text: # Copyright 2018 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.
  # ==============================================================================
  """Helper code to run complete models from within python.
  """
  
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  import os
  import shutil
  import sys
  import tempfile
  
  from absl import flags
  
  from official.utils.flags import core as flags_core
  
  
  def run_synthetic(main, tmp_root, extra_flags=None, synth=True, max_train=1):
    """Performs a minimal run of a model.
  
      This function is intended to test for syntax errors throughout a model. A
    very limited run is performed using synthetic data.
  
    Args:
      main: The primary function used to exercise a code path. Generally this
        function is "<MODULE>.main(argv)".
      tmp_root: Root path for the temp directory created by the test class.
      extra_flags: Additional flags passed by the caller of this function.
      synth: Use synthetic data.
      max_train: Maximum number of allowed training steps.
    """
  
    extra_flags = [] if extra_flags is None else extra_flags
  
    model_dir = tempfile.mkdtemp(dir=tmp_root)
  
    args = [sys.argv[0], "--model_dir", model_dir, "--train_epochs", "1",
            "--epochs_between_evals", "1"] + extra_flags
  
    if synth:
      args.append("--use_synthetic_data")
  
    if max_train is not None:
      args.extend(["--max_train_steps", str(max_train)])
  
    try:
      flags_core.parse_flags(argv=args)
      main(flags.FLAGS)
    finally:
      if os.path.exists(model_dir):
        shutil.rmtree(model_dir)
  