import argparse
import os
import datetime
import logging
import time
import math
import numpy as np
from tqdm import tqdm
from collections import OrderedDict
import re

import torch
import torch.nn.functional as F

from core.configs import cfg
from core.datasets import build_dataset
from core.models import build_model, build_adversarial_discriminator, build_feature_extractor, build_classifier
from core.solver import adjust_learning_rate
from core.utils.misc import mkdir, AverageMeter, intersectionAndUnionGPU, get_color_pallete
from core.utils.logger import setup_logger
from core.utils.metric_logger import MetricLogger

def strip_prefix_if_present(state_dict, prefix):
    keys = sorted(state_dict.keys())
    if not all(key.startswith(prefix) for key in keys):
        return state_dict
    stripped_state_dict = OrderedDict()
    for key, value in state_dict.items():
        stripped_state_dict[key.replace(prefix, "")] = value
    return stripped_state_dict

def inference(feature_extractor, classifier, image, label, flip=True):
    size = label.shape[-2:]
    if flip:
        image = torch.cat([image, torch.flip(image, [3])], 0)
    with torch.no_grad():
        output = classifier(feature_extractor(image))
    output = F.interpolate(output, size=size, mode='bilinear', align_corners=True)
    output = F.softmax(output, dim=1)
    if flip:
        output = (output[0] + output[1].flip(2)) / 2
    else:
        output = output[0]
    return output.unsqueeze(dim=0)

def multi_scale_inference(feature_extractor, classifier, image, label, scales=[0.7,1.0,1.3], flip=True):
    output = None
    size = image.shape[-2:]
    for s in scales:
        x = F.interpolate(image, size=(int(size[0]*s), int(size[1]*s)), mode='bilinear', align_corners=True)
        pred = inference(feature_extractor, classifier, x, label, flip=False)
        if output is None:
            output = pred
        else:
            output = output + pred
        if flip:
            x_flip = torch.flip(x, [3])
            pred = inference(feature_extractor, classifier, x_flip, label, flip=False)
            output = output + pred.flip(3)
    if flip:
        return output/len(scales)/2
    return output/len(scales)

def transform_color(pred):
    synthia_to_city = {
            0: 0,
            1: 1,
            2: 2,
            3: 3,
            4: 4,
            5: 5,
            6: 6,
            7: 7,
            8: 8,
            9: 10,
            10: 11,
            11: 12,
            12: 13,
            13: 15,
            14: 17,
            15: 18,
        }
    label_copy = 255 * np.ones(pred.shape, dtype=np.float32)
    for k, v in synthia_to_city.items():
        label_copy[pred == k] = v
    return label_copy.copy()

def test(cfg, saveres):
    logger = logging.getLogger("FADA.tester")
    logger.info("Start testing")
    device = torch.device(cfg.MODEL.DEVICE)

    feature_extractor = build_feature_extractor(cfg)
    feature_extractor.to(device)

    classifier = build_classifier(cfg)
    classifier.to(device)
    
    if cfg.resume:
        logger.info("Loading checkpoint from {}".format(cfg.resume))
        checkpoint = torch.load(cfg.resume, map_location=torch.device('cpu'))
        feature_extractor_weights = strip_prefix_if_present(checkpoint['feature_extractor'], 'module.')
        feature_extractor.load_state_dict(feature_extractor_weights)
        classifier_weights = strip_prefix_if_present(checkpoint['classifier'], 'module.')
        classifier.load_state_dict(classifier_weights)

    feature_extractor.eval()
    classifier.eval()
    
    intersection_meter = AverageMeter()
    union_meter = AverageMeter()
    target_meter = AverageMeter()
    
    torch.cuda.empty_cache()  # TODO check if it helps
    dataset_name = cfg.DATASETS.TEST
    output_folder = '.'
    if cfg.OUTPUT_DIR:
        output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
        mkdir(output_folder)

    test_data = build_dataset(cfg, mode='test', is_source=False)

    test_loader = torch.utils.data.DataLoader(test_data, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True, sampler=None)

    
    for batch in tqdm(test_loader):
        x, y, name = batch
        x = x.cuda(non_blocking=True)
        y = y.cuda(non_blocking=True).long()

        pred = inference(feature_extractor, classifier, x, y, flip=False)
        # pred = multi_scale_inference(feature_extractor, classifier, x, y, flip=True)

        output = pred.max(1)[1]
        print('pred.shape', pred.shape)
        print('torch.unique(output)', torch.unique(output))
        intersection, union, target = intersectionAndUnionGPU(output, y, cfg.MODEL.NUM_CLASSES, cfg.INPUT.IGNORE_LABEL)
        intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()
        intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)
     #   print('intersection', intersection)
        accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)

        if saveres:
            pred = pred.cpu().numpy().squeeze()
            pred_max = np.max(pred, 0)
            pred = pred.argmax(0)
            # uncomment the following line when visualizing SYNTHIA->Cityscapes
            # pred = transform_color(pred)
            mask = get_color_pallete(pred, "city")
            mask_filename = name[0] if len(name[0].split("/"))<2 else name[0].split("/")[1]
            mask.save(os.path.join(output_folder, mask_filename))
    
    iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
    accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
    mIoU = np.mean(iou_class)
    mAcc = np.mean(accuracy_class)
    allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)

    logger.info('Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.'.format(mIoU, mAcc, allAcc))
    for i in range(cfg.MODEL.NUM_CLASSES):
        logger.info('{} {} iou/accuracy: {:.4f}/{:.4f}.'.format(i, test_data.trainid2name[i], iou_class[i], accuracy_class[i]))

def test_all(cfg, saveres):
    logger = logging.getLogger("FADA.tester")
    logger.info("Start testing")
    device = torch.device(cfg.MODEL.DEVICE)

    feature_extractor = build_feature_extractor(cfg)
    feature_extractor.to(device)
    
