"""
Written by Ji Zhang, 2019
Some functions are adapted from Rowan Zellers
Original source:
https://github.com/rowanz/neural-motifs/blob/master/lib/evaluation/sg_eval.py
"""
import os
import numpy as np
import logging
from six.moves import cPickle as pickle
import json
import csv
from tqdm import tqdm

from core.config import cfg
from functools import reduce
from utils.boxes import bbox_overlaps
from datasets_rel.ap_eval_rel import ap_eval, prepare_mAP_dets

from .pytorch_misc import intersect_2d, argsort_desc

np.set_printoptions(precision=3)

logger = logging.getLogger(__name__)


def eval_rel_results(all_results, output_dir, do_val=True, do_vis=False, do_special=False):
    
    topk = 100

    if cfg.TEST.DATASETS[0].find('vg') >= 0:
        eval_per_img = True
        # eval_per_img = False
        prd_k = 1
    else:
        eval_per_img = False
        prd_k = 2
        
    if cfg.TEST.DATASETS[0].find('oi') >= 0:
        eval_ap = True
    else:
        eval_ap = False
    
    if eval_per_img:
        recalls = {1: [], 5: [], 10: [], 20: [], 50: [], 100: []}
    else:
        recalls = {1: 0, 5: 0, 10: 0, 20: 0, 50: 0, 100: 0}
        if do_val:
            all_gt_cnt = 0

    if do_special:
        special_img_f = open("/home/jiz/projects/100_img_special_set.txt", "r")
        special_imgs = special_img_f.readlines()
        special_imgs = [img[:-1] for img in special_imgs]
        special_img_set = set(special_imgs)
        logger.info('Special images len: {}'.format(len(special_img_set)))
    
    topk_dets = []
    for im_i, res in enumerate(tqdm(all_results)):

        if do_special:
            img_id = res['image'].split('/')[-1].split('.')[0]
            if img_id not in special_img_set:
                continue
        
        # in oi_all_rel some images have no dets
        if res['prd_scores'] is None:
            det_boxes_s_top = np.zeros((0, 4), dtype=np.float32)
            det_boxes_o_top = np.zeros((0, 4), dtype=np.float32)
            det_labels_s_top = np.zeros(0, dtype=np.int32)
            det_labels_p_top = np.zeros(0, dtype=np.int32)
            det_labels_o_top = np.zeros(0, dtype=np.int32)
            det_scores_top = np.zeros(0, dtype=np.float32)
            
            det_scores_top_vis = np.zeros(0, dtype=np.float32)
            if 'prd_scores_bias' in res:
                det_scores_top_bias = np.zeros(0, dtype=np.float32)
            if 'prd_scores_spt' in res:
                det_scores_top_spt = np.zeros(0, dtype=np.float32)
        else:
            det_boxes_sbj = res['sbj_boxes']  # (#num_rel, 4)
            det_boxes_obj = res['obj_boxes']  # (#num_rel, 4)
            det_labels_sbj = res['sbj_labels']  # (#num_rel,)
            det_labels_obj = res['obj_labels']  # (#num_rel,)
            det_scores_sbj = res['sbj_scores']  # (#num_rel,)
            det_scores_obj = res['obj_scores']  # (#num_rel,)
            if 'prd_scores_ttl' in res:
                det_scores_prd = res['prd_scores_ttl'][:, 1:]
            else:
                det_scores_prd = res['prd_scores'][:, 1:]

            det_labels_prd = np.argsort(-det_scores_prd, axis=1)
            det_scores_prd = -np.sort(-det_scores_prd, axis=1)

            det_scores_so = det_scores_sbj * det_scores_obj
            det_scores_spo = det_scores_so[:, None] * det_scores_prd[:, :prd_k]

            det_scores_inds = argsort_desc(det_scores_spo)[:topk]
            det_scores_top = det_scores_spo[det_scores_inds[:, 0], det_scores_inds[:, 1]]
            det_boxes_so_top = np.hstack(
                (det_boxes_sbj[det_scores_inds[:, 0]], det_boxes_obj[det_scores_inds[:, 0]]))
            det_labels_p_top = det_labels_prd[det_scores_inds[:, 0], det_scores_inds[:, 1]]
            det_labels_spo_top = np.vstack(
                (det_labels_sbj[det_scores_inds[:, 0]], det_labels_p_top, det_labels_obj[det_scores_inds[:, 0]])).transpose()

