from __future__ import print_function
import torch
from model import highwayNet
from utils import ngsimDataset,maskedNLL,maskedMSE,maskedNLLTest
from torch.utils.data import DataLoader
import time
import math

#ignore torch.uint8 warning
import warnings
warnings.filterwarnings("ignore", category=UserWarning)

## Network Arguments
args = {}
args['use_cuda'] = True
args['encoder_size'] = 64
args['decoder_size'] = 128
args['in_length'] = 16
args['out_length'] = 25
args['grid_size'] = (13,3)
args['soc_conv_depth'] = 64
args['conv_3x1_depth'] = 16
args['dyn_embedding_size'] = 32
args['input_embedding_size'] = 32
args['num_lat_classes'] = 3
args['num_lon_classes'] = 2
args['use_maneuvers'] = True
args['train_flag'] = True



# Initialize network
net = highwayNet(args)
if args['use_cuda']:
    net = net.cuda()


## Initialize optimizer
pretrainEpochs = 5
trainEpochs = 3
optimizer = torch.optim.Adam(net.parameters())
batch_size = 128
crossEnt = torch.nn.BCELoss()


## Initialize data loaders
trSet = ngsimDataset('data/TrainSet.mat')
valSet = ngsimDataset('data/ValSet.mat')
trDataloader = DataLoader(trSet,batch_size=batch_size,shuffle=True,num_workers=0,collate_fn=trSet.collate_fn)
valDataloader = DataLoader(valSet,batch_size=batch_size,shuffle=True,num_workers=0,collate_fn=valSet.collate_fn)


## Variables holding train and validation loss values:
train_loss = []
val_loss = []
prev_val_loss = math.inf

for epoch_num in range(pretrainEpochs+trainEpochs):
    if epoch_num == 0:
        print('Pre-training with MSE loss')
    elif epoch_num == pretrainEpochs:
        print('Training with NLL loss')


    ## Train:_________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
    net.train_flag = True

    # Variables to track training performance:
    avg_tr_loss = 0
    avg_tr_time = 0
    avg_lat_acc = 0
    avg_lon_acc = 0


    for i, data in enumerate(trDataloader):

        st_time = time.time()
        hist, nbrs, mask, lat_enc, lon_enc, fut, op_mask = data

        if args['use_cuda']:
            hist = hist.cuda()
            nbrs = nbrs.cuda()
            mask = mask.cuda()
            lat_enc = lat_enc.cuda()
            lon_enc = lon_enc.cuda()
            fut = fut.cuda()
            op_mask = op_mask.cuda()

        # Forward pass
        if args['use_maneuvers']:
            fut_pred, lat_pred, lon_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
            # Pre-train with MSE loss to speed up training
            if epoch_num < pretrainEpochs:
                l = maskedMSE(fut_pred, fut, op_mask)
            else:
            # Train with NLL loss
                l = maskedNLL(fut_pred, fut, op_mask) + crossEnt(lat_pred, lat_enc) + crossEnt(lon_pred, lon_enc)
                avg_lat_acc += (torch.sum(torch.max(lat_pred.data, 1)[1] == torch.max(lat_enc.data, 1)[1])).item() / lat_enc.size()[0]
                avg_lon_acc += (torch.sum(torch.max(lon_pred.data, 1)[1] == torch.max(lon_enc.data, 1)[1])).item() / lon_enc.size()[0]
        else:
            fut_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
            if epoch_num < pretrainEpochs:
                l = maskedMSE(fut_pred, fut, op_mask)
            else:
                l = maskedNLL(fut_pred, fut, op_mask)

        # Backprop and update weights
        optimizer.zero_grad()
        l.backward()
        a = torch.nn.utils.clip_grad_norm_(net.parameters(), 10)
        optimizer.step()

        # Track average train loss and average train time:
        batch_time = time.time()-st_time
        avg_tr_loss += l.item()
        avg_tr_time += batch_time

        if i%100 == 99:
            eta = avg_tr_time/100*(len(trSet)/batch_size-i)
            print("Epoch no:",epoch_num+1,"| Epoch progress(%):",format(i/(len(trSet)/batch_size)*100,'0.2f'), "| Avg train loss:",format(avg_tr_loss/100,'0.4f'),"| Acc:",format(avg_lat_acc,'0.4f'),format(avg_lon_acc,'0.4f'), "| Validation loss prev epoch",format(prev_val_loss,'0.4f'), "| ETA(s):",int(eta))
            train_loss.append(avg_tr_loss/100)
            avg_tr_loss = 0
            avg_lat_acc = 0
            avg_lon_acc = 0
            avg_tr_time = 0
    # _________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________



    ## Validate:______________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________
    net.train_flag = False

    print("Epoch",epoch_num+1,'complete. Calculating validation loss...')
    avg_val_loss = 0
    avg_val_lat_acc = 0
    avg_val_lon_acc = 0
    val_batch_count = 0
    total_points = 0

    for i, data  in enumerate(valDataloader):
        st_time = time.time()
        hist, nbrs, mask, lat_enc, lon_enc, fut, op_mask = data


        if args['use_cuda']:
            hist = hist.cuda()
            nbrs = nbrs.cuda()
            mask = mask.cuda()
            lat_enc = lat_enc.cuda()
            lon_enc = lon_enc.cuda()
            fut = fut.cuda()
            op_mask = op_mask.cuda()

        # Forward pass
        if args['use_maneuvers']:
            if epoch_num < pretrainEpochs:
                # During pre-training with MSE loss, validate with MSE for true maneuver class trajectory
                net.train_flag = True
                fut_pred, _ , _ = net(hist, nbrs, mask, lat_enc, lon_enc)
                l = maskedMSE(fut_pred, fut, op_mask)
            else:
                # During training with NLL loss, validate with NLL over multi-modal distribution
                fut_pred, lat_pred, lon_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
                l = maskedNLLTest(fut_pred, lat_pred, lon_pred, fut, op_mask,avg_along_time = True)
                avg_val_lat_acc += (torch.sum(torch.max(lat_pred.data, 1)[1] == torch.max(lat_enc.data, 1)[1])).item() / lat_enc.size()[0]
                avg_val_lon_acc += (torch.sum(torch.max(lon_pred.data, 1)[1] == torch.max(lon_enc.data, 1)[1])).item() / lon_enc.size()[0]
        else:
            fut_pred = net(hist, nbrs, mask, lat_enc, lon_enc)
            if epoch_num < pretrainEpochs:
                l = maskedMSE(fut_pred, fut, op_mask)
            else:
                l = maskedNLL(fut_pred, fut, op_mask)

        avg_val_loss += l.item()
        val_batch_count += 1

    print(avg_val_loss/val_batch_count)

    # Print validation loss and update display variables
    print('Validation loss :',format(avg_val_loss/val_batch_count,'0.4f'),"| Val Acc:",format(avg_val_lat_acc/val_batch_count*100,'0.4f'),format(avg_val_lon_acc/val_batch_count*100,'0.4f'))
    val_loss.append(avg_val_loss/val_batch_count)
    prev_val_loss = avg_val_loss/val_batch_count




    #__________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________________

torch.save(net.state_dict(), 'trained_models/cslstm_m.tar')



