# ── Part 2: Load data and BERT model ──

from datasets import load_dataset
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

print("\n[1] Loading SWE-Router data...")
traces = defaultdict(dict)
for model in MODELS:
    try:
        ds = load_dataset(f'SWE-Router/swebench-verified-{model}', split='test')
        for row in ds:
            iid = row['instance_id']
            traces[iid][model] = {
                'resolved': row['resolved'],
                'cost': float(row['instance_cost']),
                'problem': row['problem_statement'],
            }
        print(f"  {model}: loaded")
    except Exception as e:
        print(f"  {model}: FAILED - {e}")

print(f"  Total tasks: {len(traces)}")

print("\n[2] Loading BERT router...")
REPO = "narcolepticchicken/agent-cost-optimizer"
tokenizer = AutoTokenizer.from_pretrained(f"{REPO}", subfolder="router_models/bert_router")
bert_model = AutoModelForSequenceClassification.from_pretrained(f"{REPO}", subfolder="router_models/bert_router")
bert_model.eval()
print(f"  BERT model loaded, num_labels={bert_model.config.num_labels}")

print("\n[3] Loading v11 XGBoost router...")
from huggingface_hub import hf_hub_download
v11_path = hf_hub_download(REPO, "router_models/router_bundle_v11.pkl")
v11_bundle = pickle.load(open(v11_path, "rb"))
v11_tier_clfs = {int(k):v for k,v in v11_bundle["tier_clfs"].items()}
v11_tier_calibs = {int(k):v for k,v in v11_bundle["tier_calibrators"].items()}
v11_feat_keys = v11_bundle["feat_keys"]
print(f"  v11 loaded, features={len(v11_feat_keys)}")