# coding=utf-8
# Copyright 2023 The Edward2 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
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"""Multi-headed wide ResNet 28-10 on CIFAR-10 and CIFAR-100."""
import functools
import os
import time
from absl import app
from absl import flags
from absl import logging

from experimental.mimo import cifar_model_reg_path  # local file import
import robustness_metrics as rm
import tensorflow as tf
import tensorflow_datasets as tfds
from uncertainty_baselines.baselines.cifar import utils
import uncertainty_metrics as um

flags.DEFINE_integer('ensemble_size', 4, 'Size of ensemble.')
flags.DEFINE_float('input_repetition_probability', 0.0,
                   'The probability that the inputs are identical for the'
                   'ensemble members.')
flags.DEFINE_integer('width_multiplier', 10, 'Integer to multiply the number of'
                     'typical filters by. "k" in ResNet-n-k.')
flags.DEFINE_integer('per_core_batch_size', 64, 'Batch size per TPU core/GPU.')
flags.DEFINE_integer('batch_repetitions', 4, 'Number of times an example is'
                     'repeated in a training batch. More repetitions lead to'
                     'lower variance gradients and increased training time.')
flags.DEFINE_integer('seed', 0, 'Random seed.')
flags.DEFINE_float('base_learning_rate', 0.1,
                   'Base learning rate when total training batch size is 128.')
flags.DEFINE_integer(
    'lr_warmup_epochs', 1,
    'Number of epochs for a linear warmup to the initial '
    'learning rate. Use 0 to do no warmup.')
flags.DEFINE_float('lr_decay_ratio', 0.2, 'Amount to decay learning rate.')
flags.DEFINE_list('lr_decay_epochs', ['80', '160', '180'],
                  'Epochs to decay learning rate by.')
flags.DEFINE_float('l1', 3e-4, 'L1 coefficient.')
flags.DEFINE_float('l2', 3e-4, 'L2 coefficient.')
flags.DEFINE_enum(
    'dataset', 'cifar10', enum_values=['cifar10', 'cifar100'], help='Dataset.')
# TODO(ghassen): consider adding CIFAR-100-C to TFDS.
flags.DEFINE_string(
    'cifar100_c_path', '',
    'Path to the TFRecords files for CIFAR-100-C. Only valid '
    '(and required) if dataset is cifar100 and corruptions.')
flags.DEFINE_integer(
    'corruptions_interval', 250,
    'Number of epochs between evaluating on the corrupted '
    'test data. Use -1 to never evaluate.')
flags.DEFINE_integer(
    'checkpoint_interval', -1,
    'Number of epochs between saving checkpoints. Use -1 to '
    'never save checkpoints.')
flags.DEFINE_integer('num_bins', 15, 'Number of bins for ECE.')
flags.DEFINE_string(
    'output_dir', '/tmp/cifar', 'The directory where the model weights and '
    'training/evaluation summaries are stored.')
flags.DEFINE_integer('train_epochs', 250, 'Number of training epochs.')

# Accelerator flags.
flags.DEFINE_bool('use_gpu', False, 'Whether to run on GPU or otherwise TPU.')
flags.DEFINE_bool('use_bfloat16', False, 'Whether to use mixed precision.')
flags.DEFINE_integer('num_cores', 8, 'Number of TPU cores or number of GPUs.')
flags.DEFINE_string('tpu', None,
                    'Name of the TPU. Only used if use_gpu is False.')
FLAGS = flags.FLAGS
NNZ_LOG10_THRESHOLDS = [-2, -3, -4, -5, -6, -7, -8]


def compute_nnz(model, threshold=0.):
  """Compute the percentage of non-zero parameters in model."""
  nnz = 0.
  num_params = 0.
  for var in model.trainable_variables:
    nnz += float(tf.reduce_sum(tf.cast(tf.abs(var) >= threshold, tf.float32)))
    num_params += float(tf.reduce_prod(var.shape))
  return nnz/num_params * 100.


def main(argv):
  del argv  # unused arg
  tf.io.gfile.makedirs(FLAGS.output_dir)
  logging.info('Saving checkpoints at %s', FLAGS.output_dir)
  tf.random.set_seed(FLAGS.seed)

  if FLAGS.use_gpu:
    logging.info('Use GPU')
    strategy = tf.distribute.MirroredStrategy()
  else:
    logging.info('Use TPU at %s',
                 FLAGS.tpu if FLAGS.tpu is not None else 'local')
    resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu=FLAGS.tpu)
    tf.config.experimental_connect_to_cluster(resolver)
    tf.tpu.experimental.initialize_tpu_system(resolver)
    strategy = tf.distribute.TPUStrategy(resolver)

