import gradio as gr
import argparse
import os

import pandas as pd
from PIL import Image
import numpy as np
import torch as th
from torchvision import transforms

import diffusers
from diffusers import AutoencoderKL, DDPMScheduler, DDIMScheduler, LCMScheduler
import gc
from safetensors import safe_open

from models import SAR2OptUNetv3
from utils import update_args_from_yaml, safe_load

transform_sar = transforms.Compose([
    transforms.ToTensor(),
    transforms.Resize((256, 256)),
    transforms.Normalize((0.5), (0.5)),
])
AVAILABLE_MODELS = {
    "Sen12:LCM-Model": "models/model.safetensors",
    "Sen12:Org-Model": "models/model_org.safetensors",
}

device = th.device('cuda:0' if th.cuda.is_available() else 'cpu')

def safe_load(model_path):
    assert "safetensors" in model_path
    state_dict = {}
    with safe_open(model_path, framework="pt", device="cpu") as f:
        for k in f.keys():
            state_dict[k] = f.get_tensor(k) 
    return state_dict

unet_model = SAR2OptUNetv3(
            sample_size=256,
            in_channels=4,
            out_channels=3,
            layers_per_block=2,
            block_out_channels=(128, 128, 256, 256, 512, 512),
            down_block_types=(
                "DownBlock2D",
                "DownBlock2D",
                "DownBlock2D",
                "DownBlock2D",
                "AttnDownBlock2D",
                "DownBlock2D",
            ),
            up_block_types=(
                "UpBlock2D",
                "AttnUpBlock2D",
                "UpBlock2D",
                "UpBlock2D",
                "UpBlock2D",
                "UpBlock2D",
            ),
)

print('load unet safetensos done!')
lcm_scheduler = LCMScheduler(num_train_timesteps=1000)

unet_model.to(device)
unet_model.eval()

model_kwargs = {}


def predict(condition, nums_step, model_name):
    unet_checkpoint = AVAILABLE_MODELS[model_name]
    unet_model.load_state_dict(safe_load(unet_checkpoint), strict=True)
    unet_model.eval().to(device)
    with th.no_grad():
        lcm_scheduler.set_timesteps(nums_step, device=device)
        timesteps = lcm_scheduler.timesteps
        pred_latent = th.randn(size=[1, 3, 256, 256], device=device)
        condition = condition.convert("L")
        condition = transform_sar(condition)
        condition = th.unsqueeze(condition, 0)
        condition = condition.to(device)
        for timestep in timesteps:
            latent_to_pred = th.cat((pred_latent, condition), dim=1)
            model_pred = unet_model(latent_to_pred, timestep)
            pred_latent, denoised = lcm_scheduler.step(
                                                    model_output=model_pred,
                                                    timestep=timestep,
                                                    sample=pred_latent,
                                                    return_dict=False)
        sample = denoised.cpu()

    sample = ((sample + 1) * 127.5).clamp(0, 255).to(th.uint8)
    sample = sample.permute(0, 2, 3, 1)
    sample = sample.contiguous()
    sample = sample.cpu().numpy()
    sample = sample.squeeze(0)
    sample = Image.fromarray(sample)
    return sample


demo = gr.Interface(
    fn=predict,
    inputs=[gr.Image(type="pil"), 
            gr.Slider(1, 1000),
            gr.Dropdown(
                choices=list(AVAILABLE_MODELS.keys()),
                value=list(AVAILABLE_MODELS.keys())[0],
                label="Choose the Model"),],
            # gr.Radio(["Sent", "GF3"], label="Model", info="Which model to you want to use?"), ],
    outputs=gr.Image(type="pil"),
    examples=[
        [os.path.join(os.path.dirname(__file__), "sar_1.png"), 8, "Sen12:LCM-Model"],
        [os.path.join(os.path.dirname(__file__), "sar_2.png"), 16, "Sen12:LCM-Model"],
        [os.path.join(os.path.dirname(__file__), "sar_3.png"), 500, "Sen12:Org-Model"],
        [os.path.join(os.path.dirname(__file__), "sar_4.png"), 1000, "Sen12:Org-Model"],
    ],
    title="SAR to Optical Image🚀",
    description="""
        # 🎯 Instruction
        This is a project that converts SAR images into optical images, based on conditional diffusion. 

        Input a SAR image, and its corresponding optical image will be obtained.

        ## 📢 Inputs
        - `condition`: the SAR image that you want to transfer.
        - `timestep_respacing`: the number of iteration steps when inference.

        ## 🎉 Outputs
        - The corresponding optical image.
        
        **Paper** : [Accelerating Diffusion for SAR-to-Optical Image Translation via Adversarial Consistency Distillation](https://arxiv.org/abs/2407.06095)
        
        **Github** : https://github.com/Coordi777/Accelerating-Diffusion-for-SAR-to-Optical-Image-Translation
    """
)

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