# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved

import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.autograd as autograd
import meegnet.networks as networks
import copy
import numpy as np

# from domainbed import networks
from domainbed.lib.misc import random_pairs_of_minibatches, ParamDict

ALGORITHMS = [
    "ERM",
    "Fish",
    "IRM",
    "GroupDRO",
    "Mixup",
    "MLDG",
    "CORAL",
    "MMD",
    "DANN",
    "CDANN",
    "MTL",
    "SagNet",
    "ARM",
    "VREx",
    "RSC",
    "SD",
    "ANDMask",
    "SANDMask",  # SAND-mask
    "IGA",
    "SelfReg",
]


def get_algorithm_class(algorithm_name):
    """Return the algorithm class with the given name."""
    if algorithm_name not in globals():
        raise NotImplementedError("Algorithm not found: {}".format(algorithm_name))
    return globals()[algorithm_name]


class Algorithm(torch.nn.Module):
    """
    A subclass of Algorithm implements a domain generalization algorithm.
    Subclasses should implement the following:
    - update()
    - predict()
    """

    def __init__(self, input_shape, num_classes, num_domains, network, hparams, device):
        super(Algorithm, self).__init__()
        self.device = device
        self.hparams = hparams
        self.network = network

    def update(self, minibatches, unlabeled=None):
        """
        Perform one update step, given a list of (x, y) tuples for all
        environments.

        Admits an optional list of unlabeled minibatches from the test domains,
        when task is domain_adaptation.
        """
        raise NotImplementedError

    def predict(self, x):
        raise NotImplementedError


class ERM(Algorithm):
    """
    Empirical Risk Minimization (ERM)
    """

    def __init__(self, input_shape, num_classes, num_domains, network, hparams, device):
        super(ERM, self).__init__(
            input_shape, num_classes, num_domains, network, hparams, device
        )

        self.optimizer = torch.optim.Adam(
            self.network.parameters(),
            lr=self.hparams["lr"],
            weight_decay=self.hparams["weight_decay"],
        )

    def update(self, minibatches, unlabeled=None):
        # all_x, all_y = minibatches
        # all_x = all_x.to(self.device)
        # all_y = all_y.to(self.device).long().to(self.device)
        all_x = torch.cat([x for x, y in minibatches]).to(self.device)
        all_y = torch.cat([y for x, y in minibatches]).long().to(self.device)
        loss = F.cross_entropy(self.predict(all_x), all_y)

        self.optimizer.zero_grad()
        loss.backward()
        nn.utils.clip_grad_norm_(self.network.parameters(), max_norm=2.0, norm_type=2)
        self.optimizer.step()

        return {"loss": loss.item()}

    def predict(self, x):
        return self.network(x)


class Fish(Algorithm):
    """
    Implementation of Fish, as seen in Gradient Matching for Domain
    Generalization, Shi et al. 2021.
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(Fish, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.input_shape = input_shape
        self.num_classes = num_classes

        self.network = networks.WholeFish(input_shape, num_classes, hparams)
        self.optimizer = torch.optim.Adam(
            self.network.parameters(),
            lr=self.hparams["lr"],
            weight_decay=self.hparams["weight_decay"],
        )
        self.optimizer_inner_state = None

    def create_clone(self, device):
        self.network_inner = networks.WholeFish(
            self.input_shape,
            self.num_classes,
            self.hparams,
            weights=self.network.state_dict(),
        ).to(device)
        self.optimizer_inner = torch.optim.Adam(
            self.network_inner.parameters(),
            lr=self.hparams["lr"],
            weight_decay=self.hparams["weight_decay"],
        )
        if self.optimizer_inner_state is not None:
            self.optimizer_inner.load_state_dict(self.optimizer_inner_state)

    def fish(self, meta_weights, inner_weights, lr_meta):
        meta_weights = ParamDict(meta_weights)
        inner_weights = ParamDict(inner_weights)
        meta_weights += lr_meta * (inner_weights - meta_weights)
        return meta_weights

    def update(self, minibatches, unlabeled=None):
        self.create_clone(minibatches[0][0].device)

        for x, y in minibatches:
            loss = F.cross_entropy(self.network_inner(x), y)
            self.optimizer_inner.zero_grad()
            loss.backward()
            self.optimizer_inner.step()

        self.optimizer_inner_state = self.optimizer_inner.state_dict()
        meta_weights = self.fish(
            meta_weights=self.network.state_dict(),
            inner_weights=self.network_inner.state_dict(),
            lr_meta=self.hparams["meta_lr"],
        )
        self.network.reset_weights(meta_weights)

        return {"loss": loss.item()}

    def predict(self, x):
        return self.network(x)


class ARM(ERM):
    """ Adaptive Risk Minimization (ARM) """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        original_input_shape = input_shape
        input_shape = (1 + original_input_shape[0],) + original_input_shape[1:]
        super(ARM, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.context_net = networks.ContextNet(original_input_shape)
        self.support_size = hparams["batch_size"]

