import path from 'path'

const name = 'pix2pix'
const cwd = process.env.PIX2PIX_CWD || path.join(process.env.HOME, 'code/' + name + '/')

/*
  what are all the tasks that pix2pix has to do?
    - fetch url
    - fetch youtube
  - ffmpeg movie into frames
  - unzip zip file into sequence
    - list sequences
*/

const fetch = {
  type: 'perl',
  script: 'get.pl',
  params: (task) => {
    console.log(task)
    return [ task.module, task.opt.url ]
  },
  listen: (task, line, i) => {
    // relay the new dataset name from youtube-dl or w/e
    if ( line.match(/^created dataset: /) ) {
      let filename = line.split(': ')[1].trim()
      task.dataset = filename.split('.')[0]
      task.opt.filename = filename
      return { type: 'progress', action: 'resolve_dataset', task, }
    }
    return null
  }
}

const combine_folds = {
  type: 'pytorch',
  script: 'datasets/combine_A_and_B.py',
  params: (task) => {
    return [
      '--fold_A', task.module + '/a_b/' + task.dataset + '/A',
      '--fold_B', task.module + '/a_b/' + task.dataset + '/B',
      '--fold_AB', task.module + '/datasets/' + task.dataset,
    ]
  }
}
const train = {
  type: 'pytorch',
  script: 'train.py',
  params: (task) => {
    return [
      '--dataroot', path.join(cwd, 'datasets', task.dataset),
      '--name', task.dataset,
      '--model', 'pix2pix',
      '--loadSize', opt.load_size || 264,
      '--fineSize', 256,
      '--which_model_netG', 'unet_256',
      '--which_direction', 'AtoB',
      '--lambda_B', 100,
      '--dataset_mode', 'aligned',
      '--epoch_count', task.epochs,
      '--which_epoch', 'latest',
      '--continue_train',
      '--no_lsgan',
      '--norm', 'batch',
      '--pool_size', '0',
      '--cortex_module', task.module,
    ]
  },
}
const generate = {
  type: 'pytorch',
  script: 'test.py',
  params: (task) => {
    return [
      '--dataroot', '/sequences/' + task.module + '/' + task.dataset,
      '--name', task.dataset,
      '--start_img', '/sequences/' + task.module + '/' + task.dataset + '/frame_00001.png',
      '--how_many', 1000,
      '--model', 'test',
      '--aspect_ratio', 1.777777,
      '--which_model_netG', 'unet_256',
      '--which_direction', 'AtoB',
      '--dataset_mode', 'test',
      '--loadSize', 256,
      '--fineSize', 256,
      '--norm', 'batch'
    ]
  },
}
const live = {
  type: 'pytorch',
  script: 'live-mogrify.py',
  params: (task) => {
    console.log(task)
    return [
      '--dataroot', path.join(cwd, 'sequences', task.module, task.dataset),
      '--start_img', path.join(cwd, 'sequences', task.module, task.dataset, 'frame_00001.png'),
      '--checkpoint-name', task.checkpoint,
      '--experiment', task.checkpoint,
      '--name', task.checkpoint,
      '--module-name', task.module,
      '--sequence-name', task.dataset,
      '--recursive', '--recursive-frac', 0.1,
      '--sequence', '--sequence-frac', 0.3,
      '--process-frac', 0.5,
      '--nThreads', 1,
      '--transition',
      '--transition-min', 0.05,
      '--how_many', 1000000, '--transition-period', 1000,
      '--loadSize', 256, '--fineSize', 256,
      '--just-copy', '--poll_delay', task.opt.poll_delay || 0.09,
      '--model', 'test',
      '--which_model_netG', 'unet_256',
      '--which_direction', 'AtoB',
      '--dataset_mode', 'recursive',
      '--which_epoch', 'latest',
      '--norm', 'batch',
    ]
  },
}

export default {
  name, cwd,
  activities: {
    fetch, combine_folds, train, generate, live,
  }
}