text: # Copyright 2020 The Magenta 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.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Pipeline to create ImprovRNN dataset."""
  
  import magenta
  from magenta.music.protobuf import music_pb2
  from magenta.pipelines import dag_pipeline
  from magenta.pipelines import lead_sheet_pipelines
  from magenta.pipelines import note_sequence_pipelines
  from magenta.pipelines import pipeline
  from magenta.pipelines import pipelines_common
  from magenta.pipelines import statistics
  import tensorflow.compat.v1 as tf  # noqa
  
  
  
  class EncoderPipeline(pipeline.Pipeline):
      """A Module that converts lead sheets to a model specific encoding."""
  
      def __init__(self, config, name):
          """Constructs an EncoderPipeline.
  
          Args:
            config: An ImprovRnnConfig that specifies the encoder/decoder,
                pitch range, and transposition behavior.
            name: A unique pipeline name.
          """
          super(EncoderPipeline, self).__init__(
              input_type=magenta.music.LeadSheet,
              output_type=tf.train.SequenceExample,
              name=name)
          self._conditional_encoder_decoder = config.encoder_decoder
          self._min_note = config.min_note
          self._max_note = config.max_note
          self._transpose_to_key = config.transpose_to_key
  
      def transform(self, lead_sheet):
          lead_sheet.squash(
              self._min_note,
              self._max_note,
              self._transpose_to_key)
          try:
              encoded = [self._conditional_encoder_decoder.encode(
                  lead_sheet.chords, lead_sheet.melody)]
              stats = []
          except magenta.music.ChordEncodingError as e:
              tf.logging.warning('Skipped lead sheet: %s', e)
              encoded = []
              stats = [statistics.Counter('chord_encoding_exception', 1)]
          except magenta.music.ChordSymbolError as e:
              tf.logging.warning('Skipped lead sheet: %s', e)
              encoded = []
              stats = [statistics.Counter('chord_symbol_exception', 1)]
          self._set_stats(stats)
          return encoded
  
      def get_stats(self):
          return {}
  
  
  def get_pipeline(config, eval_ratio):
      """Returns the Pipeline instance which creates the RNN dataset.
  
      Args:
        config: An ImprovRnnConfig object.
        eval_ratio: Fraction of input to set aside for evaluation set.
  
      Returns:
        A pipeline.Pipeline instance.
      """
      all_transpositions = config.transpose_to_key is None
      partitioner = pipelines_common.RandomPartition(
          music_pb2.NoteSequence,
          ['eval_lead_sheets', 'training_lead_sheets'],
          [eval_ratio])
      dag = {partitioner: dag_pipeline.DagInput(music_pb2.NoteSequence)}
  
      for mode in ['eval', 'training']:
          time_change_splitter = note_sequence_pipelines.TimeChangeSplitter(
              name='TimeChangeSplitter_' + mode)
          quantizer = note_sequence_pipelines.Quantizer(
              steps_per_quarter=config.steps_per_quarter, name='Quantizer_' + mode)
          lead_sheet_extractor = lead_sheet_pipelines.LeadSheetExtractor(
              min_bars=7, max_steps=512, min_unique_pitches=3, gap_bars=1.0,
              ignore_polyphonic_notes=False, all_transpositions=all_transpositions,
              name='LeadSheetExtractor_' + mode)
          encoder_pipeline = EncoderPipeline(config, name='EncoderPipeline_' + mode)
  
          dag[time_change_splitter] = partitioner[mode + '_lead_sheets']
          dag[quantizer] = time_change_splitter
          dag[lead_sheet_extractor] = quantizer
          dag[encoder_pipeline] = lead_sheet_extractor
          dag[dag_pipeline.DagOutput(mode + '_lead_sheets')] = encoder_pipeline
  
      return dag_pipeline.DAGPipeline(dag)
  