# Copyright 2020 LMNT, Inc. 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 numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch_intermediary as ml

from math import sqrt

from torch.utils.checkpoint import checkpoint

from trainer.networks import register_model

Linear = ml.Linear
ConvTranspose2d = nn.ConvTranspose2d


def Conv1d(*args, **kwargs):
    layer = nn.Conv1d(*args, **kwargs)
    nn.init.kaiming_normal_(layer.weight)
    return layer


@torch.jit.script
def silu(x):
    return x * torch.sigmoid(x)


class DiffusionEmbedding(nn.Module):
    def __init__(self, max_steps):
        super().__init__()
        self.register_buffer('embedding', self._build_embedding(max_steps), persistent=False)
        self.projection1 = Linear(128, 512)
        self.projection2 = Linear(512, 512)

    def forward(self, diffusion_step):
        if diffusion_step.dtype in [torch.int32, torch.int64]:
            x = self.embedding[diffusion_step]
        else:
            x = self._lerp_embedding(diffusion_step)
        x = self.projection1(x)
        x = silu(x)
        x = self.projection2(x)
        x = silu(x)
        return x

    def _lerp_embedding(self, t):
        low_idx = torch.floor(t).long()
        high_idx = torch.ceil(t).long()
        low = self.embedding[low_idx]
        high = self.embedding[high_idx]
        return low + (high - low) * (t - low_idx)

    def _build_embedding(self, max_steps):
        steps = torch.arange(max_steps).unsqueeze(1)  # [T,1]
        dims = torch.arange(64).unsqueeze(0)  # [1,64]
        table = steps * 10.0 ** (dims * 4.0 / 63.0)  # [T,64]
        table = torch.cat([torch.sin(table), torch.cos(table)], dim=1)
        return table


class SpectrogramUpsampler(nn.Module):
    def __init__(self, n_mels):
        super().__init__()
        self.conv1 = ConvTranspose2d(1, 1, [3, 32], stride=[1, 16], padding=[1, 8])
        self.conv2 = ConvTranspose2d(1, 1, [3, 32], stride=[1, 16], padding=[1, 8])

    def forward(self, x):
        x = torch.unsqueeze(x, 1)
        x = self.conv1(x)
        x = F.leaky_relu(x, 0.4)
        x = self.conv2(x)
        x = F.leaky_relu(x, 0.4)
        x = torch.squeeze(x, 1)
        return x


class ResidualBlock(nn.Module):
    def __init__(self, n_mels, residual_channels, dilation, uncond=False):
        '''
        :param n_mels: inplanes of conv1x1 for spectrogram conditional
        :param residual_channels: audio conv
        :param dilation: audio conv dilation
        :param uncond: disable spectrogram conditional
        '''
        super().__init__()
        self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
        self.diffusion_projection = Linear(512, residual_channels)
        if not uncond:  # conditional model
            self.conditioner_projection = Conv1d(n_mels, 2 * residual_channels, 1)
        else:  # unconditional model
            self.conditioner_projection = None

        self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)

    def forward(self, x, diffusion_step, conditioner=None):
        assert (conditioner is None and self.conditioner_projection is None) or \
               (conditioner is not None and self.conditioner_projection is not None)

        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
        y = x + diffusion_step
        if self.conditioner_projection is None:  # using a unconditional model
            y = self.dilated_conv(y)
        else:
            y = self.dilated_conv(y)
            conditioner = self.conditioner_projection(conditioner)
            conditioner = F.interpolate(conditioner, size=y.shape[-1], mode='nearest')
            y = y + conditioner

        gate, filter = torch.chunk(y, 2, dim=1)
        y = torch.sigmoid(gate) * torch.tanh(filter)

        y = self.output_projection(y)
        residual, skip = torch.chunk(y, 2, dim=1)
        return (x + residual) / sqrt(2.0), skip


class DiffWave(nn.Module):
    def __init__(self, residual_layers=30, residual_channels=64, num_timesteps=4000, n_mels=128,
                 dilation_cycle_length=10, unconditional=False):
        super().__init__()
        self.input_projection = Conv1d(1, residual_channels, 1)
        self.diffusion_embedding = DiffusionEmbedding(num_timesteps)
        if unconditional:  # use unconditional model
            self.spectrogram_upsampler = None
        else:
            self.spectrogram_upsampler = SpectrogramUpsampler(n_mels)

        self.residual_layers = nn.ModuleList([
            ResidualBlock(n_mels, residual_channels, 2 ** (i % dilation_cycle_length), uncond=unconditional)
            for i in range(residual_layers)
        ])
        self.skip_projection = Conv1d(residual_channels, residual_channels, 1)
        self.output_projection = Conv1d(residual_channels, 2, 1)
        nn.init.zeros_(self.output_projection.weight)

    def forward(self, x, timesteps, spectrogram=None):
        assert (spectrogram is None and self.spectrogram_upsampler is None) or \
               (spectrogram is not None and self.spectrogram_upsampler is not None)
        x = self.input_projection(x)
        x = F.relu(x)

        timesteps = checkpoint(self.diffusion_embedding, timesteps)
        if self.spectrogram_upsampler:  # use conditional model
            spectrogram = checkpoint(self.spectrogram_upsampler, spectrogram)

        skip = None
        for layer in self.residual_layers:
            x, skip_connection = checkpoint(layer, x, timesteps, spectrogram)
            skip = skip_connection if skip is None else skip_connection + skip

        x = skip / sqrt(len(self.residual_layers))
        x = self.skip_projection(x)
        x = F.relu(x)
        x = self.output_projection(x)
        return x


@register_model
def register_diffwave(opt_net, opt):
    return DiffWave(**opt_net['kwargs'])


if __name__ == '__main__':
    model = DiffWave()
    model(torch.randn(2,1,65536), torch.tensor([500,3999]), torch.randn(2,128,256))