import sys
from typing import Dict
sys.path.insert(0, 'gradio-modified')

import gradio as gr
import numpy as np

from PIL import Image

import torch

if torch.cuda.is_available():
    t = torch.cuda.get_device_properties(0).total_memory
    r = torch.cuda.memory_reserved(0)
    a = torch.cuda.memory_allocated(0)
    f = t-a  # free inside reserved
    if f < 2**32:
        device = 'cpu'
    else:
        device = 'cuda'
else:
    device = 'cpu'
    torch._C._jit_set_bailout_depth(0)

print('Use device:', device)


net = torch.jit.load(f'weights/pkp-v1.{device}.jit.pt')


def resize_original(img: Image.Image):
    if img is None:
        return img
    if isinstance(img, dict):
        img = img["image"]
    
    guide_img = img.convert('L')
    w, h = guide_img.size
    scale = 256 / min(guide_img.size)
    guide_img = guide_img.resize([int(round(s*scale)) for s in guide_img.size], Image.Resampling.LANCZOS)

    guide = np.asarray(guide_img)
    h, w = guide.shape[-2:]
    rows = int(np.ceil(h/64))*64
    cols = int(np.ceil(w/64))*64
    ph_1 = (rows-h) // 2
    ph_2 = rows-h - (rows-h) // 2
    pw_1 = (cols-w) // 2
    pw_2 = cols-w - (cols-w) // 2
    guide = np.pad(guide, ((ph_1, ph_2), (pw_1, pw_2)), mode='constant', constant_values=255)
    guide_img = Image.fromarray(guide)

    return gr.Image.update(value=guide_img.convert('RGBA')), guide_img.convert('RGBA')


def colorize(img: Dict[str, Image.Image], guide_img: Image.Image, seed: int, hint_mode: str):
    if not isinstance(img, dict):
        return gr.update(visible=True)

    if hint_mode == "Roughly Hint":
        hint_mode_int = 0
    elif hint_mode == "Precisely Hint":
        hint_mode_int = 1
    
    guide_img = guide_img.convert('L')
    hint_img = img["mask"].convert('RGBA') # I modified gradio to enable it upload colorful mask

    guide = torch.from_numpy(np.asarray(guide_img))[None,None].float().to(device) / 255.0 * 2 - 1
    hint = torch.from_numpy(np.asarray(hint_img)).permute(2,0,1)[None].float().to(device) / 255.0 * 2 - 1
    hint_alpha = (hint[:,-1:] > 0.99).float()
    hint = hint[:,:3] * hint_alpha - 2 * (1 - hint_alpha)

    np.random.seed(int(seed))
    b, c, h, w = hint.shape
    h //= 8
    w //= 8
    noises = [torch.from_numpy(np.random.randn(b, c, h, w)).float().to(device) for _ in range(16+1)]

    with torch.inference_mode():
        sample = net(noises, guide, hint,  hint_mode_int)
        out = sample[0].cpu().numpy().transpose([1,2,0])
        out = np.uint8(((out + 1) / 2 * 255).clip(0,255))
    
    return Image.fromarray(out).convert('RGB')


with gr.Blocks() as demo:
    gr.Markdown('''<center><h1>Image Colorization With Hint</h1></center>
<h2>Colorize your images/sketches with hint points.</h2>
<br />
''')
    with gr.Row():
        with gr.Column():
            inp = gr.Image(
                source="upload", 
                tool="sketch", # tool="color-sketch", # color-sketch upload image mixed with the original
                type="pil", 
                label="Sketch", 
                interactive=True,
                elem_id="sketch-canvas"
            )
            inp_store = gr.Image(
                type="pil", 
                interactive=False
            )
            inp_store.visible = False
        with gr.Column():
            seed = gr.Slider(1, 2**32, step=1, label="Seed", interactive=True, randomize=True)
            hint_mode = gr.Radio(["Roughly Hint", "Precisely Hint"], value="Roughly Hint", label="Hint Mode")
            btn = gr.Button("Run")
        with gr.Column():
            output = gr.Image(type="pil", label="Output", interactive=False)
    gr.Markdown('''
Upon uploading an image, kindly give color hints at specific points, and then run the model. Average inference time is about 52 seconds.
''')
    inp.upload(
        resize_original, 
        inp, 
        [inp, inp_store],
    )
    btn.click(
        colorize, 
        [inp, inp_store, seed, hint_mode],
        output
    )

if __name__ == "__main__":
    demo.launch()