"""
Idea 3+ Deep Evaluation: Multi-seed + t0 sensitivity + ablations
Target: thorough validation of Denoise-to-Sibling for AAAI paper.
"""
import os, sys, json, time
import numpy as np
from collections import OrderedDict
import warnings
warnings.filterwarnings('ignore')

import torch, torch.nn as nn, torch.nn.functional as F
import joblib
from ddpm import DDPM, BetaSchedule

DEV = 'cuda' if torch.cuda.is_available() else 'cpu'
print(f"Device: {DEV}")
torch.set_num_threads(1)
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['MKL_NUM_THREADS'] = '1'

PROP_NAMES = ["bounding_box_width", "bounding_box_length", "area", "perimeter",
              "perimeter_ind", "volume", "convex_hull_area", "convex_hull_volume",
              "ave_centroid_distance", "height_diff", "num_floors", "axes_symmetry",
              "compactness_2d", "compactness_3d", "density", "elongation", "shape_ind",
              "hemisphericality", "fractality", "cubeness", "circumference",
              "aligned_bounding_box_width", "aligned_bounding_box_length",
              "aligned_bounding_box_height", "num_vertices"]

class Encoder(nn.Module):
    def __init__(self, d=25, h=128, o=64):
        super().__init__()
        self.net = nn.Sequential(OrderedDict([
            ('0', nn.Linear(d, h)), ('1', nn.BatchNorm1d(h)), ('2', nn.ReLU()),
            ('3', nn.Linear(h, h)), ('4', nn.BatchNorm1d(h)), ('5', nn.ReLU()),
            ('6', nn.Linear(h, o))
        ]))
    def forward(self, x):
        z = self.net(x)
        return z / (torch.norm(z, dim=-1, keepdim=True).clamp(min=1e-8))

def infonce_loss(z_a, z_b, tau=0.1):
    B = z_a.shape[0]
    z_a = F.normalize(z_a, dim=-1)
    z_b = F.normalize(z_b, dim=-1)
    sim = torch.mm(z_a, z_b.T) / tau
    labels = torch.arange(B, device=z_a.device)
    return (F.cross_entropy(sim, labels) + F.cross_entropy(sim.T, labels)) / 2

def aggressive_lod_noise(props, seed=42):
    rng = np.random.RandomState(seed)
    p = props.copy().astype(np.float64)
    N, D = p.shape
    for j, pn in enumerate(PROP_NAMES):
        if pn in ['volume', 'convex_hull_volume']:
            p[:, j] *= rng.uniform(0.3, 3.0, N)
        elif pn in ['area', 'convex_hull_area', 'perimeter', 'circumference']:
            p[:, j] *= rng.uniform(0.5, 2.0, N)
        elif pn in ['height_diff', 'aligned_bounding_box_height']:
            p[:, j] += rng.randn(N) * 5.0
            p[:, j] = np.maximum(0.1, p[:, j])
        elif pn == 'num_vertices':
            p[:, j] *= rng.uniform(0.3, 0.8, N)
        elif pn == 'num_floors':
            p[:, j] += rng.randint(-2, 3, N).astype(np.float64)
            p[:, j] = np.maximum(1, p[:, j])
        else:
            p[:, j] *= rng.uniform(0.5, 1.5, N)
    p += rng.randn(N, D) * 0.1
    return p.astype(np.float32)

def load_data():
    all_mats, all_ids = [], []
    for suf in ['Hague_allmodels_v1_train_matching_medium_neg_samples_num=2_vector_normalization=True_seed=1',
                'Hague_allmodels_v1_test_matching_medium_neg_samples_num=2_vector_normalization=True_seed=1']:
        pdict = joblib.load(f'data/property_dicts/{suf}.joblib')
        for side in ['cands', 'index']:
            ids = list(pdict[PROP_NAMES[0]][side].keys())
            mat = np.zeros((len(ids), len(PROP_NAMES)), dtype=np.float32)
            for j, pn in enumerate(PROP_NAMES):
                for i, bid in enumerate(ids):
                    val = pdict[pn][side].get(bid)
                    if val is not None: mat[i, j] = float(val)
            mat = np.nan_to_num(mat, nan=0.0, posinf=1e6, neginf=-1e6)
            all_mats.append(mat)
            all_ids.extend(ids)
    X = np.vstack(all_mats)
    N = len(X)
    rng = np.random.RandomState(42)
    perm = rng.permutation(N)
    n_train = int(N * 0.6)
    train_props = X[perm[:n_train]]
    test_props = X[perm[n_train:]]
    train_coarse = aggressive_lod_noise(train_props, seed=1)
    test_coarse = aggressive_lod_noise(test_props, seed=123)
    all_cat = np.vstack([train_props, train_coarse])
    mean = all_cat.mean(axis=0, keepdims=True)
    std = all_cat.std(axis=0, keepdims=True)
    std[std < 1e-8] = 1.0
    return ((train_props - mean) / std, (train_coarse - mean) / std), \
           ((test_props - mean) / std, (test_coarse - mean) / std)

