"""Generate Anonymous-Hard subset from Qwen per-law eval results.

For each prompt, computes a physics micro-avg from per-law scores
(all 13 laws), averaged across all models.
Keeps prompts with cross-model avg < threshold.

Physical laws are sourced from the canonical prompt JSONs (not from eval
JSONs, which may contain stale vocabulary).

Usage:
    # Default: threshold 1.75, overwrite existing hard subset
    python -m dataprocessing.refine.gen_hard_subset

    # Custom threshold, write to a different file
    python -m dataprocessing.refine.gen_hard_subset --threshold 1.50 \
        --output data/prompts/anonymous_hard_subset_150.json

    # Dry run: print stats without writing
    python -m dataprocessing.refine.gen_hard_subset --dry-run

    # Strict mode: fail on data quality issues
    python -m dataprocessing.refine.gen_hard_subset --strict
"""

import argparse
import json
import logging
import sys
from collections import Counter, defaultdict
from pathlib import Path

from dataprocessing.common.pipeline import PipelineCheck
from dataprocessing.common.video_id import PROMPT_SOURCES, load_source_laws

logger = logging.getLogger(__name__)

ROOT = Path(__file__).resolve().parents[2]
VIDEOS_DIR = ROOT / "data/videos"
OUTPUT_PATH = ROOT / "data/prompts/anonymous_hard_subset.json"

# Dataset suffixes that appear in video dir names.
DATASET_SUFFIXES = ["video_phy_2", "physics_iq", "openvid", "wmb"]


def parse_model_dataset(dirname: str) -> tuple[str, str] | None:
    """Extract (model, dataset) from a directory name like 'ltx-2-video_phy_2'."""
    for ds in DATASET_SUFFIXES:
        if dirname.endswith(f"-{ds}"):
            model = dirname[:-(len(ds) + 1)]
            return model, ds
    return None


def find_latest_eval(dirpath: Path, evaluator: str = "qwen") -> Path | None:
    """Find the latest batched eval JSON for the given evaluator, fallback to gemini."""
    evals = sorted(dirpath.glob(f"eval_{evaluator}_2*.json"))
    if evals:
        return evals[-1]
    if evaluator != "gemini":
        gemini = sorted(dirpath.glob("eval_gemini*_2*.json"))
        if gemini:
            return gemini[-1]
    return None


def load_eval_scores(eval_path: Path) -> list[dict]:
    """Load eval JSON and extract per-video physics micro-avg.

    Computes micro-avg from per-law scores (all laws).
    Follows the same scoring approach as score_histogram / rank.md.

    Returns list of dicts with keys: video, prompt, phys_micro_avg, n_laws_scored.
    """
    with open(eval_path) as f:
        data = json.load(f)

    entries = []
    for r in data.get("results", []):
        video = r.get("video", "")
        prompt = r.get("prompt", "")
        if not video:
            continue

        # Per-law physical scores (null-aware, supports v1 and v2 formats)
        phys = r.get("physical", {})
        if not isinstance(phys, dict):
            continue
        laws = phys.get("laws", {})

        scored_vals = []
        for law_name, law_data in laws.items():
            if not isinstance(law_data, dict):
                continue
            score = law_data.get("score")
            is_scored = (law_data.get("status") == "scored"
                         or law_data.get("valid", False))
            if is_scored and score is not None:
                scored_vals.append(score)

        if not scored_vals:
            continue

        entries.append({
            "video": video,
            "prompt": prompt,
            "phys_micro_avg": sum(scored_vals) / len(scored_vals),
            "n_laws_scored": len(scored_vals),
        })
    return entries


def main(argv: list[str] | None = None):
    parser = argparse.ArgumentParser(
        description="Generate Anonymous-Hard subset from Gemini eval scores")
    parser.add_argument("--threshold", type=float, default=3.00,
                        help="Physics micro-avg threshold (default: 3.00)")
    parser.add_argument("--output", type=str, default=None,
                        help="Output path (default: data/prompts/anonymous_hard_subset.json)")
    parser.add_argument("--dry-run", action="store_true",
                        help="Print stats without writing")
    parser.add_argument("--strict", action="store_true",
                        help="Fail on data quality issues (for CI)")
    args = parser.parse_args(argv)

    logging.basicConfig(
        level=logging.INFO,
        format="[%(asctime)s] %(message)s",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    checker = PipelineCheck(strict=args.strict)

    # ---- Step 1: Collect per-model physics micro-avg for every video ----
    # {eval_video: {model: phys_micro_avg}}
    video_scores: dict[str, dict] = defaultdict(dict)
    video_prompts: dict[str, str] = {}
    video_eval_ds: dict[str, str] = {}   # eval-side dataset suffix
    eval_paths_by_ds: dict[str, Path] = {}

    for d in sorted(VIDEOS_DIR.iterdir()):
        if not d.is_dir():
            continue
        parsed = parse_model_dataset(d.name)
        if parsed is None:
            continue
        model, dataset = parsed
        if "real_world" in model:
            continue

        eval_path = find_latest_eval(d)
        if eval_path is None:
            continue

        eval_paths_by_ds[dataset] = eval_path
        entries = load_eval_scores(eval_path)
        logger.info("Loaded %d entries from %s", len(entries), eval_path.name)

        for e in entries:
            vid = e["video"]
            video_scores[vid][model] = e["phys_micro_avg"]
            video_prompts[vid] = e["prompt"]
            video_eval_ds[vid] = dataset

    logger.info("Total unique videos with scores: %d", len(video_scores))

