# coding=utf-8
# Copyright 2023 The Tensor2Tensor 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.

r"""Perform evaluation on trained T2T models using the Estimator API."""

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

from tensor2tensor.bin import t2t_trainer          # pylint: disable=unused-import
from tensor2tensor.data_generators import problem  # pylint: disable=unused-import
from tensor2tensor.utils import trainer_lib
from tensor2tensor.utils import usr_dir
import tensorflow.compat.v1 as tf
from tensorflow.compat.v1 import estimator as tf_estimator

flags = tf.flags
FLAGS = flags.FLAGS


def main(_):
  tf.logging.set_verbosity(tf.logging.INFO)
  trainer_lib.set_random_seed(FLAGS.random_seed)
  usr_dir.import_usr_dir(FLAGS.t2t_usr_dir)

  hparams = trainer_lib.create_hparams(
      FLAGS.hparams_set, FLAGS.hparams, data_dir=FLAGS.data_dir,
      problem_name=FLAGS.problem)

  # set appropriate dataset-split, if flags.eval_use_test_set.
  dataset_split = "test" if FLAGS.eval_use_test_set else None
  dataset_kwargs = {"dataset_split": dataset_split}
  eval_input_fn = hparams.problem.make_estimator_input_fn(
      tf_estimator.ModeKeys.EVAL, hparams, dataset_kwargs=dataset_kwargs)
  config = t2t_trainer.create_run_config(hparams)

  # summary-hook in tf.estimator.EstimatorSpec requires
  # hparams.model_dir to be set.
  hparams.add_hparam("model_dir", config.model_dir)

  estimator = trainer_lib.create_estimator(
      FLAGS.model, hparams, config, use_tpu=FLAGS.use_tpu)
  ckpt_iter = trainer_lib.next_checkpoint(
      hparams.model_dir, FLAGS.eval_timeout_mins)
  for ckpt_path in ckpt_iter:
    predictions = estimator.evaluate(
        eval_input_fn, steps=FLAGS.eval_steps, checkpoint_path=ckpt_path)
    tf.logging.info(predictions)


if __name__ == "__main__":
  tf.logging.set_verbosity(tf.logging.INFO)
  tf.app.run()
