from share import *
import config
import os
import cv2
import einops
import gradio as gr
import numpy as np
import torch
import random

from pytorch_lightning import seed_everything
from annotator.util import resize_image, HWC3
from annotator.uniformer import UniformerDetector
from cldm.model import create_model, load_state_dict
from cldm.ddim_hacked import DDIMSampler

from PIL import Image

# os.environ["no_proxy"] = "localhost,127.0.0.1,::1"
device = "cpu"

model = create_model('./models/cldm_v15_cpu.yaml').cpu()
sd_model_path = "./models/sks_crack_ppl.ckpt"
controlnet_path = "./models/sks_crack_controlnet.pth"
model.load_state_dict(load_state_dict(sd_model_path, location='cpu'), strict = False)
model.load_state_dict(load_state_dict(controlnet_path, location='cpu'), strict = False)

# model = model.cuda()
ddim_sampler = DDIMSampler(model)
init_mask = Image.open("379.png").convert("L")


def model_sample(mask,
                prompt = "sks crack, pavement cracks, HDR, Asphalt road, mudded",
                a_prompt="", 
                n_prompt="", 
                num_samples=1, ddim_steps=50, guess_mode=False, strength=1.0, scale=7.0, seed=-1, eta=0.0):
    # mask --- numpy
    ddim_sampler = DDIMSampler(model)

    with torch.no_grad():
        mask = HWC3(mask)
        mask = resize_image(mask, 512)
        H, W, C= mask.shape

        control = torch.from_numpy(mask.copy()).float().to(device) / 255.0
        control = torch.stack([control for _ in range(num_samples)], dim=0)
        control = einops.rearrange(control, 'b h w c -> b c h w').clone()

        if seed == -1:
            seed = random.randint(0, 65535)
        seed_everything(seed)

        cond = {"c_concat": [control], "c_crossattn": [model.get_learned_conditioning([prompt + ', ' + a_prompt] * num_samples)]}
        un_cond = {"c_concat": None if guess_mode else [control], "c_crossattn": [model.get_learned_conditioning([n_prompt] * num_samples)]}
        shape = (4, H // 8, W // 8)


        model.control_scales = [strength * (0.825 ** float(12 - i)) for i in range(13)] if guess_mode else ([strength] * 13)  # Magic number. IDK why. Perhaps because 0.825**12<0.01 but 0.826**12>0.01
        samples, intermediates = ddim_sampler.sample(ddim_steps, num_samples,
                                                     shape, cond, verbose=False, eta=eta,
                                                     unconditional_guidance_scale=scale,
                                                     unconditional_conditioning=un_cond)


        x_samples = model.decode_first_stage(samples)
        x_samples = (einops.rearrange(x_samples, 'b c h w -> b h w c') * 127.5 + 127.5).cpu().numpy().clip(0, 255).astype(np.uint8)

        results = [x_samples[i] for i in range(num_samples)]
        
    return results

block = gr.Blocks().queue()
with block:
    with gr.Row():
        gr.Markdown("## Crack Diffusion")
    with gr.Row():
        with gr.Column():
            with gr.Row():
                with gr.Tabs(elem_id="mode_img2img"):
                    with gr.TabItem('txt2img', id='img2img', elem_id="img2img_img2img_tab") as tab_img2img:
                        init_img = gr.Image(label="Image for img2img", elem_id="img2img_image", show_label=False, source="upload", interactive=True, type="numpy", tool="editor", image_mode="L", value=init_mask).style(height=480)
                        init_run_button = gr.Button(label="Run Init")
                    with gr.TabItem('Sketch', id='img2img_sketch', elem_id="img2img_img2img_sketch_tab") as tab_sketch:
                        sketch_img = gr.Image(label="Image for img2img", elem_id="img2img_sketch", show_label=False, source="canvas", interactive=True, type="numpy", tool="color-sketch", image_mode="L").style(height=480)
                        sketch_run_button = gr.Button(label="Run Sketch")
                    prompt = gr.Textbox(label="Prompt", value="sks crack")
            with gr.Row():
                with gr.Accordion("Advanced options", open=False):
                    num_samples = gr.Slider(label="Images", minimum=1, maximum=12, value=1, step=1)
                    image_resolution = gr.Slider(label="Image Resolution", minimum=256, maximum=768, value=512, step=64)
                    strength = gr.Slider(label="Control Strength", minimum=0.0, maximum=2.0, value=1.0, step=0.01)
                    guess_mode = gr.Checkbox(label='Guess Mode', value=False)
                    detect_resolution = gr.Slider(label="Segmentation Resolution", minimum=128, maximum=1024, value=512, step=1)
                    ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=20, step=1)
                    scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=30.0, value=7.0, step=0.1)
                    seed = gr.Slider(label="Seed", minimum=-1, maximum=2147483647, step=1, randomize=True)
                    eta = gr.Number(label="eta (DDIM)", value=0.0)
                    a_prompt = gr.Textbox(label="Added Prompt", value='')
                    n_prompt = gr.Textbox(label="Negative Prompt",
                                            value='')
        with gr.Column():
            result_gallery = gr.Gallery(label='Output', show_label=False, elem_id="gallery").style(grid=2, height='auto')
 
    init_ips = [init_img, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta]
    sketch_ips = [sketch_img, prompt, a_prompt, n_prompt, num_samples, ddim_steps, guess_mode, strength, scale, seed, eta]
    init_run_button.click(fn=model_sample, inputs=init_ips, outputs=[result_gallery])
    sketch_run_button.click(fn=model_sample, inputs=sketch_ips, outputs=[result_gallery])

block.launch()