# 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 logging

import paddle
from paddle import distributed as dist
from paddle.fluid.layers import huber_loss
from paddle.nn import functional as F
from parakeet.modules.losses import masked_l1_loss
from parakeet.modules.losses import weighted_mean
from parakeet.modules.ssim import ssim
from parakeet.training.extensions.evaluator import StandardEvaluator
from parakeet.training.reporter import report
from parakeet.training.updaters.standard_updater import StandardUpdater
logging.basicConfig(
    format='%(asctime)s [%(levelname)s] [%(filename)s:%(lineno)d] %(message)s',
    datefmt='[%Y-%m-%d %H:%M:%S]')
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)


class SpeedySpeechUpdater(StandardUpdater):
    def __init__(self,
                 model,
                 optimizer,
                 dataloader,
                 init_state=None,
                 output_dir=None):
        super().__init__(model, optimizer, dataloader, init_state=None)

        log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
        self.filehandler = logging.FileHandler(str(log_file))
        logger.addHandler(self.filehandler)
        self.logger = logger
        self.msg = ""

    def update_core(self, batch):
        self.msg = "Rank: {}, ".format(dist.get_rank())
        losses_dict = {}

        decoded, predicted_durations = self.model(
            text=batch["phones"],
            tones=batch["tones"],
            durations=batch["durations"])

        target_mel = batch["feats"]
        spec_mask = F.sequence_mask(
            batch["num_frames"], dtype=target_mel.dtype).unsqueeze(-1)
        text_mask = F.sequence_mask(
            batch["num_phones"], dtype=predicted_durations.dtype)

        # spec loss
        l1_loss = masked_l1_loss(decoded, target_mel, spec_mask)

        # duration loss
        target_durations = batch["durations"]
        target_durations = paddle.maximum(
            target_durations.astype(predicted_durations.dtype),
            paddle.to_tensor([1.0]))
        duration_loss = weighted_mean(
            huber_loss(
                predicted_durations, paddle.log(target_durations), delta=1.0),
            text_mask, )

        # ssim loss
        ssim_loss = 1.0 - ssim((decoded * spec_mask).unsqueeze(1),
                               (target_mel * spec_mask).unsqueeze(1))

        loss = l1_loss + ssim_loss + duration_loss

        optimizer = self.optimizer
        optimizer.clear_grad()
        loss.backward()
        optimizer.step()

        report("train/loss", float(loss))
        report("train/l1_loss", float(l1_loss))
        report("train/duration_loss", float(duration_loss))
        report("train/ssim_loss", float(ssim_loss))

        losses_dict["l1_loss"] = float(l1_loss)
        losses_dict["duration_loss"] = float(duration_loss)
        losses_dict["ssim_loss"] = float(ssim_loss)
        losses_dict["loss"] = float(loss)
        self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
                              for k, v in losses_dict.items())


class SpeedySpeechEvaluator(StandardEvaluator):
    def __init__(self, model, dataloader, output_dir=None):
        super().__init__(model, dataloader)

        log_file = output_dir / 'worker_{}.log'.format(dist.get_rank())
        self.filehandler = logging.FileHandler(str(log_file))
        logger.addHandler(self.filehandler)
        self.logger = logger
        self.msg = ""

    def evaluate_core(self, batch):
        self.msg = "Evaluate: "
        losses_dict = {}

        decoded, predicted_durations = self.model(
            text=batch["phones"],
            tones=batch["tones"],
            durations=batch["durations"])

        target_mel = batch["feats"]
        spec_mask = F.sequence_mask(
            batch["num_frames"], dtype=target_mel.dtype).unsqueeze(-1)
        text_mask = F.sequence_mask(
            batch["num_phones"], dtype=predicted_durations.dtype)

        # spec loss
        l1_loss = masked_l1_loss(decoded, target_mel, spec_mask)

        # duration loss
        target_durations = batch["durations"]
        target_durations = paddle.maximum(
            target_durations.astype(predicted_durations.dtype),
            paddle.to_tensor([1.0]))
        duration_loss = weighted_mean(
            huber_loss(
                predicted_durations, paddle.log(target_durations), delta=1.0),
            text_mask, )

        # ssim loss
        ssim_loss = 1.0 - ssim((decoded * spec_mask).unsqueeze(1),
                               (target_mel * spec_mask).unsqueeze(1))

        loss = l1_loss + ssim_loss + duration_loss

        # import pdb; pdb.set_trace()

        report("eval/loss", float(loss))
        report("eval/l1_loss", float(l1_loss))
        report("eval/duration_loss", float(duration_loss))
        report("eval/ssim_loss", float(ssim_loss))

        losses_dict["l1_loss"] = float(l1_loss)
        losses_dict["duration_loss"] = float(duration_loss)
        losses_dict["ssim_loss"] = float(ssim_loss)
        losses_dict["loss"] = float(loss)
        self.msg += ', '.join('{}: {:>.6f}'.format(k, v)
                              for k, v in losses_dict.items())
        self.logger.info(self.msg)
