try:
    import gco
except:
    print("please install gco from https://github.com/Borda/pyGCO")
import numpy as np
import torch
import torch.nn.functional as F
from multiprocessing import Pool


def cost_matrix(width):
    """transport cost"""
    C = np.zeros([width**2, width**2], dtype=np.float32)
    for m_i in range(width**2):
        i1 = m_i // width
        j1 = m_i % width
        for m_j in range(width**2):
            i2 = m_j // width
            j2 = m_j % width
            C[m_i, m_j] = abs(i1 - i2)**2 + abs(j1 - j2)**2
    C = C / (width - 1)**2
    C = torch.tensor(C).cuda()
    return C


def graphcut_multi(unary1, unary2, pw_x, pw_y, alpha, beta, eta, n_labels=2, eps=1e-8):
    """alpha-beta swap algorithm"""
    block_num = unary1.shape[0]
    large_val = 1000 * block_num**2

    if n_labels == 2:
        prior = np.array([-np.log(alpha + eps), -np.log(1 - alpha + eps)])
    elif n_labels == 3:
        prior = np.array([
            -np.log(alpha**2 + eps), -np.log(2 * alpha * (1 - alpha) + eps),
            -np.log((1 - alpha)**2 + eps)
        ])
    elif n_labels == 4:
        prior = np.array([
            -np.log(alpha**3 + eps), -np.log(3 * alpha**2 * (1 - alpha) + eps),
            -np.log(3 * alpha * (1 - alpha)**2 + eps), -np.log((1 - alpha)**3 + eps)
        ])

    prior = eta * prior / block_num**2
    unary_cost = (large_val * np.stack([(1 - lam) * unary1 + lam * unary2 + prior[i]
                                        for i, lam in enumerate(np.linspace(0, 1, n_labels))],
                                       axis=-1)).astype(np.int32)
    pairwise_cost = np.zeros(shape=[n_labels, n_labels], dtype=np.float32)

    for i in range(n_labels):
        for j in range(n_labels):
            pairwise_cost[i, j] = (i - j)**2 / (n_labels - 1)**2

    pw_x = (large_val * (pw_x + beta)).astype(np.int32)
    pw_y = (large_val * (pw_y + beta)).astype(np.int32)

    labels = 1.0 - gco.cut_grid_graph(unary_cost, pairwise_cost, pw_x, pw_y,
                                      algorithm='swap') / (n_labels - 1)
    mask = labels.reshape(block_num, block_num)

    return mask


def neigh_penalty(input1, input2, k):
    """data local smoothness term"""
    pw_x = input1[:, :, :-1, :] - input2[:, :, 1:, :]
    pw_y = input1[:, :, :, :-1] - input2[:, :, :, 1:]

    pw_x = pw_x[:, :, k - 1::k, :]
    pw_y = pw_y[:, :, :, k - 1::k]

    pw_x = F.avg_pool2d(pw_x.abs().mean(1), kernel_size=(1, k))
    pw_y = F.avg_pool2d(pw_y.abs().mean(1), kernel_size=(k, 1))

    return pw_x, pw_y


def mask_transport(mask, grad_pool, eps=0.01):
    """optimal transport plan"""
    cost_matrix_dict = {
        '2': cost_matrix(2).unsqueeze(0),
        '4': cost_matrix(4).unsqueeze(0),
        '8': cost_matrix(8).unsqueeze(0),
        '16': cost_matrix(16).unsqueeze(0)
    }
    
    block_num = mask.shape[-1]

    n_iter = int(block_num)
    C = cost_matrix_dict[str(block_num)]

    z = (mask > 0).float()
    cost = eps * C - grad_pool.reshape(-1, block_num**2, 1) * z.reshape(-1, 1, block_num**2)

    # row and col
    for _ in range(n_iter):
        row_best = cost.min(-1)[1]
        plan = torch.zeros_like(cost).scatter_(-1, row_best.unsqueeze(-1), 1)

