content: """ 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., 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, x.device)
          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
  
  