"""Quick sweep over alpha values to find the right perturbation scale."""

import sys
import os
import json
import time
import torch

sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))

from data.longlamp import load_longlamp, select_k_profile_items
from data.templates import build_query_prompt
from models.qwen_wrapper import QwenWrapper
from models.cvh import CVHHead
from adapt.cache_hidden import cache_support_hidden_states
from adapt.fit_theta import fit_theta
from eval.metrics import evaluate_all


def run_cvh_with_params(wrapper, examples, support_sets, alpha, beta, steps, d=64, lr=0.05):
    """Run CVH with specific hyperparameters."""
    device = 'cuda:1'
    H = wrapper.hidden_size
    head = CVHHead(H, d=d, alpha=alpha, basis_seed=42).to(device)

    lm_head_bias = None
    if hasattr(wrapper.model.lm_head, 'bias') and wrapper.model.lm_head.bias is not None:
        lm_head_bias = wrapper.model.lm_head.bias.data

    predictions = []
    theta_norms = []

    for i, (ex, support) in enumerate(zip(examples, support_sets)):
        cached_h = cache_support_hidden_states(wrapper, support, ex['task'])
        if not cached_h:
            prompt = build_query_prompt(ex['query_input'], ex['task'])
            pred = wrapper.generate_base(prompt, max_new_tokens=256)
            predictions.append(pred)
            continue

        theta = fit_theta(
            cached_h=cached_h,
            lm_head_weight=wrapper.lm_head_weight,
            lm_head_bias=lm_head_bias,
            head_module=head,
            d=d, lr=lr, steps=steps, beta=beta, lam=1e-4,
            max_grad_norm=5.0, device=device, verbose=False,
        )
        theta_norms.append(theta.norm().item())

        prompt = build_query_prompt(ex['query_input'], ex['task'])
        pred = wrapper.generate_with_head(
            prompt, theta, head.forward_fn,
            max_new_tokens=256, temperature=0.0,
        )
        predictions.append(pred)

        del cached_h, theta
        torch.cuda.empty_cache()

    avg_norm = sum(theta_norms) / max(len(theta_norms), 1)
    return predictions, avg_norm


def main():
    print("Loading data...")
    examples = load_longlamp('product_review_user', split='val')[:50]
    K = 4
    support_sets = [select_k_profile_items(ex['profile_items'], K, seed=0) for ex in examples]
    references = [ex['target_output'] for ex in examples]
    support_texts = [[s['support_output'] for s in ss] for ss in support_sets]

    print("Loading model...")
    wrapper = QwenWrapper('Qwen/Qwen2.5-1.5B-Instruct', device='cuda:1')

    # Run base
    print("\n=== Base ===")
    base_preds = []
    for ex in examples:
        prompt = build_query_prompt(ex['query_input'], ex['task'])
        pred = wrapper.generate_base(prompt, max_new_tokens=256, temperature=0.0)
        base_preds.append(pred)
    base_results = evaluate_all(base_preds, references, support_texts)
    print(f"  ROUGE-L: {base_results['rougeL']:.4f}, METEOR: {base_results['meteor']:.4f}, SFD: {base_results['sfd']:.4f}")

    # Sweep
    configs = [
        {'alpha': 0.1, 'beta': 0.05, 'steps': 30, 'lr': 0.05},
        {'alpha': 0.3, 'beta': 0.05, 'steps': 30, 'lr': 0.05},
        {'alpha': 0.5, 'beta': 0.05, 'steps': 30, 'lr': 0.05},
        {'alpha': 0.3, 'beta': 0.01, 'steps': 50, 'lr': 0.05},
        {'alpha': 0.5, 'beta': 0.01, 'steps': 50, 'lr': 0.05},
        {'alpha': 0.3, 'beta': 0.01, 'steps': 50, 'lr': 0.1},
    ]

    all_results = {'Base': base_results}

    for cfg in configs:
        name = f"a{cfg['alpha']}_b{cfg['beta']}_s{cfg['steps']}_lr{cfg['lr']}"
        print(f"\n=== CVH {name} ===")
        t0 = time.time()
        preds, avg_norm = run_cvh_with_params(
            wrapper, examples, support_sets,
            alpha=cfg['alpha'], beta=cfg['beta'],
            steps=cfg['steps'], lr=cfg['lr'],
        )
        elapsed = time.time() - t0
        results = evaluate_all(preds, references, support_texts)
        all_results[name] = results
        print(f"  ROUGE-L: {results['rougeL']:.4f}, METEOR: {results['meteor']:.4f}, "
              f"SFD: {results['sfd']:.4f}, avg|theta|: {avg_norm:.3f}, time: {elapsed:.0f}s")

    # Summary
    print("\n" + "=" * 80)
    print(f"{'Config':<40} {'ROUGE-L':<10} {'METEOR':<10} {'SFD':<10}")
    print("-" * 80)
    for name, r in all_results.items():
        print(f"{name:<40} {r['rougeL']:<10.4f} {r['meteor']:<10.4f} {r['sfd']:<10.4f}")


if __name__ == '__main__':
    main()