# coding=utf-8
# Copyright 2022 The Uncertainty Baselines Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

"""BatchEnsemble of GP version of Vision Transformer."""

import functools
import itertools
import multiprocessing
import os

from absl import app
from absl import flags
from absl import logging
from clu import metric_writers
from clu import parameter_overview
from clu import periodic_actions
from clu import preprocess_spec
import flax
import jax
import jax.numpy as jnp
import ml_collections.config_flags
import numpy as np
import robustness_metrics as rm

import tensorflow as tf
import uncertainty_baselines as ub
import batchensemble_utils  # local file import from baselines.jft
import checkpoint_utils  # local file import from baselines.jft
import data_uncertainty_utils  # local file import from baselines.jft
import input_utils  # local file import from baselines.jft
import ood_utils  # local file import from baselines.jft
import preprocess_utils  # local file import from baselines.jft
import train_utils  # local file import from baselines.jft

# TODO(dusenberrymw): Open-source remaining imports.
fewshot = None

ml_collections.config_flags.DEFINE_config_file(
    'config', None, 'Training configuration.', lock_config=True)
flags.DEFINE_string('output_dir', default=None, help='Work unit directory.')
flags.DEFINE_integer(
    'num_cores', default=None, help='Unused. How many devices being used.')
flags.DEFINE_boolean(
    'use_gpu', default=None, help='Unused. Whether or not running on GPU.')
flags.DEFINE_string('tpu', None,
                    'Unused. Name of the TPU. Only used if use_gpu is False.')

FLAGS = flags.FLAGS


def get_gp_kwargs(gp_config):
  """Extract keyword argument parameters for the Gaussian process layer."""
  covmat_momentum = gp_config.get('covmat_momentum', 0.999)

  # Extracts model parameter.
  logging.info('gp_config.covmat_momentum = %s', covmat_momentum)
  covmat_momentum = None if covmat_momentum < 0. else covmat_momentum
  covmat_kwargs = dict(momentum=covmat_momentum)

  # Assembles into kwargs dictionary.
  gp_layer_kwargs = dict(covmat_kwargs=covmat_kwargs)

  return gp_layer_kwargs


def main(config, output_dir):

  seed = config.get('seed', 0)
  rng = jax.random.PRNGKey(seed)
  tf.random.set_seed(seed)

  if config.get('data_dir'):
    logging.info('data_dir=%s', config.data_dir)
  logging.info('Output dir: %s', output_dir)
  tf.io.gfile.makedirs(output_dir)

  save_checkpoint_path = None
  if config.get('checkpoint_steps'):
    save_checkpoint_path = os.path.join(output_dir, 'checkpoint.npz')

  # Create an asynchronous multi-metric writer.
  writer = metric_writers.create_default_writer(
      output_dir, just_logging=jax.process_index() > 0)

  # The pool is used to perform misc operations such as logging in async way.
  pool = multiprocessing.pool.ThreadPool()

  def write_note(note):
    if jax.process_index() == 0:
      logging.info('NOTE: %s', note)

  write_note('Initializing...')

  # Verify settings to make sure no checkpoints are accidentally missed.
  if config.get('keep_checkpoint_steps'):
    assert config.get('checkpoint_steps'), 'Specify `checkpoint_steps`.'
    assert config.keep_checkpoint_steps % config.checkpoint_steps == 0, (
        f'`keep_checkpoint_steps` ({config.checkpoint_steps}) should be'
        f'divisible by `checkpoint_steps ({config.checkpoint_steps}).`')

  batch_size = config.batch_size
  batch_size_eval = config.get('batch_size_eval', batch_size)
  if (batch_size % jax.device_count() != 0 or
      batch_size_eval % jax.device_count() != 0):
    raise ValueError(f'Batch sizes ({batch_size} and {batch_size_eval}) must '
                     f'be divisible by device number ({jax.device_count()})')

  local_batch_size = batch_size // jax.process_count()
  local_batch_size_eval = batch_size_eval // jax.process_count()
  logging.info(
      'Global batch size %d on %d hosts results in %d local batch size. '
      'With %d devices per host (%d devices total), that\'s a %d per-device '
      'batch size.', batch_size, jax.process_count(), local_batch_size,
      jax.local_device_count(), jax.device_count(),
      local_batch_size // jax.local_device_count())

  write_note('Initializing train dataset...')
  rng, train_ds_rng = jax.random.split(rng)
  train_ds_rng = jax.random.fold_in(train_ds_rng, jax.process_index())

  train_ds = input_utils.get_data(
      dataset=config.dataset,
      split=config.train_split,
      rng=train_ds_rng,
      process_batch_size=local_batch_size,
      preprocess_fn=preprocess_spec.parse(
          spec=config.pp_train, available_ops=preprocess_utils.all_ops()),
      shuffle_buffer_size=config.shuffle_buffer_size,
      prefetch_size=config.get('prefetch_to_host', 2),
      data_dir=config.get('data_dir'))

