"""Pipeline Step 17 — V4 (template-fit) 를 V3 Top-K 가 아니라 32 프레임 전체에 적용.

기존 V4 r2 는 V3 Top-5 만 평가 → 5 개 중 선택.
이 스크립트는 32 프레임 전체에 template-fit 적용 → confidence 기준 전체 랭킹.

V1/V2/V3 처럼 "32 중 Top-3" 형태 산출.

출력: v4_full32_result.yaml
"""
import datetime
import sys
from pathlib import Path
import yaml

HERE = Path(__file__).parent
sys.path.insert(0, str(HERE))

from phase_common import load_32_frames, load_frame_index
from detect_mdx import detect_mdx_analysis
from template_fit import (
    load_templates_v1, collect_anchor_vocab, compute_template_fit, route,
)
from embeddings import embed_texts, cosine
from pipeline_01_extract_nodes import MDX_SECTIONS, MDX_DIR

OUT_PATH = HERE / 'v4_full32_result.yaml'


def extract_mdx_raw(sid):
    cfg = MDX_SECTIONS[sid]
    p = MDX_DIR / cfg['file']
    lines = p.read_text(encoding='utf-8').split('\n')
    start = None
    for i, ln in enumerate(lines):
        if ln.strip() == cfg['start'].strip():
            start = i
            break
    end = len(lines)
    if cfg.get('end_prefix'):
        for i in range(start + 1, len(lines)):
            if lines[i].strip().startswith(cfg['end_prefix']):
                end = i
                break
    section = lines[start:end]
    return section[0].lstrip('#').strip(), '\n'.join(section)


def main():
    v1 = yaml.safe_load((HERE / 'mdx_matching_result.yaml').read_text(encoding='utf-8'))
    answer_map = v1['meta']['answer_map']
    holdout = v1['meta']['holdout_sections']

    templates = load_templates_v1()
    anchor_vocab = collect_anchor_vocab(templates)
    frames = load_32_frames()
    idx_data, frame_to_short = load_frame_index()
    fids = list(frames.keys())
    frame_num_map = {fid: int(frame_to_short[fid]) for fid in fids}
    frame_contents = [frames[fid].get('content', '') for fid in fids]

    print('[V4 full-32] 32 frame content 임베딩 중...')
    frame_vecs = embed_texts(frame_contents)

    out_sections = {}
    for sid in v1['mdx_sections']:
        title, raw_text = extract_mdx_raw(sid)
        mdx_analysis = detect_mdx_analysis(raw_text, title, anchor_vocab=anchor_vocab)
        mdx_summary = mdx_analysis['summary']
        mdx_vec = embed_texts([mdx_summary])[0]

        judgments = []
        for i, fid in enumerate(fids):
            if fid not in templates:
                continue
            template = templates[fid]
            content_emb = max(0.0, min(1.0, float(cosine(mdx_vec, frame_vecs[i]))))
            fit = compute_template_fit(mdx_analysis, template, content_emb)
            label = route(fit['confidence'], fit['axes'], fit['adaptation'], fit['not_suits'])
            judgments.append({
                'frame_id': fid,
                'frame_number': frame_num_map[fid],
                'template_id': template.get('template_id'),
                'confidence': round(float(fit['confidence']), 4),
                'base': round(float(fit['base']), 4),
                'penalty': round(float(fit['total_penalty']), 4),
                'label': label,
                'content_embedding': round(content_emb, 4),
                'axes': {
                    'anchor': round(float(fit['axes']['anchor']['score']), 4),
                    'cardinality': round(float(fit['axes']['cardinality']), 4),
                    'relation': round(float(fit['axes']['relation']), 4),
                    'slot': round(float(fit['axes']['slot']), 4),
                    'content': round(float(fit['axes']['content']), 4),
                },
            })

        # confidence 내림차순
        judgments.sort(key=lambda x: -x['confidence'])
        for new_rank, item in enumerate(judgments, start=1):
            item['v4_full_rank'] = new_rank

        ans = answer_map.get(sid)
        out_sections[sid] = {
            'mdx_title': title,
            'answer_frame_number': ans,
            'is_holdout': sid in holdout,
            'judgments_full32': judgments,
            'usable_count': sum(1 for j in judgments if j['label'] != 'reject'),
            'reject_count': sum(1 for j in judgments if j['label'] == 'reject'),
        }

    out = {
        'meta': {
            'pipeline_step': '8.v4.full32',
            'description': 'V4 template-fit 을 V3 Top-K 가 아니라 32 프레임 전체에 적용',
            'note': '기존 v4_template_fit_r2_result.yaml 는 V3 Top-5 만 평가. 이 파일은 32 전체.',
            'generated_at': datetime.datetime.now().isoformat(timespec='seconds'),
            'answer_map': answer_map,
            'holdout_sections': holdout,
        },
        'mdx_sections': out_sections,
    }
    OUT_PATH.write_text(
        yaml.safe_dump(out, allow_unicode=True, sort_keys=False, width=1000),
        encoding='utf-8',
    )

    print('=' * 70)
    print(f'V4 full-32 평가 완료: {OUT_PATH}')
    print('=' * 70)
    print(f'\n각 섹션별 사용 가능 프레임 (label != reject) 개수 + Top-3:')
    for sid, s in out_sections.items():
        ans = s.get('answer_frame_number')
        ans_str = f'(정답 {ans})' if ans else '(holdout)'
        print(f'\n[{sid}] {ans_str}  사용가능 {s["usable_count"]}/{s["usable_count"]+s["reject_count"]}')
        usable = [j for j in s['judgments_full32'] if j['label'] != 'reject']
        for j in usable[:3]:
            mark = ' 🎯' if ans and j['frame_number'] == ans else ''
            print(f'  Frame {j["frame_number"]:>2}{mark}  conf {j["confidence"]:.3f}  {j["label"]}')
        if not usable:
            print('  (사용 가능 후보 없음 — 모두 reject)')


if __name__ == '__main__':
    main()