import numpy as np
import torch
from sklearn.preprocessing import MinMaxScaler

def seed_everything(seed):
    """
    Set a seed for all random number generators to ensure reproducibility.

    Parameters:
    seed (int): The seed to use.
    """
    np.random.seed(seed)
    torch.manual_seed(seed)

def scale_data(data):
    """
    Scale data using MinMaxScaler.

    Parameters:
    data (np.array): The data to scale.

    Returns:
    np.array: The scaled data.
    """
    scaler = MinMaxScaler()
    scaled_data = scaler.fit_transform(data)
    return scaled_data, scaler

def save_model(model, path):
    """
    Save a PyTorch model.

    Parameters:
    model (torch.nn.Module): The model to save.
    path (str): The path where to save the model.
    """
    torch.save(model.state_dict(), path)

def load_model(model, path):
    """
    Load a PyTorch model.

    Parameters:
    model (torch.nn.Module): The model to load.
    path (str): The path from where to load the model.

    Returns:
    torch.nn.Module: The loaded model.
    """
    model.load_state_dict(torch.load(path))
    model.eval()
    return model