import sys
sys.path.append("..")

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import Parameter

import utils
from metrics import calculate_kl as KL_DIV
import config_bayesian as cfg
from ..misc import ModuleWrapper


class BBBConv2d(ModuleWrapper):

    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, bias=True, priors=None):
        super(BBBConv2d, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.kernel_size = (kernel_size, kernel_size)
        self.stride = stride
        self.padding = padding
        self.dilation = dilation
        self.groups = 1
        self.use_bias = bias
        self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

        if priors is None:
            priors = {
                'prior_mu': 0,
                'prior_sigma': 0.1,
                'posterior_mu_initial': (0, 0.1),
                'posterior_rho_initial': (-3, 0.1),
            }
        self.prior_mu = priors['prior_mu']
        self.prior_sigma = priors['prior_sigma']
        self.posterior_mu_initial = priors['posterior_mu_initial']
        self.posterior_rho_initial = priors['posterior_rho_initial']

        self.W_mu = Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size))
        self.W_rho = Parameter(torch.Tensor(out_channels, in_channels, *self.kernel_size))
        if self.use_bias:
            self.bias_mu = Parameter(torch.Tensor(out_channels))
            self.bias_rho = Parameter(torch.Tensor(out_channels))
        else:
            self.register_parameter('bias_mu', None)
            self.register_parameter('bias_rho', None)

        self.reset_parameters()

    def reset_parameters(self):
        self.W_mu.data.normal_(*self.posterior_mu_initial)
        self.W_rho.data.normal_(*self.posterior_rho_initial)

        if self.use_bias:
            self.bias_mu.data.normal_(*self.posterior_mu_initial)
            self.bias_rho.data.normal_(*self.posterior_rho_initial)

    def forward(self, x, sample=True):

        self.W_sigma = torch.log1p(torch.exp(self.W_rho))
        if self.use_bias:
            self.bias_sigma = torch.log1p(torch.exp(self.bias_rho))
            bias_var = self.bias_sigma ** 2
        else:
            self.bias_sigma = bias_var = None

        act_mu = F.conv2d(
            x, self.W_mu, self.bias_mu, self.stride, self.padding, self.dilation, self.groups)
        act_var = 1e-16 + F.conv2d(
            x ** 2, self.W_sigma ** 2, bias_var, self.stride, self.padding, self.dilation, self.groups)
        act_std = torch.sqrt(act_var)

        if self.training or sample:
            eps = torch.empty(act_mu.size()).normal_(0, 1).to(self.device)
            return act_mu + act_std * eps
        else:
            return act_mu

    def kl_loss(self):
        kl = KL_DIV(self.prior_mu, self.prior_sigma, self.W_mu, self.W_sigma)
        if self.use_bias:
            kl += KL_DIV(self.prior_mu, self.prior_sigma, self.bias_mu, self.bias_sigma)
        return kl