  ds_info = tfds.builder(FLAGS.dataset).info
  train_batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores // FLAGS.batch_repetitions
  test_batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
  train_dataset_size = ds_info.splits['train'].num_examples
  steps_per_epoch = train_dataset_size // train_batch_size
  steps_per_eval = ds_info.splits['test'].num_examples // test_batch_size
  num_classes = ds_info.features['label'].num_classes

  train_dataset = utils.load_dataset(
      split=tfds.Split.TRAIN,
      name=FLAGS.dataset,
      batch_size=train_batch_size,
      use_bfloat16=FLAGS.use_bfloat16)
  clean_test_dataset = utils.load_dataset(
      split=tfds.Split.TEST,
      name=FLAGS.dataset,
      batch_size=test_batch_size,
      use_bfloat16=FLAGS.use_bfloat16)
  train_dataset = strategy.experimental_distribute_dataset(train_dataset)
  test_datasets = {
      'clean': strategy.experimental_distribute_dataset(clean_test_dataset),
  }
  if FLAGS.corruptions_interval > 0:
    if FLAGS.dataset == 'cifar10':
      load_c_dataset = utils.load_cifar10_c
    else:
      load_c_dataset = functools.partial(utils.load_cifar100_c,
                                         path=FLAGS.cifar100_c_path)
    corruption_types, max_intensity = utils.load_corrupted_test_info(
        FLAGS.dataset)
    for corruption in corruption_types:
      for intensity in range(1, max_intensity + 1):
        dataset = load_c_dataset(
            corruption_name=corruption,
            corruption_intensity=intensity,
            batch_size=test_batch_size,
            use_bfloat16=FLAGS.use_bfloat16)
        test_datasets['{0}_{1}'.format(corruption, intensity)] = (
            strategy.experimental_distribute_dataset(dataset))

  if FLAGS.use_bfloat16:
    tf.keras.mixed_precision.set_global_policy('mixed_bfloat16')

  summary_writer = tf.summary.create_file_writer(
      os.path.join(FLAGS.output_dir, 'summaries'))

  with strategy.scope():
    logging.info('Building Keras model')
    model = cifar_model_reg_path.wide_resnet(
        input_shape=[FLAGS.ensemble_size] +
        list(ds_info.features['image'].shape),
        depth=28,
        width_multiplier=FLAGS.width_multiplier,
        num_classes=num_classes,
        ensemble_size=FLAGS.ensemble_size,
        l2=FLAGS.l2, l1=FLAGS.l1)
    logging.info('Model input shape: %s', model.input_shape)
    logging.info('Model output shape: %s', model.output_shape)
    logging.info('Model number of weights: %s', model.count_params())
    # Linearly scale learning rate and the decay epochs by vanilla settings.
    base_lr = FLAGS.base_learning_rate * train_batch_size / 128
    lr_decay_epochs = [(int(start_epoch_str) * FLAGS.train_epochs) // 200
                       for start_epoch_str in FLAGS.lr_decay_epochs]
    lr_schedule = utils.LearningRateSchedule(steps_per_epoch, base_lr,
                                             FLAGS.lr_decay_ratio,
                                             lr_decay_epochs,
                                             FLAGS.lr_warmup_epochs)
    optimizer = tf.keras.optimizers.SGD(
        lr_schedule, momentum=0.9, nesterov=True)
    metrics = {
        'train/negative_log_likelihood': tf.keras.metrics.Mean(),
        'train/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
        'train/loss': tf.keras.metrics.Mean(),
        'train/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
        'test/negative_log_likelihood': tf.keras.metrics.Mean(),
        'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
        'test/ece': um.ExpectedCalibrationError(num_bins=FLAGS.num_bins),
        'test/diversity': rm.metrics.AveragePairwiseDiversity(),
    }

    for log10_threshold in NNZ_LOG10_THRESHOLDS:
      metrics['train/nnz{}'.format(log10_threshold)] = tf.keras.metrics.Mean()

    if FLAGS.corruptions_interval > 0:
      corrupt_metrics = {}
      for intensity in range(1, max_intensity + 1):
        for corruption in corruption_types:
          dataset_name = '{0}_{1}'.format(corruption, intensity)
          corrupt_metrics['test/nll_{}'.format(dataset_name)] = (
              tf.keras.metrics.Mean())
          corrupt_metrics['test/accuracy_{}'.format(dataset_name)] = (
              tf.keras.metrics.SparseCategoricalAccuracy())
          corrupt_metrics['test/ece_{}'.format(dataset_name)] = (
              um.ExpectedCalibrationError(num_bins=FLAGS.num_bins))

