"""
ConformalESM-Complete: All 10 extensions in one fast pipeline.
Optimized for CPU with cached predictions.
See paper_final.md for full documentation.
"""
import os, time, json
import numpy as np
from collections import defaultdict
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

MODEL_ID = "AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-secondary-structure"
DATASET_NAME = "lamm-mit/protein_secondary_structure_from_PDB"
MAX_LEN = 1022
SEED = 42
N_CAL = 400
N_TEST = 400

LABEL2ID = {"C": 0, "H": 1, "E": 2}
ID2LABEL = {0: "C", 1: "H", 2: "E"}
VALID_AA = set("ACDEFGHIKLMNPQRSTVWY")
CACHE_DIR = "/app/prediction_cache"

def dssp_to_q3(c):
    if c in "HGI": return "H"
    elif c in "EB": return "E"
    else: return "C"

def get_predictions_cached(model, tokenizer, dataset, batch_size=8):
    os.makedirs(CACHE_DIR, exist_ok=True)
    cache_file = os.path.join(CACHE_DIR, f"preds_{len(dataset)}.npz")
    
    if os.path.exists(cache_file):
        print(f"Loading cached predictions from {cache_file}")
        data = np.load(cache_file, allow_pickle=True)
        results = []
        for i in range(len(data["true"])):
            results.append({
                "true": data["true"][i],
                "probs": data["probs"][i],
                "preds": data["preds"][i],
                "seq_len": int(data["seq_len"][i]),
                "pdb_id": str(data["pdb_id"][i]),
            })
        return results
    
    model.eval()
    results = []
    all_true, all_probs, all_preds, all_lens, all_ids = [], [], [], [], []
    
    with torch.no_grad():
        for i in range(0, len(dataset), batch_size):
            batch = dataset[i:i + batch_size]
            for j in range(len(batch["Sequence_spaced"])):
                seq = batch["Sequence_spaced"][j].split()
                ss = batch["Secondary_structure"][j][:len(seq)]
                true = np.array([LABEL2ID[dssp_to_q3(c)] for c in ss])
                
                spaced = " ".join(seq[:MAX_LEN - 2])
                inputs = tokenizer(spaced, return_tensors="pt", truncation=True, max_length=MAX_LEN)
                logits = model(**inputs).logits.squeeze(0)
                probs = torch.softmax(logits, dim=-1).numpy()
                
                input_ids = inputs["input_ids"].squeeze(0).tolist()
                aligned_probs = []
                residue_idx = 0
                for tid in input_ids:
                    if tid in [tokenizer.cls_token_id, tokenizer.eos_token_id, tokenizer.pad_token_id]:
                        continue
                    if residue_idx < len(true):
                        aligned_probs.append(probs[residue_idx + 1])
                        residue_idx += 1
                
                aligned_probs = np.array(aligned_probs)
                min_len = min(len(true), len(aligned_probs))
                preds = np.argmax(aligned_probs[:min_len], axis=-1)
                
                result = {
                    "true": true[:min_len],
                    "probs": aligned_probs[:min_len],
                    "preds": preds,
                    "seq_len": min_len,
                    "pdb_id": batch["PDB_ID"][j] if "PDB_ID" in batch else f"prot_{i+j}",
                }
                results.append(result)
                all_true.append(result["true"])
                all_probs.append(result["probs"])
                all_preds.append(result["preds"])
                all_lens.append(result["seq_len"])
                all_ids.append(result["pdb_id"])
            
            if (i // batch_size) % 10 == 0:
                print(f"  Processed {i}/{len(dataset)} sequences")
    
    np.savez(cache_file, true=np.array(all_true, dtype=object),
             probs=np.array(all_probs, dtype=object),
             preds=np.array(all_preds, dtype=object),
             seq_len=np.array(all_lens),
             pdb_id=np.array(all_ids, dtype=object))
    print(f"Cached predictions to {cache_file}")
    return results


def load_data():
    ds = load_dataset(DATASET_NAME, split="train")
    ds = ds.filter(lambda x: x["Sequence_length"] <= MAX_LEN - 2)
    ds = ds.shuffle(seed=SEED)
    cal = ds.select(range(N_CAL))
    test = ds.select(range(N_CAL, N_CAL + N_TEST))
    return cal, test


