""" Mixup and Cutmix

Papers:
mixup: Beyond Empirical Risk Minimization (https://arxiv.org/abs/1710.09412)

CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features (https://arxiv.org/abs/1905.04899)

Code Reference:
CutMix: https://github.com/clovaai/CutMix-PyTorch

Hacked together by / Copyright 2019, Ross Wightman
"""
import numpy as np
import torch


def one_hot(x, num_classes, on_value=1.0, off_value=0.0, device="cuda"):
    x = x.long().view(-1, 1)
    return torch.full((x.size()[0], num_classes), off_value, device=device).scatter_(
        1, x, on_value
    )


def mixup_target(target, num_classes, lam=1.0, smoothing=0.0, device="cuda"):
    off_value = smoothing / num_classes
    on_value = 1.0 - smoothing + off_value
    y1 = one_hot(
        target, num_classes, on_value=on_value, off_value=off_value, device=device
    )
    y2 = one_hot(
        target.flip(0),
        num_classes,
        on_value=on_value,
        off_value=off_value,
        device=device,
    )
    return y1 * lam + y2 * (1.0 - lam)


def rand_bbox(img_shape, lam, margin=0.0, count=None):
    """Standard CutMix bounding-box
    Generates a random square bbox based on lambda value. This impl includes
    support for enforcing a border margin as percent of bbox dimensions.

    Args:
        img_shape (tuple): Image shape as tuple
        lam (float): Cutmix lambda value
        margin (float): Percentage of bbox dimension to enforce as margin (reduce amount of box outside image)
        count (int): Number of bbox to generate
    """
    ratio = np.sqrt(1 - lam)
    img_h, img_w = img_shape[-2:]
    cut_h, cut_w = int(img_h * ratio), int(img_w * ratio)
    margin_y, margin_x = int(margin * cut_h), int(margin * cut_w)
    cy = np.random.randint(0 + margin_y, img_h - margin_y, size=count)
    cx = np.random.randint(0 + margin_x, img_w - margin_x, size=count)
    yl = np.clip(cy - cut_h // 2, 0, img_h)
    yh = np.clip(cy + cut_h // 2, 0, img_h)
    xl = np.clip(cx - cut_w // 2, 0, img_w)
    xh = np.clip(cx + cut_w // 2, 0, img_w)
    return yl, yh, xl, xh


def rand_bbox_minmax(img_shape, minmax, count=None):
    """Min-Max CutMix bounding-box
    Inspired by Darknet cutmix impl, generates a random rectangular bbox
    based on min/max percent values applied to each dimension of the input image.

    Typical defaults for minmax are usually in the  .2-.3 for min and .8-.9 range for max.

    Args:
        img_shape (tuple): Image shape as tuple
        minmax (tuple or list): Min and max bbox ratios (as percent of image size)
        count (int): Number of bbox to generate
    """
    assert len(minmax) == 2
    img_h, img_w = img_shape[-2:]
    cut_h = np.random.randint(
        int(img_h * minmax[0]), int(img_h * minmax[1]), size=count
    )
    cut_w = np.random.randint(
        int(img_w * minmax[0]), int(img_w * minmax[1]), size=count
    )
    yl = np.random.randint(0, img_h - cut_h, size=count)
    xl = np.random.randint(0, img_w - cut_w, size=count)
    yu = yl + cut_h
    xu = xl + cut_w
    return yl, yu, xl, xu


def cutmix_bbox_and_lam(
    img_shape, lam, ratio_minmax=None, correct_lam=True, count=None
):
    """Generate bbox and apply lambda correction."""
    if ratio_minmax is not None:
        yl, yu, xl, xu = rand_bbox_minmax(img_shape, ratio_minmax, count=count)
    else:
        yl, yu, xl, xu = rand_bbox(img_shape, lam, count=count)
    if correct_lam or ratio_minmax is not None:
        bbox_area = (yu - yl) * (xu - xl)
        lam = 1.0 - bbox_area / float(img_shape[-2] * img_shape[-1])
    return (yl, yu, xl, xu), lam


class Mixup:
    """Mixup/Cutmix that applies different params to each element or whole batch

    Args:
        mixup_alpha (float): mixup alpha value, mixup is active if > 0.
        cutmix_alpha (float): cutmix alpha value, cutmix is active if > 0.
        cutmix_minmax (List[float]): cutmix min/max image ratio, cutmix is active and uses this vs alpha if not None.
        prob (float): probability of applying mixup or cutmix per batch or element
        switch_prob (float): probability of switching to cutmix instead of mixup when both are active
        mode (str): how to apply mixup/cutmix params (per 'batch', 'pair' (pair of elements), 'elem' (element)
        correct_lam (bool): apply lambda correction when cutmix bbox clipped by image borders
        label_smoothing (float): apply label smoothing to the mixed target tensor
        num_classes (int): number of classes for target
    """

