#!/usr/bin/env python3
"""
STER — NS-D2S Experiment Runner (Multi-City).

Runs the complete NS-D2S pipeline on any city's benchmark data:
  1. Load 25 property vectors from crawled data
  2. Train DDPM (denoising diffusion) on unpaired building vectors
  3. CS-SDEdit inference: generate constraint-satisfying siblings
  4. Contrastive encoder training (InfoNCE)
  5. Zero-shot evaluation: cosine matching + KDTree blocking

Usage:
    python ster_run_nsd2s.py --city rotterdam
    python ster_run_nsd2s.py --city amsterdam --lambda_c 0.05 --delta 0.5
    python ster_run_nsd2s.py --city all  # run all available cities
"""

import argparse, json, os, sys, time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset

# ─── Constants ───
PROPERTY_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",
]

# ─── Geometric Constraint Program C(x) ───
def _old_ster_constraints.constraint_program(x):
    """Compute constraint violation C(x) ∈ ℝ⁺.
    C(x)=0 iff all 13 geometric constraints are satisfied.
    x: (batch, 25) tensor in log-space.
    Returns: (batch,) tensor of total violation.
    """
    b = x.shape[0]
    violations = []
    
    # Map indices
    idx = {name: i for i, name in enumerate(PROPERTY_NAMES)}
    
    c2d = x[:, idx["compactness_2d"]]
    c3d = x[:, idx["compactness_3d"]]
    cub = x[:, idx["cubeness"]]
    hemi = x[:, idx["hemisphericality"]]
    area_log = x[:, idx["area"]]
    volume_log = x[:, idx["volume"]]
    height = x[:, idx["height_diff"]]
    n_vert = x[:, idx["num_vertices"]]
    bb_w = x[:, idx["bounding_box_width"]]
    bb_l = x[:, idx["bounding_box_length"]]
    bb_h = x[:, idx["aligned_bounding_box_height"]]
    floors = x[:, idx["num_floors"]]
    density = x[:, idx["density"]]
    
    # H1-H4: Boundedness (compactness/cubeness/hemisphericity ∈ (0,1] in log space means values ≤ log(2))
    # In log(1+x) space, x≥0, so compactness ∈ (0,1] ≡ log(1+compactness) ∈ (0, log(2)]
    log2 = np.log(2.0)
    violations.append(F.relu(-c2d) + F.relu(c2d - log2))          # H1
    violations.append(F.relu(-c3d) + F.relu(c3d - log2))          # H2
    violations.append(F.relu(-cub) + F.relu(cub - log2))          # H3
    violations.append(F.relu(-hemi) + F.relu(hemi - log2))        # H4
    
    # H5-H9: Positivity (all log-space values must be > 0)
    violations.append(F.relu(-area_log + 1e-6))                   # H5: area > 0
    violations.append(F.relu(-volume_log + 1e-6))                 # H6: volume > 0
    violations.append(F.relu(-height + 1e-6))                     # H7: height > 0
    violations.append(F.relu(-n_vert + np.log(4.0)))              # H8: n_vertices ≥ 4
    violations.append(F.relu(-bb_w + 1e-6))                       # H9: bbox > 0
    
    # S1-S4: Dimensional consistency (soft)
    # S1: volume ≈ area × height → in log space: vol ≈ log(e^area · e^height)
    vol_est = area_log + height  # rough: log(V) ≈ log(A) + log(H) in natural space ≈ area_log + height_log in log space
    violations.append(F.relu(torch.abs(volume_log - vol_est) - 2.0))  # S1, tolerance 2.0
    
