import json
import logging
from math import log2
from pathlib import Path
from typing import Any, Dict, List

import numpy as np

logger = logging.getLogger(__name__)

GT_DIR = Path("/data") / "gt"

K_VALUES = [1, 5, 10, 20, 50, 100]


class Evaluator:
    def __init__(self, gt_dir: str | Path | None = None):
        self.gt_dir = Path(gt_dir) if gt_dir else GT_DIR
        self._gt_cache: Dict[str, list] = {}

    def _load_gt(self, album_id: str) -> list:
        if album_id in self._gt_cache:
            return self._gt_cache[album_id]
        gt_file = self.gt_dir / f"album{album_id}_test_answer.json"
        if not gt_file.exists():
            raise FileNotFoundError(f"Ground truth file not found: {gt_file}")
        with open(gt_file, "r", encoding="utf-8") as f:
            data = json.load(f)
        self._gt_cache[album_id] = data
        return data

    def validate_json_format(self, data: Any) -> list[str]:
        errors = []
        if not isinstance(data, list):
            return ["Root must be a JSON array"]
        if len(data) == 0:
            return ["Submission is empty"]
        for i, item in enumerate(data):
            if not isinstance(item, dict):
                errors.append(f"Item #{i} must be an object")
                continue
            if "album_id" not in item or str(item["album_id"]) not in ["1", "2", "3"]:
                errors.append(f"Item #{i} 'album_id' must be '1', '2', or '3'")
            if "query_en" not in item or not isinstance(item["query_en"], str):
                errors.append(f"Item #{i} 'query_en' must be a string")
            if (
                "pred" not in item
                or not isinstance(item["pred"], list)
                or not all(isinstance(x, str) for x in item["pred"])
            ):
                errors.append(f"Item #{i} 'pred' must be a list of strings")
        return errors

    def _dcg_at_k(self, r, k):
        r = np.asarray(r, dtype=float)[:k]
        if r.size:
            return np.sum(r / np.log2(np.arange(2, r.size + 2)))
        return 0.0

    def _ndcg_at_k(self, r, k):
        dcg_max = self._dcg_at_k(sorted(r, reverse=True), k)
        if not dcg_max:
            return 0.0
        return self._dcg_at_k(r, k) / dcg_max

    def _recall_at_k(self, ground_truth, predictions, k):
        k_preds = predictions[:k]
        hits = len(set(ground_truth) & set(k_preds))
        if len(ground_truth) == 0:
            return 0.0
        return hits / len(ground_truth)

    def _evaluate_album(self, album_submissions: dict, album_id: str) -> dict:
        """Evaluate a single album."""
        gt_data = self._load_gt(album_id)
        gt_map = {item["query_en"]: item for item in gt_data}

        metrics_accum = {f"Recall@{k}": [] for k in K_VALUES}
        metrics_accum.update({f"NDCG@{k}": [] for k in K_VALUES})
        metrics_accum["Recall"] = []
        metrics_accum["NDCG"] = []
        source_accum = {}
        empty_gt_queries = 0
        evaluated_queries = 0
        extraneous_queries = 0

        for q, pred in album_submissions.items():
            if q not in gt_map:
                extraneous_queries += 1
                continue

            gt_item = gt_map[q]
            gt_answers = gt_item.get("ground_truth", [])
            source = gt_item.get("Source")
            evaluated_queries += 1

            if not gt_answers:
                empty_gt_queries += 1
                continue

            r = [1 if p in gt_answers else 0 for p in pred]
            dcg_r = [1.0] * len(gt_answers)
            m = {}

            for k in K_VALUES:
                m[f"Recall@{k}"] = self._recall_at_k(gt_answers, pred, k)
                idcg = self._dcg_at_k(dcg_r, k)
                ndcg = self._dcg_at_k(r, k) / idcg if idcg > 0 else 0.0
                m[f"NDCG@{k}"] = ndcg
                metrics_accum[f"Recall@{k}"].append(m[f"Recall@{k}"])
                metrics_accum[f"NDCG@{k}"].append(m[f"NDCG@{k}"])