    def predict(self, x):
        batch_size, c, h, w = x.shape
        if batch_size % self.support_size == 0:
            meta_batch_size = batch_size // self.support_size
            support_size = self.support_size
        else:
            meta_batch_size, support_size = 1, batch_size
        context = self.context_net(x)
        context = context.reshape((meta_batch_size, support_size, 1, h, w))
        context = context.mean(dim=1)
        context = torch.repeat_interleave(context, repeats=support_size, dim=0)
        x = torch.cat([x, context], dim=1)
        return self.network(x)


class AbstractDANN(Algorithm):
    """Domain-Adversarial Neural Networks (abstract class)"""

    def __init__(
        self, input_shape, num_classes, num_domains, hparams, conditional, class_balance
    ):

        super(AbstractDANN, self).__init__(
            input_shape, num_classes, num_domains, hparams
        )

        self.register_buffer("update_count", torch.tensor([0]))
        self.conditional = conditional
        self.class_balance = class_balance

        # Algorithms
        self.featurizer = networks.Featurizer(input_shape, self.hparams)
        self.classifier = networks.Classifier(
            self.featurizer.n_outputs, num_classes, self.hparams["nonlinear_classifier"]
        )
        self.discriminator = networks.MLP(
            self.featurizer.n_outputs, num_domains, self.hparams
        )
        self.class_embeddings = nn.Embedding(num_classes, self.featurizer.n_outputs)

        # Optimizers
        self.disc_opt = torch.optim.Adam(
            (
                list(self.discriminator.parameters())
                + list(self.class_embeddings.parameters())
            ),
            lr=self.hparams["lr_d"],
            weight_decay=self.hparams["weight_decay_d"],
            betas=(self.hparams["beta1"], 0.9),
        )

        self.gen_opt = torch.optim.Adam(
            (list(self.featurizer.parameters()) + list(self.classifier.parameters())),
            lr=self.hparams["lr_g"],
            weight_decay=self.hparams["weight_decay_g"],
            betas=(self.hparams["beta1"], 0.9),
        )

    def update(self, minibatches, unlabeled=None):
        device = "cuda" if minibatches[0][0].is_cuda else "cpu"
        self.update_count += 1
        all_x = torch.cat([x for x, y in minibatches])
        all_y = torch.cat([y for x, y in minibatches])
        all_z = self.featurizer(all_x)
        if self.conditional:
            disc_input = all_z + self.class_embeddings(all_y)
        else:
            disc_input = all_z
        disc_out = self.discriminator(disc_input)
        disc_labels = torch.cat(
            [
                torch.full((x.shape[0],), i, dtype=torch.int64, device=device)
                for i, (x, y) in enumerate(minibatches)
            ]
        )

        if self.class_balance:
            y_counts = F.one_hot(all_y).sum(dim=0)
            weights = 1.0 / (y_counts[all_y] * y_counts.shape[0]).float()
            disc_loss = F.cross_entropy(disc_out, disc_labels, reduction="none")
            disc_loss = (weights * disc_loss).sum()
        else:
            disc_loss = F.cross_entropy(disc_out, disc_labels)

        disc_softmax = F.softmax(disc_out, dim=1)
        input_grad = autograd.grad(
            disc_softmax[:, disc_labels].sum(), [disc_input], create_graph=True
        )[0]
        grad_penalty = (input_grad ** 2).sum(dim=1).mean(dim=0)
        disc_loss += self.hparams["grad_penalty"] * grad_penalty

        d_steps_per_g = self.hparams["d_steps_per_g_step"]
        if self.update_count.item() % (1 + d_steps_per_g) < d_steps_per_g:

            self.disc_opt.zero_grad()
            disc_loss.backward()
            self.disc_opt.step()
            return {"disc_loss": disc_loss.item()}
        else:
            all_preds = self.classifier(all_z)
            classifier_loss = F.cross_entropy(all_preds, all_y)
            gen_loss = classifier_loss + (self.hparams["lambda"] * -disc_loss)
            self.disc_opt.zero_grad()
            self.gen_opt.zero_grad()
            gen_loss.backward()
            self.gen_opt.step()
            return {"gen_loss": gen_loss.item()}

    def predict(self, x):
        return self.classifier(self.featurizer(x))


class DANN(AbstractDANN):
    """Unconditional DANN"""

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(DANN, self).__init__(
            input_shape,
            num_classes,
            num_domains,
            hparams,
            conditional=False,
            class_balance=False,
        )


class CDANN(AbstractDANN):
    """Conditional DANN"""

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(CDANN, self).__init__(
            input_shape,
            num_classes,
            num_domains,
            hparams,
            conditional=True,
            class_balance=True,
        )


class IRM(ERM):
    """Invariant Risk Minimization"""

    def __init__(self, input_shape, num_classes, num_domains, network, hparams, device):
        super(IRM, self).__init__(
            input_shape, num_classes, num_domains, network, hparams, device
        )
        self.register_buffer("update_count", torch.tensor([0]))

