--[[
    Copyright (c) 2015-present, Facebook, Inc.
    All rights reserved.

    This source code is licensed under the BSD-style license found in the
    LICENSE file in the root directory of this source tree. An additional grant
    of patent rights can be found in the PATENTS file in the same directory.
]]--

require 'torch'
torch.setdefaulttensortype('torch.FloatTensor')
local ffi = require 'ffi'
local class = require('pl.class')
local dir = require 'pl.dir'
local tablex = require 'pl.tablex'
local argcheck = require 'argcheck'
require 'sys'
require 'xlua'
require 'image'

local dataset = torch.class('dataLoader')

local initcheck = argcheck{
   pack=true,
   help=[[
     A dataset class for images in a flat folder structure (folder-name is class-name).
     Optimized for extremely large datasets (upwards of 14 million images).
     Tested only on Linux (as it uses command-line linux utilities to scale up)
]],
   {check=function(paths)
       local out = true;
       for k,v in ipairs(paths) do
          if type(v) ~= 'string' then
             print('paths can only be of string input');
             out = false
          end
       end
       return out
   end,
    name="paths",
    type="table",
    help="Multiple paths of directories with images"},

   {name="sampleSize",
    type="table",
    help="a consistent sample size to resize the images"},

   {name="split",
    type="number",
    help="Percentage of split to go to Training"
   },
   {name="serial_batches",
    type="number",
    help="if randomly sample training images"},
     
   {name="samplingMode",
    type="string",
    help="Sampling mode: random | balanced ",
    default = "balanced"},

   {name="verbose",
    type="boolean",
    help="Verbose mode during initialization",
    default = false},

   {name="loadSize",
    type="table",
    help="a size to load the images to, initially",
    opt = true},

   {name="forceClasses",
    type="table",
    help="If you want this loader to map certain classes to certain indices, "
       .. "pass a classes table that has {classname : classindex} pairs."
       .. " For example: {3 : 'dog', 5 : 'cat'}"
       .. "This function is very useful when you want two loaders to have the same "
    .. "class indices (trainLoader/testLoader for example)",
    opt = true},

   {name="sampleHookTrain",
    type="function",
    help="applied to sample during training(ex: for lighting jitter). "
       .. "It takes the image path as input",
    opt = true},

   {name="sampleHookTest",
    type="function",
    help="applied to sample during testing",
    opt = true},
}

function dataset:__init(...)

   -- argcheck
   local args =  initcheck(...)
   print(args)
   for k,v in pairs(args) do self[k] = v end

   if not self.loadSize then self.loadSize = self.sampleSize; end

   if not self.sampleHookTrain then self.sampleHookTrain = self.defaultSampleHook end
   if not self.sampleHookTest then self.sampleHookTest = self.defaultSampleHook end
   self.image_count = 1
--   print('image_count_init', self.image_count)
   -- find class names
   self.classes = {}
   local classPaths = {}
   if self.forceClasses then
      for k,v in pairs(self.forceClasses) do
         self.classes[k] = v
         classPaths[k] = {}
      end
   end
   local function tableFind(t, o) for k,v in pairs(t) do if v == o then return k end end end
   -- loop over each paths folder, get list of unique class names,
   -- also store the directory paths per class
   -- for each class,
   for k,path in ipairs(self.paths) do
--      print('path', path)
      local dirs = {} -- hack
      dirs[1] = path
--      local dirs = dir.getdirectories(path);
      for k,dirpath in ipairs(dirs) do
         local class = paths.basename(dirpath)
         local idx = tableFind(self.classes, class)
--         print(class)
--         print(idx)
         if not idx then
            table.insert(self.classes, class)
            idx = #self.classes
            classPaths[idx] = {}
         end
         if not tableFind(classPaths[idx], dirpath) then
            table.insert(classPaths[idx], dirpath);
         end
      end
   end

   self.classIndices = {}
   for k,v in ipairs(self.classes) do
      self.classIndices[v] = k
   end

   -- define command-line tools, try your best to maintain OSX compatibility
   local wc = 'wc'
   local cut = 'cut'
   local find = 'find -H'  -- if folder name is symlink, do find inside it after dereferencing
   if jit.os == 'OSX' then
      wc = 'gwc'
      cut = 'gcut'
      find = 'gfind'
   end
   ----------------------------------------------------------------------
   -- Options for the GNU find command
   local extensionList = {'jpg', 'png','JPG','PNG','JPEG', 'ppm', 'PPM', 'bmp', 'BMP'}
   local findOptions = ' -iname "*.' .. extensionList[1] .. '"'
   for i=2,#extensionList do
      findOptions = findOptions .. ' -o -iname "*.' .. extensionList[i] .. '"'
   end

