from __future__ import annotations

import argparse
import concurrent.futures
import glob
import os
import re
import sys
from dataclasses import dataclass
from functools import lru_cache
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd
import torch
from scipy.ndimage import binary_dilation, label as cc_label
from tqdm import tqdm

try:
    from .common import (
        build_dataloader,
        build_dataset,
        build_model,
        get_label,
        is_ram_chunk_dataset,
        load_config,
        load_model_checkpoint,
        maybe_copy_best,
        pack_inputs,
        shutdown_dataloader,
        write_json,
    )
    from .logger import ExperimentLogger
except ImportError:
    code_root = Path(__file__).resolve().parents[2]
    if str(code_root) not in sys.path:
        sys.path.insert(0, str(code_root))
    from src.training_validation.common import (  # type: ignore
        build_dataloader,
        build_dataset,
        build_model,
        get_label,
        is_ram_chunk_dataset,
        load_config,
        load_model_checkpoint,
        maybe_copy_best,
        pack_inputs,
        shutdown_dataloader,
        write_json,
    )
    from src.training_validation.logger import ExperimentLogger  # type: ignore


@dataclass
class Cluster:
    cluster_id: int
    raw_mask: np.ndarray
    mask: np.ndarray

    @property
    def pixel_count(self) -> int:
        return int(self.mask.sum())

    @property
    def raw_pixel_count(self) -> int:
        return int(self.raw_mask.sum())


@dataclass
class PreparedTruth:
    clusters: list[Cluster]
    match_masks: list[np.ndarray]
    masks: list[np.ndarray]


def _km_to_pixels(km: float, pixel_size_km: float) -> int:
    if km <= 0:
        return 0
    return int(np.ceil(float(km) / float(pixel_size_km)))


@lru_cache(maxsize=64)
def _circular_footprint(radius: int) -> np.ndarray:
    if radius <= 0:
        return np.ones((1, 1), dtype=bool)
    y, x = np.ogrid[-radius : radius + 1, -radius : radius + 1]
    return (x * x + y * y) <= radius * radius


def _dilate(mask: np.ndarray, radius: int) -> np.ndarray:
    if radius <= 0:
        return mask.astype(bool, copy=True)
    return binary_dilation(mask.astype(bool), structure=_circular_footprint(radius))


def _structure(connectivity: int) -> np.ndarray:
    if int(connectivity) == 4:
        return np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]], dtype=bool)
    return np.ones((3, 3), dtype=bool)


def _components(mask: np.ndarray, min_pixels: int, connectivity: int) -> list[np.ndarray]:
    labeled, n_features = cc_label(mask.astype(bool), structure=_structure(connectivity))
    out = []
    for cid in range(1, n_features + 1):
        comp = labeled == cid
        if int(comp.sum()) >= int(min_pixels):
            out.append(comp)
    return out


def cluster_field(
    field: np.ndarray,
    threshold: float,
    mode: str = "mode_like",
    min_cluster_pixels: int = 3,
    pixel_size_km: float = 2.0,
    merge_buffer_km: float = 12.0,
    cluster_mask_expansion_km: float = 0.0,
    connectivity: int = 8,
) -> list[Cluster]:
    valid_mask = np.isfinite(field)
    positive = (field >= float(threshold)) & valid_mask
    merge_radius = _km_to_pixels(float(merge_buffer_km), float(pixel_size_km))
    expansion_radius = _km_to_pixels(float(cluster_mask_expansion_km), float(pixel_size_km))
    mode = str(mode)

    if mode == "connected" or merge_radius <= 0:
        components = _components(positive, min_cluster_pixels, connectivity)
        clusters = []
        for comp in components:
            final = _dilate(comp, expansion_radius)
            clusters.append(Cluster(len(clusters) + 1, comp, final))
        return clusters

    if mode not in {"mode_like", "distance_merge"}:
        raise ValueError(f"unsupported cluster_mode: {mode}")

