# Defaults for infer.py if using a TFExample file as input.
#
#
# The features from each TFExample are tokenized using the model's vocabulary.
# By default, the inputs feature is assumed to be keyed as 'inputs', but this
# can be overridden with `create_task_from_tfexample_file.inputs_key`.
#
# You must also include a binding for MODEL.
#
# Required to be set:
#
# - TF_EXAMPLE_FILE_PATHS: The path to read TF Examples from.
# - TF_EXAMPLE_FILE_TYPE: The type of file to read TF Examples from. Currently
#                         supported: 'tfrecord', 'recordio', 'sstable'.
# - FEATURE_LENGTHS: The maximum length per feature in the TF Examples.
# - CHECKPOINT_PATH: The model checkpoint to use for inference
# - INFER_OUTPUT_DIR: The dir to write results to.
#
#
# Commonly overridden options:
#
# - infer.mode
# - infer.checkpoint_period
# - infer.shard_id
# - infer.num_shards
# - create_task_from_tfexample_file.inputs_key
# - create_task_from_tfexample_file.targets_key
# - DatasetConfig.split
# - DatasetConfig.batch_size
# - RestoreCheckpointConfig.mode
# - PjitPartitioner.num_partitions
from __gin__ import dynamic_registration

import __main__ as infer_script
import seqio
from t5x import models
from t5x import partitioning
from t5x import utils

# Must be overridden
TF_EXAMPLE_FILE_PATHS = %gin.REQUIRED
TF_EXAMPLE_FILE_TYPE = %gin.REQUIRED
FEATURE_LENGTHS = %gin.REQUIRED
CHECKPOINT_PATH = %gin.REQUIRED
INFER_OUTPUT_DIR = %gin.REQUIRED

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()
  checkpoint_period = 100
  shard_id = 0
  num_shards = 1

partitioning.PjitPartitioner:
  num_partitions = 1
  logical_axis_rules = @partitioning.standard_logical_axis_rules()

utils.DatasetConfig:
  mixture_or_task_name = @infer_script.create_task_from_tfexample_file()
  task_feature_lengths = %FEATURE_LENGTHS
  split = 'infer'
  batch_size = 32
  shuffle = False
  seed = 0
  pack = False

infer_script.create_task_from_tfexample_file:
  paths = %TF_EXAMPLE_FILE_PATHS
  file_type = %TF_EXAMPLE_FILE_TYPE
  inputs_key = 'inputs'
  targets_key = None
  features = {'inputs': @inputs/seqio.Feature(), 'targets': @outputs/seqio.Feature()}

# Plumbing to extract the vocabulary directly from MODEL. This is needed to
# tokenize the features from the TFExample we aren't provided with vocabularies
# via a Task.
inputs/seqio.Feature.vocabulary = @models.get_input_vocabulary()
models.get_input_vocabulary.model = %MODEL
outputs/seqio.Feature.vocabulary = @models.get_output_vocabulary()
models.get_output_vocabulary.model = %MODEL

utils.RestoreCheckpointConfig:
  mode = 'specific'
  path = %CHECKPOINT_PATH
  dtype = 'bfloat16'