            # filter out bad relationships
            cand_inds = np.where(det_scores_top > cfg.TEST.SPO_SCORE_THRESH)[0]
            det_boxes_so_top = det_boxes_so_top[cand_inds]
            det_labels_spo_top = det_labels_spo_top[cand_inds]
            det_scores_top = det_scores_top[cand_inds]

            det_scores_vis = res['prd_scores'][:, 1:]
            for i in range(det_labels_prd.shape[0]):
                det_scores_vis[i] = det_scores_vis[i][det_labels_prd[i]]
            det_scores_vis = det_scores_vis[:, :prd_k]
            det_scores_top_vis = det_scores_vis[det_scores_inds[:, 0], det_scores_inds[:, 1]]
            det_scores_top_vis = det_scores_top_vis[cand_inds]
            if 'prd_scores_bias' in res:
                det_scores_bias = res['prd_scores_bias'][:, 1:]
                for i in range(det_labels_prd.shape[0]):
                    det_scores_bias[i] = det_scores_bias[i][det_labels_prd[i]]
                det_scores_bias = det_scores_bias[:, :prd_k]
                det_scores_top_bias = det_scores_bias[det_scores_inds[:, 0], det_scores_inds[:, 1]]
                det_scores_top_bias = det_scores_top_bias[cand_inds]
            if 'prd_scores_spt' in res:
                det_scores_spt = res['prd_scores_spt'][:, 1:]
                for i in range(det_labels_prd.shape[0]):
                    det_scores_spt[i] = det_scores_spt[i][det_labels_prd[i]]
                det_scores_spt = det_scores_spt[:, :prd_k]
                det_scores_top_spt = det_scores_spt[det_scores_inds[:, 0], det_scores_inds[:, 1]]
                det_scores_top_spt = det_scores_top_spt[cand_inds]
            
            det_boxes_s_top = det_boxes_so_top[:, :4]
            det_boxes_o_top = det_boxes_so_top[:, 4:]
            det_labels_s_top = det_labels_spo_top[:, 0]
            det_labels_p_top = det_labels_spo_top[:, 1]
            det_labels_o_top = det_labels_spo_top[:, 2]
            
        topk_dets.append(dict(image=res['image'],
                              det_boxes_s_top=det_boxes_s_top,
                              det_boxes_o_top=det_boxes_o_top,
                              det_labels_s_top=det_labels_s_top,
                              det_labels_p_top=det_labels_p_top,
                              det_labels_o_top=det_labels_o_top,
                              det_scores_top=det_scores_top))
        topk_dets[-1]['det_scores_top_vis'] = det_scores_top_vis
        if 'prd_scores_bias' in res:
            topk_dets[-1]['det_scores_top_bias'] = det_scores_top_bias
        if 'prd_scores_spt' in res:
            topk_dets[-1]['det_scores_top_spt'] = det_scores_top_spt
        if do_vis:
            topk_dets[-1].update(dict(blob_conv=res['blob_conv'],
                                      blob_conv_prd=res['blob_conv_prd']))

