import keras
from . import backend


def focal(alpha=0.25, gamma=2.0):
    """ Create a functor for computing the focal loss.

    Args
        alpha: Scale the focal weight with alpha.
        gamma: Take the power of the focal weight with gamma.

    Returns
        A functor that computes the focal loss using the alpha and gamma.
    """
    def _focal(y_true, y_pred):
        """ Compute the focal loss given the target tensor and the predicted tensor.

        As defined in https://arxiv.org/abs/1708.02002

        Args
            y_true: Tensor of target data from the generator with shape (B, N, num_classes).
            y_pred: Tensor of predicted data from the network with shape (B, N, num_classes).

        Returns
            The focal loss of y_pred w.r.t. y_true.
        """
        labels         = y_true[:, :, :-1]
        anchor_state   = y_true[:, :, -1]  # -1 for ignore, 0 for background, 1 for object
        classification = y_pred

        # filter out "ignore" anchors
        indices        = backend.where(keras.backend.not_equal(anchor_state, -1))
        labels         = backend.gather_nd(labels, indices)
        classification = backend.gather_nd(classification, indices)

        # compute the focal loss
        alpha_factor = keras.backend.ones_like(labels) * alpha
        alpha_factor = backend.where(keras.backend.equal(labels, 1), alpha_factor, 1 - alpha_factor)
        focal_weight = backend.where(keras.backend.equal(labels, 1), 1 - classification, classification)
        focal_weight = alpha_factor * focal_weight ** gamma

        cls_loss = focal_weight * keras.backend.binary_crossentropy(labels, classification)

        # compute the normalizer: the number of positive anchors
        normalizer = backend.where(keras.backend.equal(anchor_state, 1))
        normalizer = keras.backend.cast(keras.backend.shape(normalizer)[0], keras.backend.floatx())
        normalizer = keras.backend.maximum(1.0, normalizer)

        return keras.backend.sum(cls_loss) / normalizer

    return _focal


def smooth_l1(sigma=3.0):
    """ Create a smooth L1 loss functor.

    Args
        sigma: This argument defines the point where the loss changes from L2 to L1.

    Returns
        A functor for computing the smooth L1 loss given target data and predicted data.
    """
    sigma_squared = sigma ** 2

    def _smooth_l1(y_true, y_pred):
        """ Compute the smooth L1 loss of y_pred w.r.t. y_true.

        Args
            y_true: Tensor from the generator of shape (B, N, 5). The last value for each box is the state of the anchor (ignore, negative, positive).
            y_pred: Tensor from the network of shape (B, N, 4).

        Returns
            The smooth L1 loss of y_pred w.r.t. y_true.
        """
        # separate target and state
        regression        = y_pred
        regression_target = y_true[:, :, :-1]
        anchor_state      = y_true[:, :, -1]

        # filter out "ignore" anchors
        indices           = backend.where(keras.backend.equal(anchor_state, 1))
        regression        = backend.gather_nd(regression, indices)
        regression_target = backend.gather_nd(regression_target, indices)

        # compute smooth L1 loss
        # f(x) = 0.5 * (sigma * x)^2          if |x| < 1 / sigma / sigma
        #        |x| - 0.5 / sigma / sigma    otherwise
        regression_diff = regression - regression_target
        regression_diff = keras.backend.abs(regression_diff)
        regression_loss = backend.where(
            keras.backend.less(regression_diff, 1.0 / sigma_squared),
            0.5 * sigma_squared * keras.backend.pow(regression_diff, 2),
            regression_diff - 0.5 / sigma_squared
        )

        # compute the normalizer: the number of positive anchors
        normalizer = keras.backend.maximum(1, keras.backend.shape(indices)[0])
        normalizer = keras.backend.cast(normalizer, dtype=keras.backend.floatx())
        return keras.backend.sum(regression_loss) / normalizer

    return _smooth_l1