# coding=utf-8
# Copyright 2023 The TensorFlow GAN 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.
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"""Trains a GANEstimator on MNIST data using `train_and_evaluate`."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from absl import app
from absl import flags

import tensorflow.compat.v1 as tf
from tensorflow_gan.examples.mnist_estimator import train_experiment_lib

# ML Hparams.
flags.DEFINE_integer('batch_size', 32,
                     'The number of images in each batch.')
flags.DEFINE_integer(
    'noise_dims', 64, 'Dimensions of the generator noise vector')
flags.DEFINE_float('generator_lr', 0.000076421, 'The generator learning rate.')
flags.DEFINE_float('discriminator_lr', 0.0031938,
                   'The discriminator learning rate.')
flags.DEFINE_bool('joint_train', False,
                  'Whether to jointly or sequentially train the generator and '
                  'discriminator.')

# ML Infra.
flags.DEFINE_string('model_dir', '/tmp/tfgan_logdir/mnist-estimator-tae',
                    'Optional location to save model. If `None`, use a '
                    'default provided by tf.Estimator.')
flags.DEFINE_integer('num_train_steps', 20000,
                     'The maximum number of gradient steps.')
flags.DEFINE_integer('num_eval_steps', 400,
                     'The number of evaluation steps.')
flags.DEFINE_integer('num_reader_parallel_calls', 4,
                     'Number of parallel calls in the input dataset.')
flags.DEFINE_boolean('use_dummy_data', False,
                     'Whether to use fake data. Used for testing.')

FLAGS = flags.FLAGS


def main(_):
  hparams = train_experiment_lib.HParams(
      FLAGS.generator_lr, FLAGS.discriminator_lr, FLAGS.joint_train,
      FLAGS.batch_size, FLAGS.noise_dims, FLAGS.model_dir,
      FLAGS.num_train_steps, FLAGS.num_eval_steps,
      FLAGS.num_reader_parallel_calls, FLAGS.use_dummy_data)
  train_experiment_lib.train(hparams)


if __name__ == '__main__':
  tf.disable_v2_behavior()
  app.run(main)
