# Defaults for infer.py.
#
# You must also include a binding for MODEL.
#
# Required to be set:
#
# - TASK_PREFIX
# - TASK_FEATURE_LENGTHS
# - CHECKPOINT_PATH
# - INFER_OUTPUT_DIR
#
# Commonly overridden options:
#
# - infer.mode
# - infer.checkpoint_period
# - infer.shard_id
# - infer.num_shards
# - DatasetConfig.split
# - DatasetConfig.batch_size
# - DatasetConfig.use_cached
# - RestoreCheckpointConfig.is_tensorflow
# - RestoreCheckpointConfig.mode
# - PjitPartitioner.num_partitions

from __gin__ import dynamic_registration

import __main__ as infer_script
from mt3 import inference
from mt3 import preprocessors
from mt3 import tasks
from mt3 import vocabularies
from t5x import partitioning
from t5x import utils

# Must be overridden
TASK_PREFIX = %gin.REQUIRED
TASK_FEATURE_LENGTHS = %gin.REQUIRED
CHECKPOINT_PATH = %gin.REQUIRED
INFER_OUTPUT_DIR = %gin.REQUIRED

# Number of velocity bins: set to 1 (no velocity) or 127
NUM_VELOCITY_BINS = %gin.REQUIRED
VOCAB_CONFIG = @vocabularies.VocabularyConfig()
vocabularies.VocabularyConfig.num_velocity_bins = %NUM_VELOCITY_BINS

# Program granularity: set to 'flat', 'midi_class', or 'full'
PROGRAM_GRANULARITY = %gin.REQUIRED
preprocessors.map_midi_programs.granularity_type = %PROGRAM_GRANULARITY

TASK_SUFFIX = 'test'
tasks.construct_task_name:
  task_prefix = %TASK_PREFIX
  vocab_config = %VOCAB_CONFIG
  task_suffix = %TASK_SUFFIX

ONSETS_ONLY = %gin.REQUIRED
USE_TIES = %gin.REQUIRED
inference.write_inferences_to_file:
  vocab_config = %VOCAB_CONFIG
  onsets_only = %ONSETS_ONLY
  use_ties = %USE_TIES

infer_script.infer:
  mode = 'predict'
  model = %MODEL  # imported from separate gin file
  output_dir = %INFER_OUTPUT_DIR
  dataset_cfg = @utils.DatasetConfig()
  partitioner = @partitioning.PjitPartitioner()
  restore_checkpoint_cfg = @utils.RestoreCheckpointConfig()
  # This is a hack, but pass an extremely large value here to make sure the
  # entire dataset fits in a single epoch. Otherwise, segments from a single
  # example may end up in different epochs after splitting.
  checkpoint_period = 1000000
  shard_id = 0
  num_shards = 1
  write_fn = @inference.write_inferences_to_file

utils.DatasetConfig:
  mixture_or_task_name = @tasks.construct_task_name()
  task_feature_lengths = %TASK_FEATURE_LENGTHS
  use_cached = True
  split = 'eval'
  batch_size = 32
  shuffle = False
  seed = 0
  pack = False

partitioning.PjitPartitioner.num_partitions = 1

utils.RestoreCheckpointConfig:
  path = %CHECKPOINT_PATH
  mode = 'specific'