    support = _dilate(positive, merge_radius)
    support_labeled, n_support = cc_label(support, structure=_structure(connectivity))
    clusters = []
    for support_id in range(1, n_support + 1):
        support_mask = support_labeled == support_id
        raw_union = positive & support_mask
        if int(raw_union.sum()) < int(min_cluster_pixels):
            continue
        final = support_mask if mode == "mode_like" else _dilate(raw_union, expansion_radius)
        clusters.append(Cluster(len(clusters) + 1, raw_union, final))
    return clusters


def label_clusters(label_arr: np.ndarray, min_pixels: int = 1, connectivity: int = 8) -> list[Cluster]:
    comps = _components(label_arr > 0.5, min_pixels, connectivity)
    return [Cluster(i + 1, comp, comp) for i, comp in enumerate(comps)]


def prepare_truth(target: np.ndarray, val_cfg: dict[str, Any]) -> PreparedTruth:
    pixel_size_km = float(val_cfg.get("pixel_size_km", 2.0))
    label_buffer_px = _km_to_pixels(float(val_cfg.get("label_buffer_km", 0.0)), pixel_size_km)
    connectivity = int(val_cfg.get("connectivity", 8))
    clusters = label_clusters(
        target,
        min_pixels=int(val_cfg.get("label_min_cluster_pixels", 1)),
        connectivity=connectivity,
    )
    match_masks = [_dilate(truth.mask, label_buffer_px) for truth in clusters]
    masks = [truth.mask for truth in clusters]
    return PreparedTruth(clusters=clusters, match_masks=match_masks, masks=masks)


def evaluate_scene_with_prepared_truth(
    pred: np.ndarray,
    prepared_truth: PreparedTruth,
    threshold: float,
    val_cfg: dict[str, Any],
) -> dict[str, int]:
    pixel_size_km = float(val_cfg.get("pixel_size_km", 2.0))
    false_buffer_px = _km_to_pixels(float(val_cfg.get("model_false_buffer_km", 0.0)), pixel_size_km)
    connectivity = int(val_cfg.get("connectivity", 8))
    pred_clusters = cluster_field(
        pred,
        threshold=threshold,
        mode=str(val_cfg.get("cluster_mode", "mode_like")),
        min_cluster_pixels=int(val_cfg.get("min_cluster_pixels", 3)),
        pixel_size_km=pixel_size_km,
        merge_buffer_km=float(val_cfg.get("merge_buffer_km", 12.0)),
        cluster_mask_expansion_km=float(val_cfg.get("cluster_mask_expansion_km", 0.0)),
        connectivity=connectivity,
    )

    hits = 0
    misses = 0
    for truth_match in prepared_truth.match_masks:
        if any(bool(np.any(truth_match & pred_cluster.mask)) for pred_cluster in pred_clusters):
            hits += 1
        else:
            misses += 1

    falses = 0
    for pred_cluster in pred_clusters:
        pred_match = _dilate(pred_cluster.mask, false_buffer_px)
        if not any(bool(np.any(pred_match & truth_mask)) for truth_mask in prepared_truth.masks):
            falses += 1

    return {
        "hits": int(hits),
        "misses": int(misses),
        "falses": int(falses),
        "truth_clusters": int(len(prepared_truth.clusters)),
        "pred_clusters": int(len(pred_clusters)),
    }


def evaluate_scene(
    pred: np.ndarray,
    target: np.ndarray,
    threshold: float,
    val_cfg: dict[str, Any],
) -> dict[str, int]:
    return evaluate_scene_with_prepared_truth(pred, prepare_truth(target, val_cfg), threshold, val_cfg)


def scores(hits: int, misses: int, falses: int) -> dict[str, float | None]:
    pod = hits / (hits + misses) if hits + misses > 0 else None
    far = falses / (hits + falses) if hits + falses > 0 else None
    csi = hits / (hits + misses + falses) if hits + misses + falses > 0 else None
    f1 = 2 * hits / (2 * hits + misses + falses) if 2 * hits + misses + falses > 0 else None
    return {"POD": pod, "FAR": far, "CSI": csi, "F1": f1}


def parse_epoch_from_path(path: Path) -> int:
    match = re.search(r"epoch_(\d+)", path.name)
    if match is None:
        return -1
    return int(match.group(1))


