# Copyright 2019 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.

"""Inference for onset conditioned model.

A histogram summary will be written for every example processed, and the
resulting MIDI and pianoroll images will also be written for every example.
The final summary value is the mean score for all examples.
"""

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

import collections
import functools
import os
import time

from magenta.models.onsets_frames_transcription import infer_util
from magenta.models.onsets_frames_transcription import train_util
from magenta.music import midi_io
from magenta.music.protobuf import music_pb2

import numpy as np
import scipy
import tensorflow.compat.v1 as tf


FLAGS = tf.app.flags.FLAGS

tf.app.flags.DEFINE_string('master', '',
                           'Name of the TensorFlow runtime to use.')
tf.app.flags.DEFINE_string('config', 'onsets_frames',
                           'Name of the config to use.')
tf.app.flags.DEFINE_string('model_dir', None, 'Path to look for checkpoints.')
tf.app.flags.DEFINE_string(
    'checkpoint_path', None,
    'Filename of the checkpoint to use. If not specified, will use the latest '
    'checkpoint')
tf.app.flags.DEFINE_string('examples_path', None,
                           'Path to test examples TFRecord.')
tf.app.flags.DEFINE_string(
    'output_dir', '~/tmp/onsets_frames/infer',
    'Path to store output midi files and summary events.')
tf.app.flags.DEFINE_string(
    'hparams', '',
    'A comma-separated list of `name=value` hyperparameter values.')
tf.app.flags.DEFINE_string(
    'log', 'INFO',
    'The threshold for what messages will be logged: '
    'DEBUG, INFO, WARN, ERROR, or FATAL.')
tf.app.flags.DEFINE_boolean('preprocess_examples', False,
                            'Whether or not to run preprocessing on examples.')


def model_inference(model_fn,
                    model_dir,
                    checkpoint_path,
                    data_fn,
                    hparams,
                    examples_path,
                    output_dir,
                    summary_writer,
                    master,
                    preprocess_examples):
  """Runs inference for the given examples."""
  tf.logging.info('model_dir=%s', model_dir)
  tf.logging.info('checkpoint_path=%s', checkpoint_path)
  tf.logging.info('examples_path=%s', examples_path)
  tf.logging.info('output_dir=%s', output_dir)

  estimator = train_util.create_estimator(
      model_fn, model_dir, hparams, master=master)

  transcription_data = functools.partial(
      data_fn, examples=examples_path, preprocess_examples=preprocess_examples,
      is_training=False, shuffle_examples=False, skip_n_initial_records=0)

  input_fn = infer_util.labels_to_features_wrapper(transcription_data)

  start_time = time.time()
  infer_times = []
  num_frames = []

  file_num = 0

  all_metrics = collections.defaultdict(list)

  for predictions in estimator.predict(
      input_fn, checkpoint_path=checkpoint_path, yield_single_examples=False):

    # Remove batch dimension for convenience.
    for k in predictions.keys():
      if predictions[k].shape[0] != 1:
        raise ValueError(
            'All predictions must have batch size 1, but shape of '
            '{} was: {}'.format(k, + predictions[k].shape[0]))
      predictions[k] = predictions[k][0]

    end_time = time.time()
    infer_time = end_time - start_time
    infer_times.append(infer_time)
    num_frames.append(predictions['frame_predictions'].shape[0])
    tf.logging.info(
        'Infer time %f, frames %d, frames/sec %f, running average %f',
        infer_time, num_frames[-1], num_frames[-1] / infer_time,
        np.sum(num_frames) / np.sum(infer_times))

    tf.logging.info('Scoring sequence %s', predictions['sequence_ids'])

    sequence_prediction = music_pb2.NoteSequence.FromString(
        predictions['sequence_predictions'])
    sequence_label = music_pb2.NoteSequence.FromString(
        predictions['sequence_labels'])

    # Make filenames UNIX-friendly.
    filename_chars = predictions['sequence_ids'].decode('utf-8')
    filename_chars = [c if c.isalnum() else '_' for c in filename_chars]
    filename_safe = ''.join(filename_chars).rstrip()
    filename_safe = '{:04d}_{}'.format(file_num, filename_safe[:200])
    file_num += 1
    output_file = os.path.join(output_dir, filename_safe + '.mid')
    tf.logging.info('Writing inferred midi file to %s', output_file)
    midi_io.sequence_proto_to_midi_file(sequence_prediction, output_file)

    label_output_file = os.path.join(output_dir, filename_safe + '_label.mid')
    tf.logging.info('Writing label midi file to %s', label_output_file)
    midi_io.sequence_proto_to_midi_file(sequence_label, label_output_file)

    # Also write a pianoroll showing acoustic model output vs labels.
    pianoroll_output_file = os.path.join(
        output_dir, filename_safe + '_pianoroll.png')
    tf.logging.info('Writing acoustic logit/label file to %s',
                    pianoroll_output_file)
    with tf.gfile.GFile(pianoroll_output_file, mode='w') as f:
      scipy.misc.imsave(
          f,
          infer_util.posterior_pianoroll_image(
              predictions['frame_probs'],
              predictions['frame_labels']))

    # Update histogram for metrics.
    with tf.Graph().as_default(), tf.Session().as_default():
      for k, v in predictions.iteritems():
        if not k.startswith('metrics/'):
          continue
        all_metrics[k].extend(v)
        metric_summary = tf.summary.histogram(
            k,
            tf.constant(all_metrics[k], name=k),
            collections=[])
        summary_writer.add_summary(metric_summary.eval(), global_step=file_num)
      summary_writer.flush()

    start_time = time.time()

  # Write final mean values for all metrics.
  with tf.Graph().as_default(), tf.Session().as_default():
    for k, v in all_metrics.iteritems():
      name = 'final/' + k
      metric_summary = tf.summary.scalar(
          name,
          tf.constant(np.mean(all_metrics[k]), name=name),
          collections=[])
      summary_writer.add_summary(metric_summary.eval())
    summary_writer.flush()

  start_time = time.time()


def run(config_map, data_fn):
  """Run the infer script."""
  output_dir = os.path.expanduser(FLAGS.output_dir)

  config = config_map[FLAGS.config]
  hparams = config.hparams
  hparams.parse(FLAGS.hparams)

  # Batch size should always be 1 for inference.
  hparams.batch_size = 1

  tf.logging.info(hparams)

  tf.gfile.MakeDirs(output_dir)

  summary_writer = tf.summary.FileWriter(logdir=output_dir)

  with tf.Session():
    run_config = '\n\n'.join([
        'model_dir: ' + FLAGS.model_dir,
        'checkpoint_path: ' + str(FLAGS.checkpoint_path),
        'examples_path: ' + FLAGS.examples_path,
        str(hparams),
    ])
    run_config_summary = tf.summary.text(
        'run_config',
        tf.constant(run_config, name='run_config'),
        collections=[])
    summary_writer.add_summary(run_config_summary.eval())

  model_inference(
      model_fn=config.model_fn,
      model_dir=FLAGS.model_dir,
      checkpoint_path=FLAGS.checkpoint_path,
      data_fn=data_fn,
      hparams=hparams,
      examples_path=FLAGS.examples_path,
      output_dir=output_dir,
      summary_writer=summary_writer,
      preprocess_examples=FLAGS.preprocess_examples,
      master=FLAGS.master)