  # Start prefetching already.
  train_iter = input_utils.start_input_pipeline(
      train_ds, config.get('prefetch_to_device', 1))

  write_note('Initializing val dataset(s)...')

  def _get_val_split(dataset, split, pp_eval, data_dir=None):
    # We do ceil rounding such that we include the last incomplete batch.
    nval_img = input_utils.get_num_examples(
        dataset,
        split=split,
        process_batch_size=local_batch_size_eval,
        drop_remainder=False,
        data_dir=data_dir)
    val_steps = int(np.ceil(nval_img / batch_size_eval))
    logging.info('Running validation for %d steps for %s, %s', val_steps,
                 dataset, split)

    if isinstance(pp_eval, str):
      pp_eval = preprocess_spec.parse(
          spec=pp_eval, available_ops=preprocess_utils.all_ops())

    val_ds = input_utils.get_data(
        dataset=dataset,
        split=split,
        rng=None,
        process_batch_size=local_batch_size_eval,
        preprocess_fn=pp_eval,
        cache=config.get('val_cache', 'batched'),
        num_epochs=1,
        repeat_after_batching=True,
        shuffle=False,
        prefetch_size=config.get('prefetch_to_host', 2),
        drop_remainder=False,
        data_dir=data_dir)

    return val_ds

  val_ds_splits = {
      'val':
          _get_val_split(
              config.dataset,
              split=config.val_split,
              pp_eval=config.pp_eval,
              data_dir=config.get('data_dir'))
  }

  if config.get('test_split'):
    val_ds_splits.update({
        'test':
            _get_val_split(
                config.dataset,
                split=config.test_split,
                pp_eval=config.pp_eval,
                data_dir=config.get('data_dir'))
    })

  if config.get('eval_on_cifar_10h'):
    cifar10_to_cifar10h_fn = data_uncertainty_utils.create_cifar10_to_cifar10h_fn(
        config.get('data_dir', None))
    preprocess_fn = preprocess_spec.parse(
        spec=config.pp_eval_cifar_10h, available_ops=preprocess_utils.all_ops())
    pp_eval = lambda ex: preprocess_fn(cifar10_to_cifar10h_fn(ex))
    val_ds_splits['cifar_10h'] = _get_val_split(
        'cifar10',
        split=config.get('cifar_10h_split') or 'test',
        pp_eval=pp_eval,
        data_dir=config.get('data_dir'))

  elif config.get('eval_on_imagenet_real'):
    imagenet_to_real_fn = data_uncertainty_utils.create_imagenet_to_real_fn()
    preprocess_fn = preprocess_spec.parse(
        spec=config.pp_eval_imagenet_real,
        available_ops=preprocess_utils.all_ops())
    pp_eval = lambda ex: preprocess_fn(imagenet_to_real_fn(ex))
    val_ds_splits['imagenet_real'] = _get_val_split(
        'imagenet2012_real',
        split=config.get('imagenet_real_split') or 'validation',
        pp_eval=pp_eval,
        data_dir=config.get('data_dir'))

  ood_ds = {}
  if config.get('ood_datasets') and config.get('ood_methods'):
    if config.get('ood_methods'):  #  config.ood_methods is not a empty list
      logging.info('loading OOD dataset = %s', config.get('ood_datasets'))
      ood_ds, ood_ds_names = ood_utils.load_ood_datasets(
          config.dataset,
          config.ood_datasets,
          config.ood_split,
          config.pp_eval,
          config.pp_eval_ood,
          config.ood_methods,
          config.train_split,
          config.get('data_dir'),
          _get_val_split,
      )

  ntrain_img = input_utils.get_num_examples(
      config.dataset,
      split=config.train_split,
      process_batch_size=local_batch_size,
      data_dir=config.get('data_dir'))
  steps_per_epoch = ntrain_img // batch_size

  if config.get('num_epochs'):
    total_steps = int(config.num_epochs * steps_per_epoch)
    assert not config.get('total_steps'), 'Set either num_epochs or total_steps'
  else:
    total_steps = config.total_steps

  logging.info('Total train data points: %d', ntrain_img)
  logging.info(
      'Running for %d steps, that means %f epochs and %d steps per epoch',
      total_steps, total_steps * batch_size / ntrain_img, steps_per_epoch)

  write_note('Initializing model...')
  logging.info('config.model = %s', config.model)

