import os
import sys
import torch
import gradio as gr
from PIL import Image
import numpy as np
from omegaconf import OmegaConf
import subprocess
from tqdm import tqdm
import requests

# Assuming spaces is a valid module
import spaces

def download_file(url, filename):
    response = requests.get(url, stream=True)
    total_size = int(response.headers.get('content-length', 0))
    block_size = 1024
    with open(filename, 'wb') as file, tqdm(
        desc=filename,
        total=total_size,
        unit='iB',
        unit_scale=True,
        unit_divisor=1024,
    ) as progress_bar:
        for data in response.iter_content(block_size):
            size = file.write(data)
            progress_bar.update(size)

def setup_environment():
    if not os.path.exists("CCSR"):
        print("Cloning CCSR repository...")
        subprocess.run(["git", "clone", "-b", "dev", "https://github.com/camenduru/CCSR.git"])
    
    os.chdir("CCSR")
    sys.path.append(os.getcwd())
    
    os.makedirs("weights", exist_ok=True)
    if not os.path.exists("weights/real-world_ccsr.ckpt"):
        print("Downloading model checkpoint...")
        download_file(
            "https://huggingface.co./camenduru/CCSR/resolve/main/real-world_ccsr.ckpt",
            "weights/real-world_ccsr.ckpt"
        )
    else:
        print("Model checkpoint already exists. Skipping download.")

setup_environment()

# Importing from the CCSR folder
from CCSR.ldm.xformers_state import disable_xformers
from CCSR.model.q_sampler import SpacedSampler
from CCSR.model.ccsr_stage1 import ControlLDM
from CCSR.utils.common import instantiate_from_config, load_state_dict

config = OmegaConf.load("configs/model/ccsr_stage2.yaml")
model = instantiate_from_config(config)
ckpt = torch.load("weights/real-world_ccsr.ckpt", map_location="cpu")
load_state_dict(model, ckpt, strict=True)
model.freeze()
model.to("cuda")

@spaces.GPU  # Decorate the inference function with @spaces.GPU
@torch.no_grad()
def process(image, steps, t_max, t_min, color_fix_type):
    image = Image.open(image).convert("RGB")
    image = image.resize((256, 256), Image.LANCZOS)
    image = np.array(image)

    sampler = SpacedSampler(model, var_type="fixed_small")
    control = torch.tensor(np.stack([image]) / 255.0, dtype=torch.float32, device=model.device).clamp_(0, 1)
    control = einops.rearrange(control, "n h w c -> n c h w").contiguous()

    model.control_scales = [1.0] * 13

    height, width = control.size(-2), control.size(-1)
    shape = (1, 4, height // 8, width // 8)
    x_T = torch.randn(shape, device=model.device, dtype=torch.float32)

    samples = sampler.sample_ccsr(
        steps=steps, t_max=t_max, t_min=t_min, shape=shape, cond_img=control,
        positive_prompt="", negative_prompt="", x_T=x_T,
        cfg_scale=1.0, color_fix_type=color_fix_type
    )

    x_samples = samples.clamp(0, 1)
    x_samples = (einops.rearrange(x_samples, "b c h w -> b h w c") * 255).cpu().numpy().clip(0, 255).astype(np.uint8)

    return Image.fromarray(x_samples[0])

interface = gr.Interface(
    fn=process,
    inputs=[
        gr.Image(type="filepath"),
        gr.Slider(minimum=1, maximum=100, step=1, value=45),
        gr.Slider(minimum=0, maximum=1, step=0.0001, value=0.6667),
        gr.Slider(minimum=0, maximum=1, step=0.0001, value=0.3333),
        gr.Dropdown(choices=["adain", "wavelet", "none"], value="adain"),
    ],
    outputs=gr.Image(type="pil"),
    title="CCSR: Continuous Contrastive Super-Resolution",
)

interface.launch()