"""
PC-SHO-DLM Assumption Diagnostics

Empirical diagnostics to validate the theoretical assumptions:
1. Stationarity residual (envelope theorem support)
2. Local Hessian spectrum via HVP + Lanczos (curvature/kappa estimation)
3. Energy monotonicity statistics (discrete-time Lyapunov check)
4. Precision calibration (predicted precision vs empirical squared error)
5. Warm-start drift measurement (tracking theorem support)
6. Token freeze accuracy (soft-gate reliability)
7. Contraction estimation (solver contractivity)
"""

import json
import math
import os
import sys
from pathlib import Path
from typing import Optional

import torch
import torch.nn.functional as F

sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..", "src"))
from model import PCSHODLM, PCSHOConfig


# =============================================================================
# 1. Stationarity Residual
# =============================================================================

def measure_stationarity_residual(
    model: PCSHODLM,
    x_0: torch.Tensor,
    settling_steps_list: list[int] = [2, 4, 8, 16, 32],
    device: str = "cpu",
) -> dict:
    """Measure ||grad_h E_t(h^(K))|| as a function of K.

    Validates envelope theorem: low residual means post-settling local
    updates are approximately exact gradients of the reduced objective.
    """
    model = model.to(device).eval()
    x_0 = x_0.to(device)
    B, S = x_0.shape

    t = torch.full((B,), model.config.n_diffusion_steps // 2, device=device, dtype=torch.long)
    x_t, mask = model.schedule.corrupt(x_0, t, model.config.mask_token_id)
    h_0 = model.embed_input(x_t, t)
    h_init = model.amortized_forward_pass(h_0)

    results = {}
    for K in settling_steps_list:
        model.config.n_settling_steps = K
        model.config.settling_threshold = float("inf")

        h = [hi.detach() for hi in h_init]
        v = [torch.zeros_like(h_init[l + 1]) for l in range(model.config.n_layers)]

        for k in range(K):
            h, v, energy = model.settling_step(h, v, h_init, x_0, mask, t, None)

        # Compute final gradient norm
        L = model.config.n_layers
        for l in range(1, L + 1):
            h[l] = h[l].detach().requires_grad_(True)

        with torch.enable_grad():
            _, _, _, energy = model.compute_energy_gradient(h, h_init, x_0, mask, t)

        grad_norms = []
        for l in range(1, L + 1):
            if h[l].grad is not None:
                grad_norms.append(h[l].grad.detach().norm().item())

        # Re-compute via autograd
        h_params = [h[l + 1] for l in range(L)]
        for l in range(1, L + 1):
            h[l] = h[l].detach().requires_grad_(True)

        with torch.enable_grad():
            grad_h, _, _, energy_val = model.compute_energy_gradient(h, h_init, x_0, mask, t)

        total_residual = sum(g.detach().norm().item() for g in grad_h)

        results[K] = {
            "stationarity_residual": total_residual,
            "energy": energy_val,
        }
        print(f"  K={K:3d}: residual={total_residual:.4f}, energy={energy_val:.2f}")

    return results


# =============================================================================
# 2. Local Hessian Spectrum via HVP + Lanczos
# =============================================================================

def hessian_vector_product(
    model: PCSHODLM,
    h: list[torch.Tensor],
    h_init: list[torch.Tensor],
    x_0: torch.Tensor,
    mask: torch.Tensor,
    t: torch.Tensor,
    v_dir: list[torch.Tensor],
) -> list[torch.Tensor]:
    """Compute Hessian-vector product d^2 E / dh^2 @ v using double autograd."""
    L = model.config.n_layers

    with torch.enable_grad():
        # First: compute gradient with create_graph=True for second derivatives
        for l in range(1, L + 1):
            h[l] = h[l].detach().requires_grad_(True)

        # Re-compute energy directly (need graph for second derivative)
        mu_up, mu_down = model.compute_predictions(h)
        eps_up = [h[l + 1] - mu_up[l] for l in range(L)]

        energy = 0.0
        for l in range(L):
            energy += 0.5 * (eps_up[l] ** 2).sum()
        energy += model.config.anchor_rho * sum(
            ((h[l + 1] - h_init[l + 1]) ** 2).sum() for l in range(L)
        )

        h_params = [h[l + 1] for l in range(L)]
        grads = torch.autograd.grad(energy, h_params, create_graph=True)