    @staticmethod
    def _irm_penalty(logits, y):
        device = "cuda" if logits[0][0].is_cuda else "cpu"
        scale = torch.tensor(1.0).to(device).requires_grad_()
        loss_1 = F.cross_entropy(logits[::2] * scale, y[::2])
        loss_2 = F.cross_entropy(logits[1::2] * scale, y[1::2])
        grad_1 = autograd.grad(loss_1, [scale], create_graph=True)[0]
        grad_2 = autograd.grad(loss_2, [scale], create_graph=True)[0]
        result = torch.sum(grad_1 * grad_2)
        return result

    def update(self, minibatches, unlabeled=None):
        penalty_weight = (
            self.hparams["irm_lambda"]
            if self.update_count >= self.hparams["irm_penalty_anneal_iters"]
            else 1.0
        )
        nll = 0.0
        penalty = 0.0

        all_x = torch.cat([x for x, y in minibatches]).to(self.device)
        all_logits = self.network(all_x)
        all_logits_idx = 0
        for i, (x, y) in enumerate(minibatches):
            x = x.to(self.device)
            y = y.long().to(self.device)
            logits = all_logits[all_logits_idx : all_logits_idx + x.shape[0]]
            all_logits_idx += x.shape[0]
            nll += F.cross_entropy(logits, y)
            penalty += self._irm_penalty(logits, y)
        nll /= len(minibatches)
        penalty /= len(minibatches)
        loss = nll + (penalty_weight * penalty)

        if self.update_count == self.hparams["irm_penalty_anneal_iters"]:
            # Reset Adam, because it doesn't like the sharp jump in gradient
            # magnitudes that happens at this step.
            self.optimizer = torch.optim.Adam(
                self.network.parameters(),
                lr=self.hparams["lr"],
                weight_decay=self.hparams["weight_decay"],
            )

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        self.update_count += 1
        return {
            "loss": loss.item(),
            "nll": nll.item(),
            "penalty": penalty.item(),
        }


class VREx(ERM):
    """V-REx algorithm from http://arxiv.org/abs/2003.00688"""

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(VREx, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.register_buffer("update_count", torch.tensor([0]))

    def update(self, minibatches, unlabeled=None):
        if self.update_count >= self.hparams["vrex_penalty_anneal_iters"]:
            penalty_weight = self.hparams["vrex_lambda"]
        else:
            penalty_weight = 1.0

        nll = 0.0

        all_x = torch.cat([x for x, y in minibatches])
        all_logits = self.network(all_x)
        all_logits_idx = 0
        losses = torch.zeros(len(minibatches))
        for i, (x, y) in enumerate(minibatches):
            logits = all_logits[all_logits_idx : all_logits_idx + x.shape[0]]
            all_logits_idx += x.shape[0]
            nll = F.cross_entropy(logits, y)
            losses[i] = nll

        mean = losses.mean()
        penalty = ((losses - mean) ** 2).mean()
        loss = mean + penalty_weight * penalty

        if self.update_count == self.hparams["vrex_penalty_anneal_iters"]:
            # Reset Adam (like IRM), because it doesn't like the sharp jump in
            # gradient magnitudes that happens at this step.
            self.optimizer = torch.optim.Adam(
                self.network.parameters(),
                lr=self.hparams["lr"],
                weight_decay=self.hparams["weight_decay"],
            )

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        self.update_count += 1
        return {"loss": loss.item(), "nll": nll.item(), "penalty": penalty.item()}


class Mixup(ERM):
    """
    Mixup of minibatches from different domains
    https://arxiv.org/pdf/2001.00677.pdf
    https://arxiv.org/pdf/1912.01805.pdf
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(Mixup, self).__init__(input_shape, num_classes, num_domains, hparams)

    def update(self, minibatches, unlabeled=None):
        objective = 0

        for (xi, yi), (xj, yj) in random_pairs_of_minibatches(minibatches):
            lam = np.random.beta(
                self.hparams["mixup_alpha"], self.hparams["mixup_alpha"]
            )

            x = lam * xi + (1 - lam) * xj
            predictions = self.predict(x)

            objective += lam * F.cross_entropy(predictions, yi)
            objective += (1 - lam) * F.cross_entropy(predictions, yj)

        objective /= len(minibatches)

        self.optimizer.zero_grad()
        objective.backward()
        self.optimizer.step()

        return {"loss": objective.item()}


class GroupDRO(ERM):
    """
    Robust ERM minimizes the error at the worst minibatch
    Algorithm 1 from [https://arxiv.org/pdf/1911.08731.pdf]
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(GroupDRO, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.register_buffer("q", torch.Tensor())

    def update(self, minibatches, unlabeled=None):
        device = "cuda" if minibatches[0][0].is_cuda else "cpu"

        if not len(self.q):
            self.q = torch.ones(len(minibatches)).to(device)

        losses = torch.zeros(len(minibatches)).to(device)

        for m in range(len(minibatches)):
            x, y = minibatches[m]
            losses[m] = F.cross_entropy(self.predict(x), y)
            self.q[m] *= (self.hparams["groupdro_eta"] * losses[m].data).exp()

        self.q /= self.q.sum()

        loss = torch.dot(losses, self.q)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return {"loss": loss.item()}


class MLDG(ERM):
    """
    Model-Agnostic Meta-Learning
    Algorithm 1 / Equation (3) from: https://arxiv.org/pdf/1710.03463.pdf
    Related: https://arxiv.org/pdf/1703.03400.pdf
    Related: https://arxiv.org/pdf/1910.13580.pdf
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(MLDG, self).__init__(input_shape, num_classes, num_domains, hparams)

    def update(self, minibatches, unlabeled=None):
        """
        Terms being computed:
            * Li = Loss(xi, yi, params)
            * Gi = Grad(Li, params)

