"""Test SVD-based CVH vs random basis CVH vs Unconditional."""

import sys
import os
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, UnconditionalHead
from models.svd_cvh import SVDCVHHead, SVDUncondHead
from adapt.cache_hidden import cache_support_hidden_states
from adapt.fit_theta import fit_theta
from eval.metrics import evaluate_all


def run_head(wrapper, examples, support_sets, head_module, d=64,
             beta=0.05, steps=30, lr=0.05, max_new_tokens=512,
             min_new_tokens=64):
    device = 'cuda:1'
    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=max_new_tokens)
            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_module,
            d=d, lr=lr, steps=steps, beta=beta, lam=1e-4,
            max_grad_norm=5.0, device=device, verbose=(i == 0),
        )
        theta_norms.append(theta.norm().item())

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

        del cached_h, theta
        torch.cuda.empty_cache()

        if (i + 1) % 20 == 0:
            print(f"    {i+1}/{len(examples)}")

    avg_norm = sum(theta_norms) / max(len(theta_norms), 1)
    # Check avg output length
    avg_len = sum(len(p.split()) for p in predictions) / max(len(predictions), 1)
    return predictions, avg_norm, avg_len


def main():
    N = 50
    print(f"Loading data ({N} examples)...")
    examples = load_longlamp('product_review_user', split='val')[:N]
    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]

    avg_ref_len = sum(len(r.split()) for r in references) / len(references)
    print(f"Avg reference length: {avg_ref_len:.0f} words")

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

    # 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=512, temperature=0.0)
        base_preds.append(pred)
    base_r = evaluate_all(base_preds, references, support_texts)
    base_len = sum(len(p.split()) for p in base_preds) / len(base_preds)
    print(f"  ROUGE-L: {base_r['rougeL']:.4f}, METEOR: {base_r['meteor']:.4f}, "
          f"SFD: {base_r['sfd']:.4f}, avg_len: {base_len:.0f}")

    results = {}
    results['Base'] = {**base_r, 'avg_len': base_len}

    # SVD-based heads
    print("\nComputing SVD of lm_head...")
    svd_cvh = SVDCVHHead(wrapper.lm_head_weight, d=64, alpha=0.1).to(device)
    svd_uncond = SVDUncondHead(wrapper.lm_head_weight, d=64, alpha=0.1).to(device)

    configs = [
        ('Random CVH d=64', CVHHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
        ('Random Uncond d=64', UnconditionalHead(H, d=64, alpha=0.1, basis_seed=42).to(device), 64),
        ('SVD CVH d=64', svd_cvh, 64),
        ('SVD Uncond d=64', svd_uncond, 64),
        # Try different alpha with SVD
        ('SVD CVH d=64 a=0.05', SVDCVHHead(wrapper.lm_head_weight, d=64, alpha=0.05).to(device), 64),
        ('SVD CVH d=64 a=0.2', SVDCVHHead(wrapper.lm_head_weight, d=64, alpha=0.2).to(device), 64),
    ]

    for name, head, d in configs:
        print(f"\n=== {name} ===")
        t0 = time.time()
        preds, avg_norm, avg_len = run_head(
            wrapper, examples, support_sets, head, d=d,
            beta=0.05, steps=30, lr=0.05, max_new_tokens=512,
            min_new_tokens=64,
        )
        elapsed = time.time() - t0
        r = evaluate_all(preds, references, support_texts)
        results[name] = {**r, 'avg_len': avg_len}
        print(f"  ROUGE-L: {r['rougeL']:.4f}, METEOR: {r['meteor']:.4f}, "
              f"SFD: {r['sfd']:.4f}, avg|theta|: {avg_norm:.3f}, "
              f"avg_len: {avg_len:.0f}, time: {elapsed:.0f}s")

    # Summary
    print("\n" + "=" * 100)
    print(f"{'Config':<25} {'R-1':<8} {'R-L':<8} {'METEOR':<8} {'SFD':<8} {'Len':<6}")
    print("-" * 100)
    for name, r in results.items():
        print(f"{name:<25} {r['rouge1']:<8.4f} {r['rougeL']:<8.4f} "
              f"{r['meteor']:<8.4f} {r['sfd']:<8.4f} {r.get('avg_len', 0):<6.0f}")


if __name__ == '__main__':
    main()