   -- find the image path names
   self.imagePath = torch.CharTensor()  -- path to each image in dataset
   self.imageClass = torch.LongTensor() -- class index of each image (class index in self.classes)
   self.classList = {}                  -- index of imageList to each image of a particular class
   self.classListSample = self.classList -- the main list used when sampling data

   print('running "find" on each class directory, and concatenate all'
         .. ' those filenames into a single file containing all image paths for a given class')
   -- so, generates one file per class
   local classFindFiles = {}
   for i=1,#self.classes do
      classFindFiles[i] = os.tmpname()
   end
   local combinedFindList = os.tmpname();

   local tmpfile = os.tmpname()
   local tmphandle = assert(io.open(tmpfile, 'w'))
   -- iterate over classes
   for i, class in ipairs(self.classes) do
      -- iterate over classPaths
      for j,path in ipairs(classPaths[i]) do
         local command = find .. ' "' .. path .. '" ' .. findOptions
            .. ' >>"' .. classFindFiles[i] .. '" \n'
         tmphandle:write(command)
      end
   end
   io.close(tmphandle)
   os.execute('bash ' .. tmpfile)
   os.execute('rm -f ' .. tmpfile)

   print('now combine all the files to a single large file')
   local tmpfile = os.tmpname()
   local tmphandle = assert(io.open(tmpfile, 'w'))
   -- concat all finds to a single large file in the order of self.classes
   for i=1,#self.classes do
      local command = 'cat "' .. classFindFiles[i] .. '" >>' .. combinedFindList .. ' \n'
      tmphandle:write(command)
   end
   io.close(tmphandle)
   os.execute('bash ' .. tmpfile)
   os.execute('rm -f ' .. tmpfile)

   --==========================================================================
   print('load the large concatenated list of sample paths to self.imagePath')
   local cmd = wc .. " -L '"
                                                  .. combinedFindList .. "' |"
                                                  .. cut .. " -f1 -d' '"
   print('cmd..' .. cmd)
   local maxPathLength = tonumber(sys.fexecute(wc .. " -L '"
                                                  .. combinedFindList .. "' |"
                                                  .. cut .. " -f1 -d' '")) + 1
   local length = tonumber(sys.fexecute(wc .. " -l '"
                                           .. combinedFindList .. "' |"
                                           .. cut .. " -f1 -d' '"))
   assert(length > 0, "Could not find any image file in the given input paths")
   assert(maxPathLength > 0, "paths of files are length 0?")
   self.imagePath:resize(length, maxPathLength):fill(0)
   local s_data = self.imagePath:data()
   local count = 0
   for line in io.lines(combinedFindList) do
      ffi.copy(s_data, line)
      s_data = s_data + maxPathLength
      if self.verbose and count % 10000 == 0 then
         xlua.progress(count, length)
      end;
      count = count + 1
   end

   self.numSamples = self.imagePath:size(1)
   if self.verbose then print(self.numSamples ..  ' samples found.') end
   --==========================================================================
   print('Updating classList and imageClass appropriately')
   self.imageClass:resize(self.numSamples)
   local runningIndex = 0
   for i=1,#self.classes do
      if self.verbose then xlua.progress(i, #(self.classes)) end
      local length = tonumber(sys.fexecute(wc .. " -l '"
                                              .. classFindFiles[i] .. "' |"
                                              .. cut .. " -f1 -d' '"))
      if length == 0 then
         error('Class has zero samples')
      else
         self.classList[i] = torch.linspace(runningIndex + 1, runningIndex + length, length):long()
         self.imageClass[{{runningIndex + 1, runningIndex + length}}]:fill(i)
      end
      runningIndex = runningIndex + length
   end

   --==========================================================================
   -- clean up temporary files
   print('Cleaning up temporary files')
   local tmpfilelistall = ''
   for i=1,#(classFindFiles) do
      tmpfilelistall = tmpfilelistall .. ' "' .. classFindFiles[i] .. '"'
      if i % 1000 == 0 then
         os.execute('rm -f ' .. tmpfilelistall)
         tmpfilelistall = ''
      end
   end
   os.execute('rm -f '  .. tmpfilelistall)
   os.execute('rm -f "' .. combinedFindList .. '"')
   --==========================================================================

