import sys
_module = sys.modules[__name__]
del sys
assets = _module
data = _module
data_loader = _module
models = _module
base_model = _module
constants = _module
ffg_model = _module
mvg_model = _module
networks = _module
optimizers = _module
noisy_adam = _module
noisy_kfac = _module
option_parser = _module
test = _module
train = _module
utils = _module
html = _module
utils = _module
visualizer = _module

from _paritybench_helpers import _mock_config, patch_functional
from unittest.mock import mock_open, MagicMock
from torch.autograd import Function
from torch.nn import Module
import abc, collections, copy, enum, functools, inspect, itertools, logging, math, matplotlib, numbers, numpy, pandas, queue, random, re, scipy, sklearn, string, tensorflow, time, torch, torchaudio, torchtext, torchvision, types, typing, uuid, warnings
import numpy as np
from torch import Tensor
patch_functional()
open = mock_open()
yaml = logging = sys = argparse = MagicMock()
ArgumentParser = argparse.ArgumentParser
_global_config = args = argv = cfg = config = params = _mock_config()
argparse.ArgumentParser.return_value.parse_args.return_value = _global_config
yaml.load.return_value = _global_config
sys.argv = _global_config
__version__ = '1.0.0'
xrange = range
wraps = functools.wraps


from torchvision.datasets import SVHN


from torchvision.datasets import CIFAR10


from torchvision.datasets import MNIST


from torch.utils.data import DataLoader


from torchvision import transforms


import torch


from abc import abstractmethod


from collections import OrderedDict


from torch.autograd import Variable


from torch import nn


from torch.optim import lr_scheduler


from torch.nn.parameter import Parameter


from torch.nn import functional as F


import math


from torch.optim.optimizer import Optimizer


from itertools import chain


import time


import numpy as np


class BayesianLinear(nn.Module):
    """
    Bayesian type of linear network.
    similar with Linear module of torch.nn.modules.
    
    There are 2 options for Bayesian Linear, Fully Factorized Gaussian(FFG) and Matrix Variate Gaussian.
    If you pick FFG option, 
    """

    def __init__(self, in_features, out_features, n, gpu_ids, option='FFG', bias=True, eps=1e-08, lamb=1):
        super(BayesianLinear, self).__init__()
        if option not in ('FFG', 'MVG'):
            raise RuntimeError('Option should be FFG or MVG.')
        self.option = option
        self.gpu_ids = gpu_ids
        self.eps = eps
        self.lamb = lamb
        self.n = n
        self.weight = Parameter(torch.FloatTensor(out_features, in_features))
        self.bias = None
        if bias:
            self.bias = Parameter(torch.FloatTensor(out_features))
        if self.option == 'FFG':
            self.u_weight = torch.FloatTensor(torch.FloatTensor(out_features, in_features))
            self.f_weight = torch.FloatTensor(torch.FloatTensor(out_features, in_features))
            if self.bias is not None:
                self.u_bias = torch.FloatTensor(torch.FloatTensor(out_features))
                self.f_bias = torch.FloatTensor(torch.FloatTensor(out_features))
        elif self.option == 'MVG':
            self.u_weight = torch.FloatTensor(torch.FloatTensor(out_features, in_features))
            self.a_weight = torch.FloatTensor(torch.FloatTensor(in_features, in_features))
            self.s_weight = torch.FloatTensor(torch.FloatTensor(out_features, out_features))
            if self.bias is not None:
                raise Exception('MVG option does not support bias option.')
        self.reset_parameters()

    def reset_parameters(self):
        if self.option == 'FFG':
            stdv = 1.0 / math.sqrt(self.u_weight.size(1))
            self.u_weight.uniform_(-stdv, stdv)
            self.f_weight.fill_(0.0)
            if self.bias is not None:
                stdv = 1.0 / math.sqrt(self.u_bias.size(0))
                self.u_bias.uniform_(-stdv, stdv)
                self.f_bias.fill_(0.0)
        elif self.option == 'MVG':
            stdv = 1.0 / math.sqrt(self.u_weight.size(1))
            self.u_weight.uniform_(-stdv, stdv)
            self.a_weight.fill_(0.0)
            self.s_weight.fill_(0.0)
        else:
            raise Exception('option should be FFG or MVG.')

