import typing as tp
import torch
import torch.nn as nn
from torch.nn import Conv1d, ConvTranspose1d
from torch.nn.utils import weight_norm, remove_weight_norm

from fireredtts.modules.bigvgan.alias_free_torch import (
    Activation1d as TorchActivation1d,
)
from fireredtts.modules.bigvgan.activations import Snake, SnakeBeta


def init_weights(m, mean=0.0, std=0.01):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        m.weight.data.normal_(mean, std)


def get_padding(kernel_size, dilation=1):
    return int((kernel_size * dilation - dilation) / 2)


class AMPBlock1(torch.nn.Module):
    def __init__(
        self,
        channels,
        kernel_size=3,
        dilation=(1, 3, 5),
        activation=None,
        snake_logscale=True,
        use_cuda_kernel=False,
    ):
        super(AMPBlock1, self).__init__()

        self.convs1 = nn.ModuleList(
            [
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=dilation[0],
                        padding=get_padding(kernel_size, dilation[0]),
                    )
                ),
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=dilation[1],
                        padding=get_padding(kernel_size, dilation[1]),
                    )
                ),
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=dilation[2],
                        padding=get_padding(kernel_size, dilation[2]),
                    )
                ),
            ]
        )
        self.convs1.apply(init_weights)

        self.convs2 = nn.ModuleList(
            [
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=1,
                        padding=get_padding(kernel_size, 1),
                    )
                ),
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=1,
                        padding=get_padding(kernel_size, 1),
                    )
                ),
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=1,
                        padding=get_padding(kernel_size, 1),
                    )
                ),
            ]
        )
        self.convs2.apply(init_weights)

        self.num_layers = len(self.convs1) + len(
            self.convs2
        )  # total number of conv layers

        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
        if use_cuda_kernel:
            from modules.bigvgan.alias_free_cuda.activation1d import (
                Activation1d as CudaActivation1d,
            )

            Activation1d = CudaActivation1d
        else:
            Activation1d = TorchActivation1d

        if (
            activation == "snake"
        ):  # periodic nonlinearity with snake function and anti-aliasing
            self.activations = nn.ModuleList(
                [
                    Activation1d(
                        activation=Snake(channels, alpha_logscale=snake_logscale)
                    )
                    for _ in range(self.num_layers)
                ]
            )
        elif (
            activation == "snakebeta"
        ):  # periodic nonlinearity with snakebeta function and anti-aliasing
            self.activations = nn.ModuleList(
                [
                    Activation1d(
                        activation=SnakeBeta(channels, alpha_logscale=snake_logscale)
                    )
                    for _ in range(self.num_layers)
                ]
            )
        else:
            raise NotImplementedError(
                "activation incorrectly specified. check the config file and look for 'activation'."
            )

    def forward(self, x):
        acts1, acts2 = self.activations[::2], self.activations[1::2]
        for c1, c2, a1, a2 in zip(self.convs1, self.convs2, acts1, acts2):
            xt = a1(x)
            xt = c1(xt)
            xt = a2(xt)
            xt = c2(xt)
            x = xt + x

        return x

    def remove_weight_norm(self):
        for l in self.convs1:
            remove_weight_norm(l)
        for l in self.convs2:
            remove_weight_norm(l)


class AMPBlock2(torch.nn.Module):
    def __init__(
        self,
        channels,
        kernel_size=3,
        dilation=(1, 3),
        activation=None,
        snake_logscale=True,
        use_cuda_kernel=False,
    ):
        super(AMPBlock2, self).__init__()

        self.convs = nn.ModuleList(
            [
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=dilation[0],
                        padding=get_padding(kernel_size, dilation[0]),
                    )
                ),
                weight_norm(
                    Conv1d(
                        channels,
                        channels,
                        kernel_size,
                        1,
                        dilation=dilation[1],
                        padding=get_padding(kernel_size, dilation[1]),
                    )
                ),
            ]
        )
        self.convs.apply(init_weights)

        self.num_layers = len(self.convs)  # total number of conv layers

        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
        if use_cuda_kernel:
            from modules.bigvgan.alias_free_cuda.activation1d import (
                Activation1d as CudaActivation1d,
            )

            Activation1d = CudaActivation1d
        else:
            Activation1d = TorchActivation1d

        if (
            activation == "snake"
        ):  # periodic nonlinearity with snake function and anti-aliasing
            self.activations = nn.ModuleList(
                [
                    Activation1d(
                        activation=Snake(channels, alpha_logscale=snake_logscale)
                    )
                    for _ in range(self.num_layers)
                ]
            )
        elif (
            activation == "snakebeta"
        ):  # periodic nonlinearity with snakebeta function and anti-aliasing
            self.activations = nn.ModuleList(
                [
                    Activation1d(
                        activation=SnakeBeta(channels, alpha_logscale=snake_logscale)
                    )
                    for _ in range(self.num_layers)
                ]
            )
        else:
            raise NotImplementedError(
                "activation incorrectly specified. check the config file and look for 'activation'."
            )