        # Dot product with direction
        dot = sum((g * vd).sum() for g, vd in zip(grads, v_dir))

        # Second derivative
        hvp = torch.autograd.grad(dot, h_params, create_graph=False)

    return [hv.detach() for hv in hvp]


def estimate_hessian_spectrum_lanczos(
    model: PCSHODLM,
    x_0: torch.Tensor,
    n_lanczos_steps: int = 20,
    device: str = "cpu",
) -> dict:
    """Estimate extremal eigenvalues of the energy Hessian via Lanczos iteration.

    Returns approximate lambda_min, lambda_max, and effective kappa.
    """
    model = model.to(device).eval()
    x_0 = x_0.to(device)
    B, S = x_0.shape
    L = model.config.n_layers

    t = torch.full((B,), model.config.n_diffusion_steps // 2, device=device, dtype=torch.long)
    x_t, mask = model.schedule.corrupt(x_0, t, model.config.mask_token_id)
    h_0 = model.embed_input(x_t, t)
    h_init = model.amortized_forward_pass(h_0)

    # Settle to approximate fixed point
    model.config.n_settling_steps = 16
    model.config.settling_threshold = float("inf")
    h_settled, _, _, _, _ = model.settle(h_init, x_0, mask, t)

    # Lanczos iteration
    # Flatten h into a single vector for eigenvalue computation
    def flatten_h(h_list):
        return torch.cat([h_list[l + 1].reshape(-1) for l in range(L)])

    def unflatten_v(v_flat):
        parts = []
        offset = 0
        for l in range(L):
            size = h_settled[l + 1].numel()
            parts.append(v_flat[offset:offset + size].reshape_as(h_settled[l + 1]))
            offset += size
        return parts

    dim = sum(h_settled[l + 1].numel() for l in range(L))

    # Initialize random vector
    q = torch.randn(dim, device=device)
    q = q / q.norm()

    alphas = []
    betas = [0.0]
    Q = [q]

    for j in range(min(n_lanczos_steps, dim)):
        v_dir = unflatten_v(q)
        hvp = hessian_vector_product(model, list(h_settled), h_init, x_0, mask, t, v_dir)
        w = torch.cat([hv.reshape(-1) for hv in hvp])

        alpha = w.dot(q).item()
        alphas.append(alpha)

        if j > 0:
            w = w - betas[-1] * Q[-2]
        w = w - alpha * q

        beta = w.norm().item()
        if beta < 1e-10:
            break
        betas.append(beta)

        q = w / beta
        Q.append(q)

    # Build tridiagonal matrix and compute eigenvalues
    n = len(alphas)
    T_mat = torch.zeros(n, n, device=device)
    for i in range(n):
        T_mat[i, i] = alphas[i]
    for i in range(n - 1):
        T_mat[i, i + 1] = betas[i + 2] if i + 2 < len(betas) else 0
        T_mat[i + 1, i] = betas[i + 2] if i + 2 < len(betas) else 0

    eigenvalues = torch.linalg.eigvalsh(T_mat).cpu().numpy()

    lambda_min = float(max(eigenvalues.min(), 1e-8))
    lambda_max = float(eigenvalues.max())
    kappa = lambda_max / lambda_min if lambda_min > 0 else float("inf")

    print(f"  Hessian spectrum: lambda_min={lambda_min:.4f}, lambda_max={lambda_max:.4f}, kappa={kappa:.2f}")

    return {
        "eigenvalues": eigenvalues.tolist(),
        "lambda_min": lambda_min,
        "lambda_max": lambda_max,
        "effective_kappa": kappa,
    }


# =============================================================================
# 3. Energy Monotonicity Statistics
# =============================================================================

def measure_energy_monotonicity(
    model: PCSHODLM,
    x_0: torch.Tensor,
    K: int = 32,
    device: str = "cpu",
) -> dict:
    """Track energy at each microstep and report monotonicity violations.

