"""Utilities for reproducible experiment seeding."""

from __future__ import annotations

import os
import random
from typing import Mapping

import numpy as np
import torch


def resolve_base_seed(training_cfg: Mapping[str, object], default: int = 42) -> int | None:
    """Return the configured base seed, preserving explicit null as true random."""
    if "random_seed" not in training_cfg:
        return int(default)
    seed = training_cfg.get("random_seed")
    if seed is None:
        return None
    return int(seed)


def derive_seed(base_seed: int | None, offset: int = 0) -> int | None:
    """Derive a deterministic child seed from a base seed."""
    if base_seed is None:
        return None
    return int(base_seed) + int(offset)


def set_global_seed(seed: int | None, *, deterministic_torch: bool = True) -> None:
    """Seed Python, NumPy and PyTorch RNGs for reproducible training."""
    if seed is None:
        return

    seed = int(seed)
    os.environ["PYTHONHASHSEED"] = str(seed)
    os.environ.setdefault("CUBLAS_WORKSPACE_CONFIG", ":4096:8")

    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed(seed)
        torch.cuda.manual_seed_all(seed)

    if deterministic_torch:
        torch.backends.cudnn.deterministic = True
        torch.backends.cudnn.benchmark = False
        try:
            torch.use_deterministic_algorithms(True, warn_only=True)
        except Exception:
            pass