# Lint as: python3
# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
#
# 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.
# ==============================================================================
"""TFM continuous finetuning+eval training driver."""

import os
import time
from typing import Mapping, Any

from absl import app
from absl import flags
from absl import logging
import gin
import tensorflow as tf

# pylint: disable=unused-import
from official.common import registry_imports
# pylint: enable=unused-import
from official.common import flags as tfm_flags
from official.core import task_factory
from official.core import train_lib
from official.core import train_utils
from official.modeling import performance
from official.modeling.hyperparams import config_definitions
from official.utils.misc import distribution_utils


FLAGS = flags.FLAGS


def run_continuous_finetune(
    mode: str,
    params: config_definitions.ExperimentConfig,
    model_dir: str,
    run_post_eval: bool = False,
) -> Mapping[str, Any]:
  """Run modes with continuous training.

  Currently only supports continuous_train_and_eval.

  Args:
    mode: A 'str', specifying the mode.
      continuous_train_and_eval - monitors a checkpoint directory. Once a new
        checkpoint is discovered, loads the checkpoint, finetune the model by
        training it (probably on another dataset or with another task), then
        evaluate the finetuned model.
    params: ExperimentConfig instance.
    model_dir: A 'str', a path to store model checkpoints and summaries.
    run_post_eval: Whether to run post eval once after training, metrics logs
      are returned.

  Returns:
    eval logs: returns eval metrics logs when run_post_eval is set to True,
      othewise, returns {}.
  """

  assert mode == 'continuous_train_and_eval', (
      'Only continuous_train_and_eval is supported by continuous_finetune. '
      'Got mode: {}'.format(mode))

  # Sets mixed_precision policy. Using 'mixed_float16' or 'mixed_bfloat16'
  # can have significant impact on model speeds by utilizing float16 in case of
  # GPUs, and bfloat16 in the case of TPUs. loss_scale takes effect only when
  # dtype is float16
  if params.runtime.mixed_precision_dtype:
    performance.set_mixed_precision_policy(params.runtime.mixed_precision_dtype,
                                           params.runtime.loss_scale)
  distribution_strategy = distribution_utils.get_distribution_strategy(
      distribution_strategy=params.runtime.distribution_strategy,
      all_reduce_alg=params.runtime.all_reduce_alg,
      num_gpus=params.runtime.num_gpus,
      tpu_address=params.runtime.tpu)

  retry_times = 0
  while not tf.io.gfile.isdir(params.task.init_checkpoint):
    # Wait for the init_checkpoint directory to be created.
    if retry_times >= 60:
      raise ValueError(
          'ExperimentConfig.task.init_checkpoint must be a directory for '
          'continuous_train_and_eval mode.')
    retry_times += 1
    time.sleep(60)

  summary_writer = tf.summary.create_file_writer(
      os.path.join(model_dir, 'eval'))
  for pretrain_ckpt in tf.train.checkpoints_iterator(
      checkpoint_dir=params.task.init_checkpoint,
      min_interval_secs=10,
      timeout=params.trainer.continuous_eval_timeout):
    with distribution_strategy.scope():
      global_step = train_utils.read_global_step_from_checkpoint(pretrain_ckpt)

    if params.trainer.best_checkpoint_export_subdir:
      best_ckpt_subdir = '{}_{}'.format(
          params.trainer.best_checkpoint_export_subdir, global_step)
      params_replaced = params.replace(
          task={'init_checkpoint': pretrain_ckpt},
          trainer={'best_checkpoint_export_subdir': best_ckpt_subdir})
    else:
      params_replaced = params.replace(task={'init_checkpoint': pretrain_ckpt})
    params_replaced.lock()
    logging.info('Running finetuning with params: %s', params_replaced)

    with distribution_strategy.scope():
      task = task_factory.get_task(params_replaced.task, logging_dir=model_dir)

    _, eval_metrics = train_lib.run_experiment(
        distribution_strategy=distribution_strategy,
        task=task,
        mode='train_and_eval',
        # replace params.task.init_checkpoint to make sure that we load
        # exactly this pretrain checkpoint.
        params=params_replaced,
        model_dir=model_dir,
        run_post_eval=True,
        save_summary=False)
    logging.info('Evaluation finished. Pretrain global_step: %d', global_step)
    train_utils.write_json_summary(model_dir, global_step, eval_metrics)

    if not os.path.basename(model_dir):  # if model_dir.endswith('/')
      summary_grp = os.path.dirname(model_dir) + '_' + task.__class__.__name__
    else:
      summary_grp = os.path.basename(model_dir) + '_' + task.__class__.__name__
    summaries = {}
    for name, value in eval_metrics.items():
      summaries[summary_grp + '/' + name] = value
    train_utils.write_summary(summary_writer, global_step, summaries)

    train_utils.remove_ckpts(model_dir)

  if run_post_eval:
    return eval_metrics
  return {}


def main(_):
  gin.parse_config_files_and_bindings(FLAGS.gin_file, FLAGS.gin_params)
  params = train_utils.parse_configuration(FLAGS)
  model_dir = FLAGS.model_dir
  train_utils.serialize_config(params, model_dir)
  run_continuous_finetune(FLAGS.mode, params, model_dir)


if __name__ == '__main__':
  tfm_flags.define_flags()
  app.run(main)
