"""
Ref-YoutubeVOS data loader
"""
from pathlib import Path

import torch
from torch.autograd.grad_mode import F
from torch.utils.data import Dataset
import datasets.transforms_video as T

import os
from PIL import Image
import json
import numpy as np
import random

from datasets.categories import ytvos_category_dict as category_dict


class YTVOSDataset(Dataset):
    """
    A dataset class for the Refer-Youtube-VOS dataset which was first introduced in the paper:
    "URVOS: Unified Referring Video Object Segmentation Network with a Large-Scale Benchmark"
    (see https://link.springer.com/content/pdf/10.1007/978-3-030-58555-6_13.pdf).
    The original release of the dataset contained both 'first-frame' and 'full-video' expressions. However, the first
    dataset is not publicly available anymore as now only the harder 'full-video' subset is available to download
    through the Youtube-VOS referring video object segmentation competition page at:
    https://competitions.codalab.org/competitions/29139
    Furthermore, for the competition the subset's original validation set, which consists of 507 videos, was split into
    two competition 'validation' & 'test' subsets, consisting of 202 and 305 videos respectively. Evaluation can
    currently only be done on the competition 'validation' subset using the competition's server, as
    annotations were publicly released only for the 'train' subset of the competition.

    """
    def __init__(self, img_folder: Path, ann_file: Path, transforms, return_masks: bool, 
                 num_frames: int, max_skip: int):
        self.img_folder = img_folder     
        self.ann_file = ann_file         
        self._transforms = transforms    
        self.return_masks = return_masks # not used
        self.num_frames = num_frames     
        self.max_skip = max_skip
        # create video meta data
        self.prepare_metas()       

        print('\n video num: ', len(self.videos), ' clip num: ', len(self.metas))  
        print('\n')    

    def prepare_metas(self):
        # read object information
        with open(os.path.join(str(self.img_folder), 'meta.json'), 'r') as f:
            subset_metas_by_video = json.load(f)['videos']
        
        # read expression data
        with open(str(self.ann_file), 'r') as f:
            subset_expressions_by_video = json.load(f)['videos']
        self.videos = list(subset_expressions_by_video.keys())

        self.metas = []
        skip_vid_count = 0

        for vid in self.videos:
            vid_meta = subset_metas_by_video[vid]
            vid_data = subset_expressions_by_video[vid]
            vid_frames = sorted(vid_data['frames'])
            vid_len = len(vid_frames)

            if vid_len < 11:
                #print(f"Too short video: {vid} with frame length {vid_len}")
                skip_vid_count += 1
                continue

            for exp_id, exp_dict in vid_data['expressions'].items():
                # Exclude start_idx (0, 1) and end_idx (vid_len-1, vid_len-2)
                start_idx , end_idx = 2, vid_len-2
                bin_size = (end_idx - start_idx) // 4

                bins = []
                for i in range(4):
                    bin_start = start_idx + i * bin_size
                    bin_end = bin_start + bin_size if i < 3 else end_idx

                    bins.append((bin_start, bin_end))

                # Random sample one frame from each bin
                sample_indx = []
                for start_idx, end_idx in bins:
                    sample_indx.append(random.randint(start_idx, end_idx - 1))
                sample_indx.sort()  # Ensure indices are in order

                
                for frame_id in sample_indx:              
                    meta = {
                        'video': vid,
                        'exp': exp_dict['exp'],
                        'obj_id': int(exp_dict['obj_id']),
                        'frames': vid_frames,
                        'frame_id' : frame_id,
                        'sample_frames_id' : sample_indx,
                        'bins': bins,  
                        'category': vid_meta['objects'][exp_dict['obj_id']]['category']
                    }
                    self.metas.append(meta)
        print(skip_vid_count)
                

    @staticmethod
    def bounding_box(img):
        rows = np.any(img, axis=1)
        cols = np.any(img, axis=0)
        rmin, rmax = np.where(rows)[0][[0, -1]]
        cmin, cmax = np.where(cols)[0][[0, -1]]
        return rmin, rmax, cmin, cmax # y1, y2, x1, x2 
        
    def __len__(self):
        return len(self.metas)
        
    def __getitem__(self, idx):
        instance_check = False
        while not instance_check:
            meta = self.metas[idx]  # dict


            video, exp, obj_id, category, frames, frame_id, sample_frames_id, bins = \
                    meta['video'], meta['exp'], meta['obj_id'], meta['category'], meta['frames'], metas['frame_id'], metas['sample_frames_id'], meta['bins']
                
