import json
from pathlib import Path

import numpy as np
from tqdm import tqdm


def update_active_stats(x, active_sum, active_sq_sum, active_count):
    vals = x[x != 0]
    if vals.size == 0:
        return active_sum, active_sq_sum, active_count

    active_sum += vals.sum()
    active_sq_sum += (vals ** 2).sum()
    active_count += vals.size
    return active_sum, active_sq_sum, active_count


def compute_dataset_stats(root_dir):
    root_dir = Path(root_dir)

    episodes = [
        p for p in root_dir.rglob("*")
        if p.is_dir() and (p / "masks.json").exists()
    ]

    print(f"Found {len(episodes)} valid episodes")

    # ---------- global stats ----------
    contact_sum = 0.0
    contact_sq_sum = 0.0
    contact_count = 0

    force_sum = 0.0
    force_sq_sum = 0.0
    force_count = 0

    contact_min = np.inf
    contact_max = -np.inf

    force_min = np.inf
    force_max = -np.inf
    force_abs_max = 0.0

    # ---------- active / non-zero stats ----------
    contact_active_sum = 0.0
    contact_active_sq_sum = 0.0
    contact_active_count = 0

    force_active_sum = 0.0
    force_active_sq_sum = 0.0
    force_active_count = 0

    # ---------- channel stats ----------
    contact_ch_sum = np.zeros(2, dtype=np.float64)
    contact_ch_sq_sum = np.zeros(2, dtype=np.float64)
    contact_ch_count = 0

    force_ch_sum = np.zeros(6, dtype=np.float64)
    force_ch_sq_sum = np.zeros(6, dtype=np.float64)
    force_ch_count = 0

    contact_ch_min = np.full(2, np.inf, dtype=np.float64)
    contact_ch_max = np.full(2, -np.inf, dtype=np.float64)

    force_ch_min = np.full(6, np.inf, dtype=np.float64)
    force_ch_max = np.full(6, -np.inf, dtype=np.float64)

    total_frames = 0
    skipped_episodes = 0
    skipped_pairs = 0

    for ep in tqdm(episodes, desc="Episodes"):
        contact_dir = ep / "modalities" / "contact"
        force_dir = ep / "modalities" / "force"

        if not contact_dir.exists() or not force_dir.exists():
            skipped_episodes += 1
            print(f"[SKIP] missing modalities: {ep}")
            continue

        contact_files = sorted(contact_dir.glob("*.npy"))
        force_files = sorted(force_dir.glob("*.npy"))

        if len(contact_files) != len(force_files):
            skipped_episodes += 1
            print(
                f"[SKIP] file count mismatch: {ep} "
                f"contact={len(contact_files)}, force={len(force_files)}"
            )
            continue

        for cfile, ffile in zip(contact_files, force_files):
            if cfile.stem != ffile.stem:
                skipped_pairs += 1
                print(
                    f"[SKIP] frame mismatch in {ep}: "
                    f"{cfile.name} vs {ffile.name}"
                )
                continue

            contact = np.load(cfile).astype(np.float64)  # (2, H, W)
            force = np.load(ffile).astype(np.float64)    # (6, H, W)

            if contact.ndim != 3 or contact.shape[0] != 2:
                skipped_pairs += 1
                print(f"[SKIP] bad contact shape {contact.shape}: {cfile}")
                continue

            if force.ndim != 3 or force.shape[0] != 6:
                skipped_pairs += 1
                print(f"[SKIP] bad force shape {force.shape}: {ffile}")
                continue

            total_frames += 1

            # --------------------------------------------------
            # global contact stats, including background zeros
            # --------------------------------------------------
            contact_sum += contact.sum()
            contact_sq_sum += (contact ** 2).sum()
            contact_count += contact.size

            contact_min = min(contact_min, float(contact.min()))
            contact_max = max(contact_max, float(contact.max()))

            # --------------------------------------------------
            # global force stats, including background zeros
            # --------------------------------------------------
            force_sum += force.sum()
            force_sq_sum += (force ** 2).sum()
            force_count += force.size

            force_min = min(force_min, float(force.min()))
            force_max = max(force_max, float(force.max()))
            force_abs_max = max(force_abs_max, float(np.abs(force).max()))

            # --------------------------------------------------
            # active / non-zero stats
            # --------------------------------------------------
            contact_active_sum, contact_active_sq_sum, contact_active_count = (
                update_active_stats(
                    contact,
                    contact_active_sum,
                    contact_active_sq_sum,
                    contact_active_count,
                )
            )

            force_active_sum, force_active_sq_sum, force_active_count = (
                update_active_stats(
                    force,
                    force_active_sum,
                    force_active_sq_sum,
                    force_active_count,
                )
            )

