# Copyright 2023 The T5 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"""Parse TensorBoard events files and (optionally) log results to csv.


Note that `--summary_dir` *must* point directly to the directory with .events
files (e.g. `/validation_eval/`), not a parent directory.
"""

import os

from absl import app
from absl import flags
from absl import logging
from t5.evaluation import eval_utils
import tensorflow as tf

FLAGS = flags.FLAGS

flags.DEFINE_string("summary_dir", None, "Where to search for .events files.")
flags.DEFINE_string("out_file", None, "Output file to write TSV.")
flags.DEFINE_bool("perplexity_eval", False,
                  "Indicates if perplexity_eval mode was used for evaluation.")
flags.DEFINE_bool("seqio_summaries", False, "Whether summaries are generated "
                  "from SeqIO Evaluator.")


def main(_):
  if FLAGS.seqio_summaries:
    subdirs = tf.io.gfile.listdir(FLAGS.summary_dir)
    summary_dirs = [os.path.join(FLAGS.summary_dir, d) for d in subdirs]
  else:
    summary_dirs = [FLAGS.summary_dir]

  scores = None
  for d in summary_dirs:
    events = eval_utils.parse_events_files(d, FLAGS.seqio_summaries)
    if FLAGS.perplexity_eval:
      task_metrics = events
    else:
      task_metrics = eval_utils.get_eval_metric_values(
          events,
          task_name=os.path.basename(d) if FLAGS.seqio_summaries else None)
    if scores:
      scores.update(task_metrics)
    else:
      scores = task_metrics

  if not scores:
    logging.info("No evaluation events found in %s", FLAGS.summary_dir)
    return
  df = eval_utils.scores_to_df(scores)
  df = eval_utils.compute_avg_glue(df)
  df = eval_utils.sort_columns(df)
  eval_utils.log_csv(df, output_file=FLAGS.out_file)

if __name__ == "__main__":
  app.run(main)
