#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Downsample experiment: train 3 models (RetFound/ResNet/ViT) on ADAM/AIROGS/PAPILA
at 100/50/25/10/5% training data fractions (stratified per class, train only),
evaluate on the FULL val/test set. Produces a learning-vs-data curve.

Output layout:  results/downsample/<dataset>/<pct>/<model>/{metrics.json, ...}
"""
import os, sys, csv, json, math, time, argparse, subprocess, random, shutil
from collections import defaultdict
from itertools import product

PROJ = "/mnt/tidal-alsh-share2/dataset/qinshengqian/research/c3/GPT-Image"
DSROOT = f"{PROJ}/Dataset"
CODE = f"{PROJ}/Code"
RETF = f"{CODE}/RETFound"
CFP = f"{PROJ}/weights/pretrained/RETFound_mae_natureCFP.pth"
RESULTS = f"{PROJ}/results/downsample"
PY = "/root/miniconda3/envs/retfound/bin/python"
TORCHRUN = "/root/miniconda3/envs/retfound/bin/torchrun"
GPUS = [0, 1, 2, 3, 4, 5, 6, 7]
TMPROOT = "/tmp/downsample_exp"  # local, not JuiceFS (symlinks can be slow on FUSE)
SEED = 42
FRACTIONS = [1.0, 0.5, 0.25, 0.10, 0.05]
DATASETS = {
    "adam":   ("AMD/adamdataset",                2, None),
    "airogs": ("Glaucoma/eyepacs-airogs-light",  2, 30),
    "papila": ("Glaucoma/papila-retinal-fundus-images", 2, None),
}
CLASS_NAMES = {"adam": "NonAMD,AMD", "airogs": "NRG,RG", "papila": "healthy,glaucoma"}
MODELS = ["retfound", "resnet", "vit"]


def make_downsampled_dir(dsk, frac):
    """Symlink full val/test + stratified-downsampled train into a temp dir.
    Returns (data_path, n_train)."""
    rel, nc, max_ep = DATASETS[dsk]
    src = os.path.join(DSROOT, rel)
    dst = os.path.join(TMPROOT, f"{dsk}_{int(frac*100)}")
    shutil.rmtree(dst, ignore_errors=True)
    os.makedirs(dst)
    rows = list(csv.DictReader(open(os.path.join(src, "labels.csv"))))
    by = defaultdict(list)
    for r in rows:
        if r["split"] == "train":
            by[r["label"]].append(r["filepath"])
    rnd = random.Random(SEED)
    chosen = []  # relative paths like "train/0/img.jpg"
    for lab in sorted(by, key=int):
        files = sorted(by[lab])
        rnd.shuffle(files)
        n = max(1, int(round(len(files) * frac)))
        chosen.extend(files[:n])
    # symlink val + test entirely
    for sp in ["val", "test"]:
        os.symlink(os.path.join(src, sp), os.path.join(dst, sp))
    # build train from chosen files only
    os.makedirs(os.path.join(dst, "train"))
    for fp in chosen:
        parts = fp.split("/")        # ["train", "<label>", "<filename>"]
        cls_dir = parts[-2]
        os.makedirs(os.path.join(dst, "train", cls_dir), exist_ok=True)
        os.symlink(os.path.join(src, fp), os.path.join(dst, "train", cls_dir, os.path.basename(fp)))
    n_train = len(chosen)
    return dst, n_train


def train_cmd(model, dsk, frac, data_path, n_train):
    rel, nc, max_ep = DATASETS[dsk]
    # epochs: scale slightly for very small datasets; keep original for normal
    ep = max_ep or (80 if n_train < 32 else 50)
    # batch_size: must be <= n_train, and preferably n_train // 2. min 4, max 64/32
    bs = min(32 if model == "retfound" else 64, max(4, n_train // 2 + 1))
    frac_str = f"{int(frac*100)}".zfill(3)
    odir = os.path.join(RESULTS, dsk, frac_str, model)  # model-specific
    if model == "retfound":
        port = random.Random(hash(f"{dsk}_{frac_str}_{model}") % 30000 + 25000).randint(25000, 55000)
        # use --task so evaluate.py finds test_pred.npz in odir/task/ → match our odir
        return (f"cd {RETF} && {TORCHRUN} --nproc_per_node=1 --master_port={port} "
                f"main_finetune.py --model RETFound_mae --model_arch retfound_mae "
                f"--finetune {CFP} --savemodel --global_pool --batch_size {bs} --world_size 1 "
                f"--epochs {ep} --nb_classes {nc} --data_path {data_path} --input_size 224 "
                f"--task {model} --output_dir {os.path.join(RESULTS, dsk, frac_str)} --adaptation finetune")
    timm_name = "resnet50" if model == "resnet" else "vit_base_patch16_224"
    extra = "" if model == "resnet" else " --lr 1e-4 --layer_decay 0.65 --drop_path 0.1 --label_smoothing 0.1 --warmup_epochs 5"
    return (f"cd {CODE} && {PY} train_cnn_vit.py --data_path {data_path} --nb_classes {nc} "
            f"--model {timm_name} --input_size 224 --batch_size {bs} --epochs {ep} "
            f"--output_dir {os.path.join(RESULTS, dsk, frac_str)} --task {model}{extra}")


def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("--dry_run", action="store_true")
    args = ap.parse_args()

    jobs = []
    port = 29500
    for dsk in sorted(DATASETS):
        for frac in FRACTIONS:
            dst, n = make_downsampled_dir(dsk, frac)
            for model in MODELS:
                frac_str = f"{int(frac*100)}".zfill(3)
                odir = os.path.join(RESULTS, dsk, frac_str, model)
                os.makedirs(odir, exist_ok=True)
                ev = f"{PY} {CODE}/evaluate.py --run_dir {odir} --class_names {CLASS_NAMES[dsk]}"
                cmd = f"{train_cmd(model, dsk, frac, dst, n)} && {ev}"
                jobs.append({"name": f"{dsk}/{frac_str}pct/{model}", "cmd": cmd, "odir": odir})

    print(f"=== {len(jobs)} jobs over {len(GPUS)} GPUs ===")
    for j in jobs:
        print(f"  {j['name']}")
        if args.dry_run:
            print(f"    {j['cmd'][:120]}...")

    if args.dry_run:
        return

    free = list(GPUS)
    running = {}
    pending = list(jobs)
    done, failed = [], []
    while pending or running:
        while pending and free:
            g = free.pop(0)
            j = pending.pop(0)
            env = dict(os.environ, CUDA_VISIBLE_DEVICES=str(g))
            fh = open(os.path.join(j["odir"], "train.log"), "w")
            p = subprocess.Popen(["bash", "-lc", j["cmd"]], env=env, stdout=fh, stderr=subprocess.STDOUT)
            running[p.pid] = (j, g, fh, p)
            print(f"[launch] GPU{g} {j['name']} (pid {p.pid}) [{len(done)+len(failed)}/{len(jobs)}]")
        time.sleep(15)
        for pid in list(running):
            j, g, fh, p = running[pid]
            rc = p.poll()
            if rc is None:
                continue
            fh.close(); free.append(g); del running[pid]
            ok = (rc == 0 and os.path.isfile(os.path.join(j["odir"], "metrics.json")))
            (done if ok else failed).append(j["name"])
            print(f"[{'done' if ok else 'FAIL'}] GPU{g} {j['name']} rc={rc} [{len(done)+len(failed)}/{len(jobs)}]")
    print(f"\n=== finished: {len(done)} ok, {len(failed)} failed ===")
    if failed:
        print("FAILED:", failed)


if __name__ == "__main__":
    main()