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

import torch
import numpy as np
from PIL import Image
from utils.inpainting import pad_image
from torchvision import transforms
from utils.visualizer import Visualizer
from diffusers import StableDiffusionInpaintPipeline
from detectron2.utils.colormap import random_color
from detectron2.data import MetadataCatalog
from scipy import ndimage


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

pipe = StableDiffusionInpaintPipeline.from_pretrained(
    # "stabilityai/stable-diffusion-2-inpainting",
    "runwayml/stable-diffusion-inpainting",
    revision="fp16", 
    torch_dtype=torch.float16,
).to("cuda")

def crop_image(input_image):
    crop_w, crop_h = np.floor(np.array(input_image.size) / 64).astype(int) * 64
    im_cropped = Image.fromarray(np.array(input_image)[:crop_h, :crop_w])
    return im_cropped

def referring_inpainting(model, image, texts, inpainting_text, *args, **kwargs):
    model.model.metadata = metadata
    texts = [[texts if texts.strip().endswith('.') else (texts.strip() + '.')]]
    image_ori = crop_image(transform(image))

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

        batch_inputs = [{'image': images, 'height': height, 'width': width, 'groundings': {'texts': texts}}]        
        outputs = model.model.evaluate_grounding(batch_inputs, None)
        visual = Visualizer(image_ori_np, metadata=metadata)

        grd_mask = (outputs[0]['grounding_mask'] > 0).float().cpu().numpy()
        for idx, mask in enumerate(grd_mask):
            color = random_color(rgb=True, maximum=1).astype(np.int32).tolist()
            demo = visual.draw_binary_mask(mask, color=color, text=texts[idx])
        res = demo.get_image()
    
    if inpainting_text not in ['no', '']:
        # if we want to do inpainting
        image_crop = image_ori
        struct2 = ndimage.generate_binary_structure(2, 2)
        mask_dilated = ndimage.binary_dilation(grd_mask[0], structure=struct2, iterations=3).astype(grd_mask[0].dtype)
        mask = Image.fromarray(mask_dilated * 255).convert('RGB')
        image_and_mask = {
            "image": image_crop,
            "mask": mask,
        }
        width = image_crop.size[0]; height = image_crop.size[1]
        images_inpainting = pipe(prompt = inpainting_text.strip(), image=image_and_mask['image'], mask_image=image_and_mask['mask'], height=height, width=width).images[0]
        # put images_inpainting back to original image
        # image_ori.paste(images_inpainting)        
        torch.cuda.empty_cache()
        return Image.fromarray(res) ,'' , images_inpainting
    else:
        torch.cuda.empty_cache()
        return image_ori, 'text', Image.fromarray(res)