""" VIC-reg self-supervision training

"""

import argparse

import json

import math

import os

import random

import signal

import subprocess

import sys

import time

from tqdm import tqdm

from torch import nn, optim

import torch

import torchvision

import torchinfo

sys.path.append(os.getcwd())

import utilities.runUtils as rutl

import utilities.logUtils as lutl

from algorithms.vicreg import VICReg, LARS, adjust_learning_rate

from datacode.natural_image_data import Cifar100Dataset

from datacode.ultrasound_data import FetalUSFramesDataset

from datacode.augmentations import BarlowTwinsTransformOrig, CustomInfoMaxTransform

print(f"Pytorch version: {torch.__version__}")

print(f"cuda version: {torch.version.cuda}")

device = 'cuda' if torch.cuda.is_available() else 'cpu'

print("Device Used:", device)

###============================= Configure and Setup ===========================

CFG = rutl.ObjDict(

use_amp = True, #automatic Mixed precision

datapath    = "/home/USR/WERK/data/a.hdf5",

valdatapath = "/home/USR/WERK/valdata/b.hdf5",

skip_count = 5,

epochs      = 1000,

batch_size  = 2048,

workers     = 24,

image_size  = 256,

base_lr      = 0.2,

weight_decay = 1e-6,

sim_coeff    = 25.0, # Invariance

std_coeff    = 25.0, # Variance

cov_coeff    = 1.0,  # Covariance

featx_arch     = "resnet50",

featx_pretrain =  None, # "IMGNET-1K"

projector      = [8192,8192,8192],

print_freq_step   = 1000, #steps

ckpt_freq_epoch   = 5,  #epochs

valid_freq_epoch  = 5,  #epochs

disable_tqdm      = False,   #True--> to disable

checkpoint_dir  = "hypotheses/-dummy/ssl-vicreg/",

resume_training = True,

)

## --------

parser = argparse.ArgumentParser(description='VIC-Reg ISIC Training')

parser.add_argument('--load-json', type=str, metavar='JSON',

    help='Load settings from file in json format. Command line options override values in file.')

args = parser.parse_args()

if args.load_json:

    with open(args.load_json, 'rt') as f:

        CFG.__dict__.update(json.load(f))

### ----------------------------------------------------------------------------

CFG.gLogPath = CFG.checkpoint_dir

CFG.gWeightPath = CFG.checkpoint_dir + '/weights/'

### ============================================================================

def getDataLoaders():

    transform_obj = BarlowTwinsTransformOrig(image_size=CFG.image_size)

    traindataset = FetalUSFramesDataset( hdf5_file= CFG.datapath,

                                transform = transform_obj,

                                load2ram = False, frame_skip=CFG.skip_count)

    trainloader  = torch.utils.data.DataLoader( traindataset, shuffle=True,

                        batch_size=CFG.batch_size, num_workers=CFG.workers,

                        pin_memory=True)

    validdataset = FetalUSFramesDataset( hdf5_file= CFG.valdatapath,

                                transform = transform_obj,

                                load2ram = False, frame_skip=CFG.skip_count)

    validloader  = torch.utils.data.DataLoader( validdataset, shuffle=False,

                        batch_size=CFG.batch_size, num_workers=CFG.workers,

                        pin_memory=True)

    lutl.LOG2DICTXT({"TRAIN DatasetClass":traindataset.get_info(),

                    "TransformsClass": str(transform_obj.get_composition()),

                    }, CFG.gLogPath +'/misc.txt')

    lutl.LOG2DICTXT({"VALID DatasetClass":validdataset.get_info(),

                    "TransformsClass": str(transform_obj.get_composition()),

                    }, CFG.gLogPath +'/misc.txt')

    return trainloader, validloader

def getModelnOptimizer():

    model = VICReg( featx_arch=CFG.featx_arch,

                    projector_sizes=CFG.projector,

                    batch_size=CFG.batch_size,

                    sim_coeff = CFG.sim_coeff,

                    std_coeff = CFG.std_coeff,

                    cov_coeff = CFG.cov_coeff,

                    featx_pretrain=CFG.featx_pretrain,

                    ).to(device)

    optimizer = LARS(model.parameters(), lr=0, weight_decay=CFG.weight_decay,

                     weight_decay_filter=True, lars_adaptation_filter=True)

    model_info = torchinfo.summary(model, 2*[(1, 3, CFG.image_size, CFG.image_size)],

                                verbose=0)

    lutl.LOG2TXT(model_info, CFG.gLogPath +'/misc.txt', console= False)

    return model.to(device), optimizer

### ----------------------------------------------------------------------------

def simple_main():