@torch.no_grad()
def evaluate_checkpoint(
    checkpoint_path: Path,
    config: dict[str, Any],
    dataset,
    loader,
    device: torch.device,
    input_sources: list[str],
    label_key: str,
    thresholds: list[float],
    truth_cache: dict[str, PreparedTruth] | None = None,
) -> list[dict[str, Any]]:
    model = build_model(config).to(device)
    payload = load_model_checkpoint(model, checkpoint_path, device)
    model.eval()
    epoch = int(payload.get("epoch", -1))
    val_cfg = dict(config.get("validation", {}))
    eval_workers = int(val_cfg.get("eval_workers", min(32, max(1, (os.cpu_count() or 1) // 2))))
    totals = {
        threshold: {"hits": 0, "misses": 0, "falses": 0, "truth_clusters": 0, "pred_clusters": 0}
        for threshold in thresholds
    }
    samples = 0

    def _evaluate_one(scene_pred: np.ndarray, prepared_truth: PreparedTruth, threshold: float) -> tuple[float, dict[str, int]]:
        return threshold, evaluate_scene_with_prepared_truth(scene_pred, prepared_truth, threshold, val_cfg)

    def _truth_for_scene(sample_time: str | None, target: np.ndarray) -> PreparedTruth:
        if truth_cache is None or sample_time is None:
            return prepare_truth(target, val_cfg)
        prepared = truth_cache.get(sample_time)
        if prepared is None:
            prepared = prepare_truth(target, val_cfg)
            truth_cache[sample_time] = prepared
        return prepared

    def _consume_loader(active_loader, desc: str, executor: concurrent.futures.Executor | None) -> int:
        nonlocal samples
        chunk_samples = 0
        for batch in tqdm(active_loader, desc=desc, dynamic_ncols=True):
            if batch is None:
                continue
            x = pack_inputs(batch, input_sources, device)
            y = get_label(batch, label_key, device)
            pred = model(x)["ci"].detach().cpu().numpy()
            truth = y.detach().cpu().numpy()
            batch_times = batch.get("time")
            pending = []
            for i in range(pred.shape[0]):
                samples += 1
                chunk_samples += 1
                sample_time = str(batch_times[i]) if isinstance(batch_times, (list, tuple)) else None
                prepared_truth = _truth_for_scene(sample_time, truth[i])
                for threshold in thresholds:
                    if executor is None:
                        scene = evaluate_scene_with_prepared_truth(pred[i], prepared_truth, threshold, val_cfg)
                        for key, value in scene.items():
                            totals[threshold][key] += int(value)
                    else:
                        pending.append(executor.submit(_evaluate_one, pred[i], prepared_truth, threshold))
            for future in concurrent.futures.as_completed(pending):
                threshold, scene = future.result()
                for key, value in scene.items():
                    totals[threshold][key] += int(value)
        return chunk_samples

    executor = None
    if eval_workers > 1:
        executor = concurrent.futures.ThreadPoolExecutor(max_workers=eval_workers)
    try:
        chunk_count = 1
        if is_ram_chunk_dataset(dataset):
            chunk_count = int(dataset.num_chunks)
            dataset.load_chunk_sync(0, free_current_before_load=True)
            for chunk_id in range(chunk_count):
                active_loader = build_dataloader(config, dataset, mode="valid")
                iterator = iter(active_loader)
                next_chunk = chunk_id + 1
                if next_chunk < chunk_count:
                    dataset.start_preload(next_chunk)
                _consume_loader(iterator, f"valid {checkpoint_path.name} chunk {chunk_id + 1}/{chunk_count}", executor)
                shutdown_dataloader(active_loader)
                if next_chunk < chunk_count:
                    if not dataset.wait_for_preload_and_swap():
                        dataset.load_chunk_sync(next_chunk, free_current_before_load=True)
        else:
            _consume_loader(loader, f"valid {checkpoint_path.name}", executor)
    finally:
        if executor is not None:
            executor.shutdown(wait=True)