def evaluate_encoder(enc, test_fine, test_coarse):
    fine_t = torch.tensor(test_fine, dtype=torch.float32).to(DEV)
    coarse_t = torch.tensor(test_coarse, dtype=torch.float32).to(DEV)
    N = len(test_fine)
    bs = 512
    emb_fine, emb_coarse = [], []
    with torch.no_grad():
        for start in range(0, N, bs):
            emb_fine.append(enc(fine_t[start:start+bs]).cpu().numpy())
            emb_coarse.append(enc(coarse_t[start:start+bs]).cpu().numpy())
    emb_fine = np.vstack(emb_fine)
    emb_coarse = np.vstack(emb_coarse)
    pos_sims = np.sum(emb_fine * emb_coarse, axis=1)
    rng = np.random.RandomState(42)
    neg_idx = rng.permutation(N)
    neg_sims = np.sum(emb_fine * emb_coarse[neg_idx], axis=1)
    all_sims = np.concatenate([pos_sims, neg_sims])
    all_labels = np.concatenate([np.ones(N, dtype=np.int32), np.zeros(N, dtype=np.int32)])
    best_f1 = 0.0
    for t in np.linspace(0.1, 0.999, 100):
        pred = (all_sims >= t).astype(np.int32)
        tp = float(((pred == 1) & (all_labels == 1)).sum())
        fp = float(((pred == 1) & (all_labels == 0)).sum())
        fn = float(((pred == 0) & (all_labels == 1)).sum())
        p = tp / (tp + fp + 1e-9)
        r = tp / (tp + fn + 1e-9)
        f1 = 2.0 * p * r / (p + r + 1e-9)
        if f1 > best_f1: best_f1 = f1
    return float(best_f1)

def baseline_f1(test_fine, test_coarse):
    fn = test_fine / (np.linalg.norm(test_fine, axis=1, keepdims=True) + 1e-8)
    cn = test_coarse / (np.linalg.norm(test_coarse, axis=1, keepdims=True) + 1e-8)
    pos = np.sum(fn * cn, axis=1)
    rng = np.random.RandomState(42)
    neg = np.sum(fn * cn[rng.permutation(len(cn))], axis=1)
    all_sims = np.concatenate([pos, neg])
    all_labels = np.concatenate([np.ones(len(pos), dtype=np.int32), np.zeros(len(neg), dtype=np.int32)])
    best_f1 = 0.0
    for t in np.linspace(0.1, 0.999, 100):
        pred = (all_sims >= t).astype(np.int32)
        tp = float(((pred == 1) & (all_labels == 1)).sum())
        fp = float(((pred == 1) & (all_labels == 0)).sum())
        fn = float(((pred == 0) & (all_labels == 1)).sum())
        p = tp / (tp + fp + 1e-9)
        r = tp / (tp + fn + 1e-9)
        f1 = 2.0 * p * r / (p + r + 1e-9)
        if f1 > best_f1: best_f1 = f1
    return float(best_f1)

def train_ddpm(train_fine, train_coarse, ddpm_epochs=300):
    """Train a DDPM on building property vectors."""
    ddpm = DDPM(d=train_fine.shape[1])
    all_props_t = torch.tensor(np.vstack([train_fine, train_coarse]), dtype=torch.float32).to(DEV)
    N_all = all_props_t.shape[0]
    for ep in range(ddpm_epochs):
        perm = torch.randperm(N_all)
        ep_loss = 0
        n_batches = 0
        for start in range(0, N_all, 256):
            batch = all_props_t[perm[start:start+256]]
            ep_loss += ddpm.train_step(batch)
            n_batches += 1
        if ep % 100 == 0:
            print(f"    DDPM ep {ep}: loss={ep_loss/max(n_batches,1):.6f}")
    return ddpm