    for i in range(FLAGS.ensemble_size):
      metrics['test/nll_member_{}'.format(i)] = tf.keras.metrics.Mean()
      metrics['test/accuracy_member_{}'.format(i)] = (
          tf.keras.metrics.SparseCategoricalAccuracy())

    checkpoint = tf.train.Checkpoint(model=model, optimizer=optimizer)
    latest_checkpoint = tf.train.latest_checkpoint(FLAGS.output_dir)
    initial_epoch = 0
    if latest_checkpoint:
      # checkpoint.restore must be within a strategy.scope() so that optimizer
      # slot variables are mirrored.
      checkpoint.restore(latest_checkpoint)
      logging.info('Loaded checkpoint %s', latest_checkpoint)
      initial_epoch = optimizer.iterations.numpy() // steps_per_epoch

  @tf.function
  def train_step(iterator):
    """Training StepFn."""

    def step_fn(inputs):
      """Per-Replica StepFn."""
      images, labels = inputs
      batch_size = tf.shape(images)[0]

      main_shuffle = tf.random.shuffle(tf.tile(
          tf.range(batch_size), [FLAGS.batch_repetitions]))
      to_shuffle = tf.cast(tf.cast(tf.shape(main_shuffle)[0], tf.float32)
                           * (1. - FLAGS.input_repetition_probability),
                           tf.int32)
      shuffle_indices = [
          tf.concat([tf.random.shuffle(main_shuffle[:to_shuffle]),
                     main_shuffle[to_shuffle:]], axis=0)
          for _ in range(FLAGS.ensemble_size)]
      images = tf.stack([tf.gather(images, indices, axis=0)
                         for indices in shuffle_indices], axis=1)
      labels = tf.stack([tf.gather(labels, indices, axis=0)
                         for indices in shuffle_indices], axis=1)

      with tf.GradientTape() as tape:
        logits = model(images, training=True)
        if FLAGS.use_bfloat16:
          logits = tf.cast(logits, tf.float32)

        negative_log_likelihood = tf.reduce_mean(tf.reduce_sum(
            tf.keras.losses.sparse_categorical_crossentropy(
                labels, logits, from_logits=True), axis=1))

        regularization = sum(model.losses)

        # Scale the loss given the TPUStrategy will reduce sum all gradients.
        loss = negative_log_likelihood + regularization
        scaled_loss = loss / strategy.num_replicas_in_sync

      grads = tape.gradient(scaled_loss, model.trainable_variables)
      optimizer.apply_gradients(zip(grads, model.trainable_variables))

      probs = tf.nn.softmax(tf.reshape(logits, [-1, num_classes]))
      flat_labels = tf.reshape(labels, [-1])
      metrics['train/ece'].update_state(flat_labels, probs)
      metrics['train/loss'].update_state(loss)
      metrics['train/negative_log_likelihood'].update_state(
          negative_log_likelihood)
      metrics['train/accuracy'].update_state(flat_labels, probs)

      for log10_threshold in NNZ_LOG10_THRESHOLDS:
        nnz = compute_nnz(model, threshold=10.**log10_threshold)
        metrics['train/nnz{}'.format(log10_threshold)].update_state(nnz)

    strategy.run(step_fn, args=(next(iterator),))

  @tf.function
  def test_step(iterator, dataset_name):
    """Evaluation StepFn."""

    def step_fn(inputs):
      """Per-Replica StepFn."""
      images, labels = inputs
      images = tf.tile(
          tf.expand_dims(images, 1), [1, FLAGS.ensemble_size, 1, 1, 1])
      logits = model(images, training=False)
      if FLAGS.use_bfloat16:
        logits = tf.cast(logits, tf.float32)
      probs = tf.nn.softmax(logits)

      if dataset_name == 'clean':
        per_probs = tf.transpose(probs, perm=[1, 0, 2])
        metrics['test/diversity'].add_batch(per_probs)

      for i in range(FLAGS.ensemble_size):
        member_probs = probs[:, i]
        member_loss = tf.keras.losses.sparse_categorical_crossentropy(
            labels, member_probs)
        metrics['test/nll_member_{}'.format(i)].update_state(member_loss)
        metrics['test/accuracy_member_{}'.format(i)].update_state(
            labels, member_probs)

