def _getSmartIndenter(indenterName, qpart, indenter):
    """Get indenter by name.
    Available indenters are none, normal, cstyle, haskell, lilypond, lisp, python, ruby, xml
    Indenter name is not case sensitive
    Raise KeyError if not found
    indentText is indentation, which shall be used. i.e. '\t' for tabs, '    ' for 4 space symbols
    """
    indenterName = indenterName.lower()

    if indenterName in ('haskell', 'lilypond'):  # not supported yet
        logger.warning('Smart indentation for %s not supported yet. But you could be a hero who implemented it' % indenterName)
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'none' == indenterName:
        from qutepart.indenter.base import IndentAlgBase as indenterClass
    elif 'normal' == indenterName:
        from qutepart.indenter.base import IndentAlgNormal as indenterClass
    elif 'cstyle' == indenterName:
        from qutepart.indenter.cstyle import IndentAlgCStyle as indenterClass
    elif 'python' == indenterName:
        from qutepart.indenter.python import IndentAlgPython as indenterClass
    elif 'ruby' == indenterName:
        from qutepart.indenter.ruby import IndentAlgRuby as indenterClass
    elif 'xml' == indenterName:
        from qutepart.indenter.xmlindent import IndentAlgXml as indenterClass
    elif 'haskell' == indenterName:
        from qutepart.indenter.haskell import IndenterHaskell as indenterClass
    elif 'lilypond' == indenterName:
        from qutepart.indenter.lilypond import IndenterLilypond as indenterClass
    elif 'lisp' == indenterName:
        from qutepart.indenter.lisp import IndentAlgLisp as indenterClass
    elif 'scheme' == indenterName:
        from qutepart.indenter.scheme import IndentAlgScheme as indenterClass
    else:
        raise KeyError("Indenter %s not found" % indenterName)

    return indenterClass(qpart, indenter)
def main(unused_argv):
  tpu_cluster_resolver = contrib_cluster_resolver.TPUClusterResolver(
      FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)

  config = contrib_tpu.RunConfig(
      cluster=tpu_cluster_resolver,
      model_dir=FLAGS.model_dir,
      save_checkpoints_steps=FLAGS.iterations_per_loop,
      keep_checkpoint_max=None,
      tpu_config=contrib_tpu.TPUConfig(
          iterations_per_loop=FLAGS.iterations_per_loop,
          num_shards=FLAGS.num_cores,
          per_host_input_for_training=contrib_tpu.InputPipelineConfig.PER_HOST_V2))  # pylint: disable=line-too-long

  # Input pipelines are slightly different (with regards to shuffling and
  # preprocessing) between training and evaluation.
  imagenet_train = imagenet_input.ImageNetInput(
      is_training=True,
      data_dir=FLAGS.data_dir,
      use_bfloat16=True,
      transpose_input=FLAGS.transpose_input)
  imagenet_eval = imagenet_input.ImageNetInput(
      is_training=False,
      data_dir=FLAGS.data_dir,
      use_bfloat16=True,
      transpose_input=FLAGS.transpose_input)

  if FLAGS.use_fast_lr:
    resnet_main.LR_SCHEDULE = [    # (multiplier, epoch to start) tuples
        (1.0, 4), (0.1, 21), (0.01, 35), (0.001, 43)
    ]
    imagenet_train_small = imagenet_input.ImageNetInput(
        is_training=True,
        image_size=128,
        data_dir=FLAGS.data_dir_small,
        num_parallel_calls=FLAGS.num_parallel_calls,
        use_bfloat16=True,
        transpose_input=FLAGS.transpose_input,
        cache=True)
    imagenet_eval_small = imagenet_input.ImageNetInput(
        is_training=False,
        image_size=128,
        data_dir=FLAGS.data_dir_small,
        num_parallel_calls=FLAGS.num_parallel_calls,
        use_bfloat16=True,
        transpose_input=FLAGS.transpose_input,
        cache=True)
    imagenet_train_large = imagenet_input.ImageNetInput(
        is_training=True,
        image_size=288,
        data_dir=FLAGS.data_dir,
        num_parallel_calls=FLAGS.num_parallel_calls,
        use_bfloat16=True,
        transpose_input=FLAGS.transpose_input)
    imagenet_eval_large = imagenet_input.ImageNetInput(
        is_training=False,
        image_size=288,
        data_dir=FLAGS.data_dir,
        num_parallel_calls=FLAGS.num_parallel_calls,
        use_bfloat16=True,
        transpose_input=FLAGS.transpose_input)