    rows = []
    for threshold in thresholds:
        total = totals[threshold]
        metric = scores(total["hits"], total["misses"], total["falses"])
        rows.append(
            {
                "checkpoint": str(checkpoint_path),
                "epoch": epoch,
                "threshold": float(threshold),
                "samples": int(samples),
                "chunks": int(chunk_count),
                **total,
                **metric,
            }
        )
    return rows


def checkpoint_paths(config: dict[str, Any]) -> list[Path]:
    val_cfg = dict(config.get("validation", {}))
    pattern = val_cfg.get("checkpoint_glob")
    if pattern is None:
        out_dir = Path(config.get("output_dir", config.get("checkpoint_dir", "runs/default")))
        pattern = str(out_dir / "checkpoints" / "epoch_*_model.pt")
    paths = [Path(p) for p in sorted(glob.glob(str(pattern)))]
    if not paths:
        raise FileNotFoundError(f"no checkpoints matched: {pattern}")
    start_epoch = val_cfg.get("start_epoch")
    if start_epoch is not None:
        min_epoch = int(start_epoch)
        paths = [path for path in paths if parse_epoch_from_path(path) >= min_epoch]
        if not paths:
            raise FileNotFoundError(f"no checkpoints matched start_epoch >= {min_epoch}: {pattern}")
    stride = int(val_cfg.get("checkpoint_stride", 1) or 1)
    if stride > 1:
        paths = paths[::stride]
    max_checkpoints = val_cfg.get("max_checkpoints")
    if max_checkpoints is not None:
        paths = paths[: int(max_checkpoints)]
    return paths


def _threshold_key(value: Any) -> str:
    return f"{float(value):.8g}"


def _completed_epochs_from_existing(metrics_path: Path, thresholds: list[float]) -> tuple[list[dict[str, Any]], set[int]]:
    if not metrics_path.exists():
        return [], set()
    metrics_df = pd.read_csv(metrics_path)
    if metrics_df.empty:
        return [], set()
    required = {_threshold_key(v) for v in thresholds}
    completed: set[int] = set()
    for epoch, group in metrics_df.groupby("epoch"):
        present = {_threshold_key(v) for v in group["threshold"].tolist()}
        if required.issubset(present):
            completed.add(int(epoch))
    return metrics_df.to_dict("records"), completed


def _drop_epoch_rows(rows: list[dict[str, Any]], epoch: int) -> list[dict[str, Any]]:
    return [row for row in rows if int(row.get("epoch", -1)) != int(epoch)]


def _sort_metric_rows(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
    return sorted(rows, key=lambda row: (int(row.get("epoch", -1)), float(row.get("threshold", 0.0))))


def main() -> None:
    parser = argparse.ArgumentParser(description="Lightweight checkpoint validation by CSI threshold sweep.")
    parser.add_argument("--config", required=True, help="Experiment YAML path")
    parser.add_argument("--device", default=None, help="Override device, e.g. cuda:0 or cpu")
    parser.add_argument("--output-dir", default=None, help="Override validation output directory")
    args = parser.parse_args()

    config = load_config(args.config)
    requested_device = str(args.device or config.get("device") or "auto")
    if requested_device == "auto":
        requested_device = "cuda" if torch.cuda.is_available() else "cpu"
    device = torch.device(requested_device)
    val_cfg = dict(config.get("validation", {}))
    if args.output_dir is not None:
        val_cfg["output_dir"] = str(Path(args.output_dir).resolve())
    split_cfg = dict(config.get("valid", {}))
    input_sources = list(split_cfg.get("input_sources", config.get("input_sources", config.get("required_inputs", ["concat"]))))
    label_key = str(split_cfg.get("label_key", split_cfg.get("target_label", "ci")))
    config.setdefault("valid", {})
    config["valid"].setdefault("input_sources", input_sources)
    config["valid"].setdefault("required_labels", [label_key])
    thresholds = [float(v) for v in val_cfg.get("thresholds", [round(x * 0.1, 1) for x in range(1, 10)])]

