import os
import random
from typing import Optional

import numpy as np
import torch

max_seed_value = np.iinfo(np.uint32).max
min_seed_value = np.iinfo(np.uint32).min


def seed_everything(seed: Optional[int], workers: bool = False) -> int:
    """Function that sets seed for pseudo-random number generators in: pytorch, numpy, python.random In addition,
    sets the following environment variables:

    - `PL_GLOBAL_SEED`: will be passed to spawned subprocesses (e.g. ddp_spawn backend).
    - `PL_SEED_WORKERS`: (optional) is set to 1 if ``workers=True``.

    Args:
        seed: the integer value seed for global random state in Lightning.
            If `None`, will read seed from `PL_GLOBAL_SEED` env variable
            or select it randomly.
        workers: if set to ``True``, will properly configure all dataloaders passed to the
            Trainer with a ``worker_init_fn``. If the user already provides such a function
            for their dataloaders, setting this argument will have no influence. See also:
            :func:`~lightning_fabric.utilities.seed.pl_worker_init_function`.
    """

    if not isinstance(seed, int):
        seed = int(seed)

    if not (min_seed_value <= seed <= max_seed_value):
        raise ValueError(f"{seed} is not in bounds, numpy accepts from {min_seed_value} to {max_seed_value}")

    print(f"Global seed set to {seed}")
    os.environ["PL_GLOBAL_SEED"] = str(seed)
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed_all(seed)

    os.environ["PL_SEED_WORKERS"] = f"{int(workers)}"

    return seed