# coding=utf-8
# Copyright 2021 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.

"""Ensemble on ImageNet.

This script only performs evaluation, not training. We recommend training
ensembles by launching independent runs of `deterministic.py` over different
seeds.
"""

import os

from absl import app
from absl import flags
from absl import logging

import numpy as np
import robustness_metrics as rm
import tensorflow as tf
import tensorflow_datasets as tfds
import uncertainty_baselines as ub
import utils  # local file import

flags.DEFINE_integer('per_core_batch_size', 64, 'Batch size per TPU core/GPU.')
flags.DEFINE_integer('seed', 0, 'Random seed.')
flags.DEFINE_string('data_dir', None, 'Path to training and testing data.')
flags.mark_flag_as_required('data_dir')
flags.DEFINE_string('checkpoint_dir', None,
                    'The directory where the model weights are stored.')
flags.mark_flag_as_required('checkpoint_dir')
flags.DEFINE_string('output_dir', '/tmp/imagenet',
                    'The directory where to save predictions.')
flags.DEFINE_string('alexnet_errors_path', None,
                    'Path to AlexNet corruption errors file.')
flags.DEFINE_integer('num_bins', 15, 'Number of bins for ECE computation.')

# Accelerator flags.
flags.DEFINE_bool('use_gpu', True, 'Whether to run on GPU or otherwise TPU.')
flags.DEFINE_integer('num_cores', 1, 'Number of TPU cores or number of GPUs.')
flags.DEFINE_string('tpu', None,
                    'Name of the TPU. Only used if use_gpu is False.')

# Heteroscedastic flags.
flags.DEFINE_integer('num_factors', 15,
                     'Num factors to approximate full rank covariance matrix.')
flags.DEFINE_float('temperature', 1.5,
                   'Temperature for heteroscedastic head.')
flags.DEFINE_integer('num_mc_samples', 5000,
                     'Num MC samples for heteroscedastic layer.')

FLAGS = flags.FLAGS

# Number of images in eval dataset.
IMAGENET_VALIDATION_IMAGES = 50000
NUM_CLASSES = 1000


def main(argv):
  del argv  # unused arg
  if not FLAGS.use_gpu:
    raise ValueError('Only GPU is currently supported.')
  if FLAGS.num_cores > 1:
    raise ValueError('Only a single accelerator is currently supported.')
  tf.random.set_seed(FLAGS.seed)
  tf.io.gfile.makedirs(FLAGS.output_dir)

  batch_size = FLAGS.per_core_batch_size * FLAGS.num_cores
  steps_per_eval = IMAGENET_VALIDATION_IMAGES // batch_size

  builder = ub.datasets.ImageNetDataset(
      split=tfds.Split.TEST,
      use_bfloat16=False)
  clean_test_dataset = builder.load(batch_size=batch_size)
  test_datasets = {'clean': clean_test_dataset}
  corruption_types, max_intensity = utils.load_corrupted_test_info()
  for name in corruption_types:
    for intensity in range(1, max_intensity + 1):
      dataset_name = '{0}_{1}'.format(name, intensity)
      test_datasets[dataset_name] = utils.load_corrupted_test_dataset(
          corruption_name=name,
          corruption_intensity=intensity,
          batch_size=batch_size,
          drop_remainder=True,
          use_bfloat16=False)

  model = ub.models.resnet50_heteroscedastic(
      input_shape=(224, 224, 3), num_classes=NUM_CLASSES,
      temperature=FLAGS.temperature, num_factors=FLAGS.num_factors,
      num_mc_samples=FLAGS.num_mc_samples)

  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())
  # Search for checkpoints from their index file; then remove the index suffix.
  ensemble_filenames = tf.io.gfile.glob(os.path.join(FLAGS.checkpoint_dir,
                                                     '**/*.index'))
  ensemble_filenames = [filename[:-6] for filename in ensemble_filenames]
  ensemble_size = len(ensemble_filenames)
  logging.info('Ensemble size: %s', ensemble_size)
  logging.info('Ensemble number of weights: %s',
               ensemble_size * model.count_params())
  logging.info('Ensemble filenames: %s', str(ensemble_filenames))
  checkpoint = tf.train.Checkpoint(model=model)

