# 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 __future__ import division
import os
import ruamel.yaml
import argparse
from tqdm import tqdm
from tensorboardX import SummaryWriter
from paddle import fluid
import paddle.fluid.dygraph as dg

from parakeet.data import SliceDataset, TransformDataset, DataCargo, SequentialSampler, RandomSampler
from parakeet.models.wavenet import UpsampleNet, WaveNet, ConditionalWavenet
from parakeet.utils.layer_tools import summary

from data import LJSpeechMetaData, Transform, DataCollector
from utils import make_output_tree, valid_model, save_checkpoint

if __name__ == "__main__":
    parser = argparse.ArgumentParser(
        description="Train a wavenet model with LJSpeech.")
    parser.add_argument(
        "--data", type=str, help="path of the LJspeech dataset.")
    parser.add_argument("--config", type=str, help="path of the config file.")
    parser.add_argument(
        "--output",
        type=str,
        default="experiment",
        help="path to save results.")
    parser.add_argument(
        "--device", type=int, default=-1, help="device to use.")
    parser.add_argument(
        "--resume", type=str, help="checkpoint to resume from.")

    args = parser.parse_args()
    with open(args.config, 'rt') as f:
        config = ruamel.yaml.safe_load(f)

    ljspeech_meta = LJSpeechMetaData(args.data)

    data_config = config["data"]
    sample_rate = data_config["sample_rate"]
    n_fft = data_config["n_fft"]
    win_length = data_config["win_length"]
    hop_length = data_config["hop_length"]
    n_mels = data_config["n_mels"]
    train_clip_seconds = data_config["train_clip_seconds"]
    transform = Transform(sample_rate, n_fft, win_length, hop_length, n_mels)
    ljspeech = TransformDataset(ljspeech_meta, transform)

    valid_size = data_config["valid_size"]
    ljspeech_valid = SliceDataset(ljspeech, 0, valid_size)
    ljspeech_train = SliceDataset(ljspeech, valid_size, len(ljspeech))

    model_config = config["model"]
    n_loop = model_config["n_loop"]
    n_layer = model_config["n_layer"]
    filter_size = model_config["filter_size"]
    context_size = 1 + n_layer * sum([filter_size**i for i in range(n_loop)])
    print("context size is {} samples".format(context_size))
    train_batch_fn = DataCollector(context_size, sample_rate, hop_length,
                                   train_clip_seconds)
    valid_batch_fn = DataCollector(
        context_size, sample_rate, hop_length, train_clip_seconds, valid=True)

    batch_size = data_config["batch_size"]
    train_cargo = DataCargo(
        ljspeech_train,
        train_batch_fn,
        batch_size,
        sampler=RandomSampler(ljspeech_train))

    # only batch=1 for validation is enabled
    valid_cargo = DataCargo(
        ljspeech_valid,
        valid_batch_fn,
        batch_size=1,
        sampler=SequentialSampler(ljspeech_valid))

    make_output_tree(args.output)

    if args.device == -1:
        place = fluid.CPUPlace()
    else:
        place = fluid.CUDAPlace(args.device)

    with dg.guard(place):
        model_config = config["model"]
        upsampling_factors = model_config["upsampling_factors"]
        encoder = UpsampleNet(upsampling_factors)

        n_loop = model_config["n_loop"]
        n_layer = model_config["n_layer"]
        residual_channels = model_config["residual_channels"]
        output_dim = model_config["output_dim"]
        loss_type = model_config["loss_type"]
        log_scale_min = model_config["log_scale_min"]
        decoder = WaveNet(n_loop, n_layer, residual_channels, output_dim,
                          n_mels, filter_size, loss_type, log_scale_min)

        model = ConditionalWavenet(encoder, decoder)
        summary(model)

        train_config = config["train"]
        learning_rate = train_config["learning_rate"]
        anneal_rate = train_config["anneal_rate"]
        anneal_interval = train_config["anneal_interval"]
        lr_scheduler = dg.ExponentialDecay(
            learning_rate, anneal_interval, anneal_rate, staircase=True)
        optim = fluid.optimizer.Adam(
            lr_scheduler, parameter_list=model.parameters())

        gradiant_max_norm = train_config["gradient_max_norm"]
        clipper = fluid.dygraph_grad_clip.GradClipByGlobalNorm(
            gradiant_max_norm)

        if args.resume:
            model_dict, optim_dict = dg.load_dygraph(args.resume)
            print("Loading from {}.pdparams".format(args.resume))
            model.set_dict(model_dict)
            if optim_dict:
                optim.set_dict(optim_dict)
                print("Loading from {}.pdopt".format(args.resume))

        train_loader = fluid.io.DataLoader.from_generator(
            capacity=10, return_list=True)
        train_loader.set_batch_generator(train_cargo, place)

        valid_loader = fluid.io.DataLoader.from_generator(
            capacity=10, return_list=True)
        valid_loader.set_batch_generator(valid_cargo, place)

        max_iterations = train_config["max_iterations"]
        checkpoint_interval = train_config["checkpoint_interval"]
        snap_interval = train_config["snap_interval"]
        eval_interval = train_config["eval_interval"]
        checkpoint_dir = os.path.join(args.output, "checkpoints")
        log_dir = os.path.join(args.output, "log")
        writer = SummaryWriter(log_dir)

        global_step = 1
        while global_step <= max_iterations:
            epoch_loss = 0.
            for i, batch in tqdm(enumerate(train_loader)):
                audio_clips, mel_specs, audio_starts = batch

                model.train()
                y_var = model(audio_clips, mel_specs, audio_starts)
                loss_var = model.loss(y_var, audio_clips)
                loss_var.backward()
                loss_np = loss_var.numpy()

                epoch_loss += loss_np[0]

                writer.add_scalar("loss", loss_np[0], global_step)
                writer.add_scalar("learning_rate",
                                  optim._learning_rate.step().numpy()[0],
                                  global_step)
                optim.minimize(loss_var, grad_clip=clipper)
                optim.clear_gradients()
                print("loss: {:<8.6f}".format(loss_np[0]))

                if global_step % snap_interval == 0:
                    valid_model(model, valid_loader, writer, global_step,
                                sample_rate)

                if global_step % checkpoint_interval == 0:
                    save_checkpoint(model, optim, checkpoint_dir, global_step)

                global_step += 1