# MIT License
#
# Copyright (c) 2020 CNRS
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# of this software and associated documentation files (the "Software"), to deal
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# furnished to do so, subject to the following conditions:
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# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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import os
import zlib
from random import Random

import torch


def create_rng_for_worker(model) -> Random:
    """Create worker-specific random number generator

    This makes sure that
    1. training samples generation is reproducible
    2. every (worker, epoch) uses a different seed

    Parameters
    ----------
    epoch : int
        Current epoch.
    """

    # create random number generator
    rng = Random()

    global_seed = os.environ.get("PL_GLOBAL_SEED", "unset")
    worker_info = torch.utils.data.get_worker_info()

    if worker_info is None:
        worker_id = None
    else:
        worker_id = worker_info.id

    seed_tuple = (
        global_seed,
        worker_id,
        model.local_rank,
        model.global_rank,
        model.current_epoch,
    )
    # use adler32 because python's `hash` is not deterministic.
    seed = zlib.adler32(str(seed_tuple).encode())
    rng.seed(seed)

    return rng