  # Specify Gaussian process layer configs.
  use_gp_layer = config.get('use_gp_layer', True)
  gp_config = config.get('gp_layer', {})
  gp_layer_kwargs = get_gp_kwargs(gp_config)

  # Process ViT backbone model configs.
  vit_kwargs = config.get('model')
  model = ub.models.PatchTransformerBEGP(
      num_classes=config.num_classes,
      use_gp_layer=use_gp_layer,
      gp_layer_kwargs=gp_layer_kwargs,
      **vit_kwargs)
  ens_size = config.model.transformer.ens_size

  # We want all parameters to be created in host RAM, not on any device, they'll
  # be sent there later as needed, otherwise we already encountered two
  # situations where we allocate them twice.
  @functools.partial(jax.jit, backend='cpu')
  def init(rng):
    image_size = tuple(train_ds.element_spec['image'].shape[2:])
    logging.info('image_size = %s', image_size)
    dummy_input = jnp.zeros((local_batch_size,) + image_size, jnp.float32)
    variables = model.init(rng, dummy_input, train=False)

    if use_gp_layer:
      # Split model parameters into trainable and untrainable collections.
      states, params = variables.pop('params')
      del variables
      params = flax.core.unfreeze(params)
      # Set bias in the head to a low value, such that loss is small initially.
      # Modify the head parameter in the GP head.
      params['head']['output_layer']['bias'] = jnp.full_like(
          params['head']['output_layer']['bias'],
          config.get('init_head_bias', 0))
    else:
      params = variables.pop('params')
      params = flax.core.unfreeze(params)
      params['batchensemble_head']['bias'] = jnp.full_like(
          params['batchensemble_head']['bias'], config.get('init_head_bias', 0))
      states = {}

      # init head kernel to all zeros for fine-tuning
      if config.get('model_init'):
        params['batchensemble_head']['kernel'] = jnp.full_like(
            params['batchensemble_head']['kernel'], 0)

    return params, states

  rng, rng_init = jax.random.split(rng)
  params_cpu, states_cpu = init(rng_init)

  if jax.process_index() == 0:
    num_params = sum(p.size for p in jax.tree_flatten(params_cpu)[0])
    parameter_overview.log_parameter_overview(params_cpu)
    writer.write_scalars(step=0, scalars={'num_params': num_params})

  @functools.partial(jax.pmap, axis_name='batch')
  def evaluation_fn(params, states, images, labels, mask):
    # Ignore the entries with all zero labels for evaluation.
    mask *= labels.max(axis=1)
    variable_dict = {'params': flax.core.freeze(params), **states}
    tiled_logits, out = model.apply(
        variable_dict,
        images,
        train=False,
        mean_field_factor=gp_config.get('mean_field_factor', -1.))

    label_indices = config.get('label_indices')
    logging.info('!!! mask %s, label_indices %s', mask, label_indices)
    if label_indices:
      tiled_logits = tiled_logits[:, label_indices]

    loss_name = config.get('loss', 'sigmoid_xent')
    # TODO(dusenberrymw,zmariet): Clean up and generalize this.
    if loss_name == 'sigmoid_xent':
      ens_logits = batchensemble_utils.log_average_sigmoid_probs(
          jnp.asarray(jnp.split(tiled_logits, ens_size)))
    else:  # softmax
      ens_logits = batchensemble_utils.log_average_softmax_probs(
          jnp.asarray(jnp.split(tiled_logits, ens_size)))
    pre_logits = jnp.concatenate(
        jnp.split(out['pre_logits'], ens_size), axis=-1)

    losses = getattr(train_utils, loss_name)(
        logits=ens_logits,
        labels=labels[:, :(len(label_indices) if label_indices
                           else config.num_classes)],
        reduction=False)
    loss = jax.lax.psum(losses * mask, axis_name='batch')

    top1_idx = jnp.argmax(ens_logits, axis=1)
    top1_correct = jnp.take_along_axis(labels, top1_idx[:, None], axis=1)[:, 0]
    ncorrect = jax.lax.psum(top1_correct * mask, axis_name='batch')
    n = jax.lax.psum(mask, axis_name='batch')

    metric_args = jax.lax.all_gather(
        [ens_logits, labels, pre_logits, mask],
        axis_name='batch')
    return ncorrect, loss, n, metric_args

