# Copyright (c) 2021 PaddlePaddle 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.
import argparse
import os

import paddle
from model import SoundClassifier
from panns import cnn14

from paddleaudio.datasets import ESC50
from paddleaudio.features import LogMelSpectrogram
from paddleaudio.utils import logger
from paddleaudio.utils import Timer

# yapf: disable
parser = argparse.ArgumentParser(__doc__)
parser.add_argument('--device', choices=['cpu', 'gpu'], default="gpu", help="Select which device to train model, defaults to gpu.")
parser.add_argument("--epochs", type=int, default=50, help="Number of epoches for fine-tuning.")
parser.add_argument("--feat_backend", type=str, choices=['numpy', 'paddle'], default='numpy', help="Choose backend to extract features from audio files.")
parser.add_argument("--learning_rate", type=float, default=5e-5, help="Learning rate used to train with warmup.")
parser.add_argument("--batch_size", type=int, default=16, help="Total examples' number in batch for training.")
parser.add_argument("--num_workers", type=int, default=0, help="Number of workers in dataloader.")
parser.add_argument("--checkpoint_dir", type=str, default='./checkpoint', help="Directory to save model checkpoints.")
parser.add_argument("--save_freq", type=int, default=10, help="Save checkpoint every n epoch.")
parser.add_argument("--log_freq", type=int, default=10, help="Log the training infomation every n steps.")
args = parser.parse_args()
# yapf: enable

if __name__ == "__main__":
    paddle.set_device(args.device)
    nranks = paddle.distributed.get_world_size()
    if paddle.distributed.get_world_size() > 1:
        paddle.distributed.init_parallel_env()
    local_rank = paddle.distributed.get_rank()

    backbone = cnn14(pretrained=True, extract_embedding=True)
    model = SoundClassifier(backbone, num_class=len(ESC50.label_list))
    model = paddle.DataParallel(model)
    optimizer = paddle.optimizer.Adam(
        learning_rate=args.learning_rate, parameters=model.parameters())
    criterion = paddle.nn.loss.CrossEntropyLoss()

    if args.feat_backend == 'numpy':
        train_ds = ESC50(mode='train', feat_type='melspectrogram')
        dev_ds = ESC50(mode='dev', feat_type='melspectrogram')
    else:
        train_ds = ESC50(mode='train')
        dev_ds = ESC50(mode='dev')
        feature_extractor = LogMelSpectrogram(sr=16000)

    train_sampler = paddle.io.DistributedBatchSampler(
        train_ds, batch_size=args.batch_size, shuffle=True, drop_last=False)
    train_loader = paddle.io.DataLoader(
        train_ds,
        batch_sampler=train_sampler,
        num_workers=args.num_workers,
        return_list=True,
        use_buffer_reader=True, )

    steps_per_epoch = len(train_sampler)
    timer = Timer(steps_per_epoch * args.epochs)
    timer.start()

    for epoch in range(1, args.epochs + 1):
        model.train()

        avg_loss = 0
        num_corrects = 0
        num_samples = 0
        for batch_idx, batch in enumerate(train_loader):
            if args.feat_backend == 'numpy':
                feats, labels = batch
            else:
                waveforms, labels = batch
                feats = feature_extractor(
                    waveforms
                )  # Need a padding when lengths of waveforms differ in a batch.
                feats = paddle.transpose(feats,
                                         [0, 2, 1])  # To [N, length, n_mels]

            logits = model(feats)

            loss = criterion(logits, labels)
            loss.backward()
            optimizer.step()
            if isinstance(optimizer._learning_rate,
                          paddle.optimizer.lr.LRScheduler):
                optimizer._learning_rate.step()
            optimizer.clear_grad()

            # Calculate loss
            avg_loss += loss.numpy()[0]

            # Calculate metrics
            preds = paddle.argmax(logits, axis=1)
            num_corrects += (preds == labels).numpy().sum()
            num_samples += feats.shape[0]

            timer.count()

            if (batch_idx + 1) % args.log_freq == 0 and local_rank == 0:
                lr = optimizer.get_lr()
                avg_loss /= args.log_freq
                avg_acc = num_corrects / num_samples

                print_msg = 'Epoch={}/{}, Step={}/{}'.format(
                    epoch, args.epochs, batch_idx + 1, steps_per_epoch)
                print_msg += ' loss={:.4f}'.format(avg_loss)
                print_msg += ' acc={:.4f}'.format(avg_acc)
                print_msg += ' lr={:.6f} step/sec={:.2f} | ETA {}'.format(
                    lr, timer.timing, timer.eta)
                logger.train(print_msg)

                avg_loss = 0
                num_corrects = 0
                num_samples = 0

        if epoch % args.save_freq == 0 and batch_idx + 1 == steps_per_epoch and local_rank == 0:
            dev_sampler = paddle.io.BatchSampler(
                dev_ds,
                batch_size=args.batch_size,
                shuffle=False,
                drop_last=False)
            dev_loader = paddle.io.DataLoader(
                dev_ds,
                batch_sampler=dev_sampler,
                num_workers=args.num_workers,
                return_list=True, )

            model.eval()
            num_corrects = 0
            num_samples = 0
            with logger.processing('Evaluation on validation dataset'):
                for batch_idx, batch in enumerate(dev_loader):
                    if args.feat_backend == 'numpy':
                        feats, labels = batch
                    else:
                        waveforms, labels = batch
                        feats = feature_extractor(waveforms)
                        feats = paddle.transpose(feats, [0, 2, 1])

                    logits = model(feats)

                    preds = paddle.argmax(logits, axis=1)
                    num_corrects += (preds == labels).numpy().sum()
                    num_samples += feats.shape[0]

            print_msg = '[Evaluation result]'
            print_msg += ' dev_acc={:.4f}'.format(num_corrects / num_samples)

            logger.eval(print_msg)

            # Save model
            save_dir = os.path.join(args.checkpoint_dir,
                                    'epoch_{}'.format(epoch))
            logger.info('Saving model checkpoint to {}'.format(save_dir))
            paddle.save(model.state_dict(),
                        os.path.join(save_dir, 'model.pdparams'))
            paddle.save(optimizer.state_dict(),
                        os.path.join(save_dir, 'model.pdopt'))
