# Copyright (c) Alibaba, Inc. and its affiliates.
import glob
import os
import shutil
import tempfile
import unittest

import json
import numpy as np
import torch
from torch import nn

from modelscope.metainfo import Trainers
from modelscope.models.base import Model
from modelscope.trainers import build_trainer
from modelscope.utils.constant import LogKeys, ModelFile
from modelscope.utils.test_utils import create_dummy_test_dataset

dummy_dataset = create_dummy_test_dataset(
    np.random.random(size=(5, )), np.random.randint(0, 4, (1, )), 20)


class DummyModel(nn.Module, Model):

    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(5, 4)
        self.bn = nn.BatchNorm1d(4)

    def forward(self, feat, labels):
        x = self.linear(feat)

        x = self.bn(x)
        loss = torch.sum(x)
        return dict(logits=x, loss=loss)


class TensorboardHookTest(unittest.TestCase):

    def setUp(self):
        print(('Testing %s.%s' % (type(self).__name__, self._testMethodName)))
        self.tmp_dir = tempfile.TemporaryDirectory().name
        if not os.path.exists(self.tmp_dir):
            os.makedirs(self.tmp_dir)

    def tearDown(self):
        super().tearDown()
        shutil.rmtree(self.tmp_dir)

    def test_tensorboard_hook(self):
        json_cfg = {
            'task': 'image_classification',
            'train': {
                'work_dir': self.tmp_dir,
                'dataloader': {
                    'batch_size_per_gpu': 2,
                    'workers_per_gpu': 1
                },
                'optimizer': {
                    'type': 'SGD',
                    'lr': 0.01
                },
                'lr_scheduler': {
                    'type': 'StepLR',
                    'step_size': 2,
                },
                'hooks': [{
                    'type': 'TensorboardHook',
                    'interval': 2
                }]
            }
        }

        config_path = os.path.join(self.tmp_dir, ModelFile.CONFIGURATION)
        with open(config_path, 'w') as f:
            json.dump(json_cfg, f)

        trainer_name = Trainers.default
        kwargs = dict(
            cfg_file=config_path,
            model=DummyModel(),
            data_collator=None,
            train_dataset=dummy_dataset,
            max_epochs=2)

        trainer = build_trainer(trainer_name, kwargs)
        trainer.train()
        tb_out_dir = os.path.join(self.tmp_dir, 'tensorboard_output')

        events_files = glob.glob(
            os.path.join(tb_out_dir, 'events.out.tfevents.*'))
        self.assertEqual(len(events_files), 1)

        from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
        ea = EventAccumulator(events_files[0])
        ea.Reload()
        self.assertEqual(len(ea.Scalars(LogKeys.LOSS)), 10)
        self.assertEqual(len(ea.Scalars(LogKeys.LR)), 10)
        for i in range(5):
            self.assertAlmostEqual(
                ea.Scalars(LogKeys.LR)[i].value, 0.01, delta=0.001)
        for i in range(5, 10):
            self.assertAlmostEqual(
                ea.Scalars(LogKeys.LR)[i].value, 0.01, delta=0.0001)


if __name__ == '__main__':
    unittest.main()