  # Write model predictions to files.
  num_datasets = len(test_datasets)
  for m, ensemble_filename in enumerate(ensemble_filenames):
    checkpoint.restore(ensemble_filename)
    for n, (name, test_dataset) in enumerate(test_datasets.items()):
      filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
      filename = os.path.join(FLAGS.output_dir, filename)
      if not tf.io.gfile.exists(filename):
        logits = []
        test_iterator = iter(test_dataset)
        for _ in range(steps_per_eval):
          features, _ = next(test_iterator)  # pytype: disable=attribute-error
          logits.append(model(features, training=False))

        logits = tf.concat(logits, axis=0)
        with tf.io.gfile.GFile(filename, 'w') as f:
          np.save(f, logits.numpy())
      percent = (m * num_datasets + (n + 1)) / (ensemble_size * num_datasets)
      message = ('{:.1%} completion for prediction: ensemble member {:d}/{:d}. '
                 'Dataset {:d}/{:d}'.format(percent,
                                            m + 1,
                                            ensemble_size,
                                            n + 1,
                                            num_datasets))
      logging.info(message)

  metrics = {
      'test/negative_log_likelihood': tf.keras.metrics.Mean(),
      'test/gibbs_cross_entropy': tf.keras.metrics.Mean(),
      'test/accuracy': tf.keras.metrics.SparseCategoricalAccuracy(),
      'test/ece': rm.metrics.ExpectedCalibrationError(
          num_bins=FLAGS.num_bins),
  }
  corrupt_metrics = {}
  for name in test_datasets:
    corrupt_metrics['test/nll_{}'.format(name)] = tf.keras.metrics.Mean()
    corrupt_metrics['test/accuracy_{}'.format(name)] = (
        tf.keras.metrics.SparseCategoricalAccuracy())
    corrupt_metrics['test/ece_{}'.format(
        name)] = rm.metrics.ExpectedCalibrationError(num_bins=FLAGS.num_bins)

  # Evaluate model predictions.
  for n, (name, test_dataset) in enumerate(test_datasets.items()):
    logits_dataset = []
    for m in range(ensemble_size):
      filename = '{dataset}_{member}.npy'.format(dataset=name, member=m)
      filename = os.path.join(FLAGS.output_dir, filename)
      with tf.io.gfile.GFile(filename, 'rb') as f:
        logits_dataset.append(np.load(f))

    logits_dataset = tf.convert_to_tensor(logits_dataset)
    test_iterator = iter(test_dataset)
    for step in range(steps_per_eval):
      _, labels = next(test_iterator)  # pytype: disable=attribute-error
      logits = logits_dataset[:, (step*batch_size):((step+1)*batch_size)]
      labels = tf.cast(tf.reshape(labels, [-1]), tf.int32)
      negative_log_likelihood_metric = rm.metrics.EnsembleCrossEntropy()
      negative_log_likelihood_metric.add_batch(logits, labels=labels)
      negative_log_likelihood = list(
          negative_log_likelihood_metric.result().values())[0]
      per_probs = tf.nn.softmax(logits)
      probs = tf.reduce_mean(per_probs, axis=0)
      if name == 'clean':
        gibbs_ce_metric = rm.metrics.GibbsCrossEntropy()
        gibbs_ce_metric.add_batch(logits, labels=labels)
        gibbs_ce = list(gibbs_ce_metric.result().values())[0]
        metrics['test/negative_log_likelihood'].update_state(
            negative_log_likelihood)
        metrics['test/gibbs_cross_entropy'].update_state(gibbs_ce)
        metrics['test/accuracy'].update_state(labels, probs)
        metrics['test/ece'].add_batch(probs, label=labels)
      else:
        corrupt_metrics['test/nll_{}'.format(name)].update_state(
            negative_log_likelihood)
        corrupt_metrics['test/accuracy_{}'.format(name)].update_state(
            labels, probs)
        corrupt_metrics['test/ece_{}'.format(name)].add_batch(
            probs, label=labels)

    message = ('{:.1%} completion for evaluation: dataset {:d}/{:d}'.format(
        (n + 1) / num_datasets, n + 1, num_datasets))
    logging.info(message)

  corrupt_results = utils.aggregate_corrupt_metrics(corrupt_metrics,
                                                    corruption_types,
                                                    max_intensity,
                                                    FLAGS.alexnet_errors_path)
  total_results = {name: metric.result() for name, metric in metrics.items()}
  # Metrics from Robustness Metrics (like ECE) will return a dict with a
  # single key/value, instead of a scalar.
  total_results.update(corrupt_results)
  total_results = {
      k: (list(v.values())[0] if isinstance(v, dict) else v)
      for k, v in total_results.items()
  }
  logging.info('Metrics: %s', total_results)


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