grad(orig, grad):
    """Returns grad * (select(x < 0, 0, 1))."""
    x = orig.args[0]
    zeros = zeros_like(x)
    ones = ones_like(x)
    return [where(less(x, zeros), zeros, ones * grad)]


@register_gradient("add")
def add_grad(orig, grad):
    """Returns [grad, grad]"""
    return [collapse_sum_like(grad, orig.args[0]),
            collapse_sum_like(grad, orig.args[1])]


@register_gradient("subtract")
def subtract_grad(orig, grad):
    """Returns [grad, -grad]"""
    return [collapse_sum_like(grad, orig.args[0]),
            collapse_sum_like(negative(grad), orig.args[1])]


@register_gradient("multiply")
def multiply_grad(orig, grad):
    """Returns [grad * y, grad * x]"""
    x, y = orig.args
    return [collapse_sum_like(grad * y, x),
            collapse_sum_like(grad * x, y)]


@register_gradient("divide")
def divide_grad(orig, grad):
    """Returns [grad / y,  - grad * (x / y) / y]"""
    x, y = orig.args
    return [collapse_sum_like(grad / y, x),
            collapse_sum_like(- (grad * orig / y), y)]


@register_gradient("zeros_like")
def zeros_like_grad(orig, grad):
    """Returns [0]"""
    return [orig]

@register_gradient("ones_like")
def ones_like_grad(orig, grad):
    """Returns [0]"""
    return [zeros_like(orig.args[0])]

@register_gradient("collapse_sum_like")
def collapse_sum_like_grad(orig, grad):
    """Returns [broadcast_to_like(grad, x), 0]"""
    x, y = orig.args
    return [broadcast_to_like(grad, x), zeros_like(y)]
