text: # Copyright 2017 The TensorFlow Authors. All Rights Reserved.
  #
  # 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 script for TensorFlow.
  
  See the README for more information.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl import app
  from absl import flags as absl_flags
  import tensorflow.compat.v1 as tf
  
  import benchmark_cnn
  import cnn_util
  import flags
  import mlperf
  from cnn_util import log_fn
  
  class Unbuffered(object):
     def __init__(self, stream):
         self.stream = stream
     def write(self, data):
         self.stream.write(data)
         self.stream.flush()
     def writelines(self, datas):
         self.stream.writelines(datas)
         self.stream.flush()
     def __getattr__(self, attr):
         return getattr(self.stream, attr)
  import sys
  sys.stdout = Unbuffered(sys.stdout)
  
  flags.define_flags()
  for name in flags.param_specs.keys():
    absl_flags.declare_key_flag(name)
  
  absl_flags.DEFINE_boolean(
      'ml_perf_compliance_logging', False,
      'Print logs required to be compliant with MLPerf. If set, must clone the '
      'MLPerf training repo https://github.com/mlperf/training and add '
      'https://github.com/mlperf/training/tree/master/compliance to the '
      'PYTHONPATH')
  
  
  def main(positional_arguments):
    # Command-line arguments like '--distortions False' are equivalent to
    # '--distortions=True False', where False is a positional argument. To prevent
    # this from silently running with distortions, we do not allow positional
    # arguments.
    assert len(positional_arguments) >= 1
    if len(positional_arguments) > 1:
      raise ValueError('Received unknown positional arguments: %s'
                       % positional_arguments[1:])
  
    params = benchmark_cnn.make_params_from_flags()
    with mlperf.mlperf_logger(absl_flags.FLAGS.ml_perf_compliance_logging,
                              params.model):
      params = benchmark_cnn.setup(params)
      bench = benchmark_cnn.BenchmarkCNN(params)
  
      tfversion = cnn_util.tensorflow_version_tuple()
      log_fn('TensorFlow:  %i.%i' % (tfversion[0], tfversion[1]))
  
      bench.print_info()
      bench.run()
  
  
  if __name__ == '__main__':
    tf.disable_v2_behavior()
    app.run(main)  # Raises error on invalid flags, unlike tf.app.run()
  