# Copyright 2021 Huawei Technologies Co., Ltd

#

# Licensed under the Apache License, Version 2.0 (the "License");

# you may not use this file except in compliance with the License.

# You may obtain a copy of the License at

#

# http://www.apache.org/licenses/LICENSE-2.0

#

# Unless required by applicable law or agreed to in writing, software

# distributed under the License is distributed on an "AS IS" BASIS,

# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

# See the License for the specific language governing permissions and

# limitations under the License.

# ============================================================================

""" 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 2020 Ross Wightman

"""

import numpy as np

import torch





def one_hot(x, num_classes, on_value=1., off_value=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., smoothing=0.0, device='cuda'):

    off_value = smoothing / num_classes

    on_value = 1. - 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. - lam)





def rand_bbox(img_shape, lam, margin=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. - 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., cutmix_alpha=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. and self.cutmix_alpha > 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.:

                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha, size=batch_size)

            elif self.cutmix_alpha > 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.

        use_cutmix = False

        if self.mixup_enabled and np.random.rand() < self.mix_prob:

            if self.mixup_alpha > 0. and self.cutmix_alpha > 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.:

                lam_mix = np.random.beta(self.mixup_alpha, self.mixup_alpha)

            elif self.cutmix_alpha > 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.:

                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.:

                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.:

            return 1.

        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. - 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.mode == 'elem':

            lam = self._mix_elem(x)

        elif self.mode == 'pair':

            lam = self._mix_pair(x)

        else:

            lam = self._mix_batch(x)

        target = mixup_target(target, self.num_classes, lam, self.label_smoothing,device='npu')

        return x, target





class FastCollateMixup(Mixup):

    """ Fast Collate w/ Mixup/Cutmix that applies different params to each element or whole batch



    A Mixup impl that's performed while collating the batches.

    """



    def _mix_elem_collate(self, output, batch, half=False):

        batch_size = len(batch)

        num_elem = batch_size // 2 if half else batch_size

        assert len(output) == num_elem

        lam_batch, use_cutmix = self._params_per_elem(num_elem)

        for i in range(num_elem):

            j = batch_size - i - 1

            lam = lam_batch[i]

            mixed = batch[i][0]

            if lam != 1.:

                if use_cutmix[i]:

                    if not half:

                        mixed = mixed.copy()

                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(

                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)

                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]

                    lam_batch[i] = lam

                else:

                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)

                    np.rint(mixed, out=mixed)

            output[i] += torch.from_numpy(mixed.astype(np.uint8))

        if half:

            lam_batch = np.concatenate((lam_batch, np.ones(num_elem)))

        return torch.tensor(lam_batch).unsqueeze(1)



    def _mix_pair_collate(self, output, batch):

        batch_size = len(batch)

        lam_batch, use_cutmix = self._params_per_elem(batch_size // 2)

        for i in range(batch_size // 2):

            j = batch_size - i - 1

            lam = lam_batch[i]

            mixed_i = batch[i][0]

            mixed_j = batch[j][0]

            assert 0 <= lam <= 1.0

            if lam < 1.:

                if use_cutmix[i]:

                    (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(

                        output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)

                    patch_i = mixed_i[:, yl:yh, xl:xh].copy()

                    mixed_i[:, yl:yh, xl:xh] = mixed_j[:, yl:yh, xl:xh]

                    mixed_j[:, yl:yh, xl:xh] = patch_i

                    lam_batch[i] = lam

                else:

                    mixed_temp = mixed_i.astype(np.float32) * lam + mixed_j.astype(np.float32) * (1 - lam)

                    mixed_j = mixed_j.astype(np.float32) * lam + mixed_i.astype(np.float32) * (1 - lam)

                    mixed_i = mixed_temp

                    np.rint(mixed_j, out=mixed_j)

                    np.rint(mixed_i, out=mixed_i)

            output[i] += torch.from_numpy(mixed_i.astype(np.uint8))

            output[j] += torch.from_numpy(mixed_j.astype(np.uint8))

        lam_batch = np.concatenate((lam_batch, lam_batch[::-1]))

        return torch.tensor(lam_batch).unsqueeze(1)



    def _mix_batch_collate(self, output, batch):

        batch_size = len(batch)

        lam, use_cutmix = self._params_per_batch()

        if use_cutmix:

            (yl, yh, xl, xh), lam = cutmix_bbox_and_lam(

                output.shape, lam, ratio_minmax=self.cutmix_minmax, correct_lam=self.correct_lam)

        for i in range(batch_size):

            j = batch_size - i - 1

            mixed = batch[i][0]

            if lam != 1.:

                if use_cutmix:

                    mixed = mixed.copy()  # don't want to modify the original while iterating

                    mixed[:, yl:yh, xl:xh] = batch[j][0][:, yl:yh, xl:xh]

                else:

                    mixed = mixed.astype(np.float32) * lam + batch[j][0].astype(np.float32) * (1 - lam)

                    np.rint(mixed, out=mixed)

            output[i] += torch.from_numpy(mixed.astype(np.uint8))

        return lam



    def __call__(self, batch, _=None):

        batch_size = len(batch)

        assert batch_size % 2 == 0, 'Batch size should be even when using this'

        half = 'half' in self.mode

        if half:

            batch_size //= 2

        output = torch.zeros((batch_size, *batch[0][0].shape), dtype=torch.uint8)

        if self.mode == 'elem' or self.mode == 'half':

            lam = self._mix_elem_collate(output, batch, half=half)

        elif self.mode == 'pair':

            lam = self._mix_pair_collate(output, batch)

        else:

            lam = self._mix_batch_collate(output, batch)

        target = torch.tensor([b[1] for b in batch], dtype=torch.int64)

        target = mixup_target(target, self.num_classes, lam, self.label_smoothing, device='cpu')

        target = target[:batch_size]

        return output, target