""" refer from https://github.com/zceng/LVCNet """

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch import nn
from torch.nn.utils.parametrizations import weight_norm

from .amp import AMPBlock


class KernelPredictor(torch.nn.Module):
    """Kernel predictor for the location-variable convolutions"""

    def __init__(
        self,
        cond_channels,
        conv_in_channels,
        conv_out_channels,
        conv_layers,
        conv_kernel_size=3,
        kpnet_hidden_channels=64,
        kpnet_conv_size=3,
        kpnet_dropout=0.0,
        kpnet_nonlinear_activation="LeakyReLU",
        kpnet_nonlinear_activation_params={"negative_slope": 0.1},
    ):
        """
        Args:
            cond_channels (int): number of channel for the conditioning sequence,
            conv_in_channels (int): number of channel for the input sequence,
            conv_out_channels (int): number of channel for the output sequence,
            conv_layers (int): number of layers
        """
        super().__init__()

        self.conv_in_channels = conv_in_channels
        self.conv_out_channels = conv_out_channels
        self.conv_kernel_size = conv_kernel_size
        self.conv_layers = conv_layers

        kpnet_kernel_channels = conv_in_channels * conv_out_channels * conv_kernel_size * conv_layers  # l_w
        kpnet_bias_channels = conv_out_channels * conv_layers  # l_b

        self.input_conv = nn.Sequential(
            weight_norm(nn.Conv1d(cond_channels, kpnet_hidden_channels, 5, padding=2, bias=True)),
            getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
        )

        self.residual_convs = nn.ModuleList()
        padding = (kpnet_conv_size - 1) // 2
        for _ in range(3):
            self.residual_convs.append(
                nn.Sequential(
                    nn.Dropout(kpnet_dropout),
                    weight_norm(
                        nn.Conv1d(
                            kpnet_hidden_channels,
                            kpnet_hidden_channels,
                            kpnet_conv_size,
                            padding=padding,
                            bias=True,
                        )
                    ),
                    getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
                    weight_norm(
                        nn.Conv1d(
                            kpnet_hidden_channels,
                            kpnet_hidden_channels,
                            kpnet_conv_size,
                            padding=padding,
                            bias=True,
                        )
                    ),
                    getattr(nn, kpnet_nonlinear_activation)(**kpnet_nonlinear_activation_params),
                )
            )
        self.kernel_conv = weight_norm(
            nn.Conv1d(
                kpnet_hidden_channels,
                kpnet_kernel_channels,
                kpnet_conv_size,
                padding=padding,
                bias=True,
            )
        )
        self.bias_conv = weight_norm(
            nn.Conv1d(
                kpnet_hidden_channels,
                kpnet_bias_channels,
                kpnet_conv_size,
                padding=padding,
                bias=True,
            )
        )

    def forward(self, c):
        """
        Args:
            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)
        """
        batch, _, cond_length = c.shape
        c = self.input_conv(c)
        for residual_conv in self.residual_convs:
            residual_conv.to(c.device)
            c = c + residual_conv(c)
        k = self.kernel_conv(c)
        b = self.bias_conv(c)
        kernels = k.contiguous().view(
            batch,
            self.conv_layers,
            self.conv_in_channels,
            self.conv_out_channels,
            self.conv_kernel_size,
            cond_length,
        )
        bias = b.contiguous().view(
            batch,
            self.conv_layers,
            self.conv_out_channels,
            cond_length,
        )

        return kernels, bias


class LVCBlock(torch.nn.Module):
    """the location-variable convolutions"""

    def __init__(
        self,
        in_channels,
        cond_channels,
        stride,
        dilations=[1, 3, 9, 27],
        lReLU_slope=0.2,
        conv_kernel_size=3,
        cond_hop_length=256,
        kpnet_hidden_channels=64,
        kpnet_conv_size=3,
        kpnet_dropout=0.0,
        add_extra_noise=False,
        downsampling=False,
    ):
        super().__init__()

        self.add_extra_noise = add_extra_noise

        self.cond_hop_length = cond_hop_length
        self.conv_layers = len(dilations)
        self.conv_kernel_size = conv_kernel_size

        self.kernel_predictor = KernelPredictor(
            cond_channels=cond_channels,
            conv_in_channels=in_channels,
            conv_out_channels=2 * in_channels,
            conv_layers=len(dilations),
            conv_kernel_size=conv_kernel_size,
            kpnet_hidden_channels=kpnet_hidden_channels,
            kpnet_conv_size=kpnet_conv_size,
            kpnet_dropout=kpnet_dropout,
            kpnet_nonlinear_activation_params={"negative_slope": lReLU_slope},
        )