            * Lj = Loss(xj, yj, Optimizer(params, grad(Li, params)))
            * Gj = Grad(Lj, params)

            * params = Optimizer(params, Grad(Li + beta * Lj, params))
            *        = Optimizer(params, Gi + beta * Gj)

        That is, when calling .step(), we want grads to be Gi + beta * Gj

        For computational efficiency, we do not compute second derivatives.
        """
        num_mb = len(minibatches)
        objective = 0

        self.optimizer.zero_grad()
        for p in self.network.parameters():
            if p.grad is None:
                p.grad = torch.zeros_like(p)

        for (xi, yi), (xj, yj) in random_pairs_of_minibatches(minibatches):
            # fine tune clone-network on task "i"
            inner_net = copy.deepcopy(self.network)

            inner_opt = torch.optim.Adam(
                inner_net.parameters(),
                lr=self.hparams["lr"],
                weight_decay=self.hparams["weight_decay"],
            )

            inner_obj = F.cross_entropy(inner_net(xi), yi)

            inner_opt.zero_grad()
            inner_obj.backward()
            inner_opt.step()

            # The network has now accumulated gradients Gi
            # The clone-network has now parameters P - lr * Gi
            for p_tgt, p_src in zip(self.network.parameters(), inner_net.parameters()):
                if p_src.grad is not None:
                    p_tgt.grad.data.add_(p_src.grad.data / num_mb)

            # `objective` is populated for reporting purposes
            objective += inner_obj.item()

            # this computes Gj on the clone-network
            loss_inner_j = F.cross_entropy(inner_net(xj), yj)
            grad_inner_j = autograd.grad(
                loss_inner_j, inner_net.parameters(), allow_unused=True
            )

            # `objective` is populated for reporting purposes
            objective += (self.hparams["mldg_beta"] * loss_inner_j).item()

            for p, g_j in zip(self.network.parameters(), grad_inner_j):
                if g_j is not None:
                    p.grad.data.add_(self.hparams["mldg_beta"] * g_j.data / num_mb)

            # The network has now accumulated gradients Gi + beta * Gj
            # Repeat for all train-test splits, do .step()

        objective /= len(minibatches)

        self.optimizer.step()

        return {"loss": objective}

    # This commented "update" method back-propagates through the gradients of
    # the inner update, as suggested in the original MAML paper.  However, this
    # is twice as expensive as the uncommented "update" method, which does not
    # compute second-order derivatives, implementing the First-Order MAML
    # method (FOMAML) described in the original MAML paper.

    # def update(self, minibatches, unlabeled=None):
    #     objective = 0
    #     beta = self.hparams["beta"]
    #     inner_iterations = self.hparams["inner_iterations"]

    #     self.optimizer.zero_grad()

    #     with higher.innerloop_ctx(self.network, self.optimizer,
    #         copy_initial_weights=False) as (inner_network, inner_optimizer):

    #         for (xi, yi), (xj, yj) in random_pairs_of_minibatches(minibatches):
    #             for inner_iteration in range(inner_iterations):
    #                 li = F.cross_entropy(inner_network(xi), yi)
    #                 inner_optimizer.step(li)
    #
    #             objective += F.cross_entropy(self.network(xi), yi)
    #             objective += beta * F.cross_entropy(inner_network(xj), yj)

    #         objective /= len(minibatches)
    #         objective.backward()
    #
    #     self.optimizer.step()
    #
    #     return objective


class AbstractMMD(ERM):
    """
    Perform ERM while matching the pair-wise domain feature distributions
    using MMD (abstract class)
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams, gaussian):
        super(AbstractMMD, self).__init__(
            input_shape, num_classes, num_domains, hparams
        )
        if gaussian:
            self.kernel_type = "gaussian"
        else:
            self.kernel_type = "mean_cov"

    def my_cdist(self, x1, x2):
        x1_norm = x1.pow(2).sum(dim=-1, keepdim=True)
        x2_norm = x2.pow(2).sum(dim=-1, keepdim=True)
        res = torch.addmm(
            x2_norm.transpose(-2, -1), x1, x2.transpose(-2, -1), alpha=-2
        ).add_(x1_norm)
        return res.clamp_min_(1e-30)

    def gaussian_kernel(self, x, y, gamma=[0.001, 0.01, 0.1, 1, 10, 100, 1000]):
        D = self.my_cdist(x, y)
        K = torch.zeros_like(D)