   if self.split == 100 then
      self.testIndicesSize = 0
   else
      print('Splitting training and test sets to a ratio of '
               .. self.split .. '/' .. (100-self.split))
      self.classListTrain = {}
      self.classListTest  = {}
      self.classListSample = self.classListTrain
      local totalTestSamples = 0
      -- split the classList into classListTrain and classListTest
      for i=1,#self.classes do
         local list = self.classList[i]
         local count = self.classList[i]:size(1)
         local splitidx = math.floor((count * self.split / 100) + 0.5) -- +round
         local perm = torch.randperm(count)
         self.classListTrain[i] = torch.LongTensor(splitidx)
         for j=1,splitidx do
            self.classListTrain[i][j] = list[perm[j]]
         end
         if splitidx == count then -- all samples were allocated to train set
            self.classListTest[i]  = torch.LongTensor()
         else
            self.classListTest[i]  = torch.LongTensor(count-splitidx)
            totalTestSamples = totalTestSamples + self.classListTest[i]:size(1)
            local idx = 1
            for j=splitidx+1,count do
               self.classListTest[i][idx] = list[perm[j]]
               idx = idx + 1
            end
         end
      end
      -- Now combine classListTest into a single tensor
      self.testIndices = torch.LongTensor(totalTestSamples)
      self.testIndicesSize = totalTestSamples
      local tdata = self.testIndices:data()
      local tidx = 0
      for i=1,#self.classes do
         local list = self.classListTest[i]
         if list:dim() ~= 0 then
            local ldata = list:data()
            for j=0,list:size(1)-1 do
               tdata[tidx] = ldata[j]
               tidx = tidx + 1
            end
         end
      end
   end
end

-- size(), size(class)
function dataset:size(class, list)
   list = list or self.classList
   if not class then
      return self.numSamples
   elseif type(class) == 'string' then
      return list[self.classIndices[class]]:size(1)
   elseif type(class) == 'number' then
      return list[class]:size(1)
   end
end

-- getByClass
function dataset:getByClass(class)
   local index = 0
   if self.serial_batches == 1 then 
     index = math.fmod(self.image_count-1, self.classListSample[class]:nElement())+1
     self.image_count = self.image_count +1
   else
    index = math.ceil(torch.uniform() * self.classListSample[class]:nElement())
   end
--   print('serial_batches: ', self.serial_batches)
--   print('max_index:, ', self.classListSample[class]:nElement())
--   print('index: ', index)
--   print('image_count', 
   local imgpath = ffi.string(torch.data(self.imagePath[self.classListSample[class][index]]))
   return self:sampleHookTrain(imgpath),  imgpath
end

-- converts a table of samples (and corresponding labels) to a clean tensor
local function tableToOutput(self, dataTable, scalarTable)
   local data, scalarLabels, labels
   local quantity = #scalarTable
--   print(dataTable[1]:())
   assert(dataTable[1]:dim() == 3)
--   print(quantity)
--   print(self.sampleSize[1])
--   print(self.sampleSize[2])
--   print(self.sampleSize[3])
   data = torch.Tensor(quantity,
		       self.sampleSize[1], self.sampleSize[2], self.sampleSize[3])
--	 print(data:size())
   scalarLabels = torch.LongTensor(quantity):fill(-1111)
   for i=1,#dataTable do
--     print(dataTable[i]:size())
      data[i]:copy(dataTable[i])
      scalarLabels[i] = scalarTable[i]
   end
   return data, scalarLabels
end

-- sampler, samples from the training set.
function dataset:sample(quantity)
   assert(quantity)
   local dataTable = {}
   local scalarTable = {}
   local samplePaths = {}
   for i=1,quantity do
      local class = torch.random(1, #self.classes)
--      print(class)
      local out, imgpath = self:getByClass(class)
--print(out:size())
      table.insert(dataTable, out)
      table.insert(scalarTable, class)
      samplePaths[i] = imgpath
--      print(imgpath)
--      table.insert(pathTable, imgpath)
--      table.insert()
--      print('out', out:size())
   end
--   print('table')
--   print(table)
   local data, scalarLabels = tableToOutput(self, dataTable, scalarTable)
   return data, scalarLabels, samplePaths-- filePaths
end

function dataset:get(i1, i2)
   local indices = torch.range(i1, i2);
   local quantity = i2 - i1 + 1;
   assert(quantity > 0)
   -- now that indices has been initialized, get the samples
   local dataTable = {}
   local scalarTable = {}
   for i=1,quantity do
      -- load the sample
      local imgpath = ffi.string(torch.data(self.imagePath[indices[i]]))
      local out = self:sampleHookTest(imgpath)
--print(out:size())
      table.insert(dataTable, out)
      table.insert(scalarTable, self.imageClass[indices[i]])
   end
   local data, scalarLabels = tableToOutput(self, dataTable, scalarTable)
   return data, scalarLabels
end

return dataset
