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.
  """Generate drum tracks from a trained checkpoint of a drums RNN model.
  
  Uses flags to define operation.
  """
  
  import ast
  import os
  import time
  
  import magenta
  from magenta.models.drums_rnn import drums_rnn_config_flags
  from magenta.models.drums_rnn import drums_rnn_model
  from magenta.models.drums_rnn import drums_rnn_sequence_generator
  from magenta.protobuf import generator_pb2
  from magenta.protobuf import music_pb2
  import tensorflow as tf
  
  FLAGS = tf.app.flags.FLAGS
  tf.app.flags.DEFINE_string(
      'run_dir', None,
      'Path to the directory where the latest checkpoint will be loaded from.')
  tf.app.flags.DEFINE_string(
      'checkpoint_file', None,
      'Path to the checkpoint file. run_dir will take priority over this flag.')
  tf.app.flags.DEFINE_string(
      'bundle_file', None,
      'Path to the bundle file. If specified, this will take priority over '
      'run_dir and checkpoint_file, unless save_generator_bundle is True, in '
      'which case both this flag and either run_dir or checkpoint_file are '
      'required')
  tf.app.flags.DEFINE_boolean(
      'save_generator_bundle', False,
      'If true, instead of generating a sequence, will save this generator as a '
      'bundle file in the location specified by the bundle_file flag')
  tf.app.flags.DEFINE_string(
      'bundle_description', None,
      'A short, human-readable text description of the bundle (e.g., training '
      'data, hyper parameters, etc.).')
  tf.app.flags.DEFINE_string(
      'output_dir', '/tmp/drums_rnn/generated',
      'The directory where MIDI files will be saved to.')
  tf.app.flags.DEFINE_integer(
      'num_outputs', 10,
      'The number of drum tracks to generate. One MIDI file will be created for '
      'each.')
  tf.app.flags.DEFINE_integer(
      'num_steps', 128,
      'The total number of steps the generated drum tracks should be, priming '
      'drum track length + generated steps. Each step is a 16th of a bar.')
  tf.app.flags.DEFINE_string(
      'primer_drums', '',
      'A string representation of a Python list of tuples containing drum pitch '
      'values. For example: '
      '"[(36,42),(),(),(),(42,),(),(),()]". If specified, this drum track will '
      'be used as the priming drum track. If a priming drum track is not '
      'specified, drum tracks will be generated from scratch.')
  tf.app.flags.DEFINE_string(
      'primer_midi', '',
      'The path to a MIDI file containing a drum track that will be used as a '
      'priming drum track. If a primer drum track is not specified, drum tracks '
      'will be generated from scratch.')
  tf.app.flags.DEFINE_float(
      'qpm', None,
      'The quarters per minute to play generated output at. If a primer MIDI is '
      'given, the qpm from that will override this flag. If qpm is None, qpm '
      'will default to 120.')
  tf.app.flags.DEFINE_float(
      'temperature', 1.0,
      'The randomness of the generated drum tracks. 1.0 uses the unaltered '
      'softmax probabilities, greater than 1.0 makes tracks more random, less '
      'than 1.0 makes tracks less random.')
  tf.app.flags.DEFINE_integer(
      'beam_size', 1,
      'The beam size to use for beam search when generating drum tracks.')
  tf.app.flags.DEFINE_integer(
      'branch_factor', 1,
      'The branch factor to use for beam search when generating drum tracks.')
  tf.app.flags.DEFINE_integer(
      'steps_per_iteration', 1,
      'The number of steps to take per beam search iteration.')
  tf.app.flags.DEFINE_string(
      'log', 'INFO',
      'The threshold for what messages will be logged DEBUG, INFO, WARN, ERROR, '
      'or FATAL.')
  
  
  def get_checkpoint():
    """Get the training dir or checkpoint path to be used by the model."""
    if ((FLAGS.run_dir or FLAGS.checkpoint_file) and
        FLAGS.bundle_file and not FLAGS.save_generator_bundle):
      raise magenta.music.SequenceGeneratorError(
          'Cannot specify both bundle_file and run_dir or checkpoint_file')
    if FLAGS.run_dir:
      train_dir = os.path.join(os.path.expanduser(FLAGS.run_dir), 'train')
      return train_dir
    elif FLAGS.checkpoint_file:
      return os.path.expanduser(FLAGS.checkpoint_file)
    else:
      return None
  
  
  def get_bundle():
    """Returns a generator_pb2.GeneratorBundle object based read from bundle_file.
  
    Returns:
      Either a generator_pb2.GeneratorBundle or None if the bundle_file flag is
      not set or the save_generator_bundle flag is set.
    """
    if FLAGS.save_generator_bundle:
      return None
    if FLAGS.bundle_file is None:
      return None
    bundle_file = os.path.expanduser(FLAGS.bundle_file)
    return magenta.music.read_bundle_file(bundle_file)
  
  
  def run_with_flags(generator):
    """Generates drum tracks and saves them as MIDI files.
  
    Uses the options specified by the flags defined in this module.
  