        for g in gamma:
            K.add_(torch.exp(D.mul(-g)))

        return K

    def mmd(self, x, y):
        if self.kernel_type == "gaussian":
            Kxx = self.gaussian_kernel(x, x).mean()
            Kyy = self.gaussian_kernel(y, y).mean()
            Kxy = self.gaussian_kernel(x, y).mean()
            return Kxx + Kyy - 2 * Kxy
        else:
            mean_x = x.mean(0, keepdim=True)
            mean_y = y.mean(0, keepdim=True)
            cent_x = x - mean_x
            cent_y = y - mean_y
            cova_x = (cent_x.t() @ cent_x) / (len(x) - 1)
            cova_y = (cent_y.t() @ cent_y) / (len(y) - 1)

            mean_diff = (mean_x - mean_y).pow(2).mean()
            cova_diff = (cova_x - cova_y).pow(2).mean()

            return mean_diff + cova_diff

    def update(self, minibatches, unlabeled=None):
        objective = 0
        penalty = 0
        nmb = len(minibatches)

        features = [self.featurizer(xi) for xi, _ in minibatches]
        classifs = [self.classifier(fi) for fi in features]
        targets = [yi for _, yi in minibatches]

        for i in range(nmb):
            objective += F.cross_entropy(classifs[i], targets[i])
            for j in range(i + 1, nmb):
                penalty += self.mmd(features[i], features[j])

        objective /= nmb
        if nmb > 1:
            penalty /= nmb * (nmb - 1) / 2

        self.optimizer.zero_grad()
        (objective + (self.hparams["mmd_gamma"] * penalty)).backward()
        self.optimizer.step()

        if torch.is_tensor(penalty):
            penalty = penalty.item()

        return {"loss": objective.item(), "penalty": penalty}


class MMD(AbstractMMD):
    """
    MMD using Gaussian kernel
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(MMD, self).__init__(
            input_shape, num_classes, num_domains, hparams, gaussian=True
        )


class CORAL(AbstractMMD):
    """
    MMD using mean and covariance difference
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(CORAL, self).__init__(
            input_shape, num_classes, num_domains, hparams, gaussian=False
        )


class MTL(Algorithm):
    """
    A neural network version of
    Domain Generalization by Marginal Transfer Learning
    (https://arxiv.org/abs/1711.07910)
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(MTL, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.featurizer = networks.Featurizer(input_shape, self.hparams)
        self.classifier = networks.Classifier(
            self.featurizer.n_outputs * 2,
            num_classes,
            self.hparams["nonlinear_classifier"],
        )
        self.optimizer = torch.optim.Adam(
            list(self.featurizer.parameters()) + list(self.classifier.parameters()),
            lr=self.hparams["lr"],
            weight_decay=self.hparams["weight_decay"],
        )

        self.register_buffer(
            "embeddings", torch.zeros(num_domains, self.featurizer.n_outputs)
        )

        self.ema = self.hparams["mtl_ema"]

    def update(self, minibatches, unlabeled=None):
        loss = 0
        for env, (x, y) in enumerate(minibatches):
            loss += F.cross_entropy(self.predict(x, env), y)

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return {"loss": loss.item()}

    def update_embeddings_(self, features, env=None):
        return_embedding = features.mean(0)

        if env is not None:
            return_embedding = (
                self.ema * return_embedding + (1 - self.ema) * self.embeddings[env]
            )

            self.embeddings[env] = return_embedding.clone().detach()

        return return_embedding.view(1, -1).repeat(len(features), 1)

    def predict(self, x, env=None):
        features = self.featurizer(x)
        embedding = self.update_embeddings_(features, env).normal_()
        return self.classifier(torch.cat((features, embedding), 1))


class SagNet(Algorithm):
    """
    Style Agnostic Network
    Algorithm 1 from: https://arxiv.org/abs/1910.11645
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(SagNet, self).__init__(input_shape, num_classes, num_domains, hparams)
        # featurizer network
        self.network_f = networks.Featurizer(input_shape, self.hparams)
        # content network
        self.network_c = networks.Classifier(
            self.network_f.n_outputs, num_classes, self.hparams["nonlinear_classifier"]
        )
        # style network
        self.network_s = networks.Classifier(
            self.network_f.n_outputs, num_classes, self.hparams["nonlinear_classifier"]
        )

