import math
import torch
from torch import nn
from torch.nn import functional as F

from . import commons
from .modules import LayerNorm


class Encoder(nn.Module):
    def __init__(
        self,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size=1,
        p_dropout=0.0,
        window_size=4,
        isflow=False,
        **kwargs,
    ):
        super().__init__()
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.window_size = window_size

        self.drop = nn.Dropout(p_dropout)
        self.attn_layers = nn.ModuleList()
        self.norm_layers_1 = nn.ModuleList()
        self.ffn_layers = nn.ModuleList()
        self.norm_layers_2 = nn.ModuleList()
        for i in range(self.n_layers):
            self.attn_layers.append(
                MultiHeadAttention(
                    hidden_channels,
                    hidden_channels,
                    n_heads,
                    p_dropout=p_dropout,
                    window_size=window_size,
                )
            )
            self.norm_layers_1.append(LayerNorm(hidden_channels))
            self.ffn_layers.append(
                FFN(
                    hidden_channels,
                    hidden_channels,
                    filter_channels,
                    kernel_size,
                    p_dropout=p_dropout,
                )
            )
            self.norm_layers_2.append(LayerNorm(hidden_channels))
        if isflow:
            cond_layer = torch.nn.Conv1d(kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1)
            self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
            self.cond_layer = weight_norm_modules(cond_layer, name="weight")
            self.gin_channels = kwargs["gin_channels"]

    def forward(self, x, x_mask, g=None):
        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
        x = x * x_mask
        if g is not None:
            g = self.cond_layer(g)

        for i in range(self.n_layers):
            if g is not None:
                x = self.cond_pre(x)
                cond_offset = i * 2 * self.hidden_channels
                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
                x = commons.fused_add_tanh_sigmoid_multiply(x, g_l, torch.IntTensor([self.hidden_channels]))
            y = self.attn_layers[i](x, x, attn_mask)
            y = self.drop(y)
            x = self.norm_layers_1[i](x + y)

            y = self.ffn_layers[i](x, x_mask)
            y = self.drop(y)
            x = self.norm_layers_2[i](x + y)
        x = x * x_mask
        return x


class Decoder(nn.Module):
    def __init__(
        self,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers,
        kernel_size=1,
        p_dropout=0.0,
        proximal_bias=False,
        proximal_init=True,
        **kwargs,
    ):
        super().__init__()
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.proximal_bias = proximal_bias
        self.proximal_init = proximal_init

        self.drop = nn.Dropout(p_dropout)
        self.self_attn_layers = nn.ModuleList()
        self.norm_layers_0 = nn.ModuleList()
        self.encdec_attn_layers = nn.ModuleList()
        self.norm_layers_1 = nn.ModuleList()
        self.ffn_layers = nn.ModuleList()
        self.norm_layers_2 = nn.ModuleList()
        for i in range(self.n_layers):
            self.self_attn_layers.append(
                MultiHeadAttention(
                    hidden_channels,
                    hidden_channels,
                    n_heads,
                    p_dropout=p_dropout,
                    proximal_bias=proximal_bias,
                    proximal_init=proximal_init,
                )
            )
            self.norm_layers_0.append(LayerNorm(hidden_channels))
            self.encdec_attn_layers.append(
                MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout)
            )
            self.norm_layers_1.append(LayerNorm(hidden_channels))
            self.ffn_layers.append(
                FFN(
                    hidden_channels,
                    hidden_channels,
                    filter_channels,
                    kernel_size,
                    p_dropout=p_dropout,
                    causal=True,
                )
            )
            self.norm_layers_2.append(LayerNorm(hidden_channels))

    def forward(self, x, x_mask, h, h_mask):
        """
        x: decoder input
        h: encoder output
        """
        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
        encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
        x = x * x_mask
        for i in range(self.n_layers):
            y = self.self_attn_layers[i](x, x, self_attn_mask)
            y = self.drop(y)
            x = self.norm_layers_0[i](x + y)

            y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
            y = self.drop(y)
            x = self.norm_layers_1[i](x + y)

            y = self.ffn_layers[i](x, x_mask)
            y = self.drop(y)
            x = self.norm_layers_2[i](x + y)
        x = x * x_mask
        return x


class MultiHeadAttention(nn.Module):
    def __init__(
        self,
        channels,
        out_channels,
        n_heads,
        p_dropout=0.0,
        window_size=None,
        heads_share=True,
        block_length=None,
        proximal_bias=False,
        proximal_init=False,
    ):
        super().__init__()
        assert channels % n_heads == 0

        self.channels = channels
        self.out_channels = out_channels
        self.n_heads = n_heads
        self.p_dropout = p_dropout
        self.window_size = window_size
        self.heads_share = heads_share
        self.block_length = block_length
        self.proximal_bias = proximal_bias
        self.proximal_init = proximal_init
        self.attn = None

        self.k_channels = channels // n_heads
        self.conv_q = nn.Conv1d(channels, channels, 1)
        self.conv_k = nn.Conv1d(channels, channels, 1)
        self.conv_v = nn.Conv1d(channels, channels, 1)
        self.conv_o = nn.Conv1d(channels, out_channels, 1)
        self.drop = nn.Dropout(p_dropout)

        if window_size is not None:
            n_heads_rel = 1 if heads_share else n_heads
            rel_stddev = self.k_channels**-0.5
            self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
            self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)