    ### SETUP

    rutl.START_SEED()

    torch.cuda.device(device)

    torch.backends.cudnn.benchmark = True

    if os.path.exists(CFG.checkpoint_dir) and (not CFG.resume_training):

        raise Exception("CheckPoint folder already exists and restart_training not enabled; Somethings Wrong!")

    if not os.path.exists(CFG.gWeightPath): os.makedirs(CFG.gWeightPath)

    with open(CFG.gLogPath+"/exp_config.json", 'a') as f:

        json.dump(vars(CFG), f, indent=4)

    ### DATA ACCESS

    trainloader, validloader = getDataLoaders()

    ### MODEL, OPTIM

    model, optimizer = getModelnOptimizer()

    ## Automatically resume from checkpoint if it exists and enabled

    ckpt = None

    if CFG.resume_training:

        try:    ckpt = torch.load(CFG.gWeightPath+'/checkpoint-1.pth', map_location='cpu')

        except:

            try:ckpt = torch.load(CFG.gWeightPath+'/checkpoint-0.pth', map_location='cpu')

            except: print("Check points are not loadable. Starting fresh...")

    if ckpt:

        start_epoch = ckpt['epoch']

        model.load_state_dict(ckpt['model'])

        optimizer.load_state_dict(ckpt['optimizer'])

        lutl.LOG2TXT(f"Restarting Training from EPOCH:{start_epoch} of {CFG.checkpoint_dir}",  CFG.gLogPath +'/misc.txt')

    else:

        start_epoch = 0

    ### MODEL TRAINING

    start_time = time.time()

    best_loss = float('inf')

    wgt_suf   = 0  # foolproof savetime crash

    if CFG.use_amp: scaler = torch.cuda.amp.GradScaler() # for mixed precision

    for epoch in range(start_epoch, CFG.epochs):

        ## ---- Training Routine ----

        t_running_loss_ = 0

        model.train()

        for step, (y1, y2) in tqdm(enumerate(trainloader,

                                            start=epoch * len(trainloader)),

                                    disable=CFG.disable_tqdm):

            y1 = y1.to(device, non_blocking=True)

            y2 = y2.to(device, non_blocking=True)

            lr_ = adjust_learning_rate(CFG, optimizer, trainloader, step)

            optimizer.zero_grad()

            if CFG.use_amp: ## with mixed precision

                with torch.cuda.amp.autocast():

                    loss = model.forward(y1, y2)

                scaler.scale(loss).backward()

                scaler.step(optimizer)

                scaler.update()

            else:

                loss = model.forward(y1, y2)

                loss.backward()

                optimizer.step()

            t_running_loss_+=loss.item()

            if step % CFG.print_freq_step == 0:

                stats = dict(epoch=epoch, step=step,

                             time=int(time.time() - start_time),

                             step_loss=loss.item(),

                             lr= lr_,)

                lutl.LOG2DICTXT(stats, CFG.checkpoint_dir +'/train-stats.txt')

        train_epoch_loss = t_running_loss_/len(trainloader)

        # save checkpoint

        if (epoch+1) % CFG.ckpt_freq_epoch == 0:

            wgt_suf = (wgt_suf+1) %2

            state = dict(epoch=epoch, model=model.state_dict(),

                            optimizer=optimizer.state_dict())

            torch.save(state, CFG.gWeightPath +f'/checkpoint-{wgt_suf}.pth')

        ## ---- Validation Routine ----

        if (epoch+1) % CFG.valid_freq_epoch == 0:

            model.eval()

            v_running_loss_ = 0

            with torch.no_grad():

                for (y1, y2) in tqdm(validloader,  total=len(validloader),

                                    disable=CFG.disable_tqdm):

                    y1 = y1.to(device, non_blocking=True)

                    y2 = y2.to(device, non_blocking=True)

                    loss = model.forward(y1, y2)

                    v_running_loss_ += loss.item()

            valid_epoch_loss = v_running_loss_/len(validloader)

            best_flag = False

            if valid_epoch_loss < best_loss:

                best_flag = True

                best_loss = valid_epoch_loss

            v_stats = dict(epoch=epoch, best=best_flag, wgt_suf=wgt_suf,

                            train_loss=train_epoch_loss,

                            valid_loss=valid_epoch_loss)

            lutl.LOG2DICTXT(v_stats, CFG.gLogPath+'/valid-stats.txt')

if __name__ == '__main__':

    simple_main()