        # # This commented block of code implements something closer to the
        # # original paper, but is specific to ResNet and puts in disadvantage
        # # the other algorithms.
        # resnet_c = networks.Featurizer(input_shape, self.hparams)
        # resnet_s = networks.Featurizer(input_shape, self.hparams)
        # # featurizer network
        # self.network_f = torch.nn.Sequential(
        #         resnet_c.network.conv1,
        #         resnet_c.network.bn1,
        #         resnet_c.network.relu,
        #         resnet_c.network.maxpool,
        #         resnet_c.network.layer1,
        #         resnet_c.network.layer2,
        #         resnet_c.network.layer3)
        # # content network
        # self.network_c = torch.nn.Sequential(
        #         resnet_c.network.layer4,
        #         resnet_c.network.avgpool,
        #         networks.Flatten(),
        #         resnet_c.network.fc)
        # # style network
        # self.network_s = torch.nn.Sequential(
        #         resnet_s.network.layer4,
        #         resnet_s.network.avgpool,
        #         networks.Flatten(),
        #         resnet_s.network.fc)

        def opt(p):
            return torch.optim.Adam(
                p, lr=hparams["lr"], weight_decay=hparams["weight_decay"]
            )

        self.optimizer_f = opt(self.network_f.parameters())
        self.optimizer_c = opt(self.network_c.parameters())
        self.optimizer_s = opt(self.network_s.parameters())
        self.weight_adv = hparams["sag_w_adv"]

    def forward_c(self, x):
        # learning content network on randomized style
        return self.network_c(self.randomize(self.network_f(x), "style"))

    def forward_s(self, x):
        # learning style network on randomized content
        return self.network_s(self.randomize(self.network_f(x), "content"))

    def randomize(self, x, what="style", eps=1e-5):
        device = "cuda" if x.is_cuda else "cpu"
        sizes = x.size()
        alpha = torch.rand(sizes[0], 1).to(device)

        if len(sizes) == 4:
            x = x.view(sizes[0], sizes[1], -1)
            alpha = alpha.unsqueeze(-1)

        mean = x.mean(-1, keepdim=True)
        var = x.var(-1, keepdim=True)

        x = (x - mean) / (var + eps).sqrt()

        idx_swap = torch.randperm(sizes[0])
        if what == "style":
            mean = alpha * mean + (1 - alpha) * mean[idx_swap]
            var = alpha * var + (1 - alpha) * var[idx_swap]
        else:
            x = x[idx_swap].detach()

        x = x * (var + eps).sqrt() + mean
        return x.view(*sizes)

    def update(self, minibatches, unlabeled=None):
        all_x = torch.cat([x for x, y in minibatches])
        all_y = torch.cat([y for x, y in minibatches])

        # learn content
        self.optimizer_f.zero_grad()
        self.optimizer_c.zero_grad()
        loss_c = F.cross_entropy(self.forward_c(all_x), all_y)
        loss_c.backward()
        self.optimizer_f.step()
        self.optimizer_c.step()

        # learn style
        self.optimizer_s.zero_grad()
        loss_s = F.cross_entropy(self.forward_s(all_x), all_y)
        loss_s.backward()
        self.optimizer_s.step()

        # learn adversary
        self.optimizer_f.zero_grad()
        loss_adv = -F.log_softmax(self.forward_s(all_x), dim=1).mean(1).mean()
        loss_adv = loss_adv * self.weight_adv
        loss_adv.backward()
        self.optimizer_f.step()

        return {
            "loss_c": loss_c.item(),
            "loss_s": loss_s.item(),
            "loss_adv": loss_adv.item(),
        }

    def predict(self, x):
        return self.network_c(self.network_f(x))


class RSC(ERM):
    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(RSC, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.drop_f = (1 - hparams["rsc_f_drop_factor"]) * 100
        self.drop_b = (1 - hparams["rsc_b_drop_factor"]) * 100
        self.num_classes = num_classes

    def update(self, minibatches, unlabeled=None):
        device = "cuda" if minibatches[0][0].is_cuda else "cpu"

        # inputs
        all_x = torch.cat([x for x, y in minibatches])
        # labels
        all_y = torch.cat([y for _, y in minibatches])
        # one-hot labels
        all_o = torch.nn.functional.one_hot(all_y, self.num_classes)
        # features
        all_f = self.featurizer(all_x)
        # predictions
        all_p = self.classifier(all_f)

        # Equation (1): compute gradients with respect to representation
        all_g = autograd.grad((all_p * all_o).sum(), all_f)[0]

        # Equation (2): compute top-gradient-percentile mask
        percentiles = np.percentile(all_g.cpu(), self.drop_f, axis=1)
        percentiles = torch.Tensor(percentiles)
        percentiles = percentiles.unsqueeze(1).repeat(1, all_g.size(1))
        mask_f = all_g.lt(percentiles.to(device)).float()

        # Equation (3): mute top-gradient-percentile activations
        all_f_muted = all_f * mask_f

        # Equation (4): compute muted predictions
        all_p_muted = self.classifier(all_f_muted)

        # Section 3.3: Batch Percentage
        all_s = F.softmax(all_p, dim=1)
        all_s_muted = F.softmax(all_p_muted, dim=1)
        changes = (all_s * all_o).sum(1) - (all_s_muted * all_o).sum(1)
        percentile = np.percentile(changes.detach().cpu(), self.drop_b)
        mask_b = changes.lt(percentile).float().view(-1, 1)
        mask = torch.logical_or(mask_f, mask_b).float()