  @functools.partial(jax.pmap, axis_name='batch')
  def cifar_10h_evaluation_fn(params, states, images, labels, mask):
    variable_dict = {'params': flax.core.freeze(params), **states}
    tiled_logits, out = model.apply(
        variable_dict,
        images,
        train=False,
        mean_field_factor=gp_config.get('mean_field_factor', -1.))
    loss_name = config.get('loss', 'softmax_xent')
    if loss_name == 'sigmoid_xent':
      ens_logits = batchensemble_utils.log_average_sigmoid_probs(
          jnp.asarray(jnp.split(tiled_logits, ens_size)))
    else:  # softmax
      ens_logits = batchensemble_utils.log_average_softmax_probs(
          jnp.asarray(jnp.split(tiled_logits, ens_size)))
    pre_logits = jnp.concatenate(
        jnp.split(out['pre_logits'], ens_size), axis=-1)

    label_indices = config.get('label_indices')
    if label_indices:
      ens_logits = ens_logits[:, label_indices]

    losses = getattr(train_utils, config.get('loss', 'softmax_xent'))(
        logits=ens_logits, labels=labels, reduction=False)
    loss = jax.lax.psum(losses, axis_name='batch')

    top1_idx = jnp.argmax(ens_logits, axis=1)
    # Extracts the label at the highest logit index for each image.
    one_hot_labels = jnp.eye(10)[jnp.argmax(labels, axis=1)]

    top1_correct = jnp.take_along_axis(
        one_hot_labels, top1_idx[:, None], axis=1)[:, 0]
    ncorrect = jax.lax.psum(top1_correct, axis_name='batch')
    n = jax.lax.psum(one_hot_labels, axis_name='batch')

    metric_args = jax.lax.all_gather([ens_logits, labels, pre_logits, mask],
                                     axis_name='batch')
    return ncorrect, loss, n, metric_args

  # Setup function for computing representation.
  @functools.partial(jax.pmap, axis_name='batch')
  def representation_fn(params, states, images, labels, mask):
    # Return shape [batch_size, representation_size * ensemble_size]. During
    # few-shot eval, a single linear regressor is applied over all dimensions.
    variable_dict = {'params': flax.core.freeze(params), **states}
    _, outputs = model.apply(
        variable_dict,
        images,
        train=False,
        mean_field_factor=gp_config.get('mean_field_factor', -1.))
    representation = outputs[config.fewshot.representation_layer]
    representation = jnp.concatenate(
        jnp.split(representation, ens_size), axis=-1)
    representation = jax.lax.all_gather(representation, 'batch')
    labels = jax.lax.all_gather(labels, 'batch')
    mask = jax.lax.all_gather(mask, 'batch')
    return representation, labels, mask

  opt_name = config.get('optim_name')
  write_note(f'Initializing {opt_name} optimizer...')
  opt_def = getattr(flax.optim, opt_name)(**config.get('optim', {}))

  # We jit this, such that the arrays that are created are created on the same
  # device as the input is, in this case the CPU. Else they'd be on device[0].
  opt_cpu = jax.jit(opt_def.create)(params_cpu)

  weight_decay_rules = config.get('weight_decay', []) or []
  rescale_value = config.lr.base if config.get('weight_decay_decouple') else 1.
  weight_decay_fn = train_utils.get_weight_decay_fn(
      weight_decay_rules=weight_decay_rules, rescale_value=rescale_value)

  @functools.partial(jax.pmap, axis_name='batch', donate_argnums=(0,))
  def update_fn(opt, states, lr, reset_covmat, images, labels, rng):
    """Update step."""
    measurements = {}

    # Split rng and return next_rng for the following step.
    rng, next_rng = jax.random.split(rng, 2)
    rng_local = jax.random.fold_in(rng, jax.lax.axis_index('batch'))

    def loss_fn(params, states, images, labels):
      if use_gp_layer:
        (logits, _), updated_states = model.apply(
            {'params': flax.core.freeze(params), **states},
            images,
            train=True,
            rngs={'dropout': rng_local},
            # Specify mutable collection to update untrainable GP parameters.
            mutable=list(states.keys()))
      else:
        updated_states = {}
        logits, _ = model.apply(
            {'params': flax.core.freeze(params), **states},
            images,
            train=True,
            rngs={'dropout': rng_local})

      ens_size = config.model.transformer.ens_size
      labels = jnp.tile(labels, (ens_size, 1))
      loss_fn = getattr(train_utils, config.get('loss', 'sigmoid_xent'))
      loss = loss_fn(logits=logits, labels=labels)
      aux = {'logits': logits, 'states': updated_states}
      return loss, aux

