import random

import numpy as np
import torch as th


def set_random_seed(seed, using_cuda=False):
    """
    Seed the different random generators
    :param seed: (int)
    :param using_cuda: (bool)
    """
    random.seed(seed)
    np.random.seed(seed)
    th.manual_seed(seed)

    if using_cuda:
        # Make CuDNN Determinist
        th.backends.cudnn.deterministic = True
        th.cuda.manual_seed(seed)


# From stable baselines
def explained_variance(y_pred, y_true):
    """
    Computes fraction of variance that ypred explains about y.
    Returns 1 - Var[y-ypred] / Var[y]

    interpretation:
        ev=0  =>  might as well have predicted zero
        ev=1  =>  perfect prediction
        ev<0  =>  worse than just predicting zero

    :param y_pred: (np.ndarray) the prediction
    :param y_true: (np.ndarray) the expected value
    :return: (float) explained variance of ypred and y
    """
    assert y_true.ndim == 1 and y_pred.ndim == 1
    var_y = np.var(y_true)
    return np.nan if var_y == 0 else 1 - np.var(y_true - y_pred) / var_y


def update_learning_rate(optimizer, learning_rate):
    """
    Update the learning rate for a given optimizer.
    Useful when doing linear schedule.

    :param optimizer: (th.optim.Optimizer)
    :param learning_rate: (float)
    """
    for param_group in optimizer.param_groups:
        param_group['lr'] = learning_rate


def get_schedule_fn(value_schedule):
    """
    Transform (if needed) learning rate and clip range (for PPO)
    to callable.

    :param value_schedule: (callable or float)
    :return: (function)
    """
    # If the passed schedule is a float
    # create a constant function
    if isinstance(value_schedule, (float, int)):
        # Cast to float to avoid errors
        value_schedule = constant_fn(float(value_schedule))
    else:
        assert callable(value_schedule)
    return value_schedule


def constant_fn(val):
    """
    Create a function that returns a constant
    It is useful for learning rate schedule (to avoid code duplication)

    :param val: (float)
    :return: (function)
    """

    def func(_):
        return val

    return func