from __future__ import annotations
import os
import random
from pathlib import Path
import numpy as np
import torch


def seed_everything(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)


def ensure_dir(path: str | Path) -> Path:
    p = Path(path)
    p.mkdir(parents=True, exist_ok=True)
    return p


def count_trainable(model: torch.nn.Module) -> tuple[int, int]:
    total = sum(p.numel() for p in model.parameters())
    trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)
    return trainable, total


def move_to_device(batch, device):
    if isinstance(batch, torch.Tensor):
        return batch.to(device, non_blocking=True)
    if isinstance(batch, dict):
        return {k: move_to_device(v, device) for k, v in batch.items()}
    if isinstance(batch, (list, tuple)):
        return type(batch)(move_to_device(v, device) for v in batch)
    return batch


def unwrap_model(model):
    return model.module if hasattr(model, "module") else model