    # Performs exact covariance update (i.e., reset precision matrix resetting
    # at begining of new epoch) if covmat_momentum is a null value.
    if use_gp_layer and config.get('gp_layer.covmat_momentum', -1.) < 0:
      # Resets precision matrix to Identity * ridge_penalty if at the begining
      # of a new epoch. This should be done before accumulate gradient.
      ridge_penalty = config.get('gp_layer.ridge_penalty', 1.)
      prec_mat_old = states['laplace_covariance']['head']['covmat_layer'][
          'precision_matrix']
      prec_mat_new = (
          (1. - reset_covmat) * prec_mat_old +
          reset_covmat * jnp.eye(prec_mat_old.shape[0]) * ridge_penalty)

      states = flax.core.unfreeze(states)
      states['laplace_covariance']['head']['covmat_layer'][
          'precision_matrix'] = prec_mat_new
      states = flax.core.freeze(states)

    # Implementation considerations compared and summarized at
    # https://docs.google.com/document/d/1g3kMEvqu1DOawaflKNyUsIoQ4yIVEoyE5ZlIPkIl4Lc/edit?hl=en#
    (l, aux), g = train_utils.accumulate_gradient_with_states(
        jax.value_and_grad(loss_fn, has_aux=True), opt.target, states, images,
        labels, config.get('grad_accum_steps'))
    l, g = jax.lax.pmean((l, g), axis_name='batch')
    measurements['training_loss'] = l

    # Log the gradient norm only if we need to compute it anyways (clipping)
    # or if we don't use grad_accum_steps, as they interact badly.
    if config.get('grad_accum_steps', 1) == 1 or config.get('grad_clip_norm'):
      grads, _ = jax.tree_flatten(g)
      l2_g = jnp.sqrt(sum([jnp.vdot(p, p) for p in grads]))
      measurements['l2_grads'] = l2_g

    # Optionally resize the global gradient to a maximum norm. We found this
    # useful in some cases across optimizers, hence it's in the main loop.
    if config.get('grad_clip_norm'):
      g_factor = jnp.minimum(1.0, config.grad_clip_norm / l2_g)
      g = jax.tree_map(lambda p: g_factor * p, g)
    opt = opt.apply_gradient(g, learning_rate=lr)
    opt = opt.replace(target=weight_decay_fn(opt.target, lr))

    params, _ = jax.tree_flatten(opt.target)
    measurements['l2_params'] = jnp.sqrt(sum([jnp.vdot(p, p) for p in params]))

    # Compute training accuracy by the ensemble members independently to save
    # compute.
    top1_idx = jnp.argmax(aux['logits'], axis=1)
    ens_size = config.model.transformer.ens_size
    tiled_labels = jnp.tile(labels, (ens_size, 1))
    top1_correct = jnp.take_along_axis(tiled_labels,
                                       top1_idx[:, None], axis=1)[:, 0]
    prec1 = jax.lax.psum(jnp.sum(top1_correct), axis_name='batch') / (
        batch_size * ens_size)
    measurements['training_prec@1'] = prec1
    measurements['learning_rate'] = lr
    return opt, aux['states'], next_rng, measurements

  reint_params = []
  checkpoint_data = checkpoint_utils.maybe_load_checkpoint(
      train_loop_rngs=rng,
      save_checkpoint_path=save_checkpoint_path,
      init_optimizer=opt_cpu,
      init_params=params_cpu,
      init_fixed_model_states=states_cpu,
      default_reinit_params=reint_params,
      config=config)

  train_loop_rngs = checkpoint_data.train_loop_rngs
  opt_cpu = checkpoint_data.optimizer
  states_cpu = checkpoint_data.fixed_model_states
  accumulated_train_time = checkpoint_data.accumulated_train_time

  write_note('Kicking off misc stuff...')
  first_step = int(opt_cpu.state.step)  # Might be a DeviceArray type.
  if first_step == 0 and jax.process_index() == 0:
    writer.write_hparams(dict(config))
  chrono = train_utils.Chrono(
      first_step, total_steps, batch_size, accumulated_train_time)

  # Note: switch to ProfileAllHosts() if you need to profile all hosts.
  # (Xprof data become much larger and take longer to load for analysis)
  profiler = periodic_actions.Profile(
      # Create profile after every restart to analyze pre-emption related
      # problems and assure we get similar performance in every run.
      logdir=output_dir, first_profile=first_step + 10)

