import copy

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.distributions import Normal


class FlowSequential(nn.Sequential):
    """ Container for layers of a normalizing flow """

    def forward(self, x, y):
        sum_log_abs_det_jacobians = 0
        for module in self:
            x, log_abs_det_jacobian = module(x, y)
            sum_log_abs_det_jacobians = sum_log_abs_det_jacobians + log_abs_det_jacobian
        return x, sum_log_abs_det_jacobians

    def inverse(self, u, y):
        sum_log_abs_det_jacobians = 0
        for module in reversed(self):
            u, log_abs_det_jacobian = module.inverse(u, y)
            sum_log_abs_det_jacobians = sum_log_abs_det_jacobians + log_abs_det_jacobian
        return u, sum_log_abs_det_jacobians


class BatchNorm(nn.Module):
    """ RealNVP BatchNorm layer """

    def __init__(self, input_size, momentum=0.9, eps=1e-5):
        super().__init__()
        self.momentum = momentum
        self.eps = eps

        self.log_gamma = nn.Parameter(torch.zeros(input_size))
        self.beta = nn.Parameter(torch.zeros(input_size))

        self.register_buffer('running_mean', torch.zeros(input_size))
        self.register_buffer('running_var', torch.ones(input_size))

    def forward(self, x, cond_y=None):
        if self.training:
            self.batch_mean = x.view(-1, x.shape[-1]).mean(0)
            # note MAF paper uses biased variance estimate; ie x.var(0, unbiased=False)
            self.batch_var = x.view(-1, x.shape[-1]).var(0)

            # update running mean
            self.running_mean.mul_(self.momentum).add_(
                self.batch_mean.data * (1 - self.momentum))
            self.running_var.mul_(self.momentum).add_(
                self.batch_var.data * (1 - self.momentum))

            mean = self.batch_mean
            var = self.batch_var
        else:
            mean = self.running_mean
            var = self.running_var

        # compute normalized input (cf original batch norm paper algo 1)
        x_hat = (x - mean) / torch.sqrt(var + self.eps)
        y = self.log_gamma.exp() * x_hat + self.beta

        # compute log_abs_det_jacobian (cf RealNVP paper)
        log_abs_det_jacobian = self.log_gamma - 0.5 * torch.log(var + self.eps)
#        print('in sum log var {:6.3f} ; out sum log var {:6.3f}; sum log det {:8.3f}; mean log_gamma {:5.3f}; mean beta {:5.3f}'.format(
#            (var + self.eps).log().sum().data.numpy(), y.var(0).log().sum().data.numpy(), log_abs_det_jacobian.mean(0).item(), self.log_gamma.mean(), self.beta.mean()))
        return y, log_abs_det_jacobian.expand_as(x)

    def inverse(self, y, cond_y=None):
        if self.training:
            mean = self.batch_mean
            var = self.batch_var
        else:
            mean = self.running_mean
            var = self.running_var

        x_hat = (y - self.beta) * torch.exp(-self.log_gamma)
        x = x_hat * torch.sqrt(var + self.eps) + mean

        log_abs_det_jacobian = 0.5 * torch.log(var + self.eps) - self.log_gamma

        return x, log_abs_det_jacobian.expand_as(x)


class LinearMaskedCoupling(nn.Module):
    """ Modified RealNVP Coupling Layers per the MAF paper """

    def __init__(self, input_size, hidden_size, n_hidden, mask, cond_label_size=None):
        super().__init__()

        self.register_buffer('mask', mask)

        # scale function
        s_net = [nn.Linear(
            input_size + (cond_label_size if cond_label_size is not None else 0), hidden_size)]
        for _ in range(n_hidden):
            s_net += [nn.Tanh(), nn.Linear(hidden_size, hidden_size)]
        s_net += [nn.Tanh(), nn.Linear(hidden_size, input_size)]
        self.s_net = nn.Sequential(*s_net)

        # translation function
        self.t_net = copy.deepcopy(self.s_net)
        # replace Tanh with ReLU's per MAF paper
        for i in range(len(self.t_net)):
            if not isinstance(self.t_net[i], nn.Linear):
                self.t_net[i] = nn.ReLU()

    def forward(self, x, y=None):
        # apply mask
        mx = x * self.mask

        # run through model
        s = self.s_net(mx if y is None else torch.cat([y, mx], dim=-1))
        t = self.t_net(mx if y is None else torch.cat([y, mx], dim=-1))
        # cf RealNVP eq 8 where u corresponds to x (here we're modeling u)
        u = mx + (1 - self.mask) * (x - t) * torch.exp(-s)

        # log det du/dx; cf RealNVP 8 and 6; note, sum over input_size done at model log_prob
        log_abs_det_jacobian = - (1 - self.mask) * s

        return u, log_abs_det_jacobian

    def inverse(self, u, y=None):
        # apply mask
        mu = u * self.mask

        # run through model
        s = self.s_net(mu if y is None else torch.cat([y, mu], dim=-1))
        t = self.t_net(mu if y is None else torch.cat([y, mu], dim=-1))
        x = mu + (1 - self.mask) * (u * s.exp() + t)  # cf RealNVP eq 7

        log_abs_det_jacobian = (1 - self.mask) * s  # log det dx/du

        return x, log_abs_det_jacobian


class RealNVP(nn.Module):
    def __init__(self, n_blocks, input_size, hidden_size, n_hidden, cond_label_size=None, batch_norm=True):
        super().__init__()

        # base distribution for calculation of log prob under the model
        self.register_buffer('base_dist_mean', torch.zeros(input_size))
        self.register_buffer('base_dist_var', torch.ones(input_size))

        self.__scale = None

        # construct model
        modules = []
        mask = torch.arange(input_size).float() % 2
        for i in range(n_blocks):
            modules += [LinearMaskedCoupling(input_size,
                                             hidden_size,
                                             n_hidden, mask,
                                             cond_label_size)]
            mask = 1 - mask
            modules += batch_norm * [BatchNorm(input_size)]

        self.net = FlowSequential(*modules)

    @property
    def base_dist(self):
        return Normal(self.base_dist_mean, self.base_dist_var)

    @property
    def scale(self):
        return self.__scale

    @scale.setter
    def scale(self, scale):
        self.__scale = scale

    def forward(self, x, cond):
        if self.scale is not None:
            x /= self.scale
        return self.net(x, cond)

    def inverse(self, u, cond):
        x, log_abs_det_jacobian = self.net.inverse(u, cond)
        if self.scale is not None:
            x *= self.scale
        return x, log_abs_det_jacobian

    def log_prob(self, x, cond):
        u, sum_log_abs_det_jacobians = self.forward(x, cond)
        return torch.sum(self.base_dist.log_prob(u) + sum_log_abs_det_jacobians, dim=-1)

    def sample(self, sample_shape=torch.Size(), cond=None):
        if cond is not None:
            shape = cond.shape[:-1]
        else:
            shape = sample_shape

        u = self.base_dist.sample(shape)
        sample, _ = self.inverse(u, cond)
        return sample