    Tests whether discrete-time settling maintains the Lyapunov guarantee.
    """
    model = model.to(device).eval()
    x_0 = x_0.to(device)
    B, S = x_0.shape

    t = torch.full((B,), model.config.n_diffusion_steps // 2, device=device, dtype=torch.long)
    x_t, mask = model.schedule.corrupt(x_0, t, model.config.mask_token_id)
    h_0 = model.embed_input(x_t, t)
    h_init = model.amortized_forward_pass(h_0)

    model.config.n_settling_steps = K
    model.config.settling_threshold = float("inf")

    h = [hi.detach() for hi in h_init]
    v = [torch.zeros_like(h_init[l + 1]) for l in range(model.config.n_layers)]

    energies = []
    kinetic_energies = []

    for k in range(K):
        h, v, energy = model.settling_step(h, v, h_init, x_0, mask, t, None)
        energies.append(energy)

        # Kinetic energy: 0.5 * sum ||v_l||^2
        ke = 0.5 * sum(vl.detach().norm().item() ** 2 for vl in v)
        kinetic_energies.append(ke)

    # Compute total energy (potential + kinetic)
    total_energies = [e + k for e, k in zip(energies, kinetic_energies)]

    # Count monotonicity violations
    violations = 0
    for i in range(1, len(total_energies)):
        if total_energies[i] > total_energies[i - 1] + 1e-6:
            violations += 1

    print(f"  Energy monotonicity: {violations}/{K-1} violations")
    print(f"  Energy range: {energies[0]:.2f} -> {energies[-1]:.2f}")

    return {
        "potential_energies": energies,
        "kinetic_energies": kinetic_energies,
        "total_energies": total_energies,
        "n_violations": violations,
        "violation_rate": violations / max(1, K - 1),
    }


# =============================================================================
# 4. Precision Calibration
# =============================================================================

def measure_precision_calibration(
    model: PCSHODLM,
    x_0: torch.Tensor,
    n_samples: int = 10,
    device: str = "cpu",
) -> dict:
    """Compare predicted precision to empirical squared prediction errors.

    If precision heads are well-calibrated, predicted precision should
    correlate inversely with actual error magnitude.
    """
    model = model.to(device).eval()
    x_0 = x_0.to(device)
    B, S = x_0.shape

    predicted_precs = []
    empirical_sq_errors = []

    for _ in range(n_samples):
        t = torch.randint(1, model.config.n_diffusion_steps + 1, (B,), device=device)
        x_t, mask = model.schedule.corrupt(x_0, t, model.config.mask_token_id)
        h_0 = model.embed_input(x_t, t)
        h_init = model.amortized_forward_pass(h_0)

        model.config.n_settling_steps = 8
        h_settled, _, _, _, _ = model.settle(h_init, x_0, mask, t)

        # Get predictions and precisions at each layer
        for l in range(model.config.n_layers):
            with torch.no_grad():
                mu_up = model.forward_blocks[l](h_settled[l])
                tok_p, ch_p = model.precision_up[l](h_settled[l + 1], t)

                # Empirical squared error
                eps = (h_settled[l + 1] - mu_up)
                sq_err = (eps ** 2).mean(dim=-1)  # (B, S)

                # Predicted precision (token-level)
                pred_p = tok_p.squeeze(-1)  # (B, S)

                predicted_precs.append(pred_p.cpu())
                empirical_sq_errors.append(sq_err.cpu())

    # Compute correlation
    all_prec = torch.cat([p.flatten() for p in predicted_precs])
    all_err = torch.cat([e.flatten() for e in empirical_sq_errors])

    # Pearson correlation between precision and 1/error
    inv_err = 1.0 / (all_err + 1e-8)
    correlation = torch.corrcoef(torch.stack([all_prec, inv_err]))[0, 1].item()

    # Spearman rank correlation (approximate via sorting)
    n = min(len(all_prec), 10000)
    idx = torch.randperm(len(all_prec))[:n]
    prec_ranks = all_prec[idx].argsort().argsort().float()
    err_ranks = all_err[idx].argsort().argsort().float()
    # Higher precision should correlate with lower error (higher error rank)
    rank_corr = torch.corrcoef(torch.stack([prec_ranks, err_ranks]))[0, 1].item()

    print(f"  Precision calibration:")
    print(f"    Pearson(precision, 1/error): {correlation:.4f}")
    print(f"    Rank correlation(precision, error_rank): {rank_corr:.4f}")

    return {
        "pearson_prec_inv_error": correlation,
        "rank_correlation": rank_corr,
        "mean_precision": all_prec.mean().item(),
        "mean_sq_error": all_err.mean().item(),
    }


