# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
import datetime
import logging
import time
import random
import torch
import torch.distributed as dist
from maskrcnn_benchmark.utils.comm import get_world_size, synchronize, broadcast_data
from maskrcnn_benchmark.utils.metric_logger import MetricLogger
from maskrcnn_benchmark.utils.ema import ModelEma


def reduce_loss_dict(loss_dict):
    """
    Reduce the loss dictionary from all processes so that process with rank
    0 has the averaged results. Returns a dict with the same fields as
    loss_dict, after reduction.
    """
    world_size = get_world_size()
    if world_size < 2:
        return loss_dict
    with torch.no_grad():
        loss_names = []
        all_losses = []
        for k in sorted(loss_dict.keys()):
            loss_names.append(k)
            all_losses.append(loss_dict[k])
        all_losses = torch.stack(all_losses, dim=0)
        dist.reduce(all_losses, dst=0)
        if dist.get_rank() == 0:
            # only main process gets accumulated, so only divide by
            # world_size in this case
            all_losses /= world_size
        reduced_losses = {k: v for k, v in zip(loss_names, all_losses)}
    return reduced_losses


def do_train(
    cfg, model, data_loader, optimizer, scheduler, checkpointer, device, checkpoint_period, arguments, rngs=None
):
    logger = logging.getLogger("maskrcnn_benchmark.trainer")
    logger.info("Start training")
    meters = MetricLogger(delimiter="  ")
    max_iter = len(data_loader)
    start_iter = arguments["iteration"]
    model.train()
    model_ema = None
    if cfg.SOLVER.MODEL_EMA > 0:
        model_ema = ModelEma(model, decay=cfg.SOLVER.MODEL_EMA)
    start_training_time = time.time()
    end = time.time()

    for iteration, (images, targets, _) in enumerate(data_loader, start_iter):

        if any(len(target) < 1 for target in targets):
            logger.error(
                "Iteration={iteration + 1} || Image Ids used for training {_} || targets Length={[len(target) for target in targets]}"
            )
            continue
        data_time = time.time() - end
        iteration = iteration + 1
        arguments["iteration"] = iteration

        images = images.to(device)
        targets = [target.to(device) for target in targets]

        # synchronize rngs
        if rngs is None:
            if isinstance(model, torch.nn.parallel.DistributedDataParallel):
                mix_nums = model.module.mix_nums
            else:
                mix_nums = model.mix_nums
            rngs = [random.randint(0, mix - 1) for mix in mix_nums]
        rngs = broadcast_data(rngs)

        for param in model.parameters():
            param.requires_grad = False
        loss_dict = model(images, targets, rngs)

        losses = sum(loss for loss in loss_dict.values())

        # reduce losses over all GPUs for logging purposes
        loss_dict_reduced = reduce_loss_dict(loss_dict)
        losses_reduced = sum(loss for loss in loss_dict_reduced.values())
        meters.update(loss=losses_reduced, **loss_dict_reduced)

        optimizer.zero_grad()
        losses.backward()
        optimizer.step()
        scheduler.step()

        if model_ema is not None:
            model_ema.update(model)
            arguments["model_ema"] = model_ema.state_dict()

        batch_time = time.time() - end
        end = time.time()
        meters.update(time=batch_time, data=data_time)

        eta_seconds = meters.time.global_avg * (max_iter - iteration)
        eta_string = str(datetime.timedelta(seconds=int(eta_seconds)))

        if iteration % 20 == 0 or iteration == max_iter:
            logger.info(
                meters.delimiter.join(
                    [
                        "eta: {eta}",
                        "iter: {iter}",
                        "{meters}",
                        "lr: {lr:.6f}",
                        "max mem: {memory:.0f}",
                    ]
                ).format(
                    eta=eta_string,
                    iter=iteration,
                    meters=str(meters),
                    lr=optimizer.param_groups[0]["lr"],
                    memory=torch.cuda.max_memory_allocated() / 1024.0 / 1024.0,
                )
            )
        if iteration % checkpoint_period == 0:
            checkpointer.save("model_{:07d}".format(iteration), **arguments)
        if iteration == max_iter:
            if model_ema is not None:
                model.load_state_dict(model_ema.state_dict())
            checkpointer.save("model_final", **arguments)

    total_training_time = time.time() - start_training_time
    total_time_str = str(datetime.timedelta(seconds=total_training_time))
    logger.info("Total training time: {} ({:.4f} s / it)".format(total_time_str, total_training_time / (max_iter)))