# --------------------------------------------------------
# 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)
# --------------------------------------------------------
import os
import openai
import torch
import numpy as np
from scipy import ndimage
from PIL import Image
from utils.inpainting import pad_image, crop_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


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")

prompts = []
prompts.append("remove the person, task: (referring editing), source: [person], target:<clean and empty scene>;")
prompts.append("remove the person in the middle, task: (referring editing), source: [person in the middle], target:<clean and empty scene>;")
prompts.append("remove the dog on the left side, task: (referring editing), source: [dog on the left side], target:<clean and empty scene>;")
prompts.append("change the apple to a pear, task: (referring editing), source: [apple], target: <pear>;")
prompts.append("change the red apple to a green one, task: (referring editing), source: [red apple], target: <green apple>;")
prompts.append("replace the dog with a cat, task: (referring editing), source: [dot], target: <cat>;")
prompts.append("replace the red apple with a green one, task: (referring editing), source: [red apple], target: <green apple>;")

def get_gpt3_response(prompt):
    openai.api_key = os.getenv("OPENAI_API_KEY")

    response = openai.Completion.create(
    model="text-davinci-003",
    prompt=prompt,
    temperature=0.7,
    max_tokens=128,
    top_p=1,
    frequency_penalty=0,
    presence_penalty=0
    )

    return response

def referring_inpainting_gpt3(model, image, instruction, *args, **kwargs):    
    # convert instruction to source and target
    print(instruction)
    resp = get_gpt3_response(' '.join(prompts) + instruction + ',')
    resp_text = resp['choices'][0]['text']
    print(resp_text)
    ref_text = resp_text[resp_text.find('[')+1:resp_text.find(']')]
    inp_text = resp_text[resp_text.find('<')+1:resp_text.find('>')]

    model.model.metadata = metadata
    texts = [[ref_text if ref_text.strip().endswith('.') else (ref_text.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 inp_text not in ['no', '']:
        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,
        }
        # images_inpainting = inpainting(inpainting_model, image_and_mask, inp_text, ddim_steps, num_samples, scale, seed)
        width = image_ori.size[0]; height = image_ori.size[1]
        images_inpainting = pipe(prompt = inp_text.strip(), image=image_and_mask['image'], mask_image=image_and_mask['mask'], height=height, width=width).images
        torch.cuda.empty_cache()
        return Image.fromarray(res), resp_text, images_inpainting[0]
    else:
        torch.cuda.empty_cache()
        return image_ori, resp_text, Image.fromarray(res)