# =============================================================================
# 5. Contraction Estimation
# =============================================================================

def estimate_contraction_factor(
    model: PCSHODLM,
    x_0: torch.Tensor,
    perturbation_scale: float = 0.01,
    K: int = 8,
    device: str = "cpu",
) -> dict:
    """Estimate solver contraction factor by perturbation experiments.

    Perturb h^k slightly and measure how fast the perturbation decays.
    """
    model = model.to(device).eval()
    x_0 = x_0.to(device)
    B, S = x_0.shape
    L = model.config.n_layers

    t = torch.full((B,), model.config.n_diffusion_steps // 2, device=device, dtype=torch.long)
    x_t, mask = model.schedule.corrupt(x_0, t, model.config.mask_token_id)
    h_0 = model.embed_input(x_t, t)
    h_init = model.amortized_forward_pass(h_0)

    model.config.n_settling_steps = K
    model.config.settling_threshold = float("inf")

    # Run unperturbed
    h_clean = [hi.detach() for hi in h_init]
    v_clean = [torch.zeros_like(h_init[l + 1]) for l in range(L)]

    # Run perturbed
    h_pert = [hi.detach().clone() for hi in h_init]
    for l in range(1, L + 1):
        h_pert[l] = h_pert[l] + perturbation_scale * torch.randn_like(h_pert[l])
    v_pert = [torch.zeros_like(h_init[l + 1]) for l in range(L)]

    distances = []
    initial_dist = sum(
        (h_pert[l + 1] - h_clean[l + 1]).norm().item() for l in range(L)
    )
    distances.append(initial_dist)

    for k in range(K):
        h_clean, v_clean, _ = model.settling_step(
            h_clean, v_clean, h_init, x_0, mask, t, None
        )
        h_pert, v_pert, _ = model.settling_step(
            h_pert, v_pert, h_init, x_0, mask, t, None
        )

        dist = sum(
            (h_pert[l + 1] - h_clean[l + 1]).detach().norm().item() for l in range(L)
        )
        distances.append(dist)

    # Estimate contraction per step
    contraction_factors = []
    for i in range(1, len(distances)):
        if distances[i - 1] > 1e-10:
            contraction_factors.append(distances[i] / distances[i - 1])

    avg_contraction = sum(contraction_factors) / len(contraction_factors) if contraction_factors else 1.0

    print(f"  Contraction estimation:")
    print(f"    Initial perturbation distance: {initial_dist:.6f}")
    print(f"    Final distance: {distances[-1]:.6f}")
    print(f"    Average contraction factor: {avg_contraction:.4f}")
    print(f"    Contractive: {avg_contraction < 1.0}")

    return {
        "distances": distances,
        "contraction_factors": contraction_factors,
        "avg_contraction": avg_contraction,
        "is_contractive": avg_contraction < 1.0,
    }


# =============================================================================
# 6. Token Freeze Accuracy
# =============================================================================

def measure_freeze_accuracy(
    model: PCSHODLM,
    x_0: torch.Tensor,
    device: str = "cpu",
) -> dict:
    """Measure fraction of soft-frozen tokens that later change.

    Runs settling with and without soft-freezing, compares final tokens.
    Tests whether early confident predictions are reliable.
    """
    model = model.to(device).eval()
    x_0 = x_0.to(device)
    B, S = x_0.shape

    t = torch.full((B,), model.config.n_diffusion_steps // 2, device=device, dtype=torch.long)
    x_t, mask = model.schedule.corrupt(x_0, t, model.config.mask_token_id)
    h_0 = model.embed_input(x_t, t)
    h_init = model.amortized_forward_pass(h_0)

    # Run without adaptive settling (full compute)
    model.config.settling_threshold = float("inf")
    h_full, _, _, _, _ = model.settle(h_init, x_0, mask, t)
    logits_full = model.readout(model.readout_norm(h_full[-1]))
    tokens_full = logits_full.argmax(dim=-1)

