import os
import random
import time
from typing import Optional
from argparse import Namespace

import timm
import torch
import torch.nn as nn
import torch.multiprocessing as torch_mp
from torch.utils.data import DataLoader
import nni
import logging
import numpy as np

from model.transcrowd_gap import VisionTransformerGAP
from arguments import args, ret_args
import dataset
from dataset import *
from model.transcrowd_gap import base_patch16_384_gap, stn_patch16_384_gap
from checkpoint import save_checkpoint

logger = logging.getLogger("train")

def setup_process_group(
        rank: int,
        world_size: int,
        master_addr: str = "localhost",
        master_port: Optional[np.ushort] = None
):
    os.environ["MASTER_ADDR"] = master_addr
    os.environ["MASTER_PORT"] = (
        str(random.randint(40000, 65545))
        if master_port is None
        else str(master_port)
    )

    # join point!
    torch.distributed.init_process_group(
        rank=rank, world_size=world_size
    )


def build_train_loader(data_keys, args):
    train_dataset = ListDataset(
        data_keys,
        shuffle = True,
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
        ]),
        train = True,
        batch_size = args.batch_size,
        nr_workers = args.workers,
        args = args
    )
    if args.use_ddp:
        train_dist_sampler = torch.utils.data.distributed.DistributedSampler(
            dataset=train_dataset
        )
    else:
        train_dist_sampler = None
    train_loader = DataLoader(
        dataset=train_dataset,
        sampler=train_dist_sampler,
        batch_size=args.batch_size,
        drop_last=False
    )
    return train_loader


def build_test_loader(data_keys, args):
    test_dataset = ListDataset(
        data_keys,
        shuffle=False,
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean=[.485, .456, .406], std=[.229, .224, .225])
        ]),
        args=args,
        train=False
    )
    if args.use_ddp:
        test_dist_sampler = torch.utils.data.distributed.DistributedSampler(
            dataset=test_dataset
        )
    else:
        test_dist_sampler = None
    test_loader = DataLoader(
        dataset=test_dataset,
        sampler=test_dist_sampler,
        batch_size=1
    )
    return test_loader


def worker(rank: int, args: Namespace):
    world_size = args.ddp_world_size

    # (If DDP) join after process group among processes
    if args.use_ddp:
        setup_process_group(rank, world_size)

    # Setup device for one proc
    if args.use_ddp and torch.cuda.is_available():
        device = torch.device(rank)
    elif torch.cuda.is_available():
        os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
        args.gpus = [int(gpu_id) for gpu_id in args.gpus.split(",")]
        device = None
    elif torch.backends.mps.is_available():
        device = torch.device("mps")
    else:
        print("[!!!] Using CPU for inference. This will be slow...")
        device = torch.device("cpu")
    if device is not None:
        torch.set_default_device(device)

    # Prepare training data
    train_list, test_list = unpack_npy_data(args)
    train_data = convert_data(train_list, args, train=True)
    test_data = convert_data(test_list, args, train=False)
    train_loader = build_train_loader(train_data, args)
    test_loader = build_test_loader(test_data, args)

    # Instantiate model
    if args.model == "stn":
        model = stn_patch16_384_gap(args.pth_tar)
    else:
        model = base_patch16_384_gap(args.pth_tar)

    if args.use_ddp:
        model = nn.parallel.DistributedDataParallel(
            model,
            device_ids=[rank], output_device=rank,
            find_unused_parameters=True,
            gradient_as_bucket_view=True    # XXX: vital, otherwise OOM
        )
    else:
        model = nn.DataParallel(
            model,
            device_ids=args.gpus
        )

    if device is not None:
        model = model.to(device)
    elif torch.cuda.is_available():
        model = model.cuda()

    # criterion, optimizer, scheduler
    criterion = nn.L1Loss(reduction="sum")
    if device is not None:
        criterion = criterion.to(device)
    elif torch.cuda.is_available():
        criterion = criterion.cuda()
    optimizer = torch.optim.Adam(
        [{"params": model.parameters(), "lr": args.lr}],
        lr=args.lr,
        weight_decay=args.weight_decay
    )
    scheduler = torch.optim.lr_scheduler.MultiStepLR(
        optimizer, milestones=[300], gamma=.1, last_epoch=-1
    )

    # Checkpointing
    if (not args.use_ddp) or torch.distributed.get_rank() == 0:
        print("[worker-0] Saving to \'{}\'".format(args.save_path))
        if not os.path.exists(args.save_path):
            os.makedirs(args.save_path)

    if args.resume:
        if os.path.isfile(args.resume):
            print("=> Loading checkpoint \'{}\'".format(args.resume))
            checkpoint = torch.load(args.resume)
            model.load_state_dict(checkpoint['state_dict'], strict=False)
            args.start_epoch = checkpoint['epoch']
            args.best_pred = checkpoint['best_prec1']   # sic.
        else:
            print("=> Checkpoint not found!")