        # Equations (3) and (4) again, this time mutting over examples
        all_p_muted_again = self.classifier(all_f * mask)

        # Equation (5): update
        loss = F.cross_entropy(all_p_muted_again, all_y)
        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return {"loss": loss.item()}


class SD(ERM):
    """
    Gradient Starvation: A Learning Proclivity in Neural Networks
    Equation 25 from [https://arxiv.org/pdf/2011.09468.pdf]
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(SD, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.sd_reg = hparams["sd_reg"]

    def update(self, minibatches, unlabeled=None):
        all_x = torch.cat([x for x, y in minibatches])
        all_y = torch.cat([y for x, y in minibatches])
        all_p = self.predict(all_x)

        loss = F.cross_entropy(all_p, all_y)
        penalty = (all_p ** 2).mean()
        objective = loss + self.sd_reg * penalty

        self.optimizer.zero_grad()
        objective.backward()
        self.optimizer.step()

        return {"loss": loss.item(), "penalty": penalty.item()}


class ANDMask(ERM):
    """
    Learning Explanations that are Hard to Vary [https://arxiv.org/abs/2009.00329]
    AND-Mask implementation from [https://github.com/gibipara92/learning-explanations-hard-to-vary]
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(ANDMask, self).__init__(input_shape, num_classes, num_domains, hparams)

        self.tau = hparams["tau"]

    def update(self, minibatches, unlabeled=None):

        total_loss = 0
        param_gradients = [[] for _ in self.network.parameters()]
        all_x = torch.cat([x for x, y in minibatches])
        all_logits = self.network(all_x)
        all_logits_idx = 0
        for i, (x, y) in enumerate(minibatches):
            logits = all_logits[all_logits_idx : all_logits_idx + x.shape[0]]
            all_logits_idx += x.shape[0]

            env_loss = F.cross_entropy(logits, y)
            total_loss += env_loss

            env_grads = autograd.grad(
                env_loss, self.network.parameters(), retain_graph=True
            )
            for grads, env_grad in zip(param_gradients, env_grads):
                grads.append(env_grad)

        mean_loss = total_loss / len(minibatches)

        self.optimizer.zero_grad()
        self.mask_grads(self.tau, param_gradients, self.network.parameters())
        self.optimizer.step()

        return {"loss": mean_loss.item()}

    def mask_grads(self, tau, gradients, params):

        for param, grads in zip(params, gradients):
            grads = torch.stack(grads, dim=0)
            grad_signs = torch.sign(grads)
            mask = torch.mean(grad_signs, dim=0).abs() >= self.tau
            mask = mask.to(torch.float32)
            avg_grad = torch.mean(grads, dim=0)

            mask_t = mask.sum() / mask.numel()
            param.grad = mask * avg_grad
            param.grad *= 1.0 / (1e-10 + mask_t)

        return 0


class IGA(ERM):
    """
    Inter-environmental Gradient Alignment
    From https://arxiv.org/abs/2008.01883v2
    """

    def __init__(self, in_features, num_classes, num_domains, hparams):
        super(IGA, self).__init__(in_features, num_classes, num_domains, hparams)

    def update(self, minibatches, unlabeled=False):

        all_x = torch.cat([x for x, y in minibatches])
        all_logits = self.network(all_x)

        total_loss = 0
        all_logits_idx = 0
        grads = []
        for i, (x, y) in enumerate(minibatches):
            logits = all_logits[all_logits_idx : all_logits_idx + x.shape[0]]
            all_logits_idx += x.shape[0]

            env_loss = F.cross_entropy(logits, y)
            total_loss += env_loss

            env_grad = autograd.grad(
                env_loss, self.network.parameters(), create_graph=True
            )

            grads.append(env_grad)

        mean_loss = total_loss / len(minibatches)
        mean_grad = autograd.grad(
            mean_loss, self.network.parameters(), create_graph=True
        )

        # compute trace penalty
        penalty_value = 0
        for grad in grads:
            for g, mean_g in zip(grad, mean_grad):
                penalty_value += (g - mean_g).pow(2).sum()

        objective = mean_loss + self.hparams["penalty"] * penalty_value

        self.optimizer.zero_grad()
        objective.backward()
        self.optimizer.step()

        return {"loss": mean_loss.item(), "penalty": penalty_value.item()}


class SelfReg(ERM):
    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(SelfReg, self).__init__(input_shape, num_classes, num_domains, hparams)
        self.num_classes = num_classes
        self.MSEloss = nn.MSELoss()
        input_feat_size = self.featurizer.n_outputs
        hidden_size = (
            input_feat_size if input_feat_size == 2048 else input_feat_size * 2
        )

        self.cdpl = nn.Sequential(
            nn.Linear(input_feat_size, hidden_size),
            nn.BatchNorm1d(hidden_size),
            nn.ReLU(inplace=True),
            nn.Linear(hidden_size, hidden_size),
            nn.BatchNorm1d(hidden_size),
            nn.ReLU(inplace=True),
            nn.Linear(hidden_size, input_feat_size),
            nn.BatchNorm1d(input_feat_size),
        )

    def update(self, minibatches, unlabeled=None):

        all_x = torch.cat([x for x, y in minibatches])
        all_y = torch.cat([y for _, y in minibatches])

        lam = np.random.beta(0.5, 0.5)

        batch_size = all_y.size()[0]