        if do_val:
            gt_boxes_sbj = res['gt_sbj_boxes']  # (#num_gt, 4)
            gt_boxes_obj = res['gt_obj_boxes']  # (#num_gt, 4)
            gt_labels_sbj = res['gt_sbj_labels']  # (#num_gt,)
            gt_labels_obj = res['gt_obj_labels']  # (#num_gt,)
            gt_labels_prd = res['gt_prd_labels']  # (#num_gt,)
            gt_boxes_so = np.hstack((gt_boxes_sbj, gt_boxes_obj))
            gt_labels_spo = np.vstack((gt_labels_sbj, gt_labels_prd, gt_labels_obj)).transpose()
            # Compute recall. It's most efficient to match once and then do recall after
            # det_boxes_so_top is (#num_rel, 8)
            # det_labels_spo_top is (#num_rel, 3)
            pred_to_gt = _compute_pred_matches(
                gt_labels_spo, det_labels_spo_top,
                gt_boxes_so, det_boxes_so_top)
            if eval_per_img:
                for k in recalls:
                    if len(pred_to_gt):
                        match = reduce(np.union1d, pred_to_gt[:k])
                    else:
                        match = []
                    rec_i = float(len(match)) / float(gt_labels_spo.shape[0] + 1e-12)  # in case there is no gt
                    recalls[k].append(rec_i)
            else:    
                all_gt_cnt += gt_labels_spo.shape[0]
                for k in recalls:
                    if len(pred_to_gt):
                        match = reduce(np.union1d, pred_to_gt[:k])
                    else:
                        match = []
                    recalls[k] += len(match)
            
            topk_dets[-1].update(dict(gt_boxes_sbj=gt_boxes_sbj,
                                      gt_boxes_obj=gt_boxes_obj,
                                      gt_labels_sbj=gt_labels_sbj,
                                      gt_labels_obj=gt_labels_obj,
                                      gt_labels_prd=gt_labels_prd))

    if do_val:
        if eval_per_img:
            for k, v in recalls.items():
                recalls[k] = np.mean(v)
        else:
            for k in recalls:
                recalls[k] = float(recalls[k]) / (float(all_gt_cnt) + 1e-12)
        excel_str = print_stats(recalls)      
        if eval_ap:
            # prepare dets for each class
            logger.info('Preparing dets for mAP...')
            cls_image_ids, cls_dets, cls_gts, npos = prepare_mAP_dets(topk_dets, 9)
            all_npos = sum(npos)
            with open(cfg.DATA_DIR + '/openimages_v4/rel/rel_9_predicates.json') as f:
                rel_prd_cats = json.load(f)

            rel_mAP = 0.
            w_rel_mAP = 0.
            ap_str = ''
            for c in range(9):
                rec, prec, ap = ap_eval(cls_image_ids[c], cls_dets[c], cls_gts[c], npos[c], True)
                weighted_ap = ap * float(npos[c]) / float(all_npos)
                w_rel_mAP += weighted_ap
                rel_mAP += ap
                ap_str += '{:.2f}, '.format(100 * ap)
                print('rel AP for class {}: {:.2f} ({:.6f})'.format(rel_prd_cats[c], 100 * ap, float(npos[c]) / float(all_npos)))
            rel_mAP /= 9.
            print('weighted rel mAP: {:.2f}'.format(100 * w_rel_mAP))
            excel_str += ap_str

            phr_mAP = 0.
            w_phr_mAP = 0.
            ap_str = ''
            for c in range(9):
                rec, prec, ap = ap_eval(cls_image_ids[c], cls_dets[c], cls_gts[c], npos[c], False)
                weighted_ap = ap * float(npos[c]) / float(all_npos)
                w_phr_mAP += weighted_ap
                phr_mAP += ap
                ap_str += '{:.2f}, '.format(100 * ap)
                print('phr AP for class {}: {:.2f} ({:.6f})'.format(rel_prd_cats[c], 100 * ap, float(npos[c]) / float(all_npos)))
            phr_mAP /= 9.
            print('weighted phr mAP: {:.2f}'.format(100 * w_phr_mAP))
            excel_str += ap_str
            
            # total: 0.4 x rel_mAP + 0.2 x R@50 + 0.4 x phr_mAP
            final_score = 0.4 * rel_mAP + 0.2 * recalls[50] + 0.4 * phr_mAP
            