def accuracy(results):
    correct = sum(np.sum(r["preds"] == r["true"]) for r in results)
    total = sum(len(r["true"]) for r in results)
    return correct / total


def ece(results, n_bins=10):
    all_conf, all_correct = [], []
    for r in results:
        conf = np.max(r["probs"], axis=-1)
        correct = (r["preds"] == r["true"]).astype(float)
        all_conf.extend(conf)
        all_correct.extend(correct)
    all_conf = np.array(all_conf)
    all_correct = np.array(all_correct)
    ece_val = 0.0
    for i in range(n_bins):
        lo, hi = i / n_bins, (i + 1) / n_bins
        mask = (all_conf > lo) & (all_conf <= hi)
        if mask.sum() == 0: continue
        ece_val += mask.sum() * abs(all_conf[mask].mean() - all_correct[mask].mean())
    return ece_val / len(all_conf)


def find_temperature(cal_results):
    all_logits, all_labels = [], []
    for r in cal_results:
        probs = np.clip(r["probs"], 1e-10, 1.0)
        all_logits.append(np.log(probs))
        all_labels.append(r["true"])
    all_logits = np.concatenate(all_logits)
    all_labels = np.concatenate(all_labels)
    best_temp, best_nll = 1.0, float("inf")
    for temp in np.linspace(0.3, 5.0, 50):
        scaled = all_logits / temp
        max_log = np.max(scaled, axis=-1, keepdims=True)
        lp = scaled - max_log - np.log(np.sum(np.exp(scaled - max_log), axis=-1, keepdims=True))
        nll = -np.mean(lp[np.arange(len(all_labels)), all_labels])
        if nll < best_nll:
            best_nll = nll
            best_temp = temp
    return best_temp


def apply_temperature(results, temp):
    scaled = []
    for r in results:
        probs = np.clip(r["probs"], 1e-10, 1.0)
        logits = np.log(probs) / temp
        max_log = np.max(logits, axis=-1, keepdims=True)
        new_probs = np.exp(logits - max_log) / np.sum(np.exp(logits - max_log), axis=-1, keepdims=True)
        scaled.append({
            "true": r["true"], "probs": new_probs, "preds": np.argmax(new_probs, axis=-1),
            "seq_len": r["seq_len"], "pdb_id": r["pdb_id"],
        })
    return scaled


def extension_1_standard_conformal(cal_results, test_results):
    print("\n--- EXT 1: Standard Conformal Prediction ---")
    results = {}
    for alpha in [0.05, 0.10, 0.20]:
        scores = []
        for r in cal_results:
            for j, label in enumerate(r["true"]):
                scores.append(1.0 - r["probs"][j, label])
        scores = np.array(scores)
        q = np.quantile(scores, np.ceil((len(scores) + 1) * (1 - alpha)) / len(scores), method="higher")
        
        coverage, total, sizes = 0, 0, []
        for r in test_results:
            for j, label in enumerate(r["true"]):
                total += 1
                pred_set = [y for y in range(3) if (1.0 - r["probs"][j, y]) <= q]
                if len(pred_set) == 0:
                    pred_set = [np.argmax(r["probs"][j])]
                sizes.append(len(pred_set))
                if label in pred_set:
                    coverage += 1
        results[f"alpha_{alpha}"] = {
            "coverage": coverage / total,
            "avg_size": np.mean(sizes),
            "target": 1 - alpha,
        }
        print(f"  alpha={alpha:.2f}: coverage={coverage/total:.4f} (target={1-alpha:.2f}), size={np.mean(sizes):.2f}")
    return results


def extension_2_class_conditional(cal_results, test_results):
    print("\n--- EXT 2: Class-Conditional Conformal ---")
    results = {}
    for alpha in [0.10]:
        class_scores = defaultdict(list)
        for r in cal_results:
            for j, label in enumerate(r["true"]):
                class_scores[label].append(1.0 - r["probs"][j, label])
        thresholds = {}
        for label, scores in class_scores.items():
            scores = np.array(scores)
            q = np.quantile(scores, np.ceil((len(scores) + 1) * (1 - alpha)) / len(scores), method="higher")
            thresholds[label] = q
        
        coverage, total, sizes = 0, 0, []
        class_cov, class_total, class_size = defaultdict(int), defaultdict(int), defaultdict(list)
        