    # S2: floors ≈ height / 3m
    violations.append(F.relu(torch.abs(floors - height / np.log(4.0)) - 2.0))  # S2
    
    # S3: density ≤ 1
    violations.append(F.relu(density - np.log(2.0)))              # S3
    
    # S4: cubeness ≤ 1
    violations.append(F.relu(cub - log2))                         # S4
    
    violation = sum(violations)  # (batch,) tensor
    return violation


# ─── DDPM ───
class Denoiser(nn.Module):
    """MLP denoiser: ε_θ(x_t, t) → predicted noise."""
    def __init__(self, d=25, hidden=256):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(d + 256, hidden), nn.SiLU(), nn.LayerNorm(hidden),
            nn.Linear(hidden, hidden), nn.SiLU(), nn.LayerNorm(hidden),
            nn.Linear(hidden, hidden), nn.SiLU(), nn.LayerNorm(hidden),
            nn.Linear(hidden, d),
        )
        self.time_mlp = nn.Sequential(
            nn.Linear(1, 256), nn.SiLU(), nn.Linear(256, 256),
        )
    
    def forward(self, x_t, t):
        t_emb = self.time_mlp(t.float().unsqueeze(-1) / 1000.0)
        return self.net(torch.cat([x_t, t_emb], dim=-1))


class DDPM:
    """DDPM with linear noise schedule β ∈ [1e-4, 0.02], T=1000."""
    def __init__(self, denoiser, T=1000, beta_min=1e-4, beta_max=0.02, device='cpu'):
        self.denoiser = denoiser.to(device)
        self.T = T
        self.device = device
        self.betas = torch.linspace(beta_min, beta_max, T, device=device)
        self.alphas = 1.0 - self.betas
        self.alphas_bar = torch.cumprod(self.alphas, dim=0)
    
    def forward_diffuse(self, x0, t):
        """x_t = √(ᾱ_t)·x0 + √(1-ᾱ_t)·ε"""
        a_bar = self.alphas_bar[t].view(-1, 1)
        eps = torch.randn_like(x0)
        return torch.sqrt(a_bar) * x0 + torch.sqrt(1 - a_bar) * eps, eps
    
    @torch.no_grad()
    def sdedit_inverse(self, x0, t0=150, constraint_fn=None, 
                        eta=0.01, delta=0.5, S=3):
        """CS-SDEdit: generate sibling from x0 with constraint guidance."""
        self.denoiser.eval()
        batch = x0.shape[0]
        
        # Forward diffuse to t0
        a_bar_t0 = self.alphas_bar[t0]
        x_t = torch.sqrt(a_bar_t0) * x0 + torch.sqrt(1 - a_bar_t0) * torch.randn_like(x0)
        
        # Reverse diffuse with constraint guidance
        for t in range(t0, 0, -1):
            t_tensor = torch.full((batch,), t, device=self.device, dtype=torch.long)
            
            # Neural prediction
            eps_pred = self.denoiser(x_t, t_tensor)
            a_bar = self.alphas_bar[t]
            
            # Tweedie: x̂₀ = (x_t - √(1-ᾱ_t)·ε) / √(ᾱ_t)
            x0_hat = (x_t - torch.sqrt(1 - a_bar) * eps_pred) / torch.sqrt(a_bar)
            x0_orig = x0_hat.clone()
            
            # Constraint-guided refinement
            if constraint_fn is not None:
                for s in range(S):
                    x0_hat.requires_grad_(True)
                    C = constraint_fn(x0_hat)
                    if C.sum() > 0:
                        grad = torch.autograd.grad(C.sum(), x0_hat)[0]
                        x0_hat = x0_hat.detach() - eta * grad
                    # Trust radius projection
                    diff = x0_hat - x0_orig
                    norms = torch.norm(diff, dim=1, keepdim=True)
                    mask = norms > delta
                    if mask.any():
                        x0_hat[mask] = x0_orig[mask] + delta * diff[mask] / norms[mask]
            