            # total: 0.4 x w_rel_mAP + 0.2 x R@50 + 0.4 x w_phr_mAP
            w_final_score = 0.4 * w_rel_mAP + 0.2 * recalls[50] + 0.4 * w_phr_mAP
            print('weighted final_score: {:.2f}'.format(100 * w_final_score))
            
            # get excel friendly string
            # excel_str = '{:.2f}, {:.2f}, {:.2f}, {:.2f}, '.format(100 * recalls[50], 100 * w_rel_mAP, 100 * w_phr_mAP, 100 * w_final_score) + excel_str
            # print('Excel-friendly format:')
            # print(excel_str.strip()[:-1])
    
#     print('Saving topk dets...')
#     topk_dets_f = os.path.join(output_dir, 'rel_detections_topk.pkl')
#     with open(topk_dets_f, 'wb') as f:
#         pickle.dump(topk_dets, f, pickle.HIGHEST_PROTOCOL)
#     logger.info('topk_dets size: {}'.format(len(topk_dets)))
    print('Done.')


def print_stats(recalls):
    # print('====================== ' + 'sgdet' + ' ============================')
    k_str = ''
    for k in recalls.keys():
        if k == 50:
            continue
        k_str += '{}\t'.format(k)
    v_str = ''
    for k, v in recalls.items():
        print('R@%i: %.2f' % (k, 100 * v))
        if k == 50:
            continue
        v_str += '{:.2f}, '.format(100 * v)
    return v_str


# This function is adapted from Rowan Zellers' code:
# https://github.com/rowanz/neural-motifs/blob/master/lib/evaluation/sg_eval.py
# Modified for this project to work with PyTorch v0.4
def _compute_pred_matches(gt_triplets, pred_triplets,
                 gt_boxes, pred_boxes, iou_thresh=0.5, phrdet=False):
    """
    Given a set of predicted triplets, return the list of matching GT's for each of the
    given predictions
    :param gt_triplets: 
    :param pred_triplets: 
    :param gt_boxes: 
    :param pred_boxes: 
    :param iou_thresh: Do y
    :return: 
    """
    # This performs a matrix multiplication-esque thing between the two arrays
    # Instead of summing, we want the equality, so we reduce in that way
    # The rows correspond to GT triplets, columns to pred triplets
    keeps = intersect_2d(gt_triplets, pred_triplets)
    gt_has_match = keeps.any(1)
    pred_to_gt = [[] for x in range(pred_boxes.shape[0])]
    for gt_ind, gt_box, keep_inds in zip(np.where(gt_has_match)[0],
                                         gt_boxes[gt_has_match],
                                         keeps[gt_has_match],
                                         ):
        boxes = pred_boxes[keep_inds]
        if phrdet:
            # Evaluate where the union box > 0.5
            gt_box_union = gt_box.reshape((2, 4))
            gt_box_union = np.concatenate((gt_box_union.min(0)[:2], gt_box_union.max(0)[2:]), 0)

            box_union = boxes.reshape((-1, 2, 4))
            box_union = np.concatenate((box_union.min(1)[:,:2], box_union.max(1)[:,2:]), 1)

            gt_box_union = gt_box_union.astype(dtype=np.float32, copy=False)
            box_union = box_union.astype(dtype=np.float32, copy=False)
            inds = bbox_overlaps(gt_box_union[None], 
                                 box_union = box_union)[0] >= iou_thresh

        else:
            gt_box = gt_box.astype(dtype=np.float32, copy=False)
            boxes = boxes.astype(dtype=np.float32, copy=False)
            sub_iou = bbox_overlaps(gt_box[None,:4], boxes[:, :4])[0]
            obj_iou = bbox_overlaps(gt_box[None,4:], boxes[:, 4:])[0]

            inds = (sub_iou >= iou_thresh) & (obj_iou >= iou_thresh)

        for i in np.where(keep_inds)[0][inds]:
            pred_to_gt[i].append(int(gt_ind))
    return pred_to_gt