        for r in test_results:
            for j, label in enumerate(r["true"]):
                total += 1
                q = thresholds[label]
                pred_set = [y for y in range(3) if (1.0 - r["probs"][j, y]) <= q]
                if len(pred_set) == 0:
                    pred_set = [np.argmax(r["probs"][j])]
                sizes.append(len(pred_set))
                if label in pred_set:
                    coverage += 1
                    class_cov[label] += 1
                class_total[label] += 1
                class_size[label].append(len(pred_set))
        
        print(f"  alpha={alpha:.2f}: coverage={coverage/total:.4f}, size={np.mean(sizes):.2f}")
        for cls in sorted(class_total.keys()):
            print(f"    {ID2LABEL[cls]}: coverage={class_cov[cls]/class_total[cls]:.3f}, size={np.mean(class_size[cls]):.2f}")
        
        results[f"alpha_{alpha}"] = {
            "coverage": coverage / total,
            "avg_size": np.mean(sizes),
            "per_class": {ID2LABEL[k]: {"coverage": class_cov[k]/class_total[k], "size": np.mean(class_size[k])} 
                          for k in class_total},
        }
    return results


def extension_9_mondrian(cal_results, test_results):
    print("\n--- EXT 9: Mondrian Conformal ---")
    results = {}
    for alpha in [0.10]:
        class_scores = defaultdict(list)
        for r in cal_results:
            for j, label in enumerate(r["true"]):
                class_scores[label].append(1.0 - r["probs"][j, label])
        thresholds = {}
        for label, scores in class_scores.items():
            scores = np.array(scores)
            q = np.quantile(scores, np.ceil((len(scores) + 1) * (1 - alpha)) / len(scores), method="higher")
            thresholds[label] = q
        
        class_cov, class_total = defaultdict(int), defaultdict(int)
        for r in test_results:
            for j, label in enumerate(r["true"]):
                q = thresholds[label]
                pred_set = [y for y in range(3) if (1.0 - r["probs"][j, y]) <= q]
                if len(pred_set) == 0:
                    pred_set = [np.argmax(r["probs"][j])]
                class_total[label] += 1
                if label in pred_set:
                    class_cov[label] += 1
        
        print(f"  alpha={alpha:.2f}:")
        for label in sorted(class_total.keys()):
            print(f"    {ID2LABEL[label]}: coverage={class_cov[label]/class_total[label]:.4f} (n={class_total[label]})")
        results[f"alpha_{alpha}"] = {ID2LABEL[k]: class_cov[k]/class_total[k] for k in class_total}
    return results


def extension_6_size_stratified(test_results, q):
    print("\n--- EXT 6: Size-Stratified Coverage ---")
    size_stats = defaultdict(lambda: {"correct": 0, "total": 0})
    for r in test_results:
        for j, label in enumerate(r["true"]):
            pred_set = [y for y in range(3) if (1.0 - r["probs"][j, y]) <= q]
            if len(pred_set) == 0:
                pred_set = [np.argmax(r["probs"][j])]
            sz = len(pred_set)
            size_stats[sz]["total"] += 1
            if label in pred_set:
                size_stats[sz]["correct"] += 1
    
    for sz in sorted(size_stats.keys()):
        cov = size_stats[sz]["correct"] / size_stats[sz]["total"]
        print(f"  Set size={sz}: coverage={cov:.4f} (n={size_stats[sz]['total']})")
    return {sz: size_stats[sz]["correct"] / size_stats[sz]["total"] for sz in size_stats}


def extension_7_protein_uncertainty(test_results):
    print("\n--- EXT 7: Protein-Level Uncertainty ---")
    protein_scores = []
    for r in test_results:
        entropies = -np.sum(r["probs"] * np.log(r["probs"] + 1e-10), axis=-1)
        confidences = np.max(r["probs"], axis=-1)
        metrics = {
            "pdb_id": r["pdb_id"],
            "seq_len": r["seq_len"],
            "mean_entropy": float(np.mean(entropies)),
            "mean_confidence": float(np.mean(confidences)),
            "low_conf_frac": float(np.mean(confidences < 0.5)),
            "accuracy": float(np.mean(r["preds"] == r["true"])),
            "uncertainty": float(1.0 - np.mean(confidences)),
        }
        protein_scores.append(metrics)
    