    def __init__(
        self,
        mixup_alpha=1.0,
        cutmix_alpha=0.0,
        cutmix_minmax=None,
        prob=1.0,
        switch_prob=0.5,
        mode="batch",
        correct_lam=True,
        label_smoothing=0.1,
        num_classes=1000,
    ):
        self.mixup_alpha = mixup_alpha
        self.cutmix_alpha = cutmix_alpha
        self.cutmix_minmax = cutmix_minmax
        if self.cutmix_minmax is not None:
            assert len(self.cutmix_minmax) == 2
            # force cutmix alpha == 1.0 when minmax active to keep logic simple & safe
            self.cutmix_alpha = 1.0
        self.mix_prob = prob
        self.switch_prob = switch_prob
        self.label_smoothing = label_smoothing
        self.num_classes = num_classes
        self.mode = mode
        self.correct_lam = (
            correct_lam  # correct lambda based on clipped area for cutmix
        )
        self.mixup_enabled = (
            True  # set to false to disable mixing (intended tp be set by train loop)
        )

    def _params_per_elem(self, batch_size):
        lam = np.ones(batch_size, dtype=np.float32)
        use_cutmix = np.zeros(batch_size, dtype=np.bool)
        if self.mixup_enabled:
            if self.mixup_alpha > 0.0 and self.cutmix_alpha > 0.0:
                use_cutmix = np.random.rand(batch_size) < self.switch_prob
                lam_mix = np.where(
                    use_cutmix,
                    np.random.beta(
                        self.cutmix_alpha, self.cutmix_alpha, size=batch_size
                    ),
                    np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size),
                )
            elif self.mixup_alpha > 0.0:
                lam_mix = np.random.beta(
                    self.mixup_alpha, self.mixup_alpha, size=batch_size
                )
            elif self.cutmix_alpha > 0.0:
                use_cutmix = np.ones(batch_size, dtype=np.bool)
                lam_mix = np.random.beta(
                    self.cutmix_alpha, self.cutmix_alpha, size=batch_size
                )
            else:
                assert (
                    False
                ), "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
            lam = np.where(
                np.random.rand(batch_size) < self.mix_prob,
                lam_mix.astype(np.float32),
                lam,
            )
        return lam, use_cutmix

    def _params_per_batch(self):
        lam = 1.0
        use_cutmix = False
        if self.mixup_enabled and np.random.rand() < self.mix_prob:
            if self.mixup_alpha > 0.0 and self.cutmix_alpha > 0.0:
                use_cutmix = np.random.rand() < self.switch_prob
                lam_mix = (
                    np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
                    if use_cutmix
                    else np.random.beta(self.mixup_alpha, self.mixup_alpha)
                )
            elif self.mixup_alpha > 0.0:
                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)
            elif self.cutmix_alpha > 0.0:
                use_cutmix = True
                lam_mix = np.random.beta(self.cutmix_alpha, self.cutmix_alpha)
            else:
                assert (
                    False
                ), "One of mixup_alpha > 0., cutmix_alpha > 0., cutmix_minmax not None should be true."
            lam = float(lam_mix)
        return lam, use_cutmix

    def _mix_elem(self, x):
        batch_size = len(x)
        lam_batch, use_cutmix = self._params_per_elem(batch_size)
        x_orig = x.clone()  # need to keep an unmodified original for mixing source
        for i in range(batch_size):
            j = batch_size - i - 1
            lam = lam_batch[i]
            if lam != 1.0:
                if use_cutmix[i]:
                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                        x[i].shape,
                        lam,
                        ratio_minmax=self.cutmix_minmax,
                        correct_lam=self.correct_lam,
                    )
                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
                    lam_batch[i] = lam
                else:
                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)

    def _mix_pair(self, x):
        batch_size = len(x)
        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)
        x_orig = x.clone()  # need to keep an unmodified original for mixing source
        for i in range(batch_size // 2):
            j = batch_size - i - 1
            lam = lam_batch[i]
            if lam != 1.0:
                if use_cutmix[i]:
                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                        x[i].shape,
                        lam,
                        ratio_minmax=self.cutmix_minmax,
                        correct_lam=self.correct_lam,
                    )
                    x[i][:, yl:yh, xl:xh] = x_orig[j][:, yl:yh, xl:xh]
                    x[j][:, yl:yh, xl:xh] = x_orig[i][:, yl:yh, xl:xh]
                    lam_batch[i] = lam
                else:
                    x[i] = x[i] * lam + x_orig[j] * (1 - lam)
                    x[j] = x[j] * lam + x_orig[i] * (1 - lam)
        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))
        return torch.tensor(lam_batch, device=x.device, dtype=x.dtype).unsqueeze(1)

    def _mix_batch(self, x):
        lam, use_cutmix = self._params_per_batch()
        if lam == 1.0:
            return 1.0
        if use_cutmix:
            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(
                x.shape,
                lam,
                ratio_minmax=self.cutmix_minmax,
                correct_lam=self.correct_lam,
            )
            x[:, :, yl:yh, xl:xh] = x.flip(0)[:, :, yl:yh, xl:xh]
        else:
            x_flipped = x.flip(0).mul_(1.0 - lam)
            x.mul_(lam).add_(x_flipped)
        return lam

    def __call__(self, x, target):
        assert len(x) % 2 == 0, "Batch size should be even when using this"
        if self.mixup_alpha > 0 or self.cutmix_alpha > 0:
            if self.mode == "elem":
                lam = self._mix_elem(x)
            elif self.mode == "pair":
                lam = self._mix_pair(x)
            else:
                lam = self._mix_batch(x)
        else:
            lam = 1
        target = mixup_target(
            target, self.num_classes, lam, self.label_smoothing, x.device
        )
        return x, target
