import math
import sys
from typing import Iterable, Optional

import torch

from timm.data import Mixup
from timm.utils import accuracy

import util.misc as misc
import util.lr_sched as lr_sched
import numpy as np
from scipy.stats import spearmanr, pearsonr


def train_koniq_epoch(model: torch.nn.Module, criterion: torch.nn.Module,
                    data_loader: Iterable, optimizer: torch.optim.Optimizer,
                    device: torch.device, epoch: int, loss_scaler=None, log_writer=None,
                    args=None):
    model.train(True)
    metric_logger = misc.MetricLogger(delimiter="  ")
    metric_logger.add_meter('lr', misc.SmoothedValue(window_size=1, fmt='{value:.6f}'))
    header = 'Epoch: [{}]'.format(epoch)
    print_freq = 20

    accum_iter = args.accum_iter

    optimizer.zero_grad()

    if log_writer is not None:
        print('log_dir: {}'.format(log_writer.log_dir))
    pred_epoch = []
    labels_epoch = []
    for data_iter_step, (samples, targets) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):

        # we use a per iteration (instead of per epoch) lr scheduler
        if data_iter_step % accum_iter == 0:
            lr_sched.adjust_learning_rate(optimizer, data_iter_step / len(data_loader) + epoch, args)

        samples = samples.to(device, non_blocking=True)
        targets = torch.squeeze(targets.type(torch.FloatTensor))
        targets = targets.to(device, non_blocking=True)


        with torch.cuda.amp.autocast():
            outputs = model(samples)
            loss = criterion(torch.squeeze(outputs), targets)

        loss_value = loss.item()

        if not math.isfinite(loss_value):
            print("Loss is {}, stopping training".format(loss_value))
            sys.exit(1)

        loss /= accum_iter
        loss_scaler(loss, optimizer, clip_grad=None,
                    parameters=model.parameters(), create_graph=False,
                    update_grad=(data_iter_step + 1) % accum_iter == 0)
        if (data_iter_step + 1) % accum_iter == 0:
            optimizer.zero_grad()

        torch.cuda.synchronize()

        metric_logger.update(loss=loss_value)
        min_lr = 10.
        max_lr = 0.
        for group in optimizer.param_groups:
            min_lr = min(min_lr, group["lr"])
            max_lr = max(max_lr, group["lr"])

        metric_logger.update(lr=max_lr)
        pred =concat_all_gather(outputs)
        labels =concat_all_gather(targets)
        pred_batch_numpy = pred.data.cpu().numpy()
        labels_batch_numpy = labels.data.cpu().numpy()
        pred_epoch = np.append(pred_epoch, pred_batch_numpy)
        labels_epoch = np.append(labels_epoch, labels_batch_numpy)
        loss_value_reduce = misc.all_reduce_mean(loss_value)
        if log_writer is not None and (data_iter_step + 1) % accum_iter == 0:
            """ We use epoch_1000x as the x-axis in tensorboard.
            This calibrates different curves when batch size changes.
            """
            epoch_1000x = int((data_iter_step / len(data_loader) + epoch) * 1000)
            log_writer.add_scalar('loss', loss_value_reduce, epoch_1000x)
            log_writer.add_scalar('lr', max_lr, epoch_1000x)
    rho_s, _ = spearmanr(np.squeeze(pred_epoch), np.squeeze(labels_epoch))
    rho_p, _ = pearsonr(np.squeeze(pred_epoch), np.squeeze(labels_epoch))

    print('*[train] epoch:%d / SROCC:%4f / PLCC:%4f' % (epoch+1, rho_s, rho_p))

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    print("Averaged stats:", metric_logger)
    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}


@torch.no_grad()
def evaluate_koniq(data_loader, model, device):
    criterion = torch.nn.L1Loss()
    metric_logger = misc.MetricLogger(delimiter="  ")
    header = 'Test:'

    # switch to evaluation mode
    model.eval()
    pred_epoch = []
    labels_epoch = []
    for batch in metric_logger.log_every(data_loader, 10, header):
        images = batch[0]
        target = batch[-1]
        images = images.to(device, non_blocking=True)
        targets = torch.squeeze(target.type(torch.FloatTensor))
        targets = targets.to(device, non_blocking=True)

        # compute output
        with torch.cuda.amp.autocast():
            output = model(images)
            loss = criterion(torch.squeeze(output), targets)

        pred =concat_all_gather(output)
        labels =concat_all_gather(targets)
        pred_batch_numpy = pred.data.cpu().numpy()
        labels_batch_numpy = labels.data.cpu().numpy()
        pred_epoch = np.append(pred_epoch, pred_batch_numpy)
        labels_epoch = np.append(labels_epoch, labels_batch_numpy)

        batch_size = images.shape[0]
        metric_logger.update(loss=loss.item())

    # gather the stats from all processes
    metric_logger.synchronize_between_processes()
    rho_s, _ = spearmanr(np.squeeze(pred_epoch), np.squeeze(labels_epoch))
    rho_p, _ = pearsonr(np.squeeze(pred_epoch), np.squeeze(labels_epoch))

    print('*[test] / SROCC:%4f / PLCC:%4f' % ( rho_s, rho_p))

    return {k: meter.global_avg for k, meter in metric_logger.meters.items()}
# utils
@torch.no_grad()
def concat_all_gather(tensor):
    """
    Performs all_gather operation on the provided tensors.
    *** Warning ***: torch.distributed.all_gather has no gradient.
    """
    tensors_gather = [torch.ones_like(tensor)
        for _ in range(torch.distributed.get_world_size())]
    torch.distributed.all_gather(tensors_gather, tensor, async_op=False)

    output = torch.cat(tensors_gather, dim=0)
    return output
