"""
ConformalESM-Extended: Unified Uncertainty Quantification for Protein Language Models
=====================================================================================

Runs overnight on Hugging Face CPU infrastructure. Pushes results to Hub.

Coverage:
  - Task 1: Secondary Structure (Q3: H/E/C) — 8M + 650M
  - Task 2: Disorder Prediction (binary) — 35M + 650M
  - Baselines: Entropy, Max-Margin
  - Methods: Temperature Scaling, Split Conformal, Class-Conditional, Mondrian,
            Size-Stratified, Experiment Prioritization, Cross-Model Transfer
  - All post-hoc, CPU-only, no retraining

Auto-pushes results to knoxel/conformalesm-paper-starter
"""

import os
import sys
import json
import time
import numpy as np
from collections import defaultdict
from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForTokenClassification
import torch

# ======================== CONFIG ========================

SEED = 42
np.random.seed(SEED)
torch.manual_seed(SEED)

MAX_LEN = 1022
N_CAL = 500
N_TEST = 500

# Models
SS_MODEL_8M = "AmelieSchreiber/esm2_t6_8M_UR50D-finetuned-secondary-structure"
SS_MODEL_650M = "gaodrew/esm2_t33_650M_UR50D-finetuned-secondary-structure"
DIS_MODEL_35M = "CQSB/esm2_35M-LoRA-ID-DisProt7"
DIS_MODEL_650M = "CQSB/esm2_650M-LoRA-ID-DisProt7"

# Datasets
SS_DATASET = "lamm-mit/protein_secondary_structure_from_PDB"
DIS_DATASET = "CQSB/SoftDis"
DIS_CONFIG = "id05"
DIS_THRESHOLD = 0.5

# Label mappings
SS_ID2LABEL = {0: "C", 1: "H", 2: "E"}
SS_LABEL2ID = {"C": 0, "H": 1, "E": 2}
DIS_LABEL2ID = {"Ordered": 0, "Disordered": 1}

# ======================== UTILS ========================

def log(msg):
    print(f"[{time.strftime('%H:%M:%S')}] {msg}", flush=True)

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

# ======================== DATA ========================

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


def load_disorder_data():
    ds = load_dataset(DIS_DATASET, DIS_CONFIG)
    train = ds["train"].shuffle(seed=SEED)
    cal = train.select(range(min(N_CAL, len(train))))
    test = ds["test"].shuffle(seed=SEED)
    test = test.select(range(min(N_TEST, len(test))))
    return cal, test

# ======================== MODEL LOADING ========================

def load_model(model_id, is_650m=False):
    """Load model, handling LoRA adapters if needed."""
    log(f"Loading model: {model_id}")

    if "LoRA" in model_id or "lora" in model_id.lower():
        from peft import PeftModel
        if "650M" in model_id or "t33" in model_id:
            base_id = "facebook/esm2_t33_650M_UR50D"
        elif "35M" in model_id or "t12" in model_id:
            base_id = "facebook/esm2_t12_35M_UR50D"
        else:
            base_id = "facebook/esm2_t6_8M_UR50D"

        log(f"  Base model: {base_id}")
        base = AutoModelForTokenClassification.from_pretrained(base_id)
        model = PeftModel.from_pretrained(base, model_id)
    else:
        model = AutoModelForTokenClassification.from_pretrained(model_id)

    tokenizer = AutoTokenizer.from_pretrained(model_id)
    model.eval()
    log(f"  Model loaded. Parameters: {sum(p.numel() for p in model.parameters()):,}")
    return model, tokenizer

# ======================== INFERENCE ========================

def infer_ss(model, tokenizer, dataset, batch_size=2):
    """Secondary structure inference. Returns list of {true, probs, preds}."""
    results = []
    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([SS_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()

