import math

import torch
import torch.nn as nn
import torch.nn.functional as F

from math import sqrt

Linear = nn.Linear
ConvTranspose2d = nn.ConvTranspose2d


class Mish(nn.Module):
    def forward(self, x):
        return x * torch.tanh(F.softplus(x))


class SinusoidalPosEmb(nn.Module):
    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def forward(self, x):
        device = x.device
        half_dim = self.dim // 2
        emb = math.log(10000) / (half_dim - 1)
        emb = torch.exp(torch.arange(half_dim, device=device) * -emb)
        emb = x[:, None] * emb[None, :]
        emb = torch.cat((emb.sin(), emb.cos()), dim=-1)
        return emb


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


class ResidualBlock(nn.Module):
    def __init__(self, encoder_hidden, residual_channels, dilation):
        super().__init__()
        self.dilated_conv = Conv1d(residual_channels, 2 * residual_channels, 3, padding=dilation, dilation=dilation)
        self.diffusion_projection = Linear(residual_channels, residual_channels)
        self.conditioner_projection = Conv1d(encoder_hidden, 2 * residual_channels, 1)
        self.output_projection = Conv1d(residual_channels, 2 * residual_channels, 1)

    def forward(self, x, conditioner, diffusion_step):
        diffusion_step = self.diffusion_projection(diffusion_step).unsqueeze(-1)
        conditioner = self.conditioner_projection(conditioner)
        y = x + diffusion_step

        y = self.dilated_conv(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 DiffNet(nn.Module):
    def __init__(self, hparams):
        super().__init__()
        in_dims = hparams['audio_num_mel_bins']
        self.encoder_hidden = hparams['hidden_size']
        self.residual_layers = hparams['residual_layers']
        self.residual_channels = hparams['residual_channels']
        self.dilation_cycle_length = hparams['dilation_cycle_length']

        self.input_projection = Conv1d(in_dims, self.residual_channels, 1)
        self.diffusion_embedding = SinusoidalPosEmb(self.residual_channels)
        dim = self.residual_channels
        self.mlp = nn.Sequential(
            nn.Linear(dim, dim * 4),
            Mish(),
            nn.Linear(dim * 4, dim)
        )
        self.residual_layers = nn.ModuleList([
            ResidualBlock(self.encoder_hidden, self.residual_channels, 2 ** (i % self.dilation_cycle_length))
            for i in range(self.residual_layers)
        ])
        self.skip_projection = Conv1d(self.residual_channels, self.residual_channels, 1)
        self.output_projection = Conv1d(self.residual_channels, in_dims, 1)
        nn.init.zeros_(self.output_projection.weight)

    def forward(self, spec, diffusion_step, cond):
        """

        :param spec: [B, 1, M, T]
        :param diffusion_step: [B, 1]
        :param cond: [B, M, T]
        :return:
        """
        x = spec[:, 0]
        x = self.input_projection(x)  # x [B, residual_channel, T]

        x = F.relu(x)
        diffusion_step = self.diffusion_embedding(diffusion_step)
        diffusion_step = self.mlp(diffusion_step)
        skip = []
        for layer_id, layer in enumerate(self.residual_layers):
            x, skip_connection = layer(x, cond, diffusion_step)
            skip.append(skip_connection)

        x = torch.sum(torch.stack(skip), dim=0) / sqrt(len(self.residual_layers))
        x = self.skip_projection(x)
        x = F.relu(x)
        x = self.output_projection(x)  # [B, 80, T]
        return x[:, None, :, :]