text: ##################################################
  # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
  ##############################################################################
  # Random Search and Reproducibility for Neural Architecture Search, UAI 2019 # 
  ##############################################################################
  import torch, random
  import torch.nn as nn
  from copy import deepcopy
  from ..cell_operations import ResNetBasicblock
  from .search_cells     import NAS201SearchCell as SearchCell
  from .genotypes        import Structure
  
  
  class TinyNetworkRANDOM(nn.Module):
  
    def __init__(self, C, N, max_nodes, num_classes, search_space, affine, track_running_stats):
      super(TinyNetworkRANDOM, 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_cache = None
      
    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 random_genotype(self, set_cache):
      genotypes = []
      for i in range(1, self.max_nodes):
        xlist = []
        for j in range(i):
          node_str = '{:}<-{:}'.format(i, j)
          op_name  = random.choice( self.op_names )
          xlist.append((op_name, j))
        genotypes.append( tuple(xlist) )
      arch = Structure( genotypes )
      if set_cache: self.arch_cache = arch
      return arch
  
    def forward(self, inputs):
  
      feature = self.stem(inputs)
      for i, cell in enumerate(self.cells):
        if isinstance(cell, SearchCell):
          feature = cell.forward_dynamic(feature, self.arch_cache)
        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 extract_sub(self, op_indices):
      stem = deepcopy(self.stem)
      cells = nn.ModuleList()
      for cell in self.cells:
        if isinstance(cell, SearchCell):
          cells.append(cell.extract_sub(op_indices))
        else:
          cells.append(deepcopy(cell))
  
      lastact = deepcopy(self.lastact)
      global_pooling = deepcopy(self.global_pooling)
      classifier = deepcopy(self.classifier)
      return SampledRANDOM(
        stem, cells, lastact, global_pooling, classifier
      )
  
  
  class SampledRANDOM(nn.Module):
    def __init__(self, stem, cells, lastact, global_pooling, classifier):
      super(SampledRANDOM, self).__init__()
      self.stem = stem
      self.cells = cells
      self.lastact = lastact
      self.global_pooling = global_pooling
      self.classifier = classifier
  
    def forward(self, inputs):
      feature = self.stem(inputs)
      for i, cell in enumerate(self.cells):
          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