    def forward(self, x):
        for c, a in zip(self.convs, self.activations):
            xt = a(x)
            xt = c(xt)
            x = xt + x

        return x

    def remove_weight_norm(self):
        for l in self.convs:
            remove_weight_norm(l)


class BigVGAN(torch.nn.Module):
    # this is our main BigVGAN model. Applies anti-aliased periodic activation for resblocks.
    def __init__(
        self,
        num_mels: int,
        upsample_initial_channel: int,
        resblock_kernel_sizes: tp.List[int],
        resblock_dilation_sizes: tp.List[tp.List[int]],
        upsample_rates: tp.List[int],
        upsample_kernel_sizes: tp.List[int],
        resblock_type: str = "1",
        snake_logscale: bool = True,
        activation: str = "snakebeta",
        use_tanh_at_final: bool = False,
        use_bias_at_final: bool = False,
        use_cuda_kernel: bool = False,
    ):
        super(BigVGAN, self).__init__()

        self.num_kernels = len(resblock_kernel_sizes)
        self.num_upsamples = len(upsample_rates)

        # pre conv
        self.conv_pre = weight_norm(
            Conv1d(num_mels, upsample_initial_channel, 7, 1, padding=3)
        )

        # define which AMPBlock to use. BigVGAN uses AMPBlock1 as default
        resblock = AMPBlock1 if resblock_type == "1" else AMPBlock2

        # transposed conv-based upsamplers. does not apply anti-aliasing
        self.ups = nn.ModuleList()
        for i, (u, k) in enumerate(zip(upsample_rates, upsample_kernel_sizes)):
            self.ups.append(
                nn.ModuleList(
                    [
                        weight_norm(
                            ConvTranspose1d(
                                upsample_initial_channel // (2**i),
                                upsample_initial_channel // (2 ** (i + 1)),
                                k,
                                u,
                                padding=(k - u) // 2,
                            )
                        )
                    ]
                )
            )

        # residual blocks using anti-aliased multi-periodicity composition modules (AMP)
        self.resblocks = nn.ModuleList()
        for i in range(len(self.ups)):
            ch = upsample_initial_channel // (2 ** (i + 1))
            for j, (k, d) in enumerate(
                zip(resblock_kernel_sizes, resblock_dilation_sizes)
            ):
                self.resblocks.append(
                    resblock(
                        ch,
                        k,
                        d,
                        activation=activation,
                        snake_logscale=snake_logscale,
                        use_cuda_kernel=use_cuda_kernel,
                    )
                )

        # select which Activation1d, lazy-load cuda version to ensure backward compatibility
        if use_cuda_kernel:
            from modules.bigvgan.alias_free_cuda.activation1d import (
                Activation1d as CudaActivation1d,
            )

            Activation1d = CudaActivation1d
        else:
            Activation1d = TorchActivation1d

        # post conv
        if (
            activation == "snake"
        ):  # periodic nonlinearity with snake function and anti-aliasing
            activation_post = Snake(ch, alpha_logscale=snake_logscale)
            self.activation_post = Activation1d(activation=activation_post)
        elif (
            activation == "snakebeta"
        ):  # periodic nonlinearity with snakebeta function and anti-aliasing
            activation_post = SnakeBeta(ch, alpha_logscale=snake_logscale)
            self.activation_post = Activation1d(activation=activation_post)
        else:
            raise NotImplementedError(
                "activation incorrectly specified. check the config file and look for 'activation'."
            )

        # whether to use bias for the final conv_post. Defaults to True for backward compatibility
        self.use_bias_at_final = use_bias_at_final
        self.conv_post = weight_norm(
            Conv1d(ch, 1, 7, 1, padding=3, bias=self.use_bias_at_final)
        )

        # weight initialization
        for i in range(len(self.ups)):
            self.ups[i].apply(init_weights)
        self.conv_post.apply(init_weights)

        # final tanh activation. Defaults to True for backward compatibility
        self.use_tanh_at_final = use_tanh_at_final

    def forward(self, x):
        # pre conv
        x = self.conv_pre(x)

        for i in range(self.num_upsamples):
            # upsampling
            for i_up in range(len(self.ups[i])):
                x = self.ups[i][i_up](x)
            # AMP blocks
            xs = None
            for j in range(self.num_kernels):
                if xs is None:
                    xs = self.resblocks[i * self.num_kernels + j](x)
                else:
                    xs += self.resblocks[i * self.num_kernels + j](x)
            x = xs / self.num_kernels

        # post conv
        x = self.activation_post(x)
        x = self.conv_post(x)
        # final tanh activation
        if self.use_tanh_at_final:
            x = torch.tanh(x)
        else:
            x = torch.clamp(x, min=-1.0, max=1.0)  # bound the output to [-1, 1]

        return x

    def remove_weight_norm(self):
        print("Removing weight norm...")
        for l in self.ups:
            for l_i in l:
                remove_weight_norm(l_i)
        for l in self.resblocks:
            l.remove_weight_norm()
        remove_weight_norm(self.conv_pre)
        remove_weight_norm(self.conv_post)