                # Align to residues (skip special tokens)
                input_ids = inputs["input_ids"].squeeze(0).tolist()
                aligned_probs = []
                residue_idx = 0
                cls_id = tokenizer.cls_token_id
                eos_id = tokenizer.eos_token_id
                pad_id = tokenizer.pad_token_id

                for tid in input_ids:
                    if tid in [cls_id, eos_id, pad_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))
                results.append({
                    "true": true[:min_len],
                    "probs": aligned_probs[:min_len],
                    "preds": np.argmax(aligned_probs[:min_len], axis=-1),
                })
    return results


def infer_disorder(model, tokenizer, dataset, batch_size=2):
    """Disorder inference. Returns list of {true, probs, preds}."""
    results = []
    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"])):
                seq = batch["sequence"][j]
                freqs = batch["soft_disorder_frequency"][j]
                true = np.array([1 if f >= DIS_THRESHOLD else 0 for f in freqs[:len(seq)]])

                spaced = " ".join(list(seq)[:MAX_LEN - 2])
                inputs = tokenizer(
                    spaced, return_tensors="pt", truncation=True, max_length=MAX_LEN,
                    return_special_tokens_mask=True
                )
                special_mask = inputs.pop("special_tokens_mask").squeeze(0).bool().numpy()

                logits = model(**inputs).logits.squeeze(0)
                probs = torch.softmax(logits, dim=-1).numpy()

                # Remove special tokens
                aligned_probs = probs[~special_mask]
                min_len = min(len(true), len(aligned_probs))
                results.append({
                    "true": true[:min_len],
                    "probs": aligned_probs[:min_len],
                    "preds": np.argmax(aligned_probs[:min_len], axis=-1),
                })
    return results

# ======================== METRICS ========================

def compute_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 if total else 0


def compute_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) if len(all_conf) else 0


def compute_brier(results):
    scores = []
    for r in results:
        n = len(r["true"])
        if n == 0:
            continue
        n_cls = r["probs"].shape[1]
        one_hot = np.zeros((n, n_cls))
        one_hot[np.arange(n), r["true"]] = 1
        scores.append(np.mean(np.sum((r["probs"] - one_hot) ** 2, axis=-1)))
    return np.mean(scores) if scores else 0

# ======================== TEMPERATURE SCALING ========================

def find_temperature(cal_results, grid=None):
    if grid is None:
        grid = np.linspace(0.5, 5.0, 50)
    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_t, best_nll = 1.0, float("inf")
    for t in grid:
        scaled = all_logits / t
        max_log = np.max(scaled, axis=-1, keepdims=True)
        log_probs = scaled - max_log - np.log(np.sum(np.exp(scaled - max_log), axis=-1, keepdims=True))
        nll = -np.mean(log_probs[np.arange(len(all_labels)), all_labels])
        if nll < best_nll:
            best_nll = nll
            best_t = t
    return best_t


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)})
    return scaled

# ======================== CONFORMAL ========================

def conformal_qhat(cal_results, alpha=0.1):
    scores = [1.0 - r["probs"][j, label]
              for r in cal_results for j, label in enumerate(r["true"])]
    scores = np.array(scores)
    n = len(scores)
    q = np.ceil((n + 1) * (1 - alpha)) / n
    return np.quantile(scores, q, method="higher")


def conformal_qhat_class_conditional(cal_results, alpha=0.1):
    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)
        n = len(scores)
        if n == 0:
            thresholds[label] = 1.0
            continue
        q = np.ceil((n + 1) * (1 - alpha)) / n
        thresholds[label] = np.quantile(scores, q, method="higher")
    return thresholds


def evaluate_conformal(results, q_hat, n_classes, per_class_thresholds=None):
    coverage_count, total = 0, 0
    set_sizes = []
    class_cov = defaultdict(int)
    class_tot = defaultdict(int)
    class_set = defaultdict(list)
    size_strat = defaultdict(lambda: {"correct": 0, "total": 0})

    for r in results:
        for j, label in enumerate(r["true"]):
            total += 1
            threshold = per_class_thresholds.get(label, q_hat) if per_class_thresholds else q_hat
            pred_set = [y for y in range(n_classes) if (1.0 - r["probs"][j, y]) <= threshold]
            set_size = len(pred_set)
            set_sizes.append(set_size)
            size_strat[set_size]["total"] += 1

