text: # Copyright 2016 Google Inc. 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.
  """Create a dataset of SequenceExamples from NoteSequence protos.
  
  This script will extract melodies from NoteSequence protos and save them to
  TensorFlow's SequenceExample protos for input to the melody RNN models.
  """
  
  import os
  
  from magenta.models.melody_rnn import melody_rnn_config_flags
  from magenta.models.melody_rnn import melody_rnn_pipeline
  from magenta.pipelines import pipeline
  import tensorflow as tf
  
  flags = tf.app.flags
  FLAGS = tf.app.flags.FLAGS
  flags.DEFINE_string(
      'input', None,
      'TFRecord to read NoteSequence protos from.')
  flags.DEFINE_string(
      'output_dir', None,
      'Directory to write training and eval TFRecord files. The TFRecord files '
      'are populated with  SequenceExample protos.')
  flags.DEFINE_float(
      'eval_ratio', 0.1,
      'Fraction of input to set aside for eval set. Partition is randomly '
      'selected.')
  flags.DEFINE_string(
      'log', 'INFO',
      'The threshold for what messages will be logged DEBUG, INFO, WARN, ERROR, '
      'or FATAL.')
  
  
  def main(unused_argv):
    tf.logging.set_verbosity(FLAGS.log)
  
    config = melody_rnn_config_flags.config_from_flags()
    pipeline_instance = melody_rnn_pipeline.get_pipeline(
        config, eval_ratio=FLAGS.eval_ratio)
  
    FLAGS.input = os.path.expanduser(FLAGS.input)
    FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
    pipeline.run_pipeline_serial(
        pipeline_instance,
        pipeline.tf_record_iterator(FLAGS.input, pipeline_instance.input_type),
        FLAGS.output_dir)
  
  
  def console_entry_point():
    tf.app.run(main)
  
  
  if __name__ == '__main__':
    console_entry_point()
  