# Copyright 2023 The Flax 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.

"""Benchmark for the MNIST example."""
import time

from absl import flags
from absl.testing import absltest
from absl.testing.flagsaver import flagsaver
from flax.testing import Benchmark
import jax
import numpy as np

import main
from configs import default


# Parse absl flags test_srcdir and test_tmpdir.
jax.config.parse_flags_with_absl()

FLAGS = flags.FLAGS


class MnistBenchmark(Benchmark):
  """Benchmarks for the MNIST Flax example."""

  @flagsaver
  def test_cpu(self):
    """Run full training for MNIST CPU training."""
    # Prepare and set flags defined in main.py.
    workdir = self.get_tmp_model_dir()
    config = default.get_config()

    FLAGS.workdir = workdir
    FLAGS.config = config

    start_time = time.time()
    main.main([])
    benchmark_time = time.time() - start_time

    summaries = self.read_summaries(workdir)

    # Summaries contain all the information necessary for the regression
    # metrics.
    wall_time, _, eval_accuracy = zip(*summaries['eval_accuracy'])
    wall_time = np.array(wall_time)
    sec_per_epoch = np.mean(wall_time[1:] - wall_time[:-1])
    end_eval_accuracy = eval_accuracy[-1]

    # Assertions are deferred until the test finishes, so the metrics are
    # always reported and benchmark success is determined based on *all*
    # assertions.
    self.assertBetween(end_eval_accuracy, 0.98, 1.0)

    # Use the reporting API to report single or multiple metrics/extras.
    self.report_wall_time(benchmark_time)
    self.report_metrics({
        'sec_per_epoch': sec_per_epoch,
        'accuracy': end_eval_accuracy,
    })
    self.report_extras({
        'model_name': 'MNIST',
        'description': 'CPU test for MNIST.',
        'implementation': 'linen',
    })


if __name__ == '__main__':
  absltest.main()