            if label in pred_set:
                coverage_count += 1
                class_cov[label] += 1
                size_strat[set_size]["correct"] += 1
            class_tot[label] += 1
            class_set[label].append(set_size)

    coverage = coverage_count / total if total else 0
    avg_size = np.mean(set_sizes) if set_sizes else 0

    per_class = {}
    for k in sorted(class_tot.keys()):
        per_class[k] = {
            "coverage": class_cov[k] / class_tot[k] if class_tot[k] else 0,
            "avg_set_size": np.mean(class_set[k]) if class_set[k] else 0,
        }

    size_strat_out = {}
    for size in sorted(size_strat.keys()):
        d = size_strat[size]
        size_strat_out[size] = {
            "coverage": d["correct"] / d["total"] if d["total"] else 0,
            "n": d["total"],
        }

    return coverage, avg_size, per_class, size_strat_out


def evaluate_mondrian(cal_results, test_results, alpha, n_classes):
    class_cal = defaultdict(list)
    for r in cal_results:
        for j, label in enumerate(r["true"]):
            class_cal[label].append(1.0 - r["probs"][j, label])

    thresholds = {}
    for label, scores in class_cal.items():
        scores = np.array(scores)
        n = len(scores)
        if n == 0:
            thresholds[label] = 1.0
            continue
        q = np.ceil((n + 1) * (1 - alpha)) / n
        thresholds[label] = np.quantile(scores, q, method="higher")

    class_cov = defaultdict(lambda: {"correct": 0, "total": 0})
    class_set = defaultdict(list)

    for r in test_results:
        for j, label in enumerate(r["true"]):
            threshold = thresholds.get(label, 1.0)
            pred_set = [y for y in range(n_classes) if (1.0 - r["probs"][j, y]) <= threshold]
            set_size = len(pred_set)
            class_cov[label]["total"] += 1
            class_set[label].append(set_size)
            if label in pred_set:
                class_cov[label]["correct"] += 1

    mondrian = {}
    for k in sorted(class_cov.keys()):
        d = class_cov[k]
        mondrian[k] = {
            "coverage": d["correct"] / d["total"] if d["total"] else 0,
            "avg_set_size": np.mean(class_set[k]) if class_set[k] else 0,
            "n": d["total"],
        }
    return mondrian

# ======================== BASELINES ========================

def entropy_baseline(results, alpha, n_classes):
    coverage_count, total = 0, 0
    set_sizes = []
    for r in results:
        for j, label in enumerate(r["true"]):
            total += 1
            probs = r["probs"][j]
            sorted_idx = np.argsort(-probs)
            cumsum = np.cumsum(probs[sorted_idx])
            n_include = np.searchsorted(cumsum, 1 - alpha) + 1
            pred_set = sorted_idx[:n_include].tolist()
            set_sizes.append(len(pred_set))
            if label in pred_set:
                coverage_count += 1
    return coverage_count / total if total else 0, np.mean(set_sizes) if set_sizes else 0


def maxmargin_baseline(results, alpha, n_classes):
    all_margins = []
    for r in results:
        for j in range(len(r["true"])):
            probs = r["probs"][j]
            sp = np.sort(probs)[::-1]
            all_margins.append(sp[0] - sp[1] if len(sp) > 1 else 1.0)
    all_margins = np.array(all_margins)

    n = len(all_margins)
    q = np.ceil((n + 1) * (1 - alpha)) / n
    margin_thresh = np.quantile(all_margins, q, method="higher")

    coverage_count, total = 0, 0
    set_sizes = []
    for r in results:
        for j, label in enumerate(r["true"]):
            total += 1
            probs = r["probs"][j]
            sorted_idx = np.argsort(-probs)
            sp = np.sort(probs)[::-1]
            margin = sp[0] - sp[1] if len(sp) > 1 else 1.0
            if margin >= margin_thresh:
                pred_set = [sorted_idx[0]]
            else:
                pred_set = sorted_idx[:min(2, n_classes)].tolist()
            set_sizes.append(len(pred_set))
            if label in pred_set:
                coverage_count += 1
    return coverage_count / total if total else 0, np.mean(set_sizes) if set_sizes else 0