    Args:
      generator: The DrumsRnnSequenceGenerator to use for generation.
    """
    if not FLAGS.output_dir:
      tf.logging.fatal('--output_dir required')
      return
    FLAGS.output_dir = os.path.expanduser(FLAGS.output_dir)
  
    primer_midi = None
    if FLAGS.primer_midi:
      primer_midi = os.path.expanduser(FLAGS.primer_midi)
  
    if not tf.gfile.Exists(FLAGS.output_dir):
      tf.gfile.MakeDirs(FLAGS.output_dir)
  
    primer_sequence = None
    qpm = FLAGS.qpm if FLAGS.qpm else magenta.music.DEFAULT_QUARTERS_PER_MINUTE
    if FLAGS.primer_drums:
      primer_drums = magenta.music.DrumTrack(
          [frozenset(pitches)
           for pitches in ast.literal_eval(FLAGS.primer_drums)])
      primer_sequence = primer_drums.to_sequence(qpm=qpm)
    elif primer_midi:
      primer_sequence = magenta.music.midi_file_to_sequence_proto(primer_midi)
      if primer_sequence.tempos and primer_sequence.tempos[0].qpm:
        qpm = primer_sequence.tempos[0].qpm
    else:
      tf.logging.warning(
          'No priming sequence specified. Defaulting to a single bass drum hit.')
      primer_drums = magenta.music.DrumTrack([frozenset([36])])
      primer_sequence = primer_drums.to_sequence(qpm=qpm)
  
    # Derive the total number of seconds to generate based on the QPM of the
    # priming sequence and the num_steps flag.
    seconds_per_step = 60.0 / qpm / generator.steps_per_quarter
    total_seconds = FLAGS.num_steps * seconds_per_step
  
    # Specify start/stop time for generation based on starting generation at the
    # end of the priming sequence and continuing until the sequence is num_steps
    # long.
    generator_options = generator_pb2.GeneratorOptions()
    if primer_sequence:
      input_sequence = primer_sequence
      # Set the start time to begin on the next step after the last note ends.
      if primer_sequence.notes:
        last_end_time = max(n.end_time for n in primer_sequence.notes)
      else:
        last_end_time = 0
      generate_section = generator_options.generate_sections.add(
          start_time=last_end_time + seconds_per_step,
          end_time=total_seconds)
  
      if generate_section.start_time >= generate_section.end_time:
        tf.logging.fatal(
            'Priming sequence is longer than the total number of steps '
            'requested: Priming sequence length: %s, Generation length '
            'requested: %s',
            generate_section.start_time, total_seconds)
        return
    else:
      input_sequence = music_pb2.NoteSequence()
      input_sequence.tempos.add().qpm = qpm
      generate_section = generator_options.generate_sections.add(
          start_time=0,
          end_time=total_seconds)
    generator_options.args['temperature'].float_value = FLAGS.temperature
    generator_options.args['beam_size'].int_value = FLAGS.beam_size
    generator_options.args['branch_factor'].int_value = FLAGS.branch_factor
    generator_options.args[
        'steps_per_iteration'].int_value = FLAGS.steps_per_iteration
    tf.logging.debug('input_sequence: %s', input_sequence)
    tf.logging.debug('generator_options: %s', generator_options)
  
    # Make the generate request num_outputs times and save the output as midi
    # files.
    date_and_time = time.strftime('%Y-%m-%d_%H%M%S')
    digits = len(str(FLAGS.num_outputs))
    for i in range(FLAGS.num_outputs):
      generated_sequence = generator.generate(input_sequence, generator_options)
  
      midi_filename = '%s_%s.mid' % (date_and_time, str(i + 1).zfill(digits))
      midi_path = os.path.join(FLAGS.output_dir, midi_filename)
      magenta.music.sequence_proto_to_midi_file(generated_sequence, midi_path)
  
    tf.logging.info('Wrote %d MIDI files to %s',
                    FLAGS.num_outputs, FLAGS.output_dir)
  
  
  def main(unused_argv):
    """Saves bundle or runs generator based on flags."""
    tf.logging.set_verbosity(FLAGS.log)
  
    bundle = get_bundle()
  
    if bundle:
      config_id = bundle.generator_details.id
      config = drums_rnn_model.default_configs[config_id]
      config.hparams.parse(FLAGS.hparams)
    else:
      config = drums_rnn_config_flags.config_from_flags()
    # Having too large of a batch size will slow generation down unnecessarily.
    config.hparams.batch_size = min(
        config.hparams.batch_size, FLAGS.beam_size * FLAGS.branch_factor)
  
    generator = drums_rnn_sequence_generator.DrumsRnnSequenceGenerator(
        model=drums_rnn_model.DrumsRnnModel(config),
        details=config.details,
        steps_per_quarter=config.steps_per_quarter,
        checkpoint=get_checkpoint(),
        bundle=bundle)
  
    if FLAGS.save_generator_bundle:
      bundle_filename = os.path.expanduser(FLAGS.bundle_file)
      if FLAGS.bundle_description is None:
        tf.logging.warning('No bundle description provided.')
      tf.logging.info('Saving generator bundle to %s', bundle_filename)
      generator.create_bundle_file(bundle_filename, FLAGS.bundle_description)
    else:
      run_with_flags(generator)
  
  
  def console_entry_point():
    tf.app.run(main)
  
  
  if __name__ == '__main__':
    console_entry_point()
  