    
            # clean up the caption
            exp = " ".join(exp.lower().split())
            category_id = category_dict[category]
            vid_len = len(frames)

            # num_frames = self.num_frames

            # read frames and masks
            imgs, labels, boxes, masks, valid = [], [], [], [], []
            for frame_indx in sample_frames_id:
                frame_name = frames[frame_indx]
                img_path = os.path.join(str(self.img_folder), 'JPEGImages', video, frame_name + '.jpg')
                mask_path = os.path.join(str(self.img_folder), 'Annotations', video, frame_name + '.png')
                img = Image.open(img_path).convert('RGB')
                mask = Image.open(mask_path).convert('P')

                # create the target
                label =  torch.tensor(category_id) 
                mask = np.array(mask)
                mask = (mask==obj_id).astype(np.float32) # 0,1 binary
                if (mask > 0).any():
                    y1, y2, x1, x2 = self.bounding_box(mask)
                    box = torch.tensor([x1, y1, x2, y2]).to(torch.float)
                    valid.append(1)
                else: # some frame didn't contain the instance
                    box = torch.tensor([0, 0, 0, 0]).to(torch.float) 
                    valid.append(0)
                mask = torch.from_numpy(mask)

                # append
                imgs.append(img)
                labels.append(label)
                masks.append(mask)
                boxes.append(box)

            # transform
            w, h = img.size
            labels = torch.stack(labels, dim=0) 
            boxes = torch.stack(boxes, dim=0) 
            boxes[:, 0::2].clamp_(min=0, max=w)
            boxes[:, 1::2].clamp_(min=0, max=h)
            masks = torch.stack(masks, dim=0) 
            target = {
                'frames_idx': torch.tensor(sample_frames_id), # [T,]
                'labels': labels,                        # [T,]
                'boxes': boxes,                          # [T, 4], xyxy
                'masks': masks,                          # [T, H, W]
                'valid': torch.tensor(valid),            # [T,]
                'caption': exp,
                'orig_size': torch.as_tensor([int(h), int(w)]), 
                'size': torch.as_tensor([int(h), int(w)])
            }

            # "boxes" normalize to [0, 1] and transform from xyxy to cxcywh in self._transform
            if self._transforms:
                imgs, target = self._transforms(imgs, target)
                imgs = torch.stack(imgs, dim=0) # [T, 3, H, W]
            else:
                imgs = np.array(imgs)
                imgs = torch.tensor(imgs.transpose(0, 3, 1, 2))
            
            
            # FIXME: handle "valid", since some box may be removed due to random crop
            if torch.any(target['valid'] == 1):  # at leatst one instance
                instance_check = True
            else:
                idx = random.randint(0, self.__len__() - 1)

        return imgs, target


def make_coco_transforms(image_set, max_size=640):
    normalize = T.Compose([
        T.ToTensor(),
        T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])

    scales = [288, 320, 352, 392, 416, 448, 480, 512]

    if image_set == 'train':
        return T.Compose([
            T.RandomHorizontalFlip(),
            T.PhotometricDistort(),
            T.RandomSelect(
                T.Compose([
                    T.RandomResize(scales, max_size=max_size),
                    T.Check(),
                ]),
                T.Compose([
                    T.RandomResize([400, 500, 600]),
                    T.RandomSizeCrop(384, 600),
                    T.RandomResize(scales, max_size=max_size),
                    T.Check(),
                ])
            ),
            normalize,
        ])
    
    # we do not use the 'val' set since the annotations are inaccessible
    if image_set == 'val':
        return T.Compose([
            T.RandomResize([360], max_size=640),
            normalize,
        ])

    raise ValueError(f'unknown {image_set}')


def build(image_set, args):
    root = Path(args.ytvos_path)
    assert root.exists(), f'provided YTVOS path {root} does not exist'
    PATHS = {
        "train": (root / "train", root / "meta_expressions" / "train" / "meta_expressions.json"),
        "val": (root / "valid", root / "meta_expressions" / "valid" / "meta_expressions.json"),    # not used actually
    }
    img_folder, ann_file = PATHS[image_set]
    # dataset = YTVOSDataset(img_folder, ann_file, transforms=make_coco_transforms(image_set, max_size=args.max_size), return_masks=args.masks, 
    #                        num_frames=args.num_frames, max_skip=args.max_skip)
    dataset = YTVOSDataset(img_folder, ann_file, transforms=None, return_masks=args.masks, 
                           num_frames=args.num_frames, max_skip=args.max_skip)
    return dataset