    print("[worker-{}] Starting @ epoch {}: pred: {}".format(
        rank, args.start_epoch, args.best_pred
    ))

    # For each epoch:
    for epoch in range(args.start_epoch, args.epochs):
        # Tell sampler which epoch it is
        if args.use_ddp:
            train_loader.sampler.set_epoch(epoch)
            test_loader.sampler.set_epoch(epoch)

        # Train
        start = time.time()
        train_one_epoch(train_loader, model, criterion, optimizer, scheduler, epoch, device, args)
        end_train = time.time()

        # Validate
        if epoch % 5 == 0 or args.debug:
            prec1 = valid_one_epoch(test_loader, model, device, args)
            end_valid = time.time()
            is_best = prec1 < args.best_pred
            args.best_pred = min(prec1, args.best_pred)

            print("* best MAE {mae:.3f} *".format(mae=args.best_pred))

            # Save checkpoint
            if not args.use_ddp or torch.distributed.get_rank() == 0:
                save_checkpoint({
                    "epoch": epoch + 1,
                    "arch": args.resume,
                    "state_dict": model.state_dict(),
                    "best_prec1": args.best_pred,
                    "optimizer": optimizer.state_dict(),
                }, is_best, args.save_path)


    # cleanup
    if args.use_ddp:
        torch.distributed.destroy_process_group()


def train_one_epoch(
        train_loader: DataLoader,
        model: VisionTransformerGAP,
        criterion,
        optimizer,
        scheduler,
        epoch: int,
        device,
        args: Namespace
):
    # Get learning rate
    curr_lr = optimizer.param_groups[0]["lr"]
    print("Epoch %d, processed %d samples, lr %.10f" %
        (epoch, epoch * len(train_loader.dataset), curr_lr)
    )

    # Set to train mode
    model.train()

    # In one epoch, for each training sample
    for i, (fname, img, kpoint) in enumerate(train_loader):
        kpoint = kpoint.type(torch.FloatTensor)
        # fpass
        if device is not None:
            img = img.to(device)
            kpoint = kpoint.to(device)
        elif torch.cuda.is_available():
            img = img.cuda()
            kpoint = kpoint.cuda()
        out, gt_count = model(img, kpoint)

        # loss
        loss = criterion(out, gt_count)

        # free grad from mem
        optimizer.zero_grad()

        # bpass
        loss.backward()

        # optimizer
        optimizer.step()

        # periodic message
        if i % args.print_freq == 0:
            print("Epoch {}: {}/{}".format(epoch, i, len(train_loader)))

        if args.debug:
            break

    scheduler.step()


def valid_one_epoch(test_loader, model, device, args):
    print("[valid_one_epoch] Validating...")
    batch_size = 1
    model.eval()

    mae = .0
    mse = .0
    visi = []
    index = 0

    for i, (fname, img, kpoint) in enumerate(test_loader):
        kpoint = kpoint.type(torch.FloatTensor)
        if device is not None:
            img = img.to(device)
            kpoint = kpoint.to(device)
        elif torch.cuda.is_available():
            img = img.cuda()
            kpoint = kpoint.cuda()

        # XXX: what do this do
        if len(img.shape) == 5:
            img = img.squeeze(0)
        if len(img.shape) == 3:
            img = img.unsqueeze(0)

        with torch.no_grad():
            out, gt_count = model(img, kpoint)
            pred_count = torch.squeeze(out, 1)
            gt_count   = torch.squeeze(gt_count, 1)

        diff = torch.sum(torch.abs(gt_count - pred_count)).item()
        mae += diff
        mse += diff ** 2
        mae = mae * 1.0 / (len(test_loader) * batch_size)
        mse = np.sqrt(mse / (len(test_loader)) * batch_size)

        if i % 5 == 0:
            print("[valid_one_epoch] {}\t| Gt {:.2f} Pred {:.4f}\t| mae {:.4f} mse {:.4f} |".format(
                fname[0], torch.sum(gt_count).item(), torch.sum(pred_count).item(),
                mae, mse
            ))

    nni.report_intermediate_result()
    print("* MAE {mae:.3f} | MSE {mse:.3f} *".format(
        mae=mae, mse=mse
    ))
    return mae


if __name__ == "__main__":
    tuner_params = nni.get_next_parameter()
    logger.debug("Generated hyperparameters: {}", tuner_params)
    combined_params = nni.utils.merge_parameter(ret_args, tuner_params)
    if args.debug:
        os.nice(15)

    #combined_params = args
    #logger.debug("Parameters: {}", combined_params)

    if combined_params.use_ddp:
        # Use DDP, spawn threads
        torch_mp.spawn(
            worker,
            args=(combined_params, ), # rank supplied at callee as 1st param
            # also above *has* to be 1-tuple else runtime expands Namespace.
            nprocs=combined_params.world_size,
        )
    else:
        # No DDP, run in current thread
        worker(0, combined_params)