# ======================== PRIORITIZATION ========================

def experiment_prioritization(results, budgets):
    all_unc, all_errors = [], []
    for r in results:
        max_probs = np.max(r["probs"], axis=-1)
        uncertainties = 1 - max_probs
        errors = (r["preds"] != r["true"]).astype(float)
        all_unc.extend(uncertainties)
        all_errors.extend(errors)

    all_unc = np.array(all_unc)
    all_errors = np.array(all_errors)
    n_total = len(all_unc)

    out = {}
    for budget in budgets:
        b = min(budget, n_total)
        random_idx = np.random.choice(n_total, size=b, replace=False)
        random_rate = all_errors[random_idx].mean()

        sorted_idx = np.argsort(-all_unc)
        top_idx = sorted_idx[:b]
        unc_rate = all_errors[top_idx].mean()

        catch = unc_rate / random_rate if random_rate > 0 else float('inf')
        out[budget] = {
            "random_error_rate": float(random_rate),
            "uncertainty_error_rate": float(unc_rate),
            "catch_rate": float(catch),
        }
    return out

# ======================== CROSS-MODEL ========================

def cross_model_transfer(cal_results_small, test_results_large, alpha, n_classes):
    q = conformal_qhat(cal_results_small, alpha)
    cov, size, _, size_strat = evaluate_conformal(test_results_large, q, n_classes)
    return {
        "q_hat": float(q),
        "coverage": float(cov),
        "avg_set_size": float(size),
        "size_stratified": {str(k): v for k, v in size_strat.items()},
    }

# ======================== PIPELINE ========================

def run_pipeline(model_id, dataset_loader, infer_fn, task_name, n_classes, label_map,
                 budgets=[100, 500, 1000, 5000]):
    """Run full conformal pipeline for one model+task."""
    log(f"\n{'='*60}")
    log(f"TASK: {task_name}")
    log(f"MODEL: {model_id}")
    log(f"{'='*60}")

    model, tokenizer = load_model(model_id)
    cal_ds, test_ds = dataset_loader()
    log(f"  Calibration: {len(cal_ds)} seqs, Test: {len(test_ds)} seqs")

    log("  Running inference (calibration)...")
    cal_results = infer_fn(model, tokenizer, cal_ds)
    log(f"  Calibration residues: {sum(len(r['true']) for r in cal_results):,}")

    log("  Running inference (test)...")
    test_results = infer_fn(model, tokenizer, test_ds)
    log(f"  Test residues: {sum(len(r['true']) for r in test_results):,}")

    # Free model memory
    del model
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

    # Baseline
    base_acc = compute_accuracy(test_results)
    base_ece = compute_ece(test_results)
    base_brier = compute_brier(test_results)
    log(f"  Baseline: Acc={base_acc:.4f}, ECE={base_ece:.4f}, Brier={base_brier:.4f}")

    # Temperature scaling
    best_t = find_temperature(cal_results)
    scaled_cal = apply_temperature(cal_results, best_t)
    scaled_test = apply_temperature(test_results, best_t)
    ts_acc = compute_accuracy(scaled_test)
    ts_ece = compute_ece(scaled_test)
    ts_brier = compute_brier(scaled_test)
    ece_red = (base_ece - ts_ece) / base_ece * 100 if base_ece else 0
    log(f"  Temperature T={best_t:.2f}: Acc={ts_acc:.4f}, ECE={ts_ece:.4f} ({ece_red:+.0f}%), Brier={ts_brier:.4f}")