  # Prepare the learning-rate and pre-fetch it to device to avoid delays.
  lr_fn = train_utils.create_learning_rate_schedule(total_steps,
                                                    **config.get('lr', {}))
  # TODO(dusenberrymw): According to flax docs, prefetching shouldn't be
  # necessary for TPUs.
  lr_iter = train_utils.prefetch_scalar(
      map(lr_fn, range(total_steps)), config.get('prefetch_to_device', 1))

  # Prepare the precision matrix resetting schedule, and pre-fetch it to device.
  reset_steps = steps_per_epoch * 1
  reset_covmat_fn = lambda step: float(step % reset_steps == 0)
  reset_covmat_iter = train_utils.prefetch_scalar(
      map(reset_covmat_fn, range(first_step, total_steps)),
      nprefetch=config.get('prefetch_to_device', 1))

  write_note(f'Replicating...\n{chrono.note}')
  opt_repl = flax.jax_utils.replicate(opt_cpu)
  states_repl = flax.jax_utils.replicate(states_cpu)

  write_note(f'Initializing few-shotters...\n{chrono.note}')
  fewshotter = None
  if 'fewshot' in config and fewshot is not None:
    fewshotter = fewshot.FewShotEvaluator(
        representation_fn, config.fewshot,
        config.fewshot.get('batch_size') or batch_size_eval)

  checkpoint_writer = None

  # Note: we return the train loss, val loss, and fewshot best l2s for use in
  # reproducibility unit tests.
  train_loss = -jnp.inf
  val_loss = {val_name: -jnp.inf for val_name, _ in val_ds_splits.items()}
  fewshot_results = {'dummy': {(0, 1): -jnp.inf}}

  write_note(f'First step compilations...\n{chrono.note}')
  logging.info('first_step = %s', first_step)
  # Make sure log_eval_steps is same as steps_per_epoch. This is because
  # the precision matrix needs to be updated fully (at the end of each epoch)
  # when eval takes place.
  log_eval_steps = config.log_eval_steps
  if use_gp_layer:
    log_eval_steps = max(steps_per_epoch, 2)
  # Advance the iterators if we are restarting from an earlier checkpoint.
  # TODO(dusenberrymw): Look into checkpointing dataset state instead.
  if first_step > 0:
    write_note('Advancing iterators after resuming from a checkpoint...')
    lr_iter = itertools.islice(lr_iter, first_step, None)
    train_iter = itertools.islice(train_iter, first_step, None)

  # Using a python integer for step here, because opt.state.step is allocated
  # on TPU during replication.
  for step, train_batch, lr_repl, reset_covmat_repl in zip(
      range(first_step + 1, total_steps + 1), train_iter, lr_iter,
      reset_covmat_iter):
    with jax.profiler.TraceAnnotation('train_step', step_num=step, _r=1):
      if not config.get('only_eval', False):
        opt_repl, states_repl, train_loop_rngs, extra_measurements = update_fn(
            opt_repl,
            states_repl,
            lr_repl,
            reset_covmat_repl,
            train_batch['image'],
            train_batch['labels'],
            rng=train_loop_rngs)

    if jax.process_index() == 0:
      profiler(step)

    # Checkpoint saving
    if not config.get('only_eval', False) and train_utils.itstime(
        step, config.get('checkpoint_steps'), total_steps, process=0):
      write_note('Checkpointing...')
      chrono.pause()
      train_utils.checkpointing_timeout(checkpoint_writer,
                                        config.get('checkpoint_timeout', 1))
      accumulated_train_time = chrono.accum_train_time
      # We need to transfer the weights over now or else we risk keeping them
      # alive while they'll be updated in a future step, creating hard to debug
      # memory errors (see b/160593526). Also, takes device 0's params only.
      # For GP layer, we will also do the same for untrainable parameters
      # (`states`). This is ok since `random features` are frozen throughout
      # pre-training, and `precision matrix` is a finetuning-specific parameters
      # that will be re-learned in the finetuning task.
      opt_cpu = jax.tree_map(lambda x: np.array(x[0]), opt_repl)
      states_cpu = jax.tree_map(lambda x: np.array(x[0]), states_repl)

      # Check whether we want to keep a copy of the current checkpoint.
      copy_step = None
      if train_utils.itstime(step, config.get('keep_checkpoint_steps'),
                             total_steps):
        write_note('Keeping a checkpoint copy...')
        copy_step = step

