###########################################################################
# Searching for A Robust Neural Architecture in Four GPU Hours, CVPR 2019 #
###########################################################################
import torch
import torch.nn as nn
from copy import deepcopy
from .search_cells import NASNetSearchCell as SearchCell
from ..cell_operations import RAW_OP_CLASSES


# The macro structure is based on NASNet
class NASNetworkGDAS_FRC(nn.Module):

    def __init__(self, C, N, steps, multiplier, stem_multiplier, num_classes, search_space, affine,
                 track_running_stats):
        super(NASNetworkGDAS_FRC, self).__init__()
        self._C = C
        self._layerN = N
        self._steps = steps
        self._multiplier = multiplier
        self.stem = nn.Sequential(
            nn.Conv2d(3, C * stem_multiplier, kernel_size=3, padding=1, bias=False),
            nn.BatchNorm2d(C * stem_multiplier))

        # config for each layer
        layer_channels = [C] * N + [C * 2] + [C * 2] * (N - 1) + [C * 4] + [C * 4] * (N - 1)
        layer_reductions = [False] * N + [True] + [False] * (N - 1) + [True] + [False] * (N - 1)

        num_edge, edge2index = None, None
        C_prev_prev, C_prev, C_curr, reduction_prev = C * stem_multiplier, C * stem_multiplier, C, False

        self.cells = nn.ModuleList()
        for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
            if reduction:
                cell = RAW_OP_CLASSES['gdas_reduction'](C_prev_prev, C_prev, C_curr, reduction_prev, affine,
                                                        track_running_stats)
            else:
                cell = SearchCell(search_space, steps, multiplier, C_prev_prev, C_prev, C_curr, reduction,
                                  reduction_prev, affine, track_running_stats)
            if num_edge is None:
                num_edge, edge2index = cell.num_edges, cell.edge2index
            else:
                assert reduction or num_edge == cell.num_edges and edge2index == cell.edge2index, 'invalid {:} vs. {:}.'.format(
                    num_edge, cell.num_edges)
            self.cells.append(cell)
            C_prev_prev, C_prev, reduction_prev = C_prev, cell.multiplier * C_curr, reduction
        self.op_names = deepcopy(search_space)
        self._Layer = len(self.cells)
        self.edge2index = edge2index
        self.lastact = nn.Sequential(nn.BatchNorm2d(C_prev), nn.ReLU(inplace=True))
        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        self.classifier = nn.Linear(C_prev, num_classes)
        self.arch_parameters = nn.Parameter(1e-3 * torch.randn(num_edge, len(search_space)))
        self.tau = 10

    def get_weights(self):
        xlist = list(self.stem.parameters()) + list(self.cells.parameters())
        xlist += list(self.lastact.parameters()) + list(self.global_pooling.parameters())
        xlist += list(self.classifier.parameters())
        return xlist

    def set_tau(self, tau):
        self.tau = tau

    def get_tau(self):
        return self.tau

    def get_alphas(self):
        return [self.arch_parameters]

    def show_alphas(self):
        with torch.no_grad():
            A = 'arch-normal-parameters :\n{:}'.format(nn.functional.softmax(self.arch_parameters, dim=-1).cpu())
        return '{:}'.format(A)

    def get_message(self):
        string = self.extra_repr()
        for i, cell in enumerate(self.cells):
            string += '\n {:02d}/{:02d} :: {:}'.format(i, len(self.cells), cell.extra_repr())
        return string

    def extra_repr(self):
        return ('{name}(C={_C}, N={_layerN}, steps={_steps}, multiplier={_multiplier}, L={_Layer})'.format(
            name=self.__class__.__name__, **self.__dict__))

    def genotype(self):
        def _parse(weights):
            gene = []
            for i in range(self._steps):
                edges = []
                for j in range(2 + i):
                    node_str = '{:}<-{:}'.format(i, j)
                    ws = weights[self.edge2index[node_str]]
                    for k, op_name in enumerate(self.op_names):
                        if op_name == 'none': continue
                        edges.append((op_name, j, ws[k]))
                edges = sorted(edges, key=lambda x: -x[-1])
                selected_edges = edges[:2]
                gene.append(tuple(selected_edges))
            return gene

        with torch.no_grad():
            gene_normal = _parse(torch.softmax(self.arch_parameters, dim=-1).cpu().numpy())
        return {'normal': gene_normal,
                'normal_concat': list(range(2 + self._steps - self._multiplier, self._steps + 2))}

    def forward(self, inputs):
        def get_gumbel_prob(xins):
            while True:
                gumbels = -torch.empty_like(xins).exponential_().log()
                logits = (xins.log_softmax(dim=1) + gumbels) / self.tau
                probs = nn.functional.softmax(logits, dim=1)
                index = probs.max(-1, keepdim=True)[1]
                one_h = torch.zeros_like(logits).scatter_(-1, index, 1.0)
                hardwts = one_h - probs.detach() + probs
                if (torch.isinf(gumbels).any()) or (torch.isinf(probs).any()) or (torch.isnan(probs).any()):
                    continue
                else:
                    break
            return hardwts, index

        hardwts, index = get_gumbel_prob(self.arch_parameters)

        s0 = s1 = self.stem(inputs)
        for i, cell in enumerate(self.cells):
            if cell.reduction:
                s0, s1 = s1, cell(s0, s1)
            else:
                s0, s1 = s1, cell.forward_gdas(s0, s1, hardwts, index)
        out = self.lastact(s1)
        out = self.global_pooling(out)
        out = out.view(out.size(0), -1)
        logits = self.classifier(out)

        return out, logits
