#!/usr/bin/env luajit

require 'torch'
require 'nn'
require 'image'
require 'optim'

----------------------------------------------------------------------

local Graph, parent = torch.class('nn.Graph', 'nn.Container')

function Graph:__init()
   parent.__init(self)
   self.pred = {}
   self.succ = {}
end

function Graph:addEdge(a, b)
   local pred, succ = self.pred, self.succ
   if not pred[a] and not succ[a] then
      self:add(a)
   end
   if not pred[b] and not succ[b] then
      self:add(b)
   end
   pred[b] = pred[b] or {}
   pred[b][#pred[b] + 1] = a
   succ[a] = succ[a] or {}
   succ[a][#succ[a] + 1] = b
end

function Graph:setInput(i)
   if torch.type(i) == 'table' then
      self.inputModules = i
      for _, m in ipairs(i) do
         if not self.pred[m] and not self.succ[m] then
            self:add(m)
         end
      end
   else
      self:setInput({ i })
   end
end

function Graph:setOutput(o)
   if torch.type(o) == 'table' then
      self.outputModules = o
      for _, m in ipairs(o) do
         if not self.pred[m] and not self.succ[m] then
            self:add(m)
         end
      end
   else
      self:setOutput({ o })
   end
end

function Graph:order()
   local distance = {}

   for _, a in pairs(self.inputModules) do
      distance[a] = 1
   end

   local nc

   repeat
      nc = 0
      for i, isucc in pairs(self.succ) do
         for _, j in pairs(isucc) do
            if distance[i] and (not distance[j] or distance[j] < distance[i] + 1) then
               distance[j] = distance[i] + 1
               nc = nc + 1
            end
         end
      end
   until nc == 0

   self.sorted = { }
   for i, d in pairs(distance) do
      table.insert(self.sorted, { d, i })
   end

   table.sort(self.sorted, function(a, b) return a[1] < b[1] end)
   for i, a in ipairs(self.sorted) do self.sorted[i] = a[2] end
end

function Graph:print()
   for i, d in ipairs(self.sorted) do
      print('#' .. i .. ' -> ' .. torch.type(d))
   end
end

function Graph:updateOutput(input)
   if #self.inputModules == 1 then
      self.inputModules[1]:updateOutput(input)
   else
      for i, d in ipairs(self.inputModules) do
         d:updateOutput(input[i])
      end
   end

   for _, d in ipairs(self.sorted) do
      if self.pred[d] then
         if #self.pred[d] == 1 then
            d:updateOutput(self.pred[d][1].output)
         elseif #self.pred[d] > 1 then
            local c = {}
            for k = 1, #self.pred[d] do
               c[k] = self.pred[d][k].output
            end
            d:updateOutput(c)
         end
      end
   end

   if #self.outputModules == 1 then
      self.output = self.outputModules[1].output
   else
      self.output = { }
      for i, d in ipairs(self.outputModules) do
         self.output[i] = d.output
      end
   end

   return self.output
end

----------------------------------------------------------------------

a = nn.Linear(10, 10)
b = nn.ReLU()
c = nn.Linear(10, 3)
d = nn.Linear(10, 3)
e = nn.CMulTable()
f = nn.Linear(3, 2)

--[[

   a -----> b ---> c ---- e ---
             \           /
              \--> d ---/

]]--

g = Graph:new()

g:setInput(a)
g:setOutput({ e, f })
g:addEdge(c, e)
g:addEdge(a, b)
g:addEdge(d, e)
g:addEdge(b, c)
g:addEdge(b, d)
g:addEdge(d, f)

g:order()

g:print(graph)

input = torch.Tensor(3, 10):uniform()

output = g:updateOutput(input)

print(output[1])
print(output[2])