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


class TinyNetworkGDAS(nn.Module):

    # def __init__(self, C, N, max_nodes, num_classes, search_space, affine=False, track_running_stats=True):
    def __init__(
        self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats
    ):
        super(TinyNetworkGDAS, self).__init__()
        self._C = C
        self._layerN = N
        self.max_nodes = max_nodes
        self.stem = nn.Sequential(
            nn.Conv2d(3, C, kernel_size=3, padding=1, bias=False), nn.BatchNorm2d(C)
        )

        layer_channels = [C] * N + [C * 2] + [C * 2] * N + [C * 4] + [C * 4] * N
        layer_reductions = [False] * N + [True] + [False] * N + [True] + [False] * N

        C_prev, num_edge, edge2index = C, None, None
        self.cells = nn.ModuleList()
        for index, (C_curr, reduction) in enumerate(
            zip(layer_channels, layer_reductions)
        ):
            if reduction:
                cell = ResNetBasicblock(C_prev, C_curr, 2)
            else:
                cell = SearchCell(
                    C_prev,
                    C_curr,
                    1,
                    max_nodes,
                    search_space,
                    affine,
                    track_running_stats,
                )
                if num_edge is None:
                    num_edge, edge2index = cell.num_edges, cell.edge2index
                else:
                    assert (
                        num_edge == cell.num_edges and edge2index == cell.edge2index
                    ), "invalid {:} vs. {:}.".format(num_edge, cell.num_edges)
            self.cells.append(cell)
            C_prev = cell.out_dim
        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():
            return "arch-parameters :\n{:}".format(
                nn.functional.softmax(self.arch_parameters, dim=-1).cpu()
            )

    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}, Max-Nodes={max_nodes}, N={_layerN}, L={_Layer})".format(
            name=self.__class__.__name__, **self.__dict__
        )

    def genotype(self):
        genotypes = []
        for i in range(1, self.max_nodes):
            xlist = []
            for j in range(i):
                node_str = "{:}<-{:}".format(i, j)
                with torch.no_grad():
                    weights = self.arch_parameters[self.edge2index[node_str]]
                    op_name = self.op_names[weights.argmax().item()]
                xlist.append((op_name, j))
            genotypes.append(tuple(xlist))
        return Structure(genotypes)

    def genotype_prune(self, initial_metric):
        genotypes = []
        for i in range(1, self.max_nodes):
            xlist = []
            for j in range(i):
                node_str = '{:}<-{:}'.format(i, j)
                with torch.no_grad():
                    weights = initial_metric[ self.edge2index[node_str] ]
                    op_name = self.op_names[ weights.argmax().item() ]
                xlist.append((op_name, j))
            genotypes.append( tuple(xlist) )
        return Structure( genotypes )    
    
    
    
    
    def forward(self, inputs):
        while True:
            gumbels = -torch.empty_like(self.arch_parameters).exponential_().log()
            logits = (self.arch_parameters.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

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

        return out, logits
    
    def forward_new(self, inputs):
        alphas  = nn.functional.softmax(self.arch_parameters, dim=-1)

        feature = self.stem(inputs)
        for i, cell in enumerate(self.cells):
            if isinstance(cell, SearchCell):
                feature = cell(feature, alphas)
            else:
                feature = cell(feature)
        out = self.lastact(feature)
        out = self.global_pooling(out)
        out = out.view(out.size(0), -1)
        logits = self.classifier(out)

        return out, logits
   
    
    
    
    
    