    # Conformal (raw)
    log("  Conformal prediction...")
    conformal = {}
    for alpha in [0.05, 0.10, 0.20]:
        q = conformal_qhat(cal_results, alpha)
        cov, size, pclass, sstrat = evaluate_conformal(test_results, q, n_classes)
        log(f"    Raw alpha={alpha:.2f}: cov={cov:.4f}, set={size:.2f}")

        q_s = conformal_qhat(scaled_cal, alpha)
        cov_s, size_s, pclass_s, sstrat_s = evaluate_conformal(scaled_test, q_s, n_classes)
        log(f"    T-scaled alpha={alpha:.2f}: cov={cov_s:.4f}, set={size_s:.2f}")

        conformal[f"alpha_{alpha}"] = {
            "raw": {"coverage": float(cov), "avg_set_size": float(size),
                    "per_class": {label_map.get(k, str(k)): v for k, v in pclass.items()},
                    "size_stratified": {str(kk): vv for kk, vv in sstrat.items()}},
            "temperature_scaled": {"coverage": float(cov_s), "avg_set_size": float(size_s),
                                   "per_class": {label_map.get(k, str(k)): v for k, v in pclass_s.items()},
                                   "size_stratified": {str(kk): vv for kk, vv in sstrat_s.items()}},
        }

    # Class-conditional
    log("  Class-conditional conformal...")
    cc = {}
    for alpha in [0.05, 0.10, 0.20]:
        th = conformal_qhat_class_conditional(cal_results, alpha)
        cov, size, pclass, _ = evaluate_conformal(test_results, 0, n_classes, th)
        log(f"    alpha={alpha:.2f}: cov={cov:.4f}, set={size:.2f}")
        cc[f"alpha_{alpha}"] = {
            "coverage": float(cov), "avg_set_size": float(size),
            "per_class": {label_map.get(k, str(k)): v for k, v in pclass.items()},
        }

    # Mondrian
    log("  Mondrian conformal...")
    mondrian = {}
    for alpha in [0.05, 0.10, 0.20]:
        mon = evaluate_mondrian(cal_results, test_results, alpha, n_classes)
        log(f"    alpha={alpha:.2f}")
        for k, v in mon.items():
            log(f"      {label_map.get(k, str(k))}: cov={v['coverage']:.4f}, set={v['avg_set_size']:.2f}, n={v['n']}")
        mondrian[f"alpha_{alpha}"] = {label_map.get(k, str(k)): v for k, v in mon.items()}

    # Baselines
    log("  Baselines...")
    ent = {}
    mm = {}
    for alpha in [0.05, 0.10, 0.20]:
        ec, es = entropy_baseline(test_results, alpha, n_classes)
        mc, ms = maxmargin_baseline(test_results, alpha, n_classes)
        log(f"    alpha={alpha:.2f}: Entropy cov={ec:.4f} set={es:.2f}, MaxMargin cov={mc:.4f} set={ms:.2f}")
        ent[f"alpha_{alpha}"] = {"coverage": float(ec), "avg_set_size": float(es)}
        mm[f"alpha_{alpha}"] = {"coverage": float(mc), "avg_set_size": float(ms)}

    # Prioritization
    log("  Experiment prioritization...")
    prio = experiment_prioritization(test_results, budgets)
    for b, d in prio.items():
        log(f"    Budget={b}: random={d['random_error_rate']:.3f}, unc={d['uncertainty_error_rate']:.3f}, catch={d['catch_rate']:.2f}x")

    return {
        "task": task_name,
        "model": model_id,
        "baseline": {"accuracy": float(base_acc), "ece": float(base_ece), "brier": float(base_brier)},
        "temperature_scaling": {"temperature": float(best_t), "accuracy": float(ts_acc),
                                 "ece": float(ts_ece), "brier": float(ts_brier),
                                 "ece_reduction_pct": float(ece_red)},
        "conformal": conformal,
        "class_conditional": cc,
        "mondrian": mondrian,
        "entropy_baseline": ent,
        "maxmargin_baseline": mm,
        "experiment_prioritization": prio,
        "_cal_raw": cal_results,
        "_cal_scaled": scaled_cal,
        "_test_raw": test_results,
        "_test_scaled": scaled_test,
    }