        # column resolve
        cost_fight = plan * cost
        col_best = cost_fight.min(-2)[1]
        plan_win = torch.zeros_like(cost).scatter_(-2, col_best.unsqueeze(-2), 1) * plan
        plan_lose = (1 - plan_win) * plan

        cost += plan_lose
    
    return plan_win


def transport_image(img, plan, block_num, block_size):
    """apply transport plan to images"""
    batch_size = img.shape[0]
    input_patch = img.reshape([batch_size, 3, block_num, block_size,
                               block_num * block_size]).transpose(-2, -1)
    input_patch = input_patch.reshape([batch_size, 3, block_num, block_num, block_size,
                                       block_size]).transpose(-2, -1)
    input_patch = input_patch.reshape([batch_size, 3, block_num**2, block_size,
                                       block_size]).permute(0, 1, 3, 4, 2).unsqueeze(-1)

    input_transport = plan.transpose(
        -2, -1).unsqueeze(1).unsqueeze(1).unsqueeze(1).matmul(input_patch).squeeze(-1).permute(
            0, 1, 4, 2, 3)
    input_transport = input_transport.reshape(
        [batch_size, 3, block_num, block_num, block_size, block_size])
    input_transport = input_transport.transpose(-2, -1).reshape(
        [batch_size, 3, block_num, block_num * block_size, block_size])
    input_transport = input_transport.transpose(-2, -1).reshape(
        [batch_size, 3, block_num * block_size, block_num * block_size])

    return input_transport


@torch.no_grad()
def puzzlemix(img, gt_label, alpha=0.5, lam=None, dist_mode=False,
              features=None, block_num=2, beta=1.2, gamma=0.5, eta=0.2,
              neigh_size=2, n_labels=2, t_eps=10.0, t_size=-1,
              mean=None, std=None, transport=True,
              noise=None, adv_mask1=0, adv_mask2=0,
              t_batch_size=None, mp=None,
              **kwargs):
    r""" PuzzleMix augmentation.

    "Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup
    (https://arxiv.org/abs/2009.06962)". In ICML, 2020.
        https://github.com/snu-mllab/PuzzleMix

    Args:
        img (Tensor): Input images of shape (N, C, H, W).
            Typically these should be mean centered and std scaled.
        gt_label (Tensor): Ground-truth labels (one-hot).
        alpha (float): To sample Beta distribution.
        lam (float): The given mixing ratio. If lam is None, sample a lam
            from Beta distribution.
        dist_mode (bool): Whether to do cross gpus index shuffling and
            return the mixup shuffle index, which support supervised
            and self-supervised methods.
        features (tensor): Gradient of loss as the saliency information.
        block_num (int or tuple): Number of grid size in input images.
        beta (float): Label smoothness.
        gamma (float): Data local smoothness.
        eta (float): Prior term.
        neigh_size (int): Number of neighbors.
        n_labels (int): Graph cut algorithm.
        t_eps (float): Transport cost coefficient. Default: 10.
        t_size (int): Transport size in small-scale datasets. Default: -1.
        t_batch_size (int): Transporting batch size in large-scale datasets.
        transport (bool): Whether to use optimal-transport. Default: False.
        mp: Multi-process for graphcut (CPU). Default: None.
    """
    # 'alpha' in PuzzleMix used for graph-cut, equal to 'lam'
    alpha = np.random.beta(alpha, alpha)
    # basic mixup args
    if not dist_mode:
        # normal mixup process
        rand_index = torch.randperm(img.size(0)).cuda()
        if len(img.size()) == 4:  # [N, C, H, W]
            input1 = img.clone()
            input2 = img[rand_index].clone()
        else:
            assert img.dim() == 5  # semi-supervised img [N, 2, C, H, W]
            # * notice that the rank of two groups of img is fixed
            input2 = img[:, 1, ...].contiguous()
            input1 = img[:, 0, ...].contiguous()
        y_a = gt_label
        y_b = gt_label[rand_index]
    
    if isinstance(block_num, int):
        block_num = (1, block_num)
    elif isinstance(block_num, tuple):
        assert len(block_num) == 2
    block_num = 2**np.random.randint(block_num[0], block_num[1])  # given num is the range
    if mean is None:
        mean = torch.tensor([0.4914, 0.4822, 0.4465]).reshape(1, 3, 1, 1).cuda()
    if std is None:
        std  = torch.tensor([0.2023, 0.1994, 0.2010]).reshape(1, 3, 1, 1).cuda()
    if mp is not None:
        mp = Pool(mp)

    # ============== PuzzleMix (official) ==============
    batch_size, _, _, width = input1.shape
    block_size = width // block_num
    neigh_size = min(neigh_size, block_size)
    t_size = min(t_size, block_size)