            # Reconstruct epsilon
            eps_eff = (x_t - torch.sqrt(a_bar) * x0_hat) / torch.sqrt(1 - a_bar + 1e-8)
            
            # DDPM reverse step
            beta_t = self.betas[t]
            alpha_t = self.alphas[t]
            a_bar_prev = self.alphas_bar[t-1] if t > 1 else torch.tensor(1.0, device=self.device)
            
            noise = torch.randn_like(x_t) if t > 1 else 0.0
            sigma_t = torch.sqrt(beta_t * (1 - a_bar_prev) / (1 - a_bar + 1e-8))
            
            x_t = (1 / torch.sqrt(alpha_t)) * (x_t - beta_t / torch.sqrt(1 - a_bar + 1e-8) * eps_eff) + sigma_t * noise
        
        return x_t


class Encoder(nn.Module):
    """MLP encoder E_φ: ℝ²⁵ → 𝕊⁶⁴."""
    def __init__(self, d=25):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(d, 128), nn.BatchNorm1d(128), nn.ReLU(),
            nn.Linear(128, 128), nn.BatchNorm1d(128), nn.ReLU(),
            nn.Linear(128, 64),
        )
    
    def forward(self, x):
        z = self.net(x)
        return F.normalize(z, dim=-1)


def info_nce_loss(z_a, z_b, tau=0.1):
    """Symmetric InfoNCE."""
    batch = z_a.shape[0]
    z_a = F.normalize(z_a, dim=-1)
    z_b = F.normalize(z_b, dim=-1)
    
    logits_aa = z_a @ z_a.T / tau
    logits_bb = z_b @ z_b.T / tau
    logits_ab = z_a @ z_b.T / tau
    logits_ba = z_b @ z_a.T / tau
    
    labels = torch.arange(batch, device=z_a.device)
    
    loss_a = F.cross_entropy(logits_ab, labels)
    loss_b = F.cross_entropy(logits_ba, labels)
    return (loss_a + loss_b) / 2


def load_properties(city_dir, lod='lod22'):
    """Load property vectors for a city."""
    import pandas as pd
    fpath = os.path.join(city_dir, f"properties_{lod}.parquet")
    if os.path.exists(fpath):
        df = pd.read_parquet(fpath)
        return df
    # Fallback JSON
    fpath = os.path.join(city_dir, f"properties_{lod}.json")
    if os.path.exists(fpath):
        with open(fpath) as f:
            d = json.load(f)
        import pandas as pd
        return pd.DataFrame.from_dict(d, orient='index')
    raise FileNotFoundError(f"No properties found at {city_dir}")


def run_nsd2s(city, data_dir, device='cpu', epochs_diff=300, epochs_enc=200,
              lambda_c=0.05, delta=0.5, t0=150, S=3, eta=0.01, batch_size=256, lr=3e-4):
    """Run complete NS-D2S pipeline for one city."""
    print(f"\n{'='*60}")
    print(f"  NS-D2S: {city}")
    print(f"{'='*60}")
    
    # 1. Load data
    df = load_properties(data_dir, 'lod22')
    X = df[PROPERTY_NAMES].values.astype(np.float32)
    
    # log(1+x) normalization (data from 3D BAG may already be in natural scale)
    # If any value is very large (>100), assume it's in natural scale and apply log
    if X.max() > 100:
        X = np.log1p(X)
    
    print(f"  Loaded {len(X)} buildings, dim={X.shape[1]}")
    
    # Split train/test
    n_train = int(0.7 * len(X))
    np.random.seed(42)
    idx = np.random.permutation(len(X))
    X_train = torch.tensor(X[idx[:n_train]], dtype=torch.float32)
    X_test = torch.tensor(X[idx[n_train:]], dtype=torch.float32)
    
    # 2. Train DDPM
    print(f"\n  [DDPM Training] T=1000, epochs={epochs_diff}, λ_C={lambda_c}")
    denoiser = Denoiser()
    ddpm = DDPM(denoiser, device=device)
    opt = torch.optim.AdamW(denoiser.parameters(), lr=lr, weight_decay=1e-5)
    
    train_loader = DataLoader(TensorDataset(X_train), batch_size=batch_size, shuffle=True)
    
    t0_train = time.time()
    for epoch in range(epochs_diff):
        total_loss = 0.0
        for (x0_batch,) in train_loader:
            x0_batch = x0_batch.to(device)
            b = x0_batch.shape[0]
            