    dataset = build_dataset(config, split=str(split_cfg.get("split", "valid")), mode="valid")
    loader = build_dataloader(config, dataset, mode="valid")
    configured_out = val_cfg.get("output_dir")
    out_dir = Path(configured_out) if configured_out else Path(config.get("output_dir", "runs/default")) / "validation"
    out_dir.mkdir(parents=True, exist_ok=True)
    logger = ExperimentLogger(config, mode="valid")
    logger.start()

    try:
        metrics_path = out_dir / "epoch_threshold_metrics.csv"
        summary_path = out_dir / "epoch_summary.csv"
        best_path = out_dir / "best_checkpoint.json"
        resume_existing = bool(val_cfg.get("resume_existing", True))
        all_rows, completed_epochs = _completed_epochs_from_existing(metrics_path, thresholds) if resume_existing else ([], set())
        if completed_epochs:
            print(f"resuming validation: found {len(completed_epochs)} completed epochs in {metrics_path}")

        def save_partial_results() -> dict[str, Any] | None:
            if not all_rows:
                return None
            metrics_df = pd.DataFrame(_sort_metric_rows(all_rows))
            metrics_df.to_csv(metrics_path, index=False)

            valid_csi = metrics_df["CSI"].fillna(-1.0)
            best_idx = int(valid_csi.idxmax())
            best_row = metrics_df.loc[best_idx].to_dict()
            summary_df = (
                metrics_df.sort_values(["epoch", "CSI"], ascending=[True, False])
                .groupby("checkpoint", as_index=False)
                .head(1)
                .sort_values("CSI", ascending=False)
            )
            summary_df.to_csv(summary_path, index=False)

            best_payload = {"best": best_row, "metrics_path": str(metrics_path)}
            write_json(best_path, best_payload)
            maybe_copy_best(
                Path(str(best_row["checkpoint"])),
                out_dir / "best_model.pt",
                enabled=bool(val_cfg.get("copy_best_model", True)),
            )
            return best_row

        truth_cache: dict[str, PreparedTruth] | None = {} if bool(val_cfg.get("cache_truth", True)) else None
        for ckpt in checkpoint_paths(config):
            ckpt_epoch = parse_epoch_from_path(ckpt)
            if ckpt_epoch in completed_epochs:
                print(f"skip completed checkpoint: {ckpt.name} epoch={ckpt_epoch}")
                continue
            all_rows = _drop_epoch_rows(all_rows, ckpt_epoch)
            rows = evaluate_checkpoint(ckpt, config, dataset, loader, device, input_sources, label_key, thresholds, truth_cache=truth_cache)
            all_rows.extend(rows)
            completed_epochs.add(int(rows[0]["epoch"]) if rows else ckpt_epoch)
            ckpt_df = pd.DataFrame(rows)
            if not ckpt_df.empty:
                best_ckpt_row = ckpt_df.loc[int(ckpt_df["CSI"].fillna(-1.0).idxmax())].to_dict()
                logger.log(best_ckpt_row, step=int(best_ckpt_row.get("epoch", 0)), prefix="valid_checkpoint")
            best_row = save_partial_results()
            if best_row is not None:
                print(
                    "saved validation results:",
                    metrics_path,
                    "current_best_epoch=",
                    best_row["epoch"],
                    "threshold=",
                    best_row["threshold"],
                    "CSI=",
                    best_row["CSI"],
                )

        best_row = save_partial_results()
        if best_row is None:
            raise RuntimeError("validation produced no metric rows")
        logger.log(best_row, step=int(best_row.get("epoch", 0)), prefix="valid_best")
        logger.log_file(metrics_path, name="validation_threshold_metrics")
        logger.log_file(summary_path, name="validation_epoch_summary")
        logger.log_file(best_path, name="validation_best_checkpoint")
        print(
            "best checkpoint:",
            best_row["checkpoint"],
            "epoch=",
            best_row["epoch"],
            "threshold=",
            best_row["threshold"],
            "CSI=",
            best_row["CSI"],
        )
    finally:
        shutdown_dataloader(loader)
        if is_ram_chunk_dataset(dataset):
            dataset.shutdown_preload()
        logger.finish()


if __name__ == "__main__":
    main()