import json
from collections import Counter

print('Loading SPARQL updates...')
with open('data/external/templama_2023_2026_updates_clean.json') as f:
    updates = json.load(f)['updates']
print(f'  {len(updates)} changed entities')

print('Loading Dynamic-TempLAMA templates...')
dyn = {}
with open('data/external/temporal-robustness/data/dynamic-templama/dataset_from_2019-1-1_to_2022-12-31_per_quarter/test.jsonl') as f:
    for line in f:
        if not line.strip(): continue
        d = json.loads(line)
        eid = d['id'].split('_')[0]
        key = eid + '_' + d['relation']
        if key not in dyn:
            dyn[key] = {'template': d['query'], 'answers': {}}
        ans = d['answer']
        if isinstance(ans, dict): name = ans.get('name', '')
        elif isinstance(ans, list) and ans: name = ans[0].get('name', '')
        else: name = str(ans)
        dyn[key]['answers'][d['date']] = name
print(f'  {len(dyn)} entity-relation pairs')

YEARS = [2023, 2024, 2025, 2026]
CUTOFFS = {'llama2': '2022-09-01', 'mistral': '2023-12-01', 'llama31': '2023-12-01', 'qwen25': '2023-12-31', 'gemma2': '2024-06-01'}

print('Generating samples...')
samples = []
sid = 0
skipped = 0

for uid, u in updates.items():
    dd = u.get('drift_date', '')[:10]
    try: dy = int(dd[:4])
    except: skipped += 1; continue
    for year in YEARS:
        if year >= dy: exp, drifted, model_ans = u['new_answer'], True, u['old_answer']
        else: exp, drifted, model_ans = u['old_answer'], False, ''
        tz = 'pre_cutoff' if year < 2023 else ('near_cutoff' if year == 2023 else 'post_cutoff')
        query = 'In ' + str(year) + ', ' + u['query_template'].replace('_X_', '___')
        s = {'sample_id': 'sparql_' + str(sid).zfill(6), 'query': query, 'expected_answer': exp, 'year': year, 'temporal_zone': tz, 'is_drifted_query': drifted, 'model_likely_answer': model_ans, 'language': 'en', 'entity': u['entity_id'], 'relation': u['relation_name'], 'knowledge_type': 'entity_role', 'category': 'unknown_drift' if drifted else 'known_drift', 'source': 'sparql_extension', 'parent_id': u['entity_id'], 'drift_date': u.get('drift_date', '')}
        for m, c in CUTOFFS.items(): s['is_drifted_' + m] = (dd > c) and (year >= dy)
        samples.append(s)
        sid += 1

with open('data/external/sparql_extension_samples.json', 'w') as f:
    json.dump({'metadata': {'total': len(samples), 'skipped': skipped}, 'samples': samples}, f, indent=2, ensure_ascii=False)

print(f'Total: {len(samples)} samples, Skipped: {skipped}')
for label, fn in [('Categories', 'category'), ('Relations', 'relation')]:
    print(f'{label}:')
    for k, n in Counter(s[fn] for s in samples).most_common(): print(f'  {k}: {n}')
print('Years:')
for y, n in sorted(Counter(s['year'] for s in samples).items()): print(f'  {y}: {n}')
print('Per-model unknown_drift:')
for m in CUTOFFS: print(f'  {m}: {sum(1 for s in samples if s.get("is_drifted_" + m, False))}')
print(f'Differential: {sum(1 for s in samples if len(set(s.get("is_drifted_" + m, False) for m in CUTOFFS)) > 1)}')
print('Saved to: data/external/sparql_extension_samples.json')