# Copyright 2023 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.
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"""Tests for train.py."""

import json
import os
import random

from absl import flags
from absl import logging
from absl.testing import flagsaver
import tensorflow as tf

from official.projects.movinet import train as train_lib
from official.vision.dataloaders import tfexample_utils

FLAGS = flags.FLAGS


class TrainTest(tf.test.TestCase):

  def setUp(self):
    super(TrainTest, self).setUp()
    self._model_dir = os.path.join(self.get_temp_dir(), 'model_dir')
    tf.io.gfile.makedirs(self._model_dir)

    data_dir = os.path.join(self.get_temp_dir(), 'data')
    tf.io.gfile.makedirs(data_dir)
    self._data_path = os.path.join(data_dir, 'data.tfrecord')
    # pylint: disable=g-complex-comprehension
    examples = [
        tfexample_utils.make_video_test_example(
            image_shape=(32, 32, 3),
            audio_shape=(20, 128),
            label=random.randint(0, 100)) for _ in range(2)
    ]
    # pylint: enable=g-complex-comprehension
    tfexample_utils.dump_to_tfrecord(self._data_path, tf_examples=examples)

  def test_train_and_evaluation_pipeline_runs(self):
    saved_flag_values = flagsaver.save_flag_values()
    train_lib.tfm_flags.define_flags()
    FLAGS.mode = 'train'
    FLAGS.model_dir = self._model_dir
    FLAGS.experiment = 'movinet_kinetics600'
    logging.info('Test pipeline correctness.')
    num_frames = 4

    # Test model training pipeline runs.
    params_override = json.dumps({
        'runtime': {
            'distribution_strategy': 'mirrored',
            'mixed_precision_dtype': 'float32',
        },
        'trainer': {
            'train_steps': 2,
            'validation_steps': 2,
        },
        'task': {
            'train_data': {
                'input_path': self._data_path,
                'file_type': 'tfrecord',
                'feature_shape': [num_frames, 32, 32, 3],
                'global_batch_size': 2,
            },
            'validation_data': {
                'input_path': self._data_path,
                'file_type': 'tfrecord',
                'global_batch_size': 2,
                'feature_shape': [num_frames * 2, 32, 32, 3],
            }
        }
    })
    FLAGS.params_override = params_override
    train_lib.main('unused_args')

    # Test model evaluation pipeline runs on newly produced checkpoint.
    FLAGS.mode = 'eval'
    with train_lib.gin.unlock_config():
      train_lib.main('unused_args')

    flagsaver.restore_flag_values(saved_flag_values)


if __name__ == '__main__':
  tf.test.main()