# ======================== MAIN ========================

def main():
    log("=" * 60)
    log("ConformalESM-Extended: Unified Pipeline")
    log("All post-hoc, CPU-only, no retraining")
    log("=" * 60)

    all_results = {}

    # Task 1: Secondary Structure - 8M
    ss8m = run_pipeline(SS_MODEL_8M, load_ss_data, infer_ss,
                        "Secondary Structure (Q3) - ESM-2-8M", 3, SS_ID2LABEL)
    all_results["ss_8m"] = {k: v for k, v in ss8m.items() if not k.startswith("_")}

    # Task 1: Secondary Structure - 650M
    ss650m = run_pipeline(SS_MODEL_650M, load_ss_data, infer_ss,
                          "Secondary Structure (Q3) - ESM-2-650M", 3, SS_ID2LABEL)
    all_results["ss_650m"] = {k: v for k, v in ss650m.items() if not k.startswith("_")}

    # Task 2: Disorder - 35M
    dis35m = run_pipeline(DIS_MODEL_35M, load_disorder_data, infer_disorder,
                          "Disorder Prediction - ESM-2-35M", 2, {0: "Ordered", 1: "Disordered"})
    all_results["disorder_35m"] = {k: v for k, v in dis35m.items() if not k.startswith("_")}

    # Task 2: Disorder - 650M
    dis650m = run_pipeline(DIS_MODEL_650M, load_disorder_data, infer_disorder,
                           "Disorder Prediction - ESM-2-650M", 2, {0: "Ordered", 1: "Disordered"})
    all_results["disorder_650m"] = {k: v for k, v in dis650m.items() if not k.startswith("_")}

    # Cross-model transfer
    log(f"\n{'='*60}")
    log("Cross-Model Calibration Transfer")
    log(f"{'='*60}")

    log("  SS: 8M calibrate -> 650M test...")
    t1 = cross_model_transfer(ss8m["_cal_raw"], ss650m["_test_raw"], 0.10, 3)
    log(f"    Coverage: {t1['coverage']:.4f}, Avg set: {t1['avg_set_size']:.2f}")
    all_results["transfer_ss_8m_to_650m"] = t1

    log("  SS: 8M calibrate (T-scaled) -> 650M test...")
    t2 = cross_model_transfer(ss8m["_cal_scaled"], ss650m["_test_scaled"], 0.10, 3)
    log(f"    Coverage: {t2['coverage']:.4f}, Avg set: {t2['avg_set_size']:.2f}")
    all_results["transfer_ss_8m_to_650m_temperature_scaled"] = t2

    log("  Disorder: 35M calibrate -> 650M test...")
    t3 = cross_model_transfer(dis35m["_cal_raw"], dis650m["_test_raw"], 0.10, 2)
    log(f"    Coverage: {t3['coverage']:.4f}, Avg set: {t3['avg_set_size']:.2f}")
    all_results["transfer_dis_35m_to_650m"] = t3

    # Save
    log(f"\n{'='*60}")
    log("Saving Results")
    log(f"{'='*60}")

    with open("conformalesm_unified_results.json", "w") as f:
        json.dump(all_results, f, indent=2)
    log("  Saved: conformalesm_unified_results.json")

    # Push to hub
    log("  Pushing to knoxel/conformalesm-paper-starter...")
    try:
        from huggingface_hub import HfApi
        api = HfApi()
        api.upload_file(
            path_or_fileobj="conformalesm_unified_results.json",
            path_in_repo="conformalesm_unified_results.json",
            repo_id="knoxel/conformalesm-paper-starter",
            repo_type="model",
        )
        log("  Successfully pushed to Hub!")
    except Exception as e:
        log(f"  Could not push to Hub: {e}")
        log("  Results saved locally.")

    log(f"\n{'='*60}")
    log("ALL DONE")
    log(f"{'='*60}")


if __name__ == "__main__":
    main()