import torch

import torch.nn as nn

from torch.utils.data import TensorDataset,DataLoader

from torch.utils.tensorboard import SummaryWriter

from tqdm import tqdm

import os

import random

import argparse

from BRnet import IncUNet

from create_dataset import data_preprocess

from utils import testDataEval,save_model

def train(args):

    """Train model to get Breathing rate from ECG

    """

    PATH = 'data'

    X_train = torch.load(PATH + '/ecgtoBR_train_data.pt')

    y_train = torch.load(PATH + '/ecgtoBR_train_labels.pt')

    X_test = torch.load(PATH + '/ecgtoBR_test_data.pt')

    y_test = torch.load(PATH + '/ecgtoBR_test_labels.pt')

    BATCH_SIZE= 64

    NUM_EPOCHS = 400

    best_loss = 1000

    train = TensorDataset(X_train,y_train)

    val = TensorDataset(X_test,y_test)

    trainLoader = DataLoader(train,batch_size = BATCH_SIZE,shuffle = True)

    valLoader = DataLoader(val, batch_size= BATCH_SIZE, shuffle=True)

    model = IncUNet((1,1,5000))

    model.cuda()

    criterion = torch.nn.SmoothL1Loss()

    optim = torch.optim.Adam(model.parameters(),lr = 0.001)

    scheduler = torch.optim.lr_scheduler.MultiStepLR(optim,milestones=[100,200], gamma=0.1)

    NUM_EPOCHS = 400

    best_loss = 1000

    writer = SummaryWriter()

    if not(os.path.isdir("Saved_Model")):

        os.mkdir("Saved_Model")

    for epoch in tqdm(range(NUM_EPOCHS)):

        model.train()

        totalLoss = 0

        for step,(x,y) in enumerate(trainLoader):

            print('.',end = " ")

            ecg= x.cuda()

            BR = y.cuda()

            BR_pred = model(ecg)

            optim.zero_grad()

            loss = criterion(BR_pred,BR)

            totalLoss += loss.cpu().item()

            loss.backward()

            optim.step()

        print ('')

        print ("Epoch:{} Train Loss:{}".format(epoch + 1,totalLoss/(step+1)))

        totalTestLoss = testDataEval(model, valLoader, criterion)

        scheduler.step()

        if best_loss > totalTestLoss:

            print ("........Saving Best Model........")

            best_loss = totalTestLoss

            save_model("Saved_Model", epoch, model, optim, best_loss )

        writer.add_scalar("Loss/test",totalTestLoss, epoch )

        writer.add_scalar("Loss/train",totalLoss/(step+1),epoch )

    writer.close()

if __name__ == "__main__":

    parser = argparse.ArgumentParser()

    parser.add_argument('--preprocess_data',action = 'store_true', help = 'Use if True')

    parser.add_argument('--sampling_freq',default = 125, type = int, help = 'Sampling Frequency')

    parser.add_argument('--upsample_freq',default = 500, type = int, help = 'Resampling Frequency')

    parser.add_argument('--window_length',default = 5,type = int,help = 'Window Length in seconds')

    parser.add_argument('--data_path',help = 'Path to data')

    parser.add_argument('--random_seed',default = 5,type = int,help = 'Random Seed initializer')

    args = parser.parse_args()

    torch.backends.cudnn.deterministic = True

    torch.backends.cudnn.benchmark = False

    random.seed(args.random_seed)

    torch.manual_seed(args.random_seed)

    if torch.cuda.is_available():

        torch.cuda.manual_seed(args.random_seed) 

    if args.preprocess_data:

        data_preprocess(args)

    train(args)