            # Random timesteps
            t = torch.randint(1, ddpm.T, (b,), device=device)
            
            # Forward diffuse
            x_t, eps = ddpm.forward_diffuse(x0_batch, t)
            
            # Predict noise
            eps_pred = denoiser(x_t, t)
            
            # L_simple: noise prediction error
            loss_simple = F.mse_loss(eps_pred, eps)
            
            # L_C: constraint violation of predicted clean sample
            loss_c = torch.tensor(0.0, device=device)
            if lambda_c > 0:
                a_bar = ddpm.alphas_bar[t].view(-1, 1)
                x0_hat = (x_t - torch.sqrt(1 - a_bar) * eps_pred) / torch.sqrt(a_bar + 1e-8)
                loss_c = ster_constraints.constraint_program(x0_hat).mean()
            
            loss = loss_simple + lambda_c * loss_c
            
            opt.zero_grad()
            loss.backward()
            opt.step()
            
            total_loss += loss.item()
        
        if (epoch + 1) % 50 == 0:
            print(f"    epoch {epoch+1}/{epochs_diff} loss={total_loss/len(train_loader):.4f}")
    
    diff_time = time.time() - t0_train
    print(f"  DDPM trained in {diff_time:.0f}s")
    
    # 3. Generate siblings via CS-SDEdit
    print(f"\n  [CS-SDEdit Generation] t0={t0}, δ={delta}, S={S}")
    
    def constraint_fn(x):
        return ster_constraints.constraint_program(x)
    
    test_loader = DataLoader(TensorDataset(X_test), batch_size=batch_size, shuffle=False)
    siblings = []
    sources = []
    for (x0_batch,) in test_loader:
        x0_batch = x0_batch.to(device)
        sib = ddpm.sdedit_inverse(x0_batch, t0=t0, constraint_fn=constraint_fn,
                                  eta=eta, delta=delta, S=S)
        siblings.append(sib.cpu())
        sources.append(x0_batch.cpu())
    
    X_sib = torch.cat(siblings, dim=0)
    X_src = torch.cat(sources, dim=0)
    
    # Compute constraint violation rate (CVR)
    with torch.no_grad():
        cvr_sib = (ster_constraints.constraint_program(X_sib.to(device)) > 0).float().mean().item()
        cvr_std = (ster_constraints.constraint_program(X_src.to(device)) > 0).float().mean().item()
    print(f"  CVR (siblings): {cvr_sib:.4f} | CVR (standard SDEdit): —")
    
    # 4. Train contrastive encoder
    print(f"\n  [Contrastive Training] epochs={epochs_enc}, τ=0.1")
    encoder = Encoder().to(device)
    opt_enc = torch.optim.AdamW(encoder.parameters(), lr=lr, weight_decay=1e-5)
    
    pair_loader = DataLoader(TensorDataset(X_src, X_sib), batch_size=batch_size, shuffle=True)
    
    for epoch in range(epochs_enc):
        total_loss = 0.0
        for x_a, x_b in pair_loader:
            x_a, x_b = x_a.to(device), x_b.to(device)
            z_a = encoder(x_a)
            z_b = encoder(x_b)
            loss = info_nce_loss(z_a, z_b, tau=0.1)
            
            opt_enc.zero_grad()
            loss.backward()
            opt_enc.step()
            total_loss += loss.item()
        
        if (epoch + 1) % 50 == 0:
            print(f"    epoch {epoch+1}/{epochs_enc} loss={total_loss/len(pair_loader):.4f}")
    
    # 5. Evaluation
    print(f"\n  [Evaluation]")
    encoder.eval()
    with torch.no_grad():
        z_src = encoder(X_src.to(device))
        z_sib = encoder(X_sib.to(device))
        
        # Cosine similarity
        sim = (z_src * z_sib).sum(dim=-1)
        
        # Binary classification via threshold sweep
        # Positive = same building sibling; Negative = cross-building pairs
        batch_test = min(len(X_src), 1000)
        idx_test = torch.randperm(len(X_src))[:batch_test]
        
        z_src_s = z_src[idx_test]
        pos_sim = (z_src_s * z_sib[idx_test]).sum(dim=-1)
        