            # --------------------------------------------------
            # channel-wise stats
            # --------------------------------------------------
            c_flat = contact.reshape(2, -1)

            contact_ch_sum += c_flat.sum(axis=1)
            contact_ch_sq_sum += (c_flat ** 2).sum(axis=1)
            contact_ch_count += c_flat.shape[1]

            contact_ch_min = np.minimum(contact_ch_min, c_flat.min(axis=1))
            contact_ch_max = np.maximum(contact_ch_max, c_flat.max(axis=1))

            f_flat = force.reshape(6, -1)

            force_ch_sum += f_flat.sum(axis=1)
            force_ch_sq_sum += (f_flat ** 2).sum(axis=1)
            force_ch_count += f_flat.shape[1]

            force_ch_min = np.minimum(force_ch_min, f_flat.min(axis=1))
            force_ch_max = np.maximum(force_ch_max, f_flat.max(axis=1))

    if total_frames == 0:
        raise RuntimeError("No valid frames found. Please check dataset path.")

    # ======================================================
    # finalize global stats
    # ======================================================
    contact_mean = contact_sum / contact_count
    contact_std = np.sqrt(
        max(contact_sq_sum / contact_count - contact_mean ** 2, 0.0)
    )

    force_mean = force_sum / force_count
    force_std = np.sqrt(
        max(force_sq_sum / force_count - force_mean ** 2, 0.0)
    )

    # ======================================================
    # finalize active stats
    # ======================================================
    if contact_active_count > 0:
        contact_active_mean = contact_active_sum / contact_active_count
        contact_active_std = np.sqrt(
            max(
                contact_active_sq_sum / contact_active_count
                - contact_active_mean ** 2,
                0.0,
            )
        )
    else:
        contact_active_mean = 0.0
        contact_active_std = 0.0

    if force_active_count > 0:
        force_active_mean = force_active_sum / force_active_count
        force_active_std = np.sqrt(
            max(
                force_active_sq_sum / force_active_count
                - force_active_mean ** 2,
                0.0,
            )
        )
    else:
        force_active_mean = 0.0
        force_active_std = 0.0

    # ======================================================
    # finalize channel stats
    # ======================================================
    contact_ch_mean = contact_ch_sum / contact_ch_count
    contact_ch_std = np.sqrt(
        np.maximum(
            contact_ch_sq_sum / contact_ch_count - contact_ch_mean ** 2,
            0.0,
        )
    )

    force_ch_mean = force_ch_sum / force_ch_count
    force_ch_std = np.sqrt(
        np.maximum(
            force_ch_sq_sum / force_ch_count - force_ch_mean ** 2,
            0.0,
        )
    )

    stats = {
        "num_frames": int(total_frames),
        "skipped_episodes": int(skipped_episodes),
        "skipped_pairs": int(skipped_pairs),

        "contact_mean": float(contact_mean),
        "contact_std": float(contact_std),
        "contact_min": float(contact_min),
        "contact_max": float(contact_max),

        "force_mean": float(force_mean),
        "force_std": float(force_std),
        "force_min": float(force_min),
        "force_max": float(force_max),
        "force_abs_max": float(force_abs_max),

        "contact_active_mean": float(contact_active_mean),
        "contact_active_std": float(contact_active_std),
        "contact_active_count": int(contact_active_count),

        "force_active_mean": float(force_active_mean),
        "force_active_std": float(force_active_std),
        "force_active_count": int(force_active_count),

        "contact_ch_mean": contact_ch_mean.tolist(),
        "contact_ch_std": contact_ch_std.tolist(),
        "contact_ch_min": contact_ch_min.tolist(),
        "contact_ch_max": contact_ch_max.tolist(),

        "force_ch_mean": force_ch_mean.tolist(),
        "force_ch_std": force_ch_std.tolist(),
        "force_ch_min": force_ch_min.tolist(),
        "force_ch_max": force_ch_max.tolist(),
    }

    out_file = root_dir / "dataset_norm_params.json"

    with open(out_file, "w") as f:
        json.dump(stats, f, indent=2)

    print(f"\nSaved -> {out_file}")
    print(f"Frames: {total_frames:,}")
    print(f"Contact max: {contact_max:.6f}")
    print(f"Force abs max: {force_abs_max:.6f}")

    return stats


if __name__ == "__main__":
    compute_dataset_stats(
        "./grasping"
    )