    # normalize
    beta = beta / block_num / 16

    # unary term
    unary_pool = F.avg_pool2d(features, block_size)
    unary1_torch = unary_pool / unary_pool.view(batch_size, -1).sum(1).view(batch_size, 1, 1)
    unary2_torch = unary1_torch[rand_index]

    # calculate pairwise terms
    input1_pool = F.avg_pool2d(input1 * std + mean, neigh_size)
    input2_pool = input1_pool[rand_index]

    pw_x = torch.zeros([batch_size, 2, 2, block_num - 1, block_num]).cuda()
    pw_y = torch.zeros([batch_size, 2, 2, block_num, block_num - 1]).cuda()

    k = block_size // neigh_size

    pw_x[:, 0, 0], pw_y[:, 0, 0] = neigh_penalty(input2_pool, input2_pool, k)
    pw_x[:, 0, 1], pw_y[:, 0, 1] = neigh_penalty(input2_pool, input1_pool, k)
    pw_x[:, 1, 0], pw_y[:, 1, 0] = neigh_penalty(input1_pool, input2_pool, k)
    pw_x[:, 1, 1], pw_y[:, 1, 1] = neigh_penalty(input1_pool, input1_pool, k)

    pw_x = beta * gamma * pw_x
    pw_y = beta * gamma * pw_y

    # re-define unary and pairwise terms to draw graph
    unary1 = unary1_torch.clone()
    unary2 = unary2_torch.clone()

    unary2[:, :-1, :] += (pw_x[:, 1, 0] + pw_x[:, 1, 1]) / 2.
    unary1[:, :-1, :] += (pw_x[:, 0, 1] + pw_x[:, 0, 0]) / 2.
    unary2[:, 1:, :] += (pw_x[:, 0, 1] + pw_x[:, 1, 1]) / 2.
    unary1[:, 1:, :] += (pw_x[:, 1, 0] + pw_x[:, 0, 0]) / 2.

    unary2[:, :, :-1] += (pw_y[:, 1, 0] + pw_y[:, 1, 1]) / 2.
    unary1[:, :, :-1] += (pw_y[:, 0, 1] + pw_y[:, 0, 0]) / 2.
    unary2[:, :, 1:] += (pw_y[:, 0, 1] + pw_y[:, 1, 1]) / 2.
    unary1[:, :, 1:] += (pw_y[:, 1, 0] + pw_y[:, 0, 0]) / 2.

    pw_x = (pw_x[:, 1, 0] + pw_x[:, 0, 1] - pw_x[:, 1, 1] - pw_x[:, 0, 0]) / 2
    pw_y = (pw_y[:, 1, 0] + pw_y[:, 0, 1] - pw_y[:, 1, 1] - pw_y[:, 0, 0]) / 2

    unary1 = unary1.detach().cpu().numpy()
    unary2 = unary2.detach().cpu().numpy()
    pw_x = pw_x.detach().cpu().numpy()
    pw_y = pw_y.detach().cpu().numpy()

    # solve graphcut
    if mp is None:
        mask = []
        for i in range(batch_size):
            mask.append(
                graphcut_multi(unary2[i], unary1[i], pw_x[i], pw_y[i], alpha, beta, eta, n_labels))
    else:
        input_mp = []
        for i in range(batch_size):
            input_mp.append((unary2[i], unary1[i], pw_x[i], pw_y[i], alpha, beta, eta, n_labels))
        mask = mp.starmap(graphcut_multi, input_mp)

    # optimal mask
    mask = torch.tensor(mask, dtype=torch.float32).cuda()
    mask = mask.unsqueeze(1)

    # add adversarial noise
    if adv_mask1 == 1.:
        input1 = input1 * std + mean + noise
        input1 = torch.clamp(input1, 0, 1)
        input1 = (input1 - mean) / std

    if adv_mask2 == 1.:
        input2 = input2 * std + mean + noise[rand_index]
        input2 = torch.clamp(input2, 0, 1)
        input2 = (input2 - mean) / std