            m["Recall"] = sum(r) / len(gt_answers)
            idcg_all = self._dcg_at_k(dcg_r, len(gt_answers))
            ndcg_all = self._dcg_at_k(r, len(r)) / idcg_all if idcg_all > 0 else 0.0
            m["NDCG"] = ndcg_all
            metrics_accum["Recall"].append(m["Recall"])
            metrics_accum["NDCG"].append(m["NDCG"])

            if source is not None:
                if source not in source_accum:
                    source_accum[source] = {f"Recall@{_k}": [] for _k in K_VALUES}
                    source_accum[source].update({f"NDCG@{_k}": [] for _k in K_VALUES})
                    source_accum[source]["Recall"] = []
                    source_accum[source]["NDCG"] = []
                for k in K_VALUES:
                    source_accum[source][f"Recall@{k}"].append(m[f"Recall@{k}"])
                    source_accum[source][f"NDCG@{k}"].append(m[f"NDCG@{k}"])
                source_accum[source]["Recall"].append(m["Recall"])
                source_accum[source]["NDCG"].append(m["NDCG"])

        global_metrics = {
            k: float(np.mean(v)) if v else 0.0 for k, v in metrics_accum.items()
        }
        return {
            "global_metrics": global_metrics,
            "source_metrics": {
                src: {k: float(np.mean(v)) if v else 0.0 for k, v in m_dict.items()}
                for src, m_dict in source_accum.items()
            },
            "empty_gt_ratio": empty_gt_queries / evaluated_queries if evaluated_queries > 0 else 0.0,
            "evaluated_queries": evaluated_queries,
            "total_gt_queries": len(gt_data),
            "is_partial": evaluated_queries < len(gt_data),
            "extraneous_queries": extraneous_queries,
        }

    def evaluate(self, submission_data: list) -> dict:
        albums = {}
        for item in submission_data:
            a_id = str(item["album_id"])
            if a_id not in albums:
                albums[a_id] = {}
            albums[a_id][item["query_en"]] = item["pred"]

        if not albums:
            raise ValueError("No valid albums found in submission.")

        # Evaluate each album separately
        per_album = {}
        for a_id in sorted(albums.keys()):
            per_album[a_id] = self._evaluate_album(albums[a_id], a_id)

        # Compute averaged metrics across all albums
        avg_metrics = {}
        for metric_key in per_album[list(per_album.keys())[0]]["global_metrics"].keys():
            values = [alb["global_metrics"][metric_key] for alb in per_album.values() if metric_key in alb["global_metrics"]]
            avg_metrics[metric_key] = float(np.mean(values)) if values else 0.0

        total_evaluated = sum(alb["evaluated_queries"] for alb in per_album.values())
        total_gt = sum(alb["total_gt_queries"] for alb in per_album.values())
        total_extraneous = sum(alb.get("extraneous_queries", 0) for alb in per_album.values())

        result = {
            "per_album": per_album,
            "global_metrics": avg_metrics,
            "evaluated_queries": total_evaluated,
            "total_gt_queries": total_gt,
            "is_partial": total_evaluated < total_gt,
            "albums": sorted(albums.keys()),
            "extraneous_queries": total_extraneous,
        }

        # Build warning / notice messages
        msgs = []
        if total_extraneous > 0:
            msgs.append(f"{total_extraneous} extraneous queries were ignored (not in current GT). This may be caused by an outdated test.json or extra queries. Valid queries: {total_evaluated}/{total_gt}.")
        if result["is_partial"]:
            missing_albums = [a for a in ["1", "2", "3"] if a not in albums]
            missing_queries = total_gt - total_evaluated
            parts = []
            if missing_albums:
                parts.append(f"Missing albums: {', '.join(missing_albums)}")
            if missing_queries > 0:
                parts.append(f"Missing {missing_queries} queries ({total_evaluated}/{total_gt} submitted)")
            msgs.append("Submission incomplete. " + "; ".join(parts) + ". Only full submissions are eligible for leaderboard ranking.")

        if msgs:
            result["warning"] = " ".join(msgs)

        return result