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.
  """Tests for drums_rnn_create_dataset."""
  
  import tensorflow as tf
  import magenta
  
  from magenta.models.drums_rnn import drums_rnn_pipeline
  from magenta.models.shared import events_rnn_model
  from magenta.pipelines import drum_pipelines
  from magenta.pipelines import note_sequence_pipelines
  from magenta.protobuf import music_pb2
  
  
  FLAGS = tf.app.flags.FLAGS
  
  
  class DrumsRNNPipelineTest(tf.test.TestCase):
  
    def setUp(self):
      self.config = events_rnn_model.EventSequenceRnnConfig(
          None,
          magenta.music.OneHotEventSequenceEncoderDecoder(
              magenta.music.MultiDrumOneHotEncoding()),
          tf.contrib.training.HParams())
  
    def testDrumsRNNPipeline(self):
      note_sequence = magenta.common.testing_lib.parse_test_proto(
          music_pb2.NoteSequence,
          """
          time_signatures: {
            numerator: 4
            denominator: 4}
          tempos: {
            qpm: 120}""")
      magenta.music.testing_lib.add_track_to_sequence(
          note_sequence, 0,
          [(36, 100, 0.00, 2.0), (40, 55, 2.1, 5.0), (44, 80, 3.6, 5.0),
           (41, 45, 5.1, 8.0), (64, 100, 6.6, 10.0), (55, 120, 8.1, 11.0),
           (39, 110, 9.6, 9.7), (53, 99, 11.1, 14.1), (51, 40, 12.6, 13.0),
           (55, 100, 14.1, 15.0), (54, 90, 15.6, 17.0), (60, 100, 17.1, 18.0)],
          is_drum=True)
  
      quantizer = note_sequence_pipelines.Quantizer(steps_per_quarter=4)
      drums_extractor = drum_pipelines.DrumsExtractor(min_bars=7, gap_bars=1.0)
      one_hot_encoding = magenta.music.OneHotEventSequenceEncoderDecoder(
          magenta.music.MultiDrumOneHotEncoding())
      quantized = quantizer.transform(note_sequence)[0]
      drums = drums_extractor.transform(quantized)[0]
      one_hot = one_hot_encoding.encode(drums)
      expected_result = {'training_drum_tracks': [one_hot],
                         'eval_drum_tracks': []}
  
      pipeline_inst = drums_rnn_pipeline.get_pipeline(
          self.config, eval_ratio=0.0)
      result = pipeline_inst.transform(note_sequence)
      self.assertEqual(expected_result, result)
  
  
  if __name__ == '__main__':
    tf.test.main()
  