    def forward(self, x):
        if self.gpu_ids and isinstance(x.data, torch.FloatTensor):
            return nn.parallel.data_parallel(F.linear, (x, self.weight, self.bias), self.gpu_ids)
        return F.linear(x, self.weight, self.bias)

    def save_parameters(self):
        if self.option == 'FFG':
            self.weight.data = self.u_weight
            if self.bias is not None:
                self.bias.data = self.u_bias
        else:
            self.weight.data = self.u_weight
            if self.bias is not None:
                self.bias.data = self.u_bias

    def sample_parameters(self):
        if self.option == 'FFG':
            self.weight.data = self.u_weight + self.lamb / (self.f_weight + self.eps).sqrt() * torch.randn(self.f_weight.size()) / self.n
            if self.bias is not None:
                self.bias.data = self.u_bias + self.lamb / (self.f_bias + self.eps).sqrt() * torch.randn(self.f_bias.size()) / self.n
        else:
            s_eval, s_evec = self.s_weight.symeig(eigenvectors=True)
            a_eval, a_evec = self.a_weight.symeig(eigenvectors=True)
            sqrt_s_weight = torch.mm(torch.mm(s_evec, torch.diag(s_eval.sign() * s_eval.abs().sqrt())), s_evec.t())
            sqrt_a_weight = torch.mm(torch.mm(a_evec, torch.diag(a_eval.sign() * a_eval.abs().sqrt())), a_evec.t())
            self.weight.data = self.u_weight + self.lamb / self.n * torch.mm(torch.mm(sqrt_s_weight, torch.randn(self.u_weight.size())), sqrt_a_weight)

    def update_bayesian_parameters(self, u_delta_dict, f_dict):
        """
        call after step function of Noisy Adam or Noisy KFAC.
        update bayesian parameters by given params.
        :return: 
        """
        if self.option == 'FFG':
            self.u_weight += u_delta_dict[self.weight]
            self.f_weight = f_dict[self.weight]
            if self.bias is not None:
                self.u_bias += u_delta_dict[self.bias]
                self.f_bias = f_dict[self.bias]
        elif self.option == 'MVG':
            self.u_weight += u_delta_dict[self.weight]
            self.a_weight = f_dict[self.weight]['a_inverse']
            self.s_weight = f_dict[self.weight]['s_inverse']


class BayesianMultilayer(nn.Module):

    def __init__(self, gpu_ids, n, eps, option='FFG', bias=False):
        super(BayesianMultilayer, self).__init__()
        self.gpu_ids = gpu_ids
        self.n = n
        self.model = nn.Sequential(BayesianLinear(in_features=784, out_features=100, option=option, n=self.n, gpu_ids=self.gpu_ids, bias=bias, eps=eps), nn.ReLU(), BayesianLinear(in_features=100, out_features=10, option=option, n=self.n, gpu_ids=self.gpu_ids, bias=bias, eps=eps), nn.ReLU())
        self.u_delta_dict = None
        self.f_dict = None

    def forward(self, x, is_test=False):
        if not is_test:
            self.sample_parameters()
        if self.gpu_ids and isinstance(x.data, torch.FloatTensor):
            return nn.parallel.data_parallel(self.model, x, self.gpu_ids)
        return self.model(x)

    @staticmethod
    def _sample_parameters(m):
        classname = m.__class__.__name__
        if classname.find('BayesianLinear') != -1:
            m.sample_parameters()

    def _update_bayesian_parameters(self, m):
        classname = m.__class__.__name__
        if classname.find('BayesianLinear') != -1:
            m.update_bayesian_parameters(self.u_delta_dict, self.f_dict)

    def _save_parameters(self, m):
        classname = m.__class__.__name__
        if classname.find('BayesianLinear') != -1:
            m.save_parameters()

    def sample_parameters(self):
        self.model.apply(self._sample_parameters)

    def update_bayesian_parameters(self, u_delta_dict, f_dict):
        self.u_delta_dict = u_delta_dict
        self.f_dict = f_dict
        self.model.apply(self._update_bayesian_parameters)

    def save_parameters(self):
        self.model.apply(self._save_parameters)


import torch
from torch.nn import MSELoss, ReLU
from _paritybench_helpers import _mock_config, _mock_layer, _paritybench_base, _fails_compile


TESTCASES = [
    # (nn.Module, init_args, forward_args, jit_compiles)
    (BayesianLinear,
     lambda: ([], {'in_features': 4, 'out_features': 4, 'n': 4, 'gpu_ids': False}),
     lambda: ([torch.rand([4, 4, 4, 4])], {}),
     False),
]

class Test_wlwkgus_NoisyNaturalGradient(_paritybench_base):
    def test_000(self):
        self._check(*TESTCASES[0])

