# Copyright (c) 2020 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.
from tensorboardX import SummaryWriter
import os
from tqdm import tqdm
from pathlib import Path
from collections import OrderedDict
import argparse
from ruamel import yaml
from parse import add_config_options_to_parser
from pprint import pprint
import paddle.fluid as fluid
import paddle.fluid.dygraph as dg
import paddle.fluid.layers as layers
from data import LJSpeechLoader
from parakeet.models.transformer_tts.vocoder import Vocoder


def load_checkpoint(step, model_path):
    model_dict, opti_dict = dg.load_dygraph(os.path.join(model_path, step))
    new_state_dict = OrderedDict()
    for param in model_dict:
        if param.startswith('_layers.'):
            new_state_dict[param[8:]] = model_dict[param]
        else:
            new_state_dict[param] = model_dict[param]
    return new_state_dict, opti_dict


def main(args):

    local_rank = dg.parallel.Env().local_rank if args.use_data_parallel else 0
    nranks = dg.parallel.Env().nranks if args.use_data_parallel else 1

    with open(args.config_path) as f:
        cfg = yaml.load(f, Loader=yaml.Loader)

    global_step = 0
    place = (fluid.CUDAPlace(dg.parallel.Env().dev_id)
             if args.use_data_parallel else fluid.CUDAPlace(0)
             if args.use_gpu else fluid.CPUPlace())

    if not os.path.exists(args.log_dir):
        os.mkdir(args.log_dir)
    path = os.path.join(args.log_dir, 'vocoder')

    writer = SummaryWriter(path) if local_rank == 0 else None

    with dg.guard(place):
        model = Vocoder(cfg, args.batch_size)

        model.train()
        optimizer = fluid.optimizer.AdamOptimizer(
            learning_rate=dg.NoamDecay(1 / (
                cfg['warm_up_step'] * (args.lr**2)), cfg['warm_up_step']),
            parameter_list=model.parameters())

        if args.checkpoint_path is not None:
            model_dict, opti_dict = load_checkpoint(
                str(args.vocoder_step),
                os.path.join(args.checkpoint_path, "vocoder"))
            model.set_dict(model_dict)
            optimizer.set_dict(opti_dict)
            global_step = args.vocoder_step
            print("load checkpoint!!!")

        if args.use_data_parallel:
            strategy = dg.parallel.prepare_context()
            model = fluid.dygraph.parallel.DataParallel(model, strategy)

        reader = LJSpeechLoader(
            cfg, args, nranks, local_rank, is_vocoder=True).reader()

        for epoch in range(args.epochs):
            pbar = tqdm(reader)
            for i, data in enumerate(pbar):
                pbar.set_description('Processing at epoch %d' % epoch)
                mel, mag = data
                mag = dg.to_variable(mag.numpy())
                mel = dg.to_variable(mel.numpy())
                global_step += 1

                mag_pred = model(mel)
                loss = layers.mean(
                    layers.abs(layers.elementwise_sub(mag_pred, mag)))

                if args.use_data_parallel:
                    loss = model.scale_loss(loss)
                    loss.backward()
                    model.apply_collective_grads()
                else:
                    loss.backward()
                optimizer.minimize(
                    loss,
                    grad_clip=fluid.dygraph_grad_clip.GradClipByGlobalNorm(cfg[
                        'grad_clip_thresh']))
                model.clear_gradients()

                if local_rank == 0:
                    writer.add_scalars('training_loss', {
                        'loss': loss.numpy(),
                    }, global_step)

                    if global_step % args.save_step == 0:
                        if not os.path.exists(args.save_path):
                            os.mkdir(args.save_path)
                        save_path = os.path.join(args.save_path,
                                                 'vocoder/%d' % global_step)
                        dg.save_dygraph(model.state_dict(), save_path)
                        dg.save_dygraph(optimizer.state_dict(), save_path)

        if local_rank == 0:
            writer.close()


if __name__ == '__main__':
    parser = argparse.ArgumentParser(description="Train vocoder model")
    add_config_options_to_parser(parser)
    args = parser.parse_args()
    # Print the whole config setting.
    pprint(args)
    main(args)