import os
import gc
import random
import numpy as np
import torch


def set_seed(seed: int):
    """
    Sets the seed of the entire notebook so results are the same every time we run.
    This is for REPRODUCIBILITY.
    """
    np.random.seed(seed)
    random_state = np.random.RandomState(seed)
    random.seed(seed)
    torch.manual_seed(seed)
    torch.cuda.manual_seed(seed)
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    os.environ['PYTHONHASHSEED'] = str(seed)
    return random_state


def flatten_list(lis):
    """Given a list, possibly nested to any level, return it flattened."""
    new_lis = []
    for item in lis:
        if type(item) == type([]):
            new_lis.extend(flatten_list(item))
        else:
            new_lis.append(item)
    return new_lis


def clear_torch_cache():
    if torch.cuda.is_available:
        torch.cuda.empty_cache()
        torch.cuda.ipc_collect()
        gc.collect()