        if downsampling:
            self.convt_pre = nn.Sequential(
                nn.LeakyReLU(lReLU_slope),
                weight_norm(nn.Conv1d(in_channels, in_channels, 2 * stride + 1, padding="same")),
                nn.AvgPool1d(stride, stride),
            )
        else:
            if stride == 1:
                self.convt_pre = nn.Sequential(
                    nn.LeakyReLU(lReLU_slope),
                    weight_norm(nn.Conv1d(in_channels, in_channels, 1)),
                )
            else:
                self.convt_pre = nn.Sequential(
                    nn.LeakyReLU(lReLU_slope),
                    weight_norm(
                        nn.ConvTranspose1d(
                            in_channels,
                            in_channels,
                            2 * stride,
                            stride=stride,
                            padding=stride // 2 + stride % 2,
                            output_padding=stride % 2,
                        )
                    ),
                )

        self.amp_block = AMPBlock(in_channels)

        self.conv_blocks = nn.ModuleList()
        for d in dilations:
            self.conv_blocks.append(
                nn.Sequential(
                    nn.LeakyReLU(lReLU_slope),
                    weight_norm(nn.Conv1d(in_channels, in_channels, conv_kernel_size, dilation=d, padding="same")),
                    nn.LeakyReLU(lReLU_slope),
                )
            )

    def forward(self, x, c):
        """forward propagation of the location-variable convolutions.
        Args:
            x (Tensor): the input sequence (batch, in_channels, in_length)
            c (Tensor): the conditioning sequence (batch, cond_channels, cond_length)

        Returns:
            Tensor: the output sequence (batch, in_channels, in_length)
        """
        _, in_channels, _ = x.shape  # (B, c_g, L')

        x = self.convt_pre(x)  # (B, c_g, stride * L')

        # Add one amp block just after the upsampling
        x = self.amp_block(x)  # (B, c_g, stride * L')

        kernels, bias = self.kernel_predictor(c)

        if self.add_extra_noise:
            # Add extra noise to part of the feature
            a, b = x.chunk(2, dim=1)
            b = b + torch.randn_like(b) * 0.1
            x = torch.cat([a, b], dim=1)

        for i, conv in enumerate(self.conv_blocks):
            output = conv(x)  # (B, c_g, stride * L')

            k = kernels[:, i, :, :, :, :]  # (B, 2 * c_g, c_g, kernel_size, cond_length)
            b = bias[:, i, :, :]  # (B, 2 * c_g, cond_length)

            output = self.location_variable_convolution(
                output, k, b, hop_size=self.cond_hop_length
            )  # (B, 2 * c_g, stride * L'): LVC
            x = x + torch.sigmoid(output[:, :in_channels, :]) * torch.tanh(
                output[:, in_channels:, :]
            )  # (B, c_g, stride * L'): GAU

        return x

    def location_variable_convolution(self, x, kernel, bias, dilation=1, hop_size=256):
        """perform location-variable convolution operation on the input sequence (x) using the local convolution kernl.
        Time: 414 μs ± 309 ns per loop (mean ± std. dev. of 7 runs, 1000 loops each), test on NVIDIA V100.
        Args:
            x (Tensor): the input sequence (batch, in_channels, in_length).
            kernel (Tensor): the local convolution kernel (batch, in_channel, out_channels, kernel_size, kernel_length)
            bias (Tensor): the bias for the local convolution (batch, out_channels, kernel_length)
            dilation (int): the dilation of convolution.
            hop_size (int): the hop_size of the conditioning sequence.
        Returns:
            (Tensor): the output sequence after performing local convolution. (batch, out_channels, in_length).
        """
        batch, _, in_length = x.shape
        batch, _, out_channels, kernel_size, kernel_length = kernel.shape

        assert in_length == (
            kernel_length * hop_size
        ), f"length of (x, kernel) is not matched, {in_length} != {kernel_length} * {hop_size}"

        padding = dilation * int((kernel_size - 1) / 2)
        x = F.pad(x, (padding, padding), "constant", 0)  # (batch, in_channels, in_length + 2*padding)
        x = x.unfold(2, hop_size + 2 * padding, hop_size)  # (batch, in_channels, kernel_length, hop_size + 2*padding)

        if hop_size < dilation:
            x = F.pad(x, (0, dilation), "constant", 0)
        x = x.unfold(
            3, dilation, dilation
        )  # (batch, in_channels, kernel_length, (hop_size + 2*padding)/dilation, dilation)
        x = x[:, :, :, :, :hop_size]
        x = x.transpose(3, 4)  # (batch, in_channels, kernel_length, dilation, (hop_size + 2*padding)/dilation)
        x = x.unfold(4, kernel_size, 1)  # (batch, in_channels, kernel_length, dilation, _, kernel_size)

        o = torch.einsum("bildsk,biokl->bolsd", x, kernel)
        o = o.to(memory_format=torch.channels_last_3d)
        bias = bias.unsqueeze(-1).unsqueeze(-1).to(memory_format=torch.channels_last_3d)
        o = o + bias
        o = o.contiguous().view(batch, out_channels, -1)

        return o