    # optimal tranport
    if transport:
        if t_size == -1:
            t_block_num = block_num
            t_size = block_size
        elif t_size < block_size:
            # block_size % t_size should be 0
            t_block_num = width // t_size
            mask = F.interpolate(mask, size=t_block_num)
            grad1_pool = F.avg_pool2d(features, t_size)
            unary1_torch = grad1_pool / grad1_pool.reshape(batch_size, -1).sum(1).reshape(
                batch_size, 1, 1)
            unary2_torch = unary1_torch[rand_index]
        else:
            t_block_num = block_num
        
        plan1 = mask_transport(mask, unary1_torch, eps=t_eps)
        plan2 = mask_transport(1 - mask, unary2_torch, eps=t_eps)
        
        if t_batch_size is not None:
            # imagenet
            t_batch_size = min(t_batch_size, 16)
            try:
                for i in range((batch_size - 1) // t_batch_size + 1):
                    idx_from = i * t_batch_size
                    idx_to = min((i + 1) * t_batch_size, batch_size)
                    input1[idx_from:idx_to] = transport_image(
                                                input1[idx_from:idx_to], plan1[idx_from:idx_to],
                                                t_block_num, t_size)
                    input2[idx_from:idx_to] = transport_image(
                                                input2[idx_from:idx_to], plan2[idx_from:idx_to],
                                                t_block_num, t_size)
            except:
                raise ValueError(
                    "*** GPU memory is lacking while transporting. ***" 
                    "*** Reduce the t_batch_size value in this function (mixup.transprort) ***"
                )
        else:
            # small-scale datasets
            input1 = transport_image(input1, plan1, t_block_num, t_size)
            input2 = transport_image(input2, plan2, t_block_num, t_size)

    # final mask and mixing ratio lam
    mask = F.interpolate(mask, size=width)
    lam = mask.reshape(batch_size, -1).mean(-1)
    img = mask * input1 + (1 - mask) * input2

    return img, (y_a, y_b, lam)


@torch.no_grad()
def attentivemix(img, gt_label, alpha=1.0, lam=None, dist_mode=False,
                 features=None, grid_scale=32, top_k=6, **kwargs):
    r""" AttentiveMix augmentation

    "Attentive CutMix: An Enhanced Data Augmentation Approach for Deep Learning
    Based Image Classification (https://arxiv.org/abs/2003.13048)". In ICASSP, 2020.
        https://github.com/xden2331/attentive_cutmix
    
    Args:
        img (Tensor): Input images of shape (N, C, H, W).
            Typically these should be mean centered and std scaled.
        gt_label (Tensor): Ground-truth labels (one-hot).
        alpha (float): To sample Beta distribution.
        lam (float): The given mixing ratio. If lam is None, sample a lam
            from Beta distribution.
        dist_mode (bool): Whether to do cross gpus index shuffling and
            return the mixup shuffle index, which support supervised
            and self-supervised methods.
        features (tensor): Feature maps for attentive regions.
        grid_scale (float): The upsampling scale of attentive grids.
        top_k (int): Using top_k attentive regions in feature maps.
    """
    
    # basic mixup args
    bs, _, att_size, _ = features.size()
    att_grid = att_size**2
    if att_size * grid_scale != img.size(2):
        grid_scale = img.size(2) / att_size
    if lam is None:
        lam = np.random.beta(alpha, alpha)
    # Notice: official attentivemix uses fixed lam by top_k, while attentivemix+
    #   in this repo uses lam\in\Beta(a,a) to choose top_k for better preformances.
    if top_k is None:
        top_k = min(max(1, int(att_grid * lam)), att_grid)
    
    if not dist_mode:
        # normal mixup process
        rand_index = torch.randperm(img.size(0)).cuda()
        if len(img.size()) == 4:  # [N, C, H, W]
            img_ = img[rand_index]
        else:
            assert img.dim() == 5  # semi-supervised img [N, 2, C, H, W]
            # Notice that the rank of two groups of img is fixed
            img_ = img[:, 1, ...].contiguous()
            img = img[:, 0, ...].contiguous()
        y_a = gt_label
        y_b = gt_label[rand_index]
    
    # select top_k attentive regions
    features = features.mean(1)
    _, att_idx = features.view(bs, att_grid).topk(top_k)
    att_idx = torch.cat([
        (att_idx // att_size).unsqueeze(1),
        (att_idx  % att_size).unsqueeze(1),], dim=1)
    mask = torch.zeros(bs, 1, att_size, att_size).cuda()
    for i in range(bs):
        mask[i, 0, att_idx[i, 0, :], att_idx[i, 1, :]] = 1
    mask = F.upsample(mask, scale_factor=grid_scale, mode="nearest")
    lam = float(mask[0, 0, ...].mean().cpu().numpy())
    img = mask * img + (1 - mask) * img_

    return img, (y_a, y_b, lam)
