import torch
from torch import nn

from ....core.alg_frame.client_trainer import ClientTrainer


class MyModelTrainer(ClientTrainer):
    def get_model_params(self):
        return self.model.cpu().state_dict()

    def set_model_params(self, model_parameters):
        self.model.load_state_dict(model_parameters)

    def train(self, train_data, device, args):
        model = self.model

        model.to(device)
        model.train()

        # train and update
        criterion = nn.CrossEntropyLoss().to(device)
        if args.client_optimizer == "sgd":
            optimizer = torch.optim.SGD(
                filter(lambda p: p.requires_grad, self.model.parameters()),
                lr=args.lr,
                weight_decay=args.wd,
            )
        else:
            optimizer = torch.optim.Adam(
                filter(lambda p: p.requires_grad, self.model.parameters()),
                lr=args.lr,
                weight_decay=args.wd,
                amsgrad=True,
            )

        epoch_loss = []
        for epoch in range(args.epochs):
            batch_loss = []
            for batch_idx, (x, labels) in enumerate(train_data):
                x, labels = x.to(device), labels.to(device)
                # logging.info("x.size = " + str(x.size()))
                # logging.info("labels.size = " + str(labels.size()))
                model.zero_grad()
                log_probs = model(x)
                loss = criterion(log_probs, labels)
                loss.backward()

                # to avoid nan loss
                torch.nn.utils.clip_grad_norm_(self.model.parameters(), 1.0)

                optimizer.step()
                # logging.info('Update Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                #     epoch, (batch_idx + 1) * self.args.batch_size, len(self.local_training_data) * self.args.batch_size,
                #            100. * (batch_idx + 1) / len(self.local_training_data), loss.item()))
                batch_loss.append(loss.item())
            epoch_loss.append(sum(batch_loss) / len(batch_loss))
            # logging.info('Client Index = {}\tEpoch: {}\tLoss: {:.6f}'.format(
            #     self.client_idx, epoch, sum(epoch_loss) / len(epoch_loss)))

    def test(self, test_data, device, args):
        model = self.model

        model.to(device)
        model.eval()

        metrics = {"test_correct": 0, "test_loss": 0, "test_total": 0}

        criterion = nn.CrossEntropyLoss().to(device)

        with torch.no_grad():
            for batch_idx, (x, target) in enumerate(test_data):
                x = x.to(device)
                target = target.to(device)
                pred = model(x)
                loss = criterion(pred, target)

                _, predicted = torch.max(pred, -1)
                correct = predicted.eq(target).sum()

                metrics["test_correct"] += correct.item()
                metrics["test_loss"] += loss.item() * target.size(0)
                metrics["test_total"] += target.size(0)
        return metrics

    def test_on_the_server(
        self, train_data_local_dict, test_data_local_dict, device, args=None
    ) -> bool:
        return False