      # Checkpoint should be a nested dictionary or FLAX datataclasses from
      # `flax.struct`. Both can be present in a checkpoint.
      checkpoint_data = checkpoint_utils.CheckpointData(
          optimizer=opt_cpu,
          fixed_model_states=states_cpu,
          train_loop_rngs=train_loop_rngs,
          accumulated_train_time=accumulated_train_time)
      checkpoint_writer = pool.apply_async(
          checkpoint_utils.checkpoint_trained_model,
          (checkpoint_data, save_checkpoint_path, copy_step))
      chrono.resume()

    # Report training progress
    if not config.get('only_eval', False) and train_utils.itstime(
        step, config.log_training_steps, total_steps, process=0):
      write_note('Reporting training progress...')
      timing_measurements, note = chrono.tick(step)
      write_note(note)
      train_measurements = {}
      train_measurements.update(flax.jax_utils.unreplicate(extra_measurements))
      train_measurements.update(timing_measurements)
      writer.write_scalars(step, train_measurements)
      # Keep to return for reproducibility tests.
      train_loss = train_measurements['training_loss']

    # Report validation performance
    if config.get('only_eval', False) or train_utils.itstime(
        step, log_eval_steps, total_steps):
      write_note('Evaluating on the validation set...')
      chrono.pause()
      for val_name, val_ds in val_ds_splits.items():
        # Sets up evaluation metrics.
        ece_num_bins = config.get('ece_num_bins', 15)
        auc_num_bins = config.get('auc_num_bins', 1000)
        ece = rm.metrics.ExpectedCalibrationError(num_bins=ece_num_bins)
        calib_auc = rm.metrics.CalibrationAUC(correct_pred_as_pos_label=False)
        oc_auc_0_5 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.005,
                                                       num_bins=auc_num_bins)
        oc_auc_1 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.01,
                                                     num_bins=auc_num_bins)
        oc_auc_2 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.02,
                                                     num_bins=auc_num_bins)
        oc_auc_5 = rm.metrics.OracleCollaborativeAUC(oracle_fraction=0.05,
                                                     num_bins=auc_num_bins)
        label_diversity = tf.keras.metrics.Mean()
        sample_diversity = tf.keras.metrics.Mean()
        ged = tf.keras.metrics.Mean()

        # Runs evaluation loop.
        val_iter = input_utils.start_input_pipeline(
            val_ds, config.get('prefetch_to_device', 1))
        ncorrect, loss, nseen = 0, 0, 0
        for batch in val_iter:
          if val_name == 'cifar_10h':
            batch_ncorrect, batch_losses, batch_n, batch_metric_args = (
                cifar_10h_evaluation_fn(opt_repl.target,
                                        states_repl,
                                        batch['image'],
                                        batch['labels'],
                                        batch['mask']))
          else:
            batch_ncorrect, batch_losses, batch_n, batch_metric_args = (
                evaluation_fn(opt_repl.target,
                              states_repl,
                              batch['image'],
                              batch['labels'],
                              batch['mask']))
          # All results are a replicated array shaped as follows:
          # (local_devices, per_device_batch_size, elem_shape...)
          # with each local device's entry being identical as they got psum'd.
          # So let's just take the first one to the host as numpy.
          ncorrect += np.sum(np.array(batch_ncorrect[0]))
          loss += np.sum(np.array(batch_losses[0]))
          nseen += np.sum(np.array(batch_n[0]))
          if config.get('loss', 'sigmoid_xent') != 'sigmoid_xent':
            # Here we parse batch_metric_args to compute uncertainty metrics.
            # (e.g., ECE or Calibration AUC).
            logits, labels, _, masks = batch_metric_args
            masks = np.array(masks[0], dtype=np.bool)
            logits = np.array(logits[0])
            probs = jax.nn.softmax(logits)
            # From one-hot to integer labels, as required by ECE.
            int_labels = np.argmax(np.array(labels[0]), axis=-1)
            int_preds = np.argmax(logits, axis=-1)
            confidence = np.max(probs, axis=-1)
            for p, c, l, d, m, label in zip(probs, confidence, int_labels,
                                            int_preds, masks, labels[0]):
              ece.add_batch(p[m, :], label=l[m])
              calib_auc.add_batch(d[m], label=l[m], confidence=c[m])
              # TODO(jereliu): Extend to support soft multi-class probabilities.
              oc_auc_0_5.add_batch(d[m], label=l[m], custom_binning_score=c[m])
              oc_auc_1.add_batch(d[m], label=l[m], custom_binning_score=c[m])
              oc_auc_2.add_batch(d[m], label=l[m], custom_binning_score=c[m])
              oc_auc_5.add_batch(d[m], label=l[m], custom_binning_score=c[m])

