# --------------------------------------------------------
# X-Decoder -- Generalized Decoding for Pixel, Image, and Language
# Copyright (c) 2022 Microsoft
# Licensed under The MIT License [see LICENSE for details]
# Written by Xueyan Zou (xueyan@cs.wisc.edu)
# --------------------------------------------------------

import torch
import numpy as np
from PIL import Image
from torchvision import transforms
from utils.visualizer import Visualizer
from detectron2.utils.colormap import random_color
from detectron2.data import MetadataCatalog


t = []
t.append(transforms.Resize(512, interpolation=Image.BICUBIC))
transform = transforms.Compose(t)
metadata = MetadataCatalog.get('ade20k_panoptic_train')

def open_panoseg(model, image, texts, inpainting_text, *args, **kwargs):
    stuff_classes = [x.strip() for x in texts.split(';')[0].replace('stuff:','').split(',')]
    thing_classes = [x.strip() for x in texts.split(';')[1].replace('thing:','').split(',')]
    thing_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(thing_classes))]
    stuff_colors = [random_color(rgb=True, maximum=255).astype(np.int32).tolist() for _ in range(len(stuff_classes))]
    thing_dataset_id_to_contiguous_id = {x:x for x in range(len(thing_classes))}
    stuff_dataset_id_to_contiguous_id = {x+len(thing_classes):x for x in range(len(stuff_classes))}

    MetadataCatalog.get("demo").set(
        thing_colors=thing_colors,
        thing_classes=thing_classes,
        thing_dataset_id_to_contiguous_id=thing_dataset_id_to_contiguous_id,
        stuff_colors=stuff_colors,
        stuff_classes=stuff_classes,
        stuff_dataset_id_to_contiguous_id=stuff_dataset_id_to_contiguous_id,
    )
    model.model.sem_seg_head.predictor.lang_encoder.get_text_embeddings(thing_classes + stuff_classes + ["background"], is_eval=True)
    metadata = MetadataCatalog.get('demo')
    model.model.metadata = metadata
    model.model.sem_seg_head.num_classes = len(thing_classes + stuff_classes)

    with torch.no_grad():
        image_ori = transform(image)
        width = image_ori.size[0]
        height = image_ori.size[1]
        image = transform(image_ori)
        image = np.asarray(image)
        images = torch.from_numpy(image.copy()).permute(2,0,1).cuda()

        batch_inputs = [{'image': images, 'height': height, 'width': width}]
        outputs = model.forward(batch_inputs)
        visual = Visualizer(image_ori, metadata=metadata)

        pano_seg = outputs[-1]['panoptic_seg'][0]
        pano_seg_info = outputs[-1]['panoptic_seg'][1]

        for i in range(len(pano_seg_info)):
            if pano_seg_info[i]['category_id'] in metadata.thing_dataset_id_to_contiguous_id.keys():
                pano_seg_info[i]['category_id'] = metadata.thing_dataset_id_to_contiguous_id[pano_seg_info[i]['category_id']]
            else:
                pano_seg_info[i]['isthing'] = False
                pano_seg_info[i]['category_id'] = metadata.stuff_dataset_id_to_contiguous_id[pano_seg_info[i]['category_id']]

        demo = visual.draw_panoptic_seg(pano_seg.cpu(), pano_seg_info) # rgb Image
        res = demo.get_image()

    MetadataCatalog.remove('demo')
    torch.cuda.empty_cache()
    return Image.fromarray(res), '', None