old_contents: # Copyright 2021 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 ImageNet example using fake data for quick perf results."""
  
  import pathlib
  import time
  
  from absl.testing import absltest
  from absl.testing.flagsaver import flagsaver
  from flax.testing import Benchmark
  import jax
  
  import tensorflow_datasets as tfds
  
  # Local imports.
  from configs import fake_data_benchmark as config_lib
  import train
  
  # Parse absl flags test_srcdir and test_tmpdir.
  jax.config.parse_flags_with_absl()
  
  
  class ImagenetBenchmarkFakeData(Benchmark):
    """Runs ImageNet using fake data for quickly measuring performance."""
  
    def test_fake_data(self):
      workdir = self.get_tmp_model_dir()
      config = config_lib.get_config()
      # Go two directories up to the root of the flax directory.
      flax_root_dir = pathlib.Path(__file__).parents[2]
      data_dir = str(flax_root_dir) + '/.tfds/metadata'
  
      start_time = time.time()
      with tfds.testing.mock_data(num_examples=1024, data_dir=data_dir):
        train.train_and_evaluate(config, workdir)
      benchmark_time = time.time() - start_time
  
      self.report_wall_time(benchmark_time)
      self.report_extras({
          'description': 'ImageNet ResNet50 with fake data',
          'model_name': 'resnet50',
          'parameters': f'hp=true,bs={config.batch_size}',
      })
  
  
  if __name__ == '__main__':
    absltest.main()
  
new_contents: # Copyright 2021 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 ImageNet example using fake data for quick perf results."""
  
  import pathlib
  import time
  
  from absl.testing import absltest
  from absl.testing.flagsaver import flagsaver
  from flax.testing import Benchmark
  import jax
  
  import tensorflow_datasets as tfds
  
  # Local imports.
  from configs import fake_data_benchmark as config_lib
  import train
  
  # Parse absl flags test_srcdir and test_tmpdir.
  jax.config.parse_flags_with_absl()
  
  
  class ImagenetBenchmarkFakeData(Benchmark):
    """Runs ImageNet using fake data for quickly measuring performance."""
  
    def test_fake_data(self):
      workdir = self.get_tmp_model_dir()
      config = config_lib.get_config()
      # Go two directories up to the root of the flax directory.
      flax_root_dir = pathlib.Path(__file__).parents[2]
      data_dir = str(flax_root_dir) + '/.tfds/metadata'
  
      # Warm-up first so that we are not measuring just compilation.
      with tfds.testing.mock_data(num_examples=1024, data_dir=data_dir):
        train.train_and_evaluate(config, workdir)
      
      start_time = time.time()
      with tfds.testing.mock_data(num_examples=1024, data_dir=data_dir):
        train.train_and_evaluate(config, workdir)
      benchmark_time = time.time() - start_time
  
      self.report_wall_time(benchmark_time)
      self.report_extras({
          'description': 'ImageNet ResNet50 with fake data',
          'model_name': 'resnet50',
          'parameters': f'hp=true,bs={config.batch_size}',
      })
  
  
  if __name__ == '__main__':
    absltest.main()
  
subject: Update imagenet_fake_data_benchmark to include warmup phase