text: # Copyright 2019 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.
  # ==============================================================================
  """Common flags for importing hyperparameters."""
  
  from __future__ import absolute_import
  from __future__ import division
  # from __future__ import google_type_annotations
  from __future__ import print_function
  
  from absl import flags
  from official.utils.flags import core as flags_core
  
  FLAGS = flags.FLAGS
  
  
  def define_common_hparams_flags():
    """Define the common flags across models."""
  
    flags.DEFINE_string(
        'model_dir',
        default=None,
        help=('The directory where the model and training/evaluation summaries'
              'are stored.'))
  
    flags.DEFINE_integer(
        'train_batch_size', default=None, help='Batch size for training.')
  
    flags.DEFINE_integer(
        'eval_batch_size', default=None, help='Batch size for evaluation.')
  
    flags.DEFINE_string(
        'precision',
        default=None,
        help=('Precision to use; one of: {bfloat16, float32}'))
  
    flags.DEFINE_string(
        'config_file',
        default=None,
        help=('A YAML file which specifies overrides. Note that this file can be '
              'used as an override template to override the default parameters '
              'specified in Python. If the same parameter is specified in both '
              '`--config_file` and `--params_override`, the one in '
              '`--params_override` will be used finally.'))
  
    flags.DEFINE_string(
        'params_override',
        default=None,
        help=('a YAML/JSON string or a YAML file which specifies additional '
              'overrides over the default parameters and those specified in '
              '`--config_file`. Note that this is supposed to be used only to '
              'override the model parameters, but not the parameters like TPU '
              'specific flags. One canonical use case of `--config_file` and '
              '`--params_override` is users first define a template config file '
              'using `--config_file`, then use `--params_override` to adjust the '
              'minimal set of tuning parameters, for example setting up different'
              ' `train_batch_size`. '
              'The final override order of parameters: default_model_params --> '
              'params from config_file --> params in params_override.'
              'See also the help message of `--config_file`.'))
    flags.DEFINE_integer('save_checkpoint_freq', None,
                         'Number of steps to save checkpoint.')
  
  
  def initialize_common_flags():
    """Define the common flags across models."""
    define_common_hparams_flags()
  
    flags_core.define_device(tpu=True)
    flags_core.define_base(
        num_gpu=True, model_dir=False, data_dir=False, batch_size=False)
    flags_core.define_distribution(worker_hosts=True, task_index=True)
    flags_core.define_performance(all_reduce_alg=True, num_packs=True)
  
    # Reset the default value of num_gpus to zero.
    FLAGS.num_gpus = 0
  
    flags.DEFINE_string(
        'strategy_type', 'mirrored', 'Type of distribute strategy.'
        'One of mirrored, tpu and multiworker.')
  
  
  def strategy_flags_dict():
    """Returns TPU and/or GPU related flags in a dictionary."""
    return {
        # TPUStrategy related flags.
        'tpu': FLAGS.tpu,
        # MultiWorkerMirroredStrategy related flags.
        'all_reduce_alg': FLAGS.all_reduce_alg,
        'worker_hosts': FLAGS.worker_hosts,
        'task_index': FLAGS.task_index,
        # MirroredStrategy and OneDeviceStrategy
        'num_gpus': FLAGS.num_gpus,
        'num_packs': FLAGS.num_packs,
    }
  
  
  def hparam_flags_dict():
    """Returns model params related flags in a dictionary."""
    return {
        'data_dir': FLAGS.data_dir,
        'model_dir': FLAGS.model_dir,
        'train_batch_size': FLAGS.train_batch_size,
        'eval_batch_size': FLAGS.eval_batch_size,
        'precision': FLAGS.precision,
        'config_file': FLAGS.config_file,
        'params_override': FLAGS.params_override,
    }
  