#!/usr/bin/env python3
"""H9: BP + KAFT depth sweep on Cora/CiteSeer/PubMed.

E1 already did DBLP L={8,12,16,20,24,32}. This fills the gap for Cora/CiteSeer/PubMed
at L={8,10,12,16,20} so we can plot Figure 4(a)-style depth curves on 4 datasets.

BP + KAFT only (KAFT+ResGCN not needed for this figure — that's stacking table).
"""

import torch
import numpy as np
import json
import os
from src.data import load_dataset
from src.trainers import BPTrainer, KAFTTrainer

device = 'cuda:0'
SEEDS = list(range(20))
EPOCHS = 200
DEPTHS = [8, 10, 12, 16, 20]
OUT_DIR = 'results/bp_kaft_depth_20seeds'

grape_extra = dict(diffusion_alpha=0.5, diffusion_iters=10,
                   lr_feedback=0.5, num_probes=64, topo_mode='fixed_A')

METHODS = {
    'BP':    (BPTrainer, {}),
    'KAFT': (KAFTTrainer, grape_extra),
}


def train_one(cls, common, extra, seed):
    torch.manual_seed(seed); np.random.seed(seed); torch.cuda.manual_seed_all(seed)
    t = cls(**common, **extra)
    if hasattr(t, 'align_mode'):
        t.align_mode = 'chain_norm'
    bv, bt = 0, 0
    for ep in range(EPOCHS):
        t.train_step()
        if ep % 5 == 0:
            v = t.evaluate('val_mask')
            te = t.evaluate('test_mask')
            if v > bv: bv, bt = v, te
    del t; torch.cuda.empty_cache()
    return bt


def main():
    os.makedirs(OUT_DIR, exist_ok=True)
    per_seed_file = os.path.join(OUT_DIR, 'per_seed_data.json')
    if os.path.exists(per_seed_file):
        with open(per_seed_file) as f:
            per_seed_data = json.load(f)
    else:
        per_seed_data = {}

    datasets_cfg = {
        'Cora': lambda: load_dataset('Cora', device=device),
        'CiteSeer': lambda: load_dataset('CiteSeer', device=device),
        'PubMed': lambda: load_dataset('PubMed', device=device),
    }

    for ds_name, loader in datasets_cfg.items():
        data = loader()
        for L in DEPTHS:
            common = dict(data=data, hidden_dim=64, lr=0.01, weight_decay=5e-4,
                          num_layers=L, residual_alpha=0.0, backbone='gcn')

            for mname, (cls, extra) in METHODS.items():
                key = f"{ds_name}_L{L}_{mname}"
                if key not in per_seed_data:
                    per_seed_data[key] = {}

                print(f"\n=== {key} (20 seeds) ===", flush=True)
                for seed in SEEDS:
                    sk = str(seed)
                    if sk in per_seed_data[key]:
                        print(f"  seed {seed}: cached ({per_seed_data[key][sk]*100:.1f}%)", flush=True)
                        continue
                    try:
                        acc = train_one(cls, common, extra, seed)
                        per_seed_data[key][sk] = acc
                        print(f"  seed {seed}: {acc*100:.1f}%", flush=True)
                    except Exception as e:
                        print(f"  seed {seed}: FAILED - {e}", flush=True)
                        per_seed_data[key][sk] = 0.0

                    with open(per_seed_file, 'w') as f:
                        json.dump(per_seed_data, f, indent=2)
        del data; torch.cuda.empty_cache()

    # Summary
    print(f"\n{'=' * 70}\nBP/KAFT depth sweep summary\n{'=' * 70}")
    results = {}
    for ds in datasets_cfg:
        print(f"\n{ds}:")
        for L in DEPTHS:
            for m in METHODS:
                key = f"{ds}_L{L}_{m}"
                vals = np.array([per_seed_data[key][str(s)] for s in SEEDS]) * 100
                results[key] = {'mean': float(vals.mean()), 'std': float(vals.std()),
                                 'per_seed': vals.tolist()}
                print(f"  L={L:2d} {m:<6} {vals.mean():5.1f} ± {vals.std():4.1f}")

    with open(os.path.join(OUT_DIR, 'results.json'), 'w') as f:
        json.dump(results, f, indent=2)
    print(f"\nSaved to {OUT_DIR}/results.json")


if __name__ == '__main__':
    main()