import os
import wandb as wdb
from dataset_ref import load_data_duo
import torch
import torch.nn as nn
from model import Classification_model_duo_contrastive
import torch.optim as optim

def train_duo(model, data_train, optimizer, loss_function, epoch, wandb):
    model.train()
    losses = 0.
    acc = 0.
    for param in model.parameters():
        param.requires_grad = True

    for imaer, imana, img_ref, label in data_train:
        imaer = imaer.float()
        imana = imana.float()
        img_ref = img_ref.float()
        label = label.long()
        if torch.cuda.is_available():
            imaer = imaer.cuda()
            imana = imana.cuda()
            img_ref = img_ref.cuda()
            label = label.cuda()
        pred_logits = model.forward(imaer, imana, img_ref)
        pred_class = torch.argmax(pred_logits, dim=1)
        acc += (pred_class == label).sum().item()
        loss = loss_function(pred_logits, label)
        losses += loss.item()
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
    losses = losses / len(data_train.dataset)
    acc = acc / len(data_train.dataset)
    print('Train epoch {}, loss : {:.3f} acc : {:.3f}'.format(epoch, losses, acc))

    wdb.log({"train loss": losses, 'train epoch': epoch, "train contrastive accuracy": acc})

    return losses, acc


def val_duo(model, data_test, loss_function, epoch, wandb):
    model.eval()
    losses = 0.
    acc = 0.
    acc_contrastive = 0.
    for param in model.parameters():
        param.requires_grad = False

    for imaer, imana, img_ref, label in data_test:
        imaer = imaer.float()
        imana = imana.float()
        img_ref = img_ref.float()
        imaer = imaer.transpose(0, 1)
        imana = imana.transpose(0, 1)
        img_ref = img_ref.transpose(0, 1)
        label = label.transpose(0, 1)
        label = label.squeeze()
        label = label.long()
        if torch.cuda.is_available():
            imaer = imaer.cuda()
            imana = imana.cuda()
            img_ref = img_ref.cuda()
            label = label.cuda()
        label_class = torch.argmin(label).data.cpu().numpy()
        pred_logits = model.forward(imaer, imana, img_ref)
        pred_class = torch.argmax(pred_logits[:, 0]).tolist()
        acc_contrastive += (
                    torch.argmax(pred_logits, dim=1).data.cpu().numpy() == label.data.cpu().numpy()).sum().item()
        acc += (pred_class == label_class)
        loss = loss_function(pred_logits, label)
        losses += loss.item()
    losses = losses / (label.shape[0] * len(data_test.dataset))
    acc = acc / (len(data_test.dataset))
    acc_contrastive = acc_contrastive / (label.shape[0] * len(data_test.dataset))
    print('Test epoch  {}, loss : {:.3f} acc : {:.3f} acc contrastive : {:.3f}'.format(epoch, losses, acc,
                                                                                       acc_contrastive))

    wdb.log({"validation loss": losses, 'validation epoch': epoch, "validation classification accuracy": acc,
                 "validation contrastive accuracy": acc_contrastive})

    return losses, acc, acc_contrastive


def run_duo(args):
    # wandb init
    os.environ["WANDB_API_KEY"] = 'b4a27ac6b6145e1a5d0ee7f9e2e8c20bd101dccd'

    os.environ["WANDB_MODE"] = "offline"
    os.environ["WANDB_DIR"] = os.path.abspath("./wandb_run")

    wdb.init(project="param_sweep_contrastive", dir='./wandb_run')

    print('Wandb initialised')
    # load data
    data_train, data_val_batch, data_test_batch = load_data_duo(base_dir_train=args.dataset_train_dir,
                                                                base_dir_val=args.dataset_val_dir,
                                                                base_dir_test=None,
                                                                batch_size=args.batch_size,
                                                                ref_dir=args.dataset_ref_dir,
                                                                positive_prop=args.positive_prop, sampler=args.sampler)

    # load model
    model = Classification_model_duo_contrastive(model=args.model, n_class=2)
    model.float()
    # move parameters to GPU
    if torch.cuda.is_available():
        print('Model loaded on GPU')
        model = model.cuda()

    # init accumulators
    best_loss = 100
    train_acc = []
    train_loss = []
    val_acc = []
    val_cont_acc = []
    val_loss = []
    # init training
    loss_function = nn.CrossEntropyLoss()
    if args.opti == 'adam':
        optimizer = optim.Adam(model.parameters(), lr=args.lr)
    # train model
    for e in range(args.epoches):
        loss, acc = train_duo(model, data_train, optimizer, loss_function, e, args.wandb)
        train_loss.append(loss)
        train_acc.append(acc)
        if e % args.eval_inter == 0:
            loss, acc, acc_contrastive = val_duo(model, data_val_batch, loss_function, e, args.wandb)
            val_loss.append(loss)
            val_acc.append(acc)
            val_cont_acc.append(acc_contrastive)
        wdb.finish()


if __name__ == '__main__':
    config = wdb.config
    print(config)
    run_duo(config)