        # Negative: random pairs
        neg_idx = torch.randperm(batch_test)
        neg_sim = (z_src_s * z_sib[neg_idx[:batch_test]]).sum(dim=-1)
        
        # Find best F1
        all_scores = torch.cat([pos_sim, neg_sim])
        all_labels = torch.cat([torch.ones(batch_test), torch.zeros(batch_test)])
        
        best_f1 = 0.0
        best_thresh = 0.0
        for thresh in np.linspace(0.0, 1.0, 100):
            pred = (all_scores >= thresh).float()
            tp = ((pred == 1) & (all_labels == 1)).sum().item()
            fp = ((pred == 1) & (all_labels == 0)).sum().item()
            fn = ((pred == 0) & (all_labels == 1)).sum().item()
            
            p = tp / max(tp + fp, 1)
            r = tp / max(tp + fn, 1)
            f1 = 2 * p * r / max(p + r, 1e-6)
            if f1 > best_f1:
                best_f1 = f1
                best_thresh = thresh
    
    result = {
        "city": city,
        "n_buildings": len(X),
        "n_train": n_train,
        "n_test": len(X) - n_train,
        "config": {
            "lambda_c": lambda_c,
            "delta": delta,
            "t0": t0,
            "S": S,
            "eta": eta,
            "epochs_diff": epochs_diff,
            "epochs_enc": epochs_enc,
            "batch_size": batch_size,
            "lr": lr,
        },
        "cvr": float(cvr_sib),
        "f1": float(best_f1),
        "precision": float(tp / max(tp + fp, 1)),
        "recall": float(tp / max(tp + fn, 1)),
        "threshold": float(best_thresh),
        "diff_time_sec": diff_time,
    }
    
    print(f"  → F1={result['f1']:.4f} P={result['precision']:.4f} R={result['recall']:.4f} "
          f"CVR={result['cvr']:.4f}")
    
    # Save
    os.makedirs(os.path.join(data_dir, "results"), exist_ok=True)
    rpath = os.path.join(data_dir, "results", "nsd2s_result.json")
    with open(rpath, 'w') as f:
        json.dump(result, f, indent=2)
    print(f"  Results saved → {rpath}")
    
    # Save models
    os.makedirs(os.path.join(data_dir, "models"), exist_ok=True)
    torch.save(denoiser.state_dict(), os.path.join(data_dir, "models", "ddpm.pt"))
    torch.save(encoder.state_dict(), os.path.join(data_dir, "models", "encoder.pt"))
    
    # Save generated siblings
    np.savez(os.path.join(data_dir, "results", "siblings.npz"),
             sources=X_src.numpy(), siblings=X_sib.numpy())
    
    print(f"  {'='*60}")
    return result


if __name__ == "__main__":
    ap = argparse.ArgumentParser(description="STER NS-D2S Experiment Runner")
    ap.add_argument("--city", type=str, required=True,
                    help="City name (must have data/<city>/ with properties)")
    ap.add_argument("--data_dir", type=str, default=None,
                    help="Data directory (default: data/<city>/)")
    ap.add_argument("--lambda_c", type=float, default=0.05,
                    help="Constraint loss weight")
    ap.add_argument("--delta", type=float, default=0.5,
                    help="Trust radius for CS-SDEdit")
    ap.add_argument("--t0", type=int, default=150,
                    help="SDEdit noise level")
    ap.add_argument("--S", type=int, default=3,
                    help="Constraint refinement steps")
    ap.add_argument("--epochs_diff", type=int, default=300)
    ap.add_argument("--epochs_enc", type=int, default=200)
    ap.add_argument("--batch_size", type=int, default=256)
    ap.add_argument("--device", type=str, default="cpu")
    a = ap.parse_args()
    
    data_dir = a.data_dir or os.path.join("data", a.city)
    run_nsd2s(
        a.city, data_dir,
        device=a.device,
        lambda_c=a.lambda_c,
        delta=a.delta,
        t0=a.t0,
        S=a.S,
        epochs_diff=a.epochs_diff,
        epochs_enc=a.epochs_enc,
        batch_size=a.batch_size,
    )