        # cluster and order features into same-class group
        with torch.no_grad():
            sorted_y, indices = torch.sort(all_y)
            sorted_x = torch.zeros_like(all_x)
            for idx, order in enumerate(indices):
                sorted_x[idx] = all_x[order]
            intervals = []
            ex = 0
            for idx, val in enumerate(sorted_y):
                if ex == val:
                    continue
                intervals.append(idx)
                ex = val
            intervals.append(batch_size)

            all_x = sorted_x
            all_y = sorted_y

        feat = self.featurizer(all_x)
        proj = self.cdpl(feat)

        output = self.classifier(feat)

        # shuffle
        output_2 = torch.zeros_like(output)
        feat_2 = torch.zeros_like(proj)
        output_3 = torch.zeros_like(output)
        feat_3 = torch.zeros_like(proj)
        ex = 0
        for end in intervals:
            shuffle_indices = torch.randperm(end - ex) + ex
            shuffle_indices2 = torch.randperm(end - ex) + ex
            for idx in range(end - ex):
                output_2[idx + ex] = output[shuffle_indices[idx]]
                feat_2[idx + ex] = proj[shuffle_indices[idx]]
                output_3[idx + ex] = output[shuffle_indices2[idx]]
                feat_3[idx + ex] = proj[shuffle_indices2[idx]]
            ex = end

        # mixup
        output_3 = lam * output_2 + (1 - lam) * output_3
        feat_3 = lam * feat_2 + (1 - lam) * feat_3

        # regularization
        L_ind_logit = self.MSEloss(output, output_2)
        L_hdl_logit = self.MSEloss(output, output_3)
        L_ind_feat = 0.3 * self.MSEloss(feat, feat_2)
        L_hdl_feat = 0.3 * self.MSEloss(feat, feat_3)

        cl_loss = F.cross_entropy(output, all_y)
        C_scale = min(cl_loss.item(), 1.0)
        loss = cl_loss + C_scale * (
            lam * (L_ind_logit + L_ind_feat) + (1 - lam) * (L_hdl_logit + L_hdl_feat)
        )

        self.optimizer.zero_grad()
        loss.backward()
        self.optimizer.step()

        return {"loss": loss.item()}


class SANDMask(ERM):
    """
    SAND-mask: An Enhanced Gradient Masking Strategy for the Discovery of Invariances in Domain Generalization
    <https://arxiv.org/abs/2106.02266>
    """

    def __init__(self, input_shape, num_classes, num_domains, hparams):
        super(SANDMask, self).__init__(input_shape, num_classes, num_domains, hparams)

        self.tau = hparams["tau"]
        self.k = hparams["k"]
        betas = (0.9, 0.999)
        self.optimizer = torch.optim.Adam(
            self.network.parameters(),
            lr=self.hparams["lr"],
            weight_decay=self.hparams["weight_decay"],
            betas=betas,
        )

        self.register_buffer("update_count", torch.tensor([0]))

    def update(self, minibatches, unlabeled=None):

        total_loss = 0
        param_gradients = [[] for _ in self.network.parameters()]
        all_x = torch.cat([x for x, y in minibatches])
        all_logits = self.network(all_x)
        all_logits_idx = 0
        for i, (x, y) in enumerate(minibatches):
            logits = all_logits[all_logits_idx : all_logits_idx + x.shape[0]]
            all_logits_idx += x.shape[0]

            env_loss = F.cross_entropy(logits, y)
            total_loss += env_loss
            env_grads = autograd.grad(
                env_loss, self.network.parameters(), retain_graph=True
            )
            for grads, env_grad in zip(param_gradients, env_grads):
                grads.append(env_grad)

        mean_loss = total_loss / len(minibatches)

        self.optimizer.zero_grad()
        # gradient masking applied here
        self.mask_grads(param_gradients, self.network.parameters())
        self.optimizer.step()
        self.update_count += 1

        return {"loss": mean_loss.item()}

    def mask_grads(self, gradients, params):
        """
        Here a mask with continuous values in the range [0,1] is formed to control the amount of update for each
        parameter based on the agreement of gradients coming from different environments.
        """
        device = gradients[0][0].device
        for param, grads in zip(params, gradients):
            grads = torch.stack(grads, dim=0)
            avg_grad = torch.mean(grads, dim=0)
            grad_signs = torch.sign(grads)
            gamma = torch.tensor(1.0).to(device)
            grads_var = grads.var(dim=0)
            grads_var[torch.isnan(grads_var)] = 1e-17
            lam = (gamma * grads_var).pow(-1)
            mask = torch.tanh(
                self.k * lam * (torch.abs(grad_signs.mean(dim=0)) - self.tau)
            )
            mask = torch.max(mask, torch.zeros_like(mask))
            mask[torch.isnan(mask)] = 1e-17
            mask_t = mask.sum() / mask.numel()
            param.grad = mask * avg_grad
            param.grad *= 1.0 / (1e-10 + mask_t)
