import numpy as np

from pyquaternion import Quaternion

import math

import pandas as pd

import matplotlib.pyplot as pp

import skvideo.io

import os

import pickle

from copy import deepcopy

import torch

def create_log_gaussian(mean, log_std, t):

    quadratic = -((0.5 * (t - mean) / (log_std.exp())).pow(2))

    l = mean.shape

    log_z = log_std

    z = l[-1] * math.log(2 * math.pi)

    log_p = quadratic.sum(dim=-1) - log_z.sum(dim=-1) - 0.5 * z

    return log_p

def logsumexp(inputs, dim=None, keepdim=False):

    if dim is None:

        inputs = inputs.view(-1)

        dim = 0

    s, _ = torch.max(inputs, dim=dim, keepdim=True)

    outputs = s + (inputs - s).exp().sum(dim=dim, keepdim=True).log()

    if not keepdim:

        outputs = outputs.squeeze(dim)

    return outputs

def soft_update(target, source, tau):

    for target_param, param in zip(target.parameters(), source.parameters()):

        target_param.data.copy_(target_param.data * (1.0 - tau) + param.data * tau)

def hard_update(target, source):

    for target_param, param in zip(target.parameters(), source.parameters()):

        target_param.data.copy_(param.data)