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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019.01 #
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import torch
import torch.nn as nn
from copy import deepcopy
from .cells import NASNetInferCell as InferCell, AuxiliaryHeadCIFAR


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

    def __init__(self, C, N, stem_multiplier, num_classes, genotype, auxiliary, affine=True, track_running_stats=True):
        super(NASNetonCIFAR, self).__init__()
        self._C = C
        self._layerN = N
        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)

        C_prev_prev, C_prev, C_curr, reduction_prev = C * stem_multiplier, C * stem_multiplier, C, False
        self.auxiliary_index = None
        self.auxiliary_head = None
        self.cells = nn.ModuleList()
        for index, (C_curr, reduction) in enumerate(zip(layer_channels, layer_reductions)):
            cell = InferCell(genotype, C_prev_prev, C_prev, C_curr, reduction, reduction_prev, affine,
                             track_running_stats)
            self.cells.append(cell)
            C_prev_prev, C_prev, reduction_prev = C_prev, cell._multiplier * C_curr, reduction
            if reduction and C_curr == C * 4 and auxiliary:
                self.auxiliary_head = AuxiliaryHeadCIFAR(C_prev, num_classes)
                self.auxiliary_index = index
        self._Layer = len(self.cells)
        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.drop_path_prob = -1

    def update_drop_path(self, drop_path_prob):
        self.drop_path_prob = drop_path_prob

    def auxiliary_param(self):
        if self.auxiliary_head is None:
            return []
        else:
            return list(self.auxiliary_head.parameters())

    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}, L={_Layer})'.format(name=self.__class__.__name__, **self.__dict__))

    def forward(self, inputs):
        stem_feature, logits_aux = self.stem(inputs), None
        cell_results = [stem_feature, stem_feature]
        for i, cell in enumerate(self.cells):
            cell_feature = cell(cell_results[-2], cell_results[-1], self.drop_path_prob)
            cell_results.append(cell_feature)
            if self.auxiliary_index is not None and i == self.auxiliary_index and self.training:
                logits_aux = self.auxiliary_head(cell_results[-1])
        out = self.lastact(cell_results[-1])
        out = self.global_pooling(out)
        out = out.view(out.size(0), -1)
        logits = self.classifier(out)
        if logits_aux is None:
            return out, logits
        else:
            return out, [logits, logits_aux]