  resnet_classifier = contrib_tpu.TPUEstimator(
      use_tpu=FLAGS.use_tpu,
      model_fn=resnet_main.resnet_model_fn,
      config=config,
      train_batch_size=FLAGS.train_batch_size,
      eval_batch_size=FLAGS.eval_batch_size)

  if FLAGS.mode == 'train':
    current_step = estimator._load_global_step_from_checkpoint_dir(FLAGS.model_dir)  # pylint: disable=protected-access,line-too-long
    batches_per_epoch = NUM_TRAIN_IMAGES / FLAGS.train_batch_size
    tf.logging.info('Training for %d steps (%.2f epochs in total). Current'
                    ' step %d.' % (FLAGS.train_steps,
                                   FLAGS.train_steps / batches_per_epoch,
                                   current_step))

    start_timestamp = time.time()  # This time will include compilation time

    # Write a dummy file at the start of training so that we can measure the
    # runtime at each checkpoint from the file write time.
    tf.gfile.MkDir(FLAGS.model_dir)
    if not tf.gfile.Exists(os.path.join(FLAGS.model_dir, 'START')):
      with tf.gfile.GFile(os.path.join(FLAGS.model_dir, 'START'), 'w') as f:
        f.write(str(start_timestamp))

    if FLAGS.use_fast_lr:
      small_steps = int(18 * NUM_TRAIN_IMAGES / FLAGS.train_batch_size)
      normal_steps = int(41 * NUM_TRAIN_IMAGES / FLAGS.train_batch_size)
      large_steps = int(min(50 * NUM_TRAIN_IMAGES / FLAGS.train_batch_size,
                            FLAGS.train_steps))

      resnet_classifier.train(
          input_fn=imagenet_train_small.input_fn, max_steps=small_steps)
      resnet_classifier.train(
          input_fn=imagenet_train.input_fn, max_steps=normal_steps)
      resnet_classifier.train(
          input_fn=imagenet_train_large.input_fn,
          max_steps=large_steps)
    else:
      resnet_classifier.train(
          input_fn=imagenet_train.input_fn, max_steps=FLAGS.train_steps)

  else:
    assert FLAGS.mode == 'eval'

    start_timestamp = tf.gfile.Stat(
        os.path.join(FLAGS.model_dir, 'START')).mtime_nsec
    results = []
    eval_steps = NUM_EVAL_IMAGES // FLAGS.eval_batch_size

    ckpt_steps = set()
    all_files = tf.gfile.ListDirectory(FLAGS.model_dir)
    for f in all_files:
      mat = re.match(CKPT_PATTERN, f)
      if mat is not None:
        ckpt_steps.add(int(mat.group('gs')))
    ckpt_steps = sorted(list(ckpt_steps))
    tf.logging.info('Steps to be evaluated: %s' % str(ckpt_steps))

    for step in ckpt_steps:
      ckpt = os.path.join(FLAGS.model_dir, 'model.ckpt-%d' % step)

      batches_per_epoch = NUM_TRAIN_IMAGES // FLAGS.train_batch_size
      current_epoch = step // batches_per_epoch

      if FLAGS.use_fast_lr:
        if current_epoch < 18:
          eval_input_fn = imagenet_eval_small.input_fn
        if current_epoch >= 18 and current_epoch < 41:
          eval_input_fn = imagenet_eval.input_fn
        if current_epoch >= 41:  # 41:
          eval_input_fn = imagenet_eval_large.input_fn
      else:
        eval_input_fn = imagenet_eval.input_fn

      end_timestamp = tf.gfile.Stat(ckpt + '.index').mtime_nsec
      elapsed_hours = (end_timestamp - start_timestamp) / (1e9 * 3600.0)

      tf.logging.info('Starting to evaluate.')
      eval_start = time.time()  # This time will include compilation time
      eval_results = resnet_classifier.evaluate(
          input_fn=eval_input_fn,
          steps=eval_steps,
          checkpoint_path=ckpt)
      eval_time = int(time.time() - eval_start)
      tf.logging.info('Eval results: %s. Elapsed seconds: %d' %
                      (eval_results, eval_time))
      results.append([
          current_epoch,
          elapsed_hours,
          '%.2f' % (eval_results['top_1_accuracy'] * 100),
          '%.2f' % (eval_results['top_5_accuracy'] * 100),
      ])

      time.sleep(60)

    with tf.gfile.GFile(os.path.join(FLAGS.model_dir, 'results.tsv'), 'wb') as tsv_file:   # pylint: disable=line-too-long
      writer = csv.writer(tsv_file, delimiter='\t')
      writer.writerow(['epoch', 'hours', 'top1Accuracy', 'top5Accuracy'])
      writer.writerows(results)