from __future__ import annotations

import traceback
from concurrent.futures import ThreadPoolExecutor, as_completed
from pathlib import Path

from .io import append_jsonl, metadata_path, read_json, read_jsonl, write_jsonl
from .judge import GeminiJudge, sample_video_frames, validate_scores
from .prompts import last_frame_prompt, mme_cof_prompt, video_prompt


def parse_seeds(value: str) -> list[int]:
    seeds = [int(part.strip()) for part in value.split(",") if part.strip()]
    if not seeds:
        raise ValueError("At least one seed is required")
    return seeds


def video_path(
    videos_dir: str | Path,
    row: dict,
    seed: int,
    filename_template: str,
) -> Path:
    name = filename_template.format(
        id=row["id"],
        id06=f"{row['id']:06d}",
        seed=seed,
    )
    return Path(videos_dir) / name


def _evaluate_vwg_job(
    judge: GeminiJudge,
    row: dict,
    seed: int,
    path: Path,
    max_frames: int,
) -> dict:
    frames = sample_video_frames(path, max_frames=max_frames)
    final_result = judge.evaluate_frames(last_frame_prompt(row), [frames[-1]])
    final_result = validate_scores(final_result, ["last_frame_goal"], 1, 5)

    prompt, metric_names = video_prompt(row)
    video_result = judge.evaluate_frames(prompt, frames)
    video_result = validate_scores(video_result, metric_names, 1, 5)
    return {
        "id": row["id"],
        "seed": seed,
        "result_id": f"{row['id']}_seed{seed}",
        "video_file": str(path),
        "dimension_id": row["dimension_id"],
        "task_group_id": row["task_group_id"],
        **video_result,
        **final_result,
    }


def evaluate_vwg(
    dataset_root: str | Path,
    videos_dir: str | Path,
    output_path: str | Path,
    seeds: list[int],
    model: str,
    filename_template: str = "{id}_seed{seed}.mp4",
    workers: int = 4,
    max_frames: int = 16,
    limit: int | None = None,
    strict_missing: bool = False,
) -> dict:
    rows = read_jsonl(metadata_path(dataset_root))
    if limit is not None:
        rows = rows[:limit]
    output = Path(output_path)
    existing = read_jsonl(output) if output.exists() else []
    done = {row["result_id"] for row in existing}
    errors_path = output.with_name(output.stem + ".errors.jsonl")

    jobs = []
    missing = []
    for row in rows:
        for seed in seeds:
            result_id = f"{row['id']}_seed{seed}"
            if result_id in done:
                continue
            path = video_path(videos_dir, row, seed, filename_template)
            if not path.is_file():
                missing.append({"result_id": result_id, "video_file": str(path)})
                continue
            jobs.append((row, seed, path))

    if strict_missing and missing:
        raise FileNotFoundError(
            f"{len(missing)} expected videos are missing; first: {missing[0]}"
        )

    completed = list(existing)
    failed = 0
    judge = GeminiJudge(model=model) if jobs else None
    with ThreadPoolExecutor(max_workers=max(1, workers)) as executor:
        future_map = {
            executor.submit(
                _evaluate_vwg_job,
                judge,
                row,
                seed,
                path,
                max_frames,
            ): (row, seed, path)
            for row, seed, path in jobs
        }
        for future in as_completed(future_map):
            row, seed, path = future_map[future]
            try:
                result = future.result()
                completed.append(result)
                append_jsonl(output, result)
            except Exception as exc:
                failed += 1
                append_jsonl(
                    errors_path,
                    {
                        "result_id": f"{row['id']}_seed{seed}",
                        "video_file": str(path),
                        "error_type": type(exc).__name__,
                        "error": str(exc),
                        "traceback": traceback.format_exc(),
                    },
                )

    completed.sort(key=lambda row: (row["id"], row["seed"]))
    write_jsonl(output, completed)
    return {
        "expected": len(rows) * len(seeds),
        "already_present": len(existing),
        "submitted": len(jobs),
        "completed_total": len(completed),
        "missing_videos": len(missing),
        "failed": failed,
        "output_path": str(output),
        "errors_path": str(errors_path) if failed else None,
    }


def _evaluate_mme_job(
    judge: GeminiJudge,
    row: dict,
    seed: int,
    path: Path,
    max_frames: int,
) -> dict:
    metrics = [
        "instruction_alignment",
        "temporal_consistency",
        "visual_stability",
        "content_fidelity",
        "focus_relevance",
    ]
    frames = sample_video_frames(path, max_frames=max_frames)
    result = judge.evaluate_frames(mme_cof_prompt(row["user_prompt"]), frames)
    result = validate_scores(result, metrics, 0, 4)
    return {
        "id": row["id"],
        "seed": seed,
        "result_id": f"{row['id']}_seed{seed}",
        "video_file": str(path),
        "task_name": row.get("task_name"),
        **result,
    }


def evaluate_mme_cof(
    metadata_json: str | Path,
    videos_dir: str | Path,
    output_path: str | Path,
    seeds: list[int],
    model: str,
    filename_template: str = "{id}_seed{seed}.mp4",
    workers: int = 4,
    max_frames: int = 16,
) -> dict:
    rows = read_json(metadata_json)
    output = Path(output_path)
    existing = read_jsonl(output) if output.exists() else []
    done = {row["result_id"] for row in existing}
    completed = list(existing)
    missing = 0
    failed = 0
    errors_path = output.with_name(output.stem + ".errors.jsonl")
    jobs = []
    for row in rows:
        for seed in seeds:
            result_id = f"{row['id']}_seed{seed}"
            if result_id in done:
                continue
            path = video_path(videos_dir, row, seed, filename_template)
            if not path.is_file():
                missing += 1
                continue
            jobs.append((row, seed, path))

    judge = GeminiJudge(model=model) if jobs else None
    with ThreadPoolExecutor(max_workers=max(1, workers)) as executor:
        future_map = {
            executor.submit(_evaluate_mme_job, judge, row, seed, path, max_frames): (
                row,
                seed,
                path,
            )
            for row, seed, path in jobs
        }
        for future in as_completed(future_map):
            row, seed, path = future_map[future]
            try:
                result = future.result()
                completed.append(result)
                append_jsonl(output, result)
            except Exception as exc:
                failed += 1
                append_jsonl(
                    errors_path,
                    {
                        "result_id": f"{row['id']}_seed{seed}",
                        "video_file": str(path),
                        "error_type": type(exc).__name__,
                        "error": str(exc),
                    },
                )

    completed.sort(key=lambda row: (row["id"], row["seed"]))
    write_jsonl(output, completed)
    return {
        "expected": len(rows) * len(seeds),
        "completed_total": len(completed),
        "missing_videos": missing,
        "failed": failed,
        "output_path": str(output),
    }