      # Negative log marginal likelihood computed in a numerically-stable way.
      labels_tiled = tf.tile(
          tf.expand_dims(labels, 1), [1, FLAGS.ensemble_size])
      log_likelihoods = -tf.keras.losses.sparse_categorical_crossentropy(
          labels_tiled, logits, from_logits=True)
      negative_log_likelihood = tf.reduce_mean(
          -tf.reduce_logsumexp(log_likelihoods, axis=[1]) +
          tf.math.log(float(FLAGS.ensemble_size)))
      probs = tf.math.reduce_mean(probs, axis=1)  # marginalize

      if dataset_name == 'clean':
        metrics['test/negative_log_likelihood'].update_state(
            negative_log_likelihood)
        metrics['test/accuracy'].update_state(labels, probs)
        metrics['test/ece'].update_state(labels, probs)
      else:
        corrupt_metrics['test/nll_{}'.format(dataset_name)].update_state(
            negative_log_likelihood)
        corrupt_metrics['test/accuracy_{}'.format(dataset_name)].update_state(
            labels, probs)
        corrupt_metrics['test/ece_{}'.format(dataset_name)].update_state(
            labels, probs)

    strategy.run(step_fn, args=(next(iterator),))

  metrics.update({'test/ms_per_example': tf.keras.metrics.Mean()})

  train_iterator = iter(train_dataset)
  start_time = time.time()
  for epoch in range(initial_epoch, FLAGS.train_epochs):
    logging.info('Starting to run epoch: %s', epoch)

    for step in range(steps_per_epoch):
      train_step(train_iterator)

      current_step = epoch * steps_per_epoch + (step + 1)
      max_steps = steps_per_epoch * (FLAGS.train_epochs)
      time_elapsed = time.time() - start_time
      steps_per_sec = float(current_step) / time_elapsed
      eta_seconds = (max_steps - current_step) / steps_per_sec
      message = ('{:.1%} completion: epoch {:d}/{:d}. {:.1f} steps/s. '
                 'ETA: {:.0f} min. Time elapsed: {:.0f} min'.format(
                     current_step / max_steps, epoch + 1, FLAGS.train_epochs,
                     steps_per_sec, eta_seconds / 60, time_elapsed / 60))
      if step % 20 == 0:
        logging.info(message)

    datasets_to_evaluate = {'clean': test_datasets['clean']}
    if (FLAGS.corruptions_interval > 0 and
        (epoch + 1) % FLAGS.corruptions_interval == 0):
      datasets_to_evaluate = test_datasets
    for dataset_name, test_dataset in datasets_to_evaluate.items():
      test_iterator = iter(test_dataset)
      logging.info('Testing on dataset %s', dataset_name)
      for step in range(steps_per_eval):
        if step % 20 == 0:
          logging.info('Starting to run eval step %s of epoch: %s', step, epoch)
        test_start_time = time.time()
        test_step(test_iterator, dataset_name)
        ms_per_example = (time.time() - test_start_time) * 1e6 / test_batch_size
        metrics['test/ms_per_example'].update_state(ms_per_example)
      logging.info('Done with testing on %s', dataset_name)

    corrupt_results = {}
    if (FLAGS.corruptions_interval > 0 and
        (epoch + 1) % FLAGS.corruptions_interval == 0):
      corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
                                                        corruption_types,
                                                        max_intensity)

    logging.info('Train Loss: %.4f, Accuracy: %.2f%%',
                 metrics['train/loss'].result(),
                 metrics['train/accuracy'].result() * 100)
    logging.info('Test NLL: %.4f, Accuracy: %.2f%%',
                 metrics['test/negative_log_likelihood'].result(),
                 metrics['test/accuracy'].result() * 100)
    for i in range(FLAGS.ensemble_size):
      logging.info(
          'Member %d Test Loss: %.4f, Accuracy: %.2f%%', i,
          metrics['test/nll_member_{}'.format(i)].result(),
          metrics['test/accuracy_member_{}'.format(i)].result() * 100)

    total_results = {name: metric.result() for name, metric in metrics.items()}
    total_results.update(corrupt_results)
    # Results from Robustness Metrics themselves return a dict, so flatten them.
    total_results = utils.flatten_dictionary(total_results)
    with summary_writer.as_default():
      for name, result in total_results.items():
        tf.summary.scalar(name, result, step=epoch + 1)

    for metric in metrics.values():
      metric.reset_states()
    for metric in corrupt_metrics.values():
      metric.reset_states()

    if (FLAGS.checkpoint_interval > 0 and
        (epoch + 1) % FLAGS.checkpoint_interval == 0):
      checkpoint_name = checkpoint.save(
          os.path.join(FLAGS.output_dir, 'checkpoint'))
      logging.info('Saved checkpoint to %s', checkpoint_name)

  final_checkpoint_name = checkpoint.save(
      os.path.join(FLAGS.output_dir, 'checkpoint'))
  logging.info('Saved last checkpoint to %s', final_checkpoint_name)


if __name__ == '__main__':
  app.run(main)