    # Run with adaptive settling (soft-freeze)
    model.config.settling_threshold = 0.5
    h_adaptive, _, _, _, _ = model.settle(h_init, x_0, mask, t)
    logits_adaptive = model.readout(model.readout_norm(h_adaptive[-1]))
    tokens_adaptive = logits_adaptive.argmax(dim=-1)

    # Check: at masked positions, how often do frozen-early tokens match full-compute tokens
    masked_match = (tokens_full[mask] == tokens_adaptive[mask]).float()
    match_rate = masked_match.mean().item() if masked_match.numel() > 0 else 1.0

    # Compute mid-settling predictions to identify "early frozen" tokens
    model.config.n_settling_steps = model.config.n_settling_steps // 2
    model.config.settling_threshold = float("inf")
    h_mid, _, _, _, _ = model.settle(h_init, x_0, mask, t)
    logits_mid = model.readout(model.readout_norm(h_mid[-1]))
    tokens_mid = logits_mid.argmax(dim=-1)

    # "Early confident" = tokens that don't change from mid to full
    early_stable = (tokens_mid[mask] == tokens_full[mask]).float()
    early_stable_rate = early_stable.mean().item() if early_stable.numel() > 0 else 1.0

    print(f"  Token freeze accuracy:")
    print(f"    Adaptive vs full match rate: {match_rate:.4f}")
    print(f"    Early-stable token rate: {early_stable_rate:.4f}")

    return {
        "adaptive_vs_full_match": match_rate,
        "early_stable_rate": early_stable_rate,
    }


# =============================================================================
# Run All Diagnostics
# =============================================================================

def run_all_diagnostics(
    output_dir: str = "diagnostics_output",
    device: str = "cpu",
):
    """Run complete assumption diagnostic suite."""
    output_path = Path(output_dir)
    output_path.mkdir(parents=True, exist_ok=True)

    config = PCSHOConfig(
        vocab_size=257,
        max_seq_len=128,
        d_model=256,
        n_heads=4,
        n_layers=4,
        d_ff=512,
        n_diffusion_steps=100,
        n_settling_steps=8,
        mask_token_id=0,
    )
    model = PCSHODLM(config)
    x_0 = torch.randint(1, 257, (4, 128))

    all_results = {}

    print("=" * 60)
    print("PC-SHO-DLM Assumption Diagnostics")
    print("=" * 60)

    print("\n1. Stationarity Residual (Envelope Theorem)")
    all_results["stationarity"] = measure_stationarity_residual(
        model, x_0, [2, 4, 8, 16], device
    )

    print("\n2. Local Hessian Spectrum (HVP + Lanczos)")
    all_results["hessian"] = estimate_hessian_spectrum_lanczos(
        model, x_0, n_lanczos_steps=10, device=device
    )

    print("\n3. Energy Monotonicity")
    all_results["monotonicity"] = measure_energy_monotonicity(
        model, x_0, K=16, device=device
    )

    print("\n4. Precision Calibration")
    all_results["precision"] = measure_precision_calibration(
        model, x_0, n_samples=5, device=device
    )

    print("\n5. Contraction Estimation")
    all_results["contraction"] = estimate_contraction_factor(
        model, x_0, device=device
    )

    print("\n6. Token Freeze Accuracy")
    all_results["freeze_accuracy"] = measure_freeze_accuracy(
        model, x_0, device=device
    )

    # Save
    # Convert numpy arrays to lists for JSON
    def convert(obj):
        import numpy as np
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        if isinstance(obj, dict):
            return {k: convert(v) for k, v in obj.items()}
        if isinstance(obj, list):
            return [convert(i) for i in obj]
        return obj

    with open(output_path / "diagnostics.json", "w") as f:
        json.dump(convert(all_results), f, indent=2)

    print(f"\nResults saved to {output_path}/diagnostics.json")
    return all_results


if __name__ == "__main__":
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument("--output", type=str, default="diagnostics_output")
    parser.add_argument("--device", type=str, default="cpu")
    args = parser.parse_args()

    run_all_diagnostics(args.output, args.device)