    # classifier = build_classifier(cfg)
    classifier = build_classifier(cfg)
    classifier.to(device)
    
    test_data = build_dataset(cfg, mode='test', is_source=False)

    test_loader = torch.utils.data.DataLoader(test_data, batch_size=cfg.TEST.BATCH_SIZE, shuffle=False, num_workers=4, pin_memory=True, sampler=None)
    
    test_stats = []
    best_iter = 0
    best_miou = 0
    
    for fname in sorted(os.listdir(cfg.resume)):
        if not fname.endswith('.pth'):
            continue
        logger.info("Loading checkpoint from {}".format(cfg.resume+'/'+fname))
        checkpoint = torch.load(cfg.resume+'/'+fname)
        
        feature_extractor_weights = strip_prefix_if_present(checkpoint['feature_extractor'], 'module.')
        feature_extractor.load_state_dict(feature_extractor_weights)
        classifier_weights = strip_prefix_if_present(checkpoint['classifier'], 'module.')
        classifier.load_state_dict(classifier_weights)

        feature_extractor.eval()
        classifier.eval()
    
        intersection_meter = AverageMeter()
        union_meter = AverageMeter()
        target_meter = AverageMeter()
    
        torch.cuda.empty_cache()
        dataset_name = cfg.DATASETS.TEST
        output_folder = '.'
        if cfg.OUTPUT_DIR:
            output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name)
            mkdir(output_folder)
            if saveres:
                output_folder = os.path.join(cfg.OUTPUT_DIR, "inference", dataset_name, fname.replace('.pth',''))
                mkdir(output_folder)
    
        for batch in tqdm(test_loader):
            x, y, name = batch
            x = x.cuda(non_blocking=True)
            y = y.cuda(non_blocking=True).long()

            pred = inference(feature_extractor, classifier, x, y, flip=False)

            output = pred.max(1)[1]
            intersection, union, target = intersectionAndUnionGPU(output, y, cfg.MODEL.NUM_CLASSES, cfg.INPUT.IGNORE_LABEL)
            intersection, union, target = intersection.cpu().numpy(), union.cpu().numpy(), target.cpu().numpy()
            intersection_meter.update(intersection), union_meter.update(union), target_meter.update(target)

            accuracy = sum(intersection_meter.val) / (sum(target_meter.val) + 1e-10)

            if saveres:
                pred = pred.cpu().numpy().squeeze().argmax(0)
                mask = get_color_pallete(pred, "city")
                mask_filename = name[0].split("/")[1]
                mask.save(os.path.join(output_folder, mask_filename))
    
        iou_class = intersection_meter.sum / (union_meter.sum + 1e-10)
        accuracy_class = intersection_meter.sum / (target_meter.sum + 1e-10)
        mIoU = np.mean(iou_class)
        mAcc = np.mean(accuracy_class)
        allAcc = sum(intersection_meter.sum) / (sum(target_meter.sum) + 1e-10)
        
        iter_num = int(re.findall(r'\d+', fname)[0])
        rec = {'iters':iter_num, 'mIoU':mIoU}
        logger.info('Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.'.format(mIoU, mAcc, allAcc))
        print('cfg.MODEL.NUM_CLASSES', cfg.MODEL.NUM_CLASSES)        
        
        for i in range(cfg.MODEL.NUM_CLASSES):
            rec[test_data.trainid2name[i]] = iou_class[i]
            logger.info('{} {} iou/accuracy: {:.4f}/{:.4f}.'.format(i, test_data.trainid2name[i], iou_class[i], accuracy_class[i]))
        test_stats.append(rec)
        
        if mIoU>best_miou:
            best_iter = iter_num
            best_miou = mIoU

    logger.info('Best result is got at iters {} with mIoU {:.4f}.'.format(best_iter, best_miou))
    with open(os.path.join(output_folder, 'test_results.csv'),'w') as handle:
        for i, rec in enumerate(test_stats):
            if i==0:
                handle.write(','.join(list(rec.keys()))+'\n')
            line = [str(rec[key]) for key in rec.keys()]
            handle.write(','.join(line)+'\n')


def main():
    parser = argparse.ArgumentParser(description="PyTorch Semantic Segmentation Testing")
    parser.add_argument("-cfg",
        "--config-file",
        default="",
        metavar="FILE",
        help="path to config file",
        type=str,
    )
    parser.add_argument('--saveres', action="store_true",
                        help='save the result')
    parser.add_argument(
        "opts",
        help="Modify config options using the command-line",
        default=None,
        nargs=argparse.REMAINDER,
    )

    args = parser.parse_args()

    torch.backends.cudnn.benchmark = True

    cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    save_dir = ""
    logger = setup_logger("FADA", save_dir, 0)
    logger.info(cfg)

    logger.info("Loaded configuration file {}".format(args.config_file))
    with open(args.config_file, "r") as cf:
        config_str = "\n" + cf.read()
        logger.info(config_str)
    logger.info("Running with config:\n{}".format(cfg))
    print('cfg.MODEL.NUM_CLASSES', cfg.MODEL.NUM_CLASSES)   
    print('cfg.MODEL.NUM_CLASSES')      
    if os.path.isdir(cfg.resume):
        test_all(cfg, args.saveres)
    else:
        test(cfg, args.saveres)


if __name__ == "__main__":
    main()

