from __future__ import annotations

import pickle
import random
from dataclasses import dataclass
from pathlib import Path
from typing import Any

import numpy as np
import torch
from torch.utils.data import Dataset

from .features import bone_features, dynamics_features, mask_keypoints


@dataclass
class ClipSample:
    video_id: str
    label: int
    joint: np.ndarray


def load_pickle(path: str | Path) -> Any:
    with open(path, "rb") as f:
        return pickle.load(f)


def save_pickle(obj: Any, path: str | Path) -> None:
    path = Path(path)
    path.parent.mkdir(parents=True, exist_ok=True)
    with open(path, "wb") as f:
        pickle.dump(obj, f)


class FallClipDataset(Dataset):
    def __init__(
        self,
        pkl_path: str | Path,
        robustness: str = "clean",
        missing_amount: float = 0.0,
        train: bool = False,
        confidence_dropout: bool = False,
        random_dropout: bool = False,
        random_dropout_prob: float = 0.1,
        high_conf_prob: float = 0.1,
        low_conf_prob: float = 0.5,
        low_conf_threshold: float = 0.3,
        seed: int = 7,
    ) -> None:
        self.samples = load_pickle(pkl_path)
        self.robustness = robustness
        self.missing_amount = missing_amount
        self.train = train
        self.confidence_dropout = confidence_dropout
        self.random_dropout = random_dropout
        self.random_dropout_prob = random_dropout_prob
        self.high_conf_prob = high_conf_prob
        self.low_conf_prob = low_conf_prob
        self.low_conf_threshold = low_conf_threshold
        self.rng = np.random.default_rng(seed)

    def __len__(self) -> int:
        return len(self.samples)

    def __getitem__(self, idx: int) -> dict[str, torch.Tensor | str]:
        sample = self.samples[idx]
        joint = np.asarray(sample["joint"], dtype=np.float32)
        if self.train and self.random_dropout:
            joint = self._random_dropout(joint)
        if self.train and self.confidence_dropout:
            joint = self._confidence_dropout(joint)
        if self.robustness != "clean":
            joint = mask_keypoints(joint, self.robustness, self.missing_amount, self.rng)
        bone = bone_features(joint)
        dyn = dynamics_features(joint)
        return {
            "video_id": sample["video_id"],
            "joint": torch.from_numpy(joint),
            "bone": torch.from_numpy(bone),
            "dyn": torch.from_numpy(dyn),
            "label": torch.tensor(sample["label"], dtype=torch.long),
        }

    def _confidence_dropout(self, joint: np.ndarray) -> np.ndarray:
        conf = joint[..., 2]
        probs = np.where(conf < self.low_conf_threshold, self.low_conf_prob, self.high_conf_prob)
        mask = self.rng.random(conf.shape) < probs
        out = joint.copy()
        out[mask] = 0
        return out.astype(np.float32)

    def _random_dropout(self, joint: np.ndarray) -> np.ndarray:
        mask = self.rng.random(joint.shape[:2]) < self.random_dropout_prob
        out = joint.copy()
        out[mask] = 0
        return out.astype(np.float32)


def split_video_ids(video_ids: list[str], ratios: dict[str, float], seed: int) -> dict[str, set[str]]:
    ids = sorted(set(video_ids))
    random.Random(seed).shuffle(ids)
    n = len(ids)
    n_train = max(1, int(round(n * ratios["train"])))
    n_val = max(1, int(round(n * ratios["val"]))) if n >= 3 else 0
    if n_train + n_val >= n:
        n_train = max(1, n - 2)
        n_val = 1 if n >= 3 else 0
    return {
        "train": set(ids[:n_train]),
        "val": set(ids[n_train:n_train + n_val]),
        "test": set(ids[n_train + n_val:]),
    }


def split_video_records(
    records: list[dict[str, Any]],
    ratios: dict[str, float],
    seed: int,
    group_key: str = "video",
) -> dict[str, set[str]]:
    """Stratified split over video ids or higher-level scenario groups."""
    rng = random.Random(seed)
    group_labels: dict[str, int] = {}
    for rec in records:
        gid = record_group_id(rec, group_key)
        label = int(rec["label"])
        if gid in group_labels and group_labels[gid] != label:
            raise ValueError(f"Mixed labels inside group {gid}")
        group_labels[gid] = label
    by_label: dict[int, list[str]] = {}
    for gid, label in group_labels.items():
        by_label.setdefault(label, []).append(gid)
    group_buckets = {"train": set(), "val": set(), "test": set()}
    for groups in by_label.values():
        groups = sorted(set(groups))
        rng.shuffle(groups)
        n = len(groups)
        n_train = max(1, int(round(n * ratios["train"])))
        n_val = max(1, int(round(n * ratios["val"]))) if n >= 3 else 0
        if n_train + n_val >= n:
            n_train = max(1, n - 2)
            n_val = 1 if n >= 3 else 0
        group_buckets["train"].update(groups[:n_train])
        group_buckets["val"].update(groups[n_train:n_train + n_val])
        group_buckets["test"].update(groups[n_train + n_val:])
    video_buckets = {"train": set(), "val": set(), "test": set()}
    for rec in records:
        gid = record_group_id(rec, group_key)
        split = next(k for k, groups in group_buckets.items() if gid in groups)
        video_buckets[split].add(str(rec["video_id"]))
    return video_buckets


def record_group_id(rec: dict[str, Any], group_key: str) -> str:
    video_id = str(rec["video_id"])
    if group_key == "video":
        return video_id
    if group_key == "scenario":
        parts = video_id.split("/")
        return "/".join(parts[:2]) if len(parts) >= 2 else video_id
    raise ValueError(f"Unknown group_key: {group_key}")