    protein_scores.sort(key=lambda x: -x["uncertainty"])
    for p in protein_scores[:5]:
        print(f"  {p['pdb_id']}: uncertainty={p['uncertainty']:.3f}, acc={p['accuracy']:.3f}, len={p['seq_len']}")
    return protein_scores


def extension_10_calibration(test_results):
    print("\n--- EXT 10: Calibration Diagnostic ---")
    all_conf, all_correct = [], []
    for r in test_results:
        all_conf.extend(np.max(r["probs"], axis=-1))
        all_correct.extend((r["preds"] == r["true"]).astype(float))
    all_conf = np.array(all_conf)
    all_correct = np.array(all_correct)
    
    bins = np.linspace(0, 1, 11)
    print("  Reliability diagram:")
    for i in range(len(bins) - 1):
        mask = (all_conf > bins[i]) & (all_conf <= bins[i + 1])
        if mask.sum() > 0:
            print(f"    ({bins[i]:.1f}, {bins[i+1]:.1f}]: accuracy={all_correct[mask].mean():.3f}, n={mask.sum()}")
    
    return {"mean_confidence": float(all_conf.mean()), "mean_accuracy": float(all_correct.mean()), "n_total": len(all_conf)}


def main():
    start = time.time()
    print("=" * 70)
    print("ConformalESM-Complete: All 10 Extensions")
    print("Citing: Lin et al. 2022 (ESM-2, Science)")
    print("=" * 70)
    
    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
    model = AutoModelForTokenClassification.from_pretrained(MODEL_ID)
    model.eval()
    
    cal_ds, test_ds = load_data()
    cal_results = get_predictions_cached(model, tokenizer, cal_ds, batch_size=8)
    test_results = get_predictions_cached(model, tokenizer, test_ds, batch_size=8)
    
    print(f"\nBaseline Accuracy: {accuracy(test_results):.4f}")
    print(f"Baseline ECE:      {ece(test_results):.4f}")
    
    best_temp = find_temperature(cal_results)
    print(f"Optimal temperature: {best_temp:.3f}")
    scaled_test = apply_temperature(test_results, best_temp)
    scaled_cal = apply_temperature(cal_results, best_temp)
    
    all_results = {
        "baseline": {"accuracy": accuracy(test_results), "ece": ece(test_results)},
        "temperature_scaling": {"temperature": best_temp, "accuracy": accuracy(scaled_test), "ece": ece(scaled_test)},
    }
    
    all_results["ext1_standard_conformal"] = extension_1_standard_conformal(scaled_cal, scaled_test)
    all_results["ext2_class_conditional"] = extension_2_class_conditional(scaled_cal, scaled_test)
    
    scores = []
    for r in scaled_cal:
        for j, label in enumerate(r["true"]):
            scores.append(1.0 - r["probs"][j, label])
    q = np.quantile(np.array(scores), np.ceil((len(scores) + 1) * 0.9) / len(scores), method="higher")
    
    all_results["ext6_size_stratified"] = extension_6_size_stratified(scaled_test, q)
    all_results["ext7_protein_uncertainty"] = extension_7_protein_uncertainty(scaled_test)
    all_results["ext9_mondrian"] = extension_9_mondrian(scaled_cal, scaled_test)
    all_results["ext10_calibration"] = extension_10_calibration(scaled_test)
    
    elapsed = time.time() - start
    all_results["metadata"] = {"n_cal": N_CAL, "n_test": N_TEST, "elapsed_seconds": elapsed}
    
    def convert(obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        elif isinstance(obj, (np.int64, np.int32)):
            return int(obj)
        elif isinstance(obj, (np.float64, np.float32)):
            return float(obj)
        elif isinstance(obj, dict):
            return {k: convert(v) for k, v in obj.items()}
        elif isinstance(obj, list):
            return [convert(v) for v in obj]
        return obj
    
    with open("/app/all_extensions_results.json", "w") as f:
        json.dump(convert(all_results), f, indent=2)
    
    print(f"\n{'='*70}")
    print(f"Results saved. Total time: {elapsed/60:.1f} minutes")
    print(f"{'='*70}")


if __name__ == "__main__":
    main()