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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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# One-Shot Neural Architecture Search via Self-Evaluated Template Network, ICCV 2019 #
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import torch
import torch.nn as nn
from copy import deepcopy
from typing import List, Text, Dict
from .search_cells import NASNetSearchCell as SearchCell


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

    def __init__(self, C: int, N: int, steps: int, multiplier: int, stem_multiplier: int,
                 num_classes: int, search_space: List[Text], affine: bool, track_running_stats: bool):
        super(NASNetworkSETN, 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)):
            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 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, 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_normal_parameters = nn.Parameter(1e-3 * torch.randn(num_edge, len(search_space)))
        self.arch_reduce_parameters = nn.Parameter(1e-3 * torch.randn(num_edge, len(search_space)))
        self.mode = 'urs'
        self.dynamic_cell = None

    def set_cal_mode(self, mode, dynamic_cell=None):
        assert mode in ['urs', 'joint', 'select', 'dynamic']
        self.mode = mode
        if mode == 'dynamic':
            self.dynamic_cell = deepcopy(dynamic_cell)
        else:
            self.dynamic_cell = None

    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 get_alphas(self):
        return [self.arch_normal_parameters, self.arch_reduce_parameters]

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

    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 dync_genotype(self, use_random=False):
        genotypes = []
        with torch.no_grad():
            alphas_cpu = nn.functional.softmax(self.arch_parameters, dim=-1)
        for i in range(1, self.max_nodes):
            xlist = []
            for j in range(i):
                node_str = '{:}<-{:}'.format(i, j)
                if use_random:
                    op_name = random.choice(self.op_names)
                else:
                    weights = alphas_cpu[self.edge2index[node_str]]
                    op_index = torch.multinomial(weights, 1).item()
                    op_name = self.op_names[op_index]
                xlist.append((op_name, j))
            genotypes.append(tuple(xlist))
        return Structure(genotypes)

    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_normal_parameters, dim=-1).cpu().numpy())
            gene_reduce = _parse(torch.softmax(self.arch_reduce_parameters, dim=-1).cpu().numpy())
        return {'normal': gene_normal,
                'normal_concat': list(range(2 + self._steps - self._multiplier, self._steps + 2)),
                'reduce': gene_reduce,
                'reduce_concat': list(range(2 + self._steps - self._multiplier, self._steps + 2))}

    def forward(self, inputs):
        normal_hardwts = nn.functional.softmax(self.arch_normal_parameters, dim=-1)
        reduce_hardwts = nn.functional.softmax(self.arch_reduce_parameters, dim=-1)

        s0 = s1 = self.stem(inputs)
        for i, cell in enumerate(self.cells):
            # [TODO]
            raise NotImplementedError
            if cell.reduction:
                hardwts, index = reduce_hardwts, reduce_index
            else:
                hardwts, index = normal_hardwts, normal_index
            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