    # ---- Step 2: Compute cross-model average ----
    video_difficulty = {}
    for vid, model_scores in video_scores.items():
        vals = list(model_scores.values())
        avg = sum(vals) / len(vals)
        video_difficulty[vid] = {
            "phys_micro_avg": round(avg, 3),
            "n_models": len(model_scores),
        }

    # ---- Step 3: Filter by threshold ----
    hard_vids = [
        vid for vid, diff in video_difficulty.items()
        if diff["phys_micro_avg"] < args.threshold
    ]
    hard_vids.sort(key=lambda v: video_difficulty[v]["phys_micro_avg"])

    logger.info("Threshold < %.2f: %d / %d videos",
                args.threshold, len(hard_vids), len(video_difficulty))

    # ---- Step 4: Look up physical_laws via canonical vid matching ----
    source = load_source_laws()

    # Check staleness: only compare each source JSON against its matching eval
    DS_TO_EVAL_SUFFIX = {
        "wmb": "wmb", "video_phy_2": "video_phy_2", "physics_iq": "physics_iq",
        "openvid": "openvid",
    }
    for ds_name, src_path in PROMPT_SOURCES:
        eval_suffix = DS_TO_EVAL_SUFFIX.get(ds_name)
        if eval_suffix and eval_suffix in eval_paths_by_ds:
            checker.check_staleness(src_path, eval_paths_by_ds[eval_suffix])

    missing = 0
    prompts_out = []
    seen_disk_vids: dict[str, int] = {}  # disk_vid -> index in prompts_out
    for eval_vid in hard_vids:
        eval_ds = video_eval_ds.get(eval_vid, "")
        matched = source.resolve_eval(eval_vid, eval_ds)

        if matched:
            cvid, entry = matched
            laws = entry["laws"]
            dataset = entry["dataset"]
            prompt = entry["prompt"] or video_prompts.get(eval_vid, "")
            legacy_ids = source.cvid_to_legacies.get(cvid, set())
            disk_vid = max(legacy_ids, key=len) if legacy_ids else eval_vid
        else:
            # Not in kept source — skip (removed, safety-blocked, etc.)
            missing += 1
            continue

        # Deduplicate: different eval_vid values can resolve to the same
        # disk_vid via legacy aliases.  Keep the entry with more models.
        if disk_vid in seen_disk_vids:
            idx = seen_disk_vids[disk_vid]
            existing = prompts_out[idx]
            if video_difficulty[eval_vid]["n_models"] > existing["difficulty"]["n_models"]:
                prompts_out[idx] = {
                    "video": disk_vid,
                    "dataset": dataset,
                    "prompt": prompt,
                    "physical_laws": laws,
                    "difficulty": video_difficulty[eval_vid],
                    "per_model_scores": dict(video_scores[eval_vid]),
                }
            continue

        checker.check_empty_laws(disk_vid, laws, dataset,
                                 resolved=matched is not None)

        seen_disk_vids[disk_vid] = len(prompts_out)
        prompts_out.append({
            "video": disk_vid,
            "dataset": dataset,
            "prompt": prompt,
            "physical_laws": laws,
            "difficulty": video_difficulty[eval_vid],
            "per_model_scores": dict(video_scores[eval_vid]),
        })

    checker.check_missing_ratio(missing, len(hard_vids))

    # ---- Step 5: Compute stats and report ----
    by_dataset = Counter(p["dataset"] for p in prompts_out)
    law_counts = Counter()
    for p in prompts_out:
        for law in p["physical_laws"]:
            law_counts[law] += 1

    output = {
        "description": (
            f"Anonymous-Hard: prompts where cross-model physics micro-avg < {args.threshold} "
            f"(Qwen, per-law scores, all 13 laws)"
        ),
        "threshold": args.threshold,
        "scoring_mode": "phys_micro_avg",
        "judge": "qwen",
        "num_prompts": len(prompts_out),
        "by_dataset": dict(by_dataset.most_common()),
        "prompts": prompts_out,
    }

    logger.info("=" * 60)
    logger.info("Hard subset: %d prompts", len(prompts_out))
    logger.info("By dataset:")
    for ds, cnt in by_dataset.most_common():
        logger.info("  %s: %d", ds, cnt)
    logger.info("Physical law counts:")
    for law, cnt in law_counts.most_common():
        logger.info("  %s: %d", law, cnt)

    score = checker.report()

    if args.dry_run:
        logger.info("(dry-run — no file written)")
        checker.finalize()
        return

    out_path = Path(args.output) if args.output else OUTPUT_PATH
    with open(out_path, "w") as f:
        json.dump(output, f, indent=2, ensure_ascii=False)
    logger.info("Saved → %s", out_path)

    checker.finalize()


if __name__ == "__main__":
    main()