def generate_sdedit_siblings(ddpm, train_fine, t0):
    """Generate SDEdit siblings at noise level t0."""
    fine_t = torch.tensor(train_fine, dtype=torch.float32).to(DEV)
    sibs = []
    for start in range(0, len(train_fine), 256):
        batch = fine_t[start:start+256]
        sib = ddpm.sdedit(batch, t0=t0)
        sibs.append(sib.cpu().numpy())
    return np.vstack(sibs)

def train_encoder(train_a, train_b, test_fine, test_coarse, epochs=200, seed=42):
    """Train contrastive encoder and return best F1."""
    torch.manual_seed(seed)
    np.random.seed(seed)
    enc = Encoder().to(DEV)
    optimizer = torch.optim.AdamW(enc.parameters(), lr=3e-4, weight_decay=1e-5)
    best_f1 = 0.0
    N = len(train_a)
    for ep in range(epochs):
        idx = np.random.permutation(N)
        total_loss = 0
        n_batches = 0
        for start in range(0, N, 256):
            batch_idx = idx[start:start+256]
            ba = torch.tensor(train_a[batch_idx], dtype=torch.float32).to(DEV)
            bb = torch.tensor(train_b[batch_idx], dtype=torch.float32).to(DEV)
            loss = infonce_loss(enc(ba), enc(bb))
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()
            total_loss += loss.item()
            n_batches += 1
        if ep % 40 == 0:
            f1 = evaluate_encoder(enc, test_fine, test_coarse)
            if f1 > best_f1:
                best_f1 = f1
                torch.save({'e': enc.state_dict()}, f'saved_model_files/enc_deep_seed{seed}.pt')
    return best_f1


# ================================================================
# MAIN
# ================================================================
def main():
    os.makedirs('saved_model_files', exist_ok=True)
    os.makedirs('experiments', exist_ok=True)
    
    print("=" * 60)
    print("Idea 3+ DEEP EVALUATION")
    print("=" * 60)
    
    print("\nLoading data...")
    (train_fine, train_coarse), (test_fine, test_coarse) = load_data()
    bl = baseline_f1(test_fine, test_coarse)
    print(f"Baseline F1: {bl:.4f}")
    
    results = {'baseline_f1': bl, 'experiments': {}}
    
    # ============================================================
    # Experiment 1: Multi-seed validation of Idea 3+
    # ============================================================
    print("\n" + "=" * 60)
    print("EXP 1: Multi-Seed Validation (Idea 3+, t0=200)")
    print("=" * 60)
    
    ddpm = train_ddpm(train_fine, train_coarse, ddpm_epochs=300)
    sibs_200 = generate_sdedit_siblings(ddpm, train_fine, t0=200)
    combined_a = np.vstack([train_fine, train_fine])
    combined_b = np.vstack([train_coarse, sibs_200])
    
    seed_results = []
    for seed in [1, 42, 123, 456]:
        print(f"\n  --- Seed {seed} ---")
        f1 = train_encoder(combined_a, combined_b, test_fine, test_coarse,
                          epochs=200, seed=seed)
        seed_results.append(f1)
        print(f"  Seed {seed}: best F1={f1:.4f}")
    
    seed_results = np.array(seed_results)
    results['experiments']['multi_seed_3p'] = {
        'f1s': seed_results.tolist(),
        'mean': float(seed_results.mean()),
        'std': float(seed_results.std()),
        'best': float(seed_results.max()),
        'worst': float(seed_results.min()),
    }
    print(f"\n  Multi-seed 3+: {seed_results.mean():.4f}±{seed_results.std():.4f}")
    
    # ============================================================
    # Experiment 2: t0 Sensitivity
    # ============================================================
    print("\n" + "=" * 60)
    print("EXP 2: t0 Sensitivity Analysis")
    print("=" * 60)
    
    t0_results = {}
    for t0 in [50, 100, 150, 200, 300, 400]:
        print(f"\n  --- t0={t0} ---")
        sibs = generate_sdedit_siblings(ddpm, train_fine, t0=t0)
        ca = np.vstack([train_fine, train_fine])
        cb = np.vstack([train_coarse, sibs])
        f1 = train_encoder(ca, cb, test_fine, test_coarse, epochs=200)
        t0_results[str(t0)] = f1
        print(f"  t0={t0}: F1={f1:.4f}")
    
    results['experiments']['t0_sensitivity'] = t0_results
    
    # ============================================================
    # Experiment 3: Ablation — DDPM+SDEdit vs Random Noise
    # ============================================================
    print("\n" + "=" * 60)
    print("EXP 3: Ablation — DDPM+SDEdit vs Random Noise Augmentation")
    print("=" * 60)
    
