import torch
import requests
from tqdm import tqdm
import zipfile
import shutil
from pathlib import Path
import os
from functools import partial
from total.model import Unet_TS_CT
from total_mr.model import Unet_TS_MR
import json

def convert_torchScript_full(model_name: str, model: torch.nn.Module, task: str, type: int, mri: bool, url: str) -> None:
    state_dict = download(url, model_name, mri)
    tmp = {}
    with open("Destination_Unet_{}.txt".format(type)) as f2:
        it = iter(state_dict.keys())
        for l1 in f2:
            print(l1)

            key = next(it)
            print(key)
            while "decoder.seg_layers" in key:
                if type == 1:
                    if "decoder.seg_layers.4" in key :
                        break
                if type == 2:
                    if "decoder.seg_layers.3"  in key:
                        break
                if type == 3:
                    if "decoder.seg_layers.2" in key:
                        break
                key = next(it)
                    
            while "all_modules" in key or "decoder.encoder" in key:
                key = next(it)
            tmp[l1.replace("\n", "")] = state_dict[key]
    if not mri:
        tmp["ClipAndNormalize.mean"] = state_dict["mean"]
        tmp["ClipAndNormalize.std"] = state_dict["std"]
        tmp["ClipAndNormalize.clip_min"] = state_dict["percentile_00_5"]
        tmp["ClipAndNormalize.clip_max"] = state_dict["percentile_99_5"]
    state_dict = {"Model" : {model.name : tmp}}
    model.load(state_dict)
    dest_path = Path(f"./{task}")
    dest_path.mkdir(exist_ok=True)
    torch.save(state_dict, str(dest_path/f"{model_name}.pt"))

def download(url: str, model_name: str, mri: bool) -> dict[str, torch.Tensor]:
    with open(url.split("/")[-1], 'wb') as f:
        with requests.get(url, stream=True) as r:
            r.raise_for_status()

            total_size = int(r.headers.get('content-length', 0))
            progress_bar = tqdm(total=total_size, unit='B', unit_scale=True, desc=f"Downloading {model_name}")
            for chunk in r.iter_content(chunk_size=8192 * 16):
                progress_bar.update(len(chunk))
                f.write(chunk)
            progress_bar.close()
    with zipfile.ZipFile(url.split("/")[-1], 'r') as zip_f:
        zip_f.extractall(url.split("/")[-1].replace(".zip", ""))
    os.remove(url.split("/")[-1])
    zip_path = Path(url.split("/")[-1].replace(".zip", ""))

    state_dict = torch.load(next(zip_path.rglob("checkpoint_final.pth"), None), map_location="cpu", weights_only=False)["network_weights"]
    if not mri:
        dataset_fingerprint_path = next(zip_path.rglob("dataset_fingerprint.json"), None)
        with open(dataset_fingerprint_path, "r") as f:
            data = json.load(f)

        ch0 = data["foreground_intensity_properties_per_channel"]["0"]

        state_dict["mean"] = torch.tensor([ch0["mean"]])
        state_dict["std"] = torch.tensor([ch0["std"]])
        state_dict["percentile_00_5"] = torch.tensor([ch0["percentile_00_5"]])
        state_dict["percentile_99_5"] = torch.tensor([ch0["percentile_99_5"]])
    shutil.rmtree(zip_path)
    return state_dict

url = "https://github.com/wasserth/TotalSegmentator/releases/download/"

UnetCPP_1_CT = partial(Unet_TS_CT, channels = [1,32,64,128,256,320,320])
UnetCPP_2_CT = partial(Unet_TS_CT, channels = [1,32,64,128,256,320])
UnetCPP_3_CT = partial(Unet_TS_CT, channels = [1,32,64,128,256])

UnetCPP_1_MR = partial(Unet_TS_MR, channels = [1,32,64,128,256,320,320])
UnetCPP_2_MR = partial(Unet_TS_MR, channels = [1,32,64,128,256,320])
UnetCPP_3_MR = partial(Unet_TS_MR, channels = [1,32,64,128,256])

models = {
         "M291" : (UnetCPP_1_CT(), "total", 1, False, url+"v2.0.0-weights/Dataset291_TotalSegmentator_part1_organs_1559subj.zip"), 
         "M292" : (UnetCPP_1_CT(), "total", 1, False, url+"v2.0.0-weights/Dataset292_TotalSegmentator_part2_vertebrae_1532subj.zip"),
         "M293" : (UnetCPP_1_CT(), "total", 1, False, url+"v2.0.0-weights/Dataset293_TotalSegmentator_part3_cardiac_1559subj.zip"),
         "M294" : (UnetCPP_1_CT(), "total", 1, False, url+"v2.0.0-weights/Dataset294_TotalSegmentator_part4_muscles_1559subj.zip"),
         "M295" : (UnetCPP_1_CT(), "total", 1, False, url+"v2.0.0-weights/Dataset295_TotalSegmentator_part5_ribs_1559subj.zip"),
         "M297" : (UnetCPP_2_CT(), "total-3mm", 2, False, url+"v2.0.4-weights/Dataset297_TotalSegmentator_total_3mm_1559subj_v204.zip"),
         #"M298" : (UnetCPP_2_CT(), 2, False, url+"v2.0.0-weights/Dataset298_TotalSegmentator_total_6mm_1559subj.zip"),
         #"M730" : (UnetCPP_1_MR(), True, 1, url+"v2.2.0-weights/Dataset730_TotalSegmentatorMRI_part1_organs_495subj.zip"),
         #"M731" : (UnetCPP_1_MR(), True, 1, url+"v2.2.0-weights/Dataset731_TotalSegmentatorMRI_part2_muscles_495subj.zip"),
         #"M732" : (UnetCPP_2_MR(), False, 2, url+"v2.2.0-weights/Dataset732_TotalSegmentatorMRI_total_3mm_495subj.zip"),
         #"M733" : (UnetCPP_3_MR(), False, 3, url+"v2.2.0-weights/Dataset733_TotalSegmentatorMRI_total_6mm_495subj.zip"),
         "M850" : (UnetCPP_1_MR(), "total_mr", 1, True, url+"v2.5.0-weights/Dataset850_TotalSegMRI_part1_organs_1088subj.zip"),
         "M851" : (UnetCPP_1_MR(), "total_mr", 1, True, url+"v2.5.0-weights/Dataset851_TotalSegMRI_part2_muscles_1088subj.zip"),
         "M852" : (UnetCPP_2_MR(), "total_mr-3mm", 2, True, url+"v2.5.0-weights/Dataset852_TotalSegMRI_total_3mm_1088subj.zip"),
         #"M853" : (UnetCPP_3_MR(), False, 3, url+"v2.5.0-weights/Dataset853_TotalSegMRI_total_6mm_1088subj.zip")
         }
if __name__ == "__main__":
    for name, model in models.items():
        convert_torchScript_full(name, *model)