# 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 time

from paddle import DataParallel
from paddle import distributed as dist
from paddle.io import DataLoader
from paddle.nn.clip import ClipGradByGlobalNorm
from paddle.optimizer import Adam

from parakeet.exps.ge2e.config import get_cfg_defaults
from parakeet.exps.ge2e.speaker_verification_dataset import Collate
from parakeet.exps.ge2e.speaker_verification_dataset import MultiSpeakerMelDataset
from parakeet.exps.ge2e.speaker_verification_dataset import MultiSpeakerSampler
from parakeet.models.lstm_speaker_encoder import LSTMSpeakerEncoder
from parakeet.training import default_argument_parser
from parakeet.training import ExperimentBase


class Ge2eExperiment(ExperimentBase):
    def setup_model(self):
        config = self.config
        model = LSTMSpeakerEncoder(config.data.n_mels, config.model.num_layers,
                                   config.model.hidden_size,
                                   config.model.embedding_size)
        optimizer = Adam(
            config.training.learning_rate_init,
            parameters=model.parameters(),
            grad_clip=ClipGradByGlobalNorm(3))
        self.model = DataParallel(model) if self.parallel else model
        self.model_core = model
        self.optimizer = optimizer

    def setup_dataloader(self):
        config = self.config
        train_dataset = MultiSpeakerMelDataset(self.args.data)
        sampler = MultiSpeakerSampler(train_dataset,
                                      config.training.speakers_per_batch,
                                      config.training.utterances_per_speaker)
        train_loader = DataLoader(
            train_dataset,
            batch_sampler=sampler,
            collate_fn=Collate(config.data.partial_n_frames),
            num_workers=16)

        self.train_dataset = train_dataset
        self.train_loader = train_loader

    def train_batch(self):
        start = time.time()
        batch = self.read_batch()
        data_loader_time = time.time() - start

        self.optimizer.clear_grad()
        self.model.train()
        specs = batch
        loss, eer = self.model(specs, self.config.training.speakers_per_batch)
        loss.backward()
        self.model_core.do_gradient_ops()
        self.optimizer.step()
        iteration_time = time.time() - start

        # logging
        loss_value = float(loss)
        msg = "Rank: {}, ".format(dist.get_rank())
        msg += "step: {}, ".format(self.iteration)
        msg += "time: {:>.3f}s/{:>.3f}s, ".format(data_loader_time,
                                                  iteration_time)
        msg += 'loss: {:>.6f} err: {:>.6f}'.format(loss_value, eer)
        self.logger.info(msg)

        if dist.get_rank() == 0:
            self.visualizer.add_scalar("train/loss", loss_value, self.iteration)
            self.visualizer.add_scalar("train/eer", eer, self.iteration)
            self.visualizer.add_scalar("param/w",
                                       float(self.model_core.similarity_weight),
                                       self.iteration)
            self.visualizer.add_scalar("param/b",
                                       float(self.model_core.similarity_bias),
                                       self.iteration)

    def valid(self):
        pass


def main_sp(config, args):
    exp = Ge2eExperiment(config, args)
    exp.setup()
    exp.resume_or_load()
    exp.run()


def main(config, args):
    if args.nprocs > 1 and args.device == "gpu":
        dist.spawn(main_sp, args=(config, args), nprocs=args.nprocs)
    else:
        main_sp(config, args)


if __name__ == "__main__":
    config = get_cfg_defaults()
    parser = default_argument_parser()
    args = parser.parse_args()
    if args.config:
        config.merge_from_file(args.config)
    if args.opts:
        config.merge_from_list(args.opts)
    config.freeze()
    print(config)
    print(args)

    main(config, args)