              if val_name == 'cifar_10h' or val_name == 'imagenet_real':
                num_classes = config.num_classes
                if config.get('label_indices'):
                  num_classes = len(config.get('label_indices'))
                batch_label_diversity, batch_sample_diversity, batch_ged = data_uncertainty_utils.generalized_energy_distance(
                    label[m], p[m, :], num_classes)
                label_diversity.update_state(batch_label_diversity)
                sample_diversity.update_state(batch_sample_diversity)
                ged.update_state(batch_ged)

        val_loss[val_name] = loss / nseen  # Keep for reproducibility tests.
        val_measurements = {
            f'{val_name}_prec@1': ncorrect / nseen,
            f'{val_name}_loss': val_loss[val_name],
        }
        if config.get('loss', 'sigmoid_xent') != 'sigmoid_xent':
          val_measurements[f'{val_name}_ece'] = ece.result()['ece']
          val_measurements[f'{val_name}_calib_auc'] = calib_auc.result()[
              'calibration_auc']
          val_measurements[f'{val_name}_oc_auc_0.5%'] = oc_auc_0_5.result()[
              'collaborative_auc']
          val_measurements[f'{val_name}_oc_auc_1%'] = oc_auc_1.result()[
              'collaborative_auc']
          val_measurements[f'{val_name}_oc_auc_2%'] = oc_auc_2.result()[
              'collaborative_auc']
          val_measurements[f'{val_name}_oc_auc_5%'] = oc_auc_5.result()[
              'collaborative_auc']
        writer.write_scalars(step, val_measurements)

        if val_name == 'cifar_10h' or val_name == 'imagenet_real':
          cifar_10h_measurements = {
              f'{val_name}_label_diversity': label_diversity.result(),
              f'{val_name}_sample_diversity': sample_diversity.result(),
              f'{val_name}_ged': ged.result(),
          }
          writer.write_scalars(step, cifar_10h_measurements)

      # OOD eval
      # Entries in the ood_ds dict include:
      # (ind_dataset, ood_dataset1, ood_dataset2, ...).
      # OOD metrics are computed using ind_dataset paired with each of the
      # ood_dataset. When Mahalanobis distance method is applied, train_ind_ds
      # is also included in the ood_ds.
      if ood_ds and config.ood_methods:

        def make_sngp_eval_fn(states):

          def sngp_eval_fn(params, images, labels, mask):
            return evaluation_fn(
                params=params,
                states=states,
                images=images,
                labels=labels,
                mask=mask)

          return sngp_eval_fn

        ood_measurements = ood_utils.eval_ood_metrics(
            ood_ds,
            ood_ds_names,
            config.ood_methods,
            make_sngp_eval_fn(states_repl),
            opt_repl.target,
            n_prefetch=config.get('prefetch_to_device', 1))
        writer.write_scalars(step, ood_measurements)

      chrono.resume()

    if 'fewshot' in config and fewshotter is not None:
      # Compute few-shot on-the-fly evaluation.
      if config.get('only_eval', False) or train_utils.itstime(
          step, config.fewshot.log_steps, total_steps):
        chrono.pause()
        write_note(f'Few-shot evaluation...\n{chrono.note}')
        # Keep `results` to return for reproducibility tests.
        fewshot_results, best_l2 = fewshotter.run_all(opt_repl.target,
                                                      config.fewshot.datasets)

        # TODO(dusenberrymw): Remove this once fewshot.py is updated.
        def make_writer_measure_fn(step):

          def writer_measure(name, value):
            writer.write_scalars(step, {name: value})

          return writer_measure

        fewshotter.walk_results(
            make_writer_measure_fn(step), fewshot_results, best_l2)
        chrono.resume()

    if config.get('only_eval', False):
      break

  write_note(f'Done!\n{chrono.note}')
  pool.close()
  pool.join()
  writer.close()

  # Return final training loss, validation loss, and fewshot results for
  # reproducibility test cases.
  return train_loss, val_loss, fewshot_results

if __name__ == '__main__':
  # Adds jax flags to the program.
  jax.config.parse_flags_with_absl()

  def _main(argv):
    del argv
    config = FLAGS.config
    output_dir = FLAGS.output_dir
    main(config, output_dir)

  app.run(_main)  # Ignore the returned values from `main`.

tensorflow.random.set_seed tensorflow.io.gfile.makedirs numpy.ceil numpy.max numpy.argmax numpy.array tensorflow.keras.metrics.Mean
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