# coding=utf-8
# Copyright 2024 The Google Research 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.

"""Debugging script to visualize the dataset video and frame sampling."""

import sys

from absl import app
from absl import flags
from absl import logging
from base_configs import validate_config
import matplotlib.pyplot as plt
from ml_collections import config_flags
import torchvision
from xirl.common import get_pretraining_dataloaders

# pylint: disable=logging-fstring-interpolation

FLAGS = flags.FLAGS

flags.DEFINE_boolean("debug", False, "Turn off shuffling and data aug.")

config_flags.DEFINE_config_file(
    "config",
    "base_configs/pretrain.py",
    "File path to the training hyperparameter configuration.",
)


def main(_):
  validate_config(FLAGS.config, mode="pretrain")
  config = FLAGS.config
  if FLAGS.debug:
    config.data.pretraining_video_sampler = "same_class"
  num_ctx_frames = config.frame_sampler.num_context_frames
  num_frames = config.frame_sampler.num_frames_per_sequence
  pretrain_loaders = get_pretraining_dataloaders(config, FLAGS.debug)
  try:
    loader = pretrain_loaders["train"]
    logging.info("Total videos: %d", loader.dataset.total_vids)
    for batch_idx, batch in enumerate(loader):
      logging.info("Batch #%d", batch_idx)
      frames = batch["frames"]
      b, _, c, h, w = frames.shape
      frames = frames.view(b, num_frames, num_ctx_frames, c, h, w)
      for b in range(frames.shape[0]):
        logging.info("\tBatch Item %s", str(b))
        grid_img = torchvision.utils.make_grid(frames[b, :, -1], nrow=5)
        plt.imshow(grid_img.permute(1, 2, 0))
        plt.show()
  except KeyboardInterrupt:
    sys.exit()


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