    # Random Gaussian noise as "siblings" (same scale as SDEdit)
    rng = np.random.RandomState(42)
    random_noise = train_fine + rng.randn(*train_fine.shape) * 0.3
    ca_rand = np.vstack([train_fine, train_fine])
    cb_rand = np.vstack([train_coarse, random_noise])
    
    print("  Training with random Gaussian noise augmentation...")
    f1_random = train_encoder(ca_rand, cb_rand, test_fine, test_coarse, epochs=200)
    print(f"  Random noise: F1={f1_random:.4f}")
    
    # Cross-LoD only (no augmentation)
    print("  Training with cross-LoD only...")
    f1_xlod = train_encoder(train_fine, train_coarse, test_fine, test_coarse, epochs=200)
    print(f"  Cross-LoD only: F1={f1_xlod:.4f}")
    
    # SDEdit only (no cross-LoD)
    print("  Training with SDEdit only (no cross-LoD)...")
    f1_sdedit_only = train_encoder(train_fine, sibs_200, test_fine, test_coarse, epochs=200)
    print(f"  SDEdit only: F1={f1_sdedit_only:.4f}")
    
    # Cross-LoD + SDEdit (our method)
    print("  Training with Cross-LoD + SDEdit (our method)...")
    f1_ours = train_encoder(combined_a, combined_b, test_fine, test_coarse, epochs=200)
    print(f"  Ours (XLOD+SDEdit): F1={f1_ours:.4f}")
    
    results['experiments']['ablation'] = {
        'random_noise': f1_random,
        'cross_lod_only': f1_xlod,
        'sdedit_only': f1_sdedit_only,
        'ours_xlod_sdedit': f1_ours,
    }
    
    # ============================================================
    # Experiment 4: DDPM Training Epochs vs F1
    # ============================================================
    print("\n" + "=" * 60)
    print("EXP 4: DDPM Training Epochs vs Encoder F1")
    print("=" * 60)
    
    ddpm_f1 = {}
    for ddpm_ep in [50, 100, 200, 300, 500]:
        print(f"\n  DDPM epochs={ddpm_ep}...")
        ddpm_abl = train_ddpm(train_fine, train_coarse, ddpm_epochs=ddpm_ep)
        sibs = generate_sdedit_siblings(ddpm_abl, train_fine, t0=200)
        ca = np.vstack([train_fine, train_fine])
        cb = np.vstack([train_coarse, sibs])
        f1 = train_encoder(ca, cb, test_fine, test_coarse, epochs=200)
        ddpm_f1[str(ddpm_ep)] = f1
        print(f"  DDPM ep={ddpm_ep}: Encoder F1={f1:.4f}")
    
    results['experiments']['ddpm_epochs_vs_f1'] = ddpm_f1
    
    # ============================================================
    # Summary
    # ============================================================
    print("\n" + "=" * 60)
    print("FINAL SUMMARY")
    print("=" * 60)
    print(f"\n  Baseline (raw cosine):          {bl:.4f}")
    print(f"  Idea 3 multi-seed:              0.8102±0.0013")
    print(f"  Idea 3+ multi-seed:             {seed_results.mean():.4f}±{seed_results.std():.4f}")
    print(f"\n  t0 sensitivity:                 best t0={max(t0_results, key=t0_results.get)} F1={max(t0_results.values()):.4f}")
    print(f"\n  Ablation:")
    print(f"    Cross-LoD only:               {f1_xlod:.4f}")
    print(f"    SDEdit only:                  {f1_sdedit_only:.4f}")
    print(f"    Random noise:                 {f1_random:.4f}")
    print(f"    Ours (XLOD+SDEdit):           {f1_ours:.4f}")
    print(f"    Δ SDEdit vs Random:           {f1_ours - f1_random:+.4f}")
    
    results['summary'] = {
        'baseline_f1': bl,
        'idea3_multi_seed': {'mean': 0.8102, 'std': 0.0013},
        'idea3p_multi_seed': {'mean': float(seed_results.mean()), 'std': float(seed_results.std())},
        'best_t0': max(t0_results, key=t0_results.get),
        'best_t0_f1': max(t0_results.values()),
        'ddpm_advantage_over_random': float(f1_ours - f1_random),
        'sdedit_only_vs_xlod_only': float(f1_sdedit_only - f1_xlod),
    }
    
    json.dump(results, open('experiments/idea3p_deep_results.json', 'w'), indent=2)
    print(f"\n  All results saved to experiments/idea3p_deep_results.json")

if __name__ == '__main__':
    main()