        nn.init.xavier_uniform_(self.conv_q.weight)
        nn.init.xavier_uniform_(self.conv_k.weight)
        nn.init.xavier_uniform_(self.conv_v.weight)
        if proximal_init:
            with torch.no_grad():
                self.conv_k.weight.copy_(self.conv_q.weight)
                self.conv_k.bias.copy_(self.conv_q.bias)

    def forward(self, x, c, attn_mask=None):
        q = self.conv_q(x)
        k = self.conv_k(c)
        v = self.conv_v(c)

        x, self.attn = self.attention(q, k, v, mask=attn_mask)

        x = self.conv_o(x)
        return x

    def attention(self, query, key, value, mask=None):
        # reshape [b, d, t] -> [b, n_h, t, d_k]
        b, d, t_s, t_t = (*key.size(), query.size(2))
        query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
        key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
        value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)

        scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
        if self.window_size is not None:
            assert t_s == t_t, "Relative attention is only available for self-attention."
            key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
            rel_logits = self._matmul_with_relative_keys(query / math.sqrt(self.k_channels), key_relative_embeddings)
            scores_local = self._relative_position_to_absolute_position(rel_logits)
            scores = scores + scores_local
        if self.proximal_bias:
            assert t_s == t_t, "Proximal bias is only available for self-attention."
            scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
        if mask is not None:
            scores = scores.masked_fill(mask == 0, -1e4)
            if self.block_length is not None:
                assert t_s == t_t, "Local attention is only available for self-attention."
                block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
                scores = scores.masked_fill(block_mask == 0, -1e4)
        p_attn = F.softmax(scores, dim=-1)  # [b, n_h, t_t, t_s]
        p_attn = self.drop(p_attn)
        output = torch.matmul(p_attn, value)
        if self.window_size is not None:
            relative_weights = self._absolute_position_to_relative_position(p_attn)
            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
            output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
        output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, d_k] -> [b, d, t_t]
        return output, p_attn

    def _matmul_with_relative_values(self, x, y):
        """
        x: [b, h, l, m]
        y: [h or 1, m, d]
        ret: [b, h, l, d]
        """
        ret = torch.matmul(x, y.unsqueeze(0))
        return ret

    def _matmul_with_relative_keys(self, x, y):
        """
        x: [b, h, l, d]
        y: [h or 1, m, d]
        ret: [b, h, l, m]
        """
        ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
        return ret

    def _get_relative_embeddings(self, relative_embeddings, length):
        max_relative_position = 2 * self.window_size + 1
        # Pad first before slice to avoid using cond ops.
        pad_length = max(length - (self.window_size + 1), 0)
        slice_start_position = max((self.window_size + 1) - length, 0)
        slice_end_position = slice_start_position + 2 * length - 1
        if pad_length > 0:
            padded_relative_embeddings = F.pad(
                relative_embeddings,
                commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
            )
        else:
            padded_relative_embeddings = relative_embeddings
        used_relative_embeddings = padded_relative_embeddings[:, slice_start_position:slice_end_position]
        return used_relative_embeddings

    def _relative_position_to_absolute_position(self, x):
        """
        x: [b, h, l, 2*l-1]
        ret: [b, h, l, l]
        """
        batch, heads, length, _ = x.size()
        # Concat columns of pad to shift from relative to absolute indexing.
        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))

        # Concat extra elements so to add up to shape (len+1, 2*len-1).
        x_flat = x.view([batch, heads, length * 2 * length])
        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]]))

        # Reshape and slice out the padded elements.
        x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[:, :, :length, length - 1 :]
        return x_final

    def _absolute_position_to_relative_position(self, x):
        """
        x: [b, h, l, l]
        ret: [b, h, l, 2*l-1]
        """
        batch, heads, length, _ = x.size()
        # padd along column
        x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]]))
        x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
        # add 0's in the beginning that will skew the elements after reshape
        x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
        x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
        return x_final

    def _attention_bias_proximal(self, length):
        """Bias for self-attention to encourage attention to close positions.
        Args:
          length: an integer scalar.
        Returns:
          a Tensor with shape [1, 1, length, length]
        """
        r = torch.arange(length, dtype=torch.float32)
        diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
        return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)


class FFN(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        filter_channels,
        kernel_size,
        p_dropout=0.0,
        activation=None,
        causal=False,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels
        self.filter_channels = filter_channels
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.activation = activation
        self.causal = causal

        if causal:
            self.padding = self._causal_padding
        else:
            self.padding = self._same_padding

        self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
        self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
        self.drop = nn.Dropout(p_dropout)

    def forward(self, x, x_mask):
        x = self.conv_1(self.padding(x * x_mask))
        if self.activation == "gelu":
            x = x * torch.sigmoid(1.702 * x)
        else:
            x = torch.relu(x)
        x = self.drop(x)
        x = self.conv_2(self.padding(x * x_mask))
        return x * x_mask

    def _causal_padding(self, x):
        if self.kernel_size == 1:
            return x
        pad_l = self.kernel_size - 1
        pad_r = 0
        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
        x = F.pad(x, commons.convert_pad_shape(padding))
        return x

    def _same_padding(self, x):
        if self.kernel_size == 1:
            return x
        pad_l = (self.kernel_size - 1) // 2
        pad_r = self.kernel_size // 2
        padding = [[0, 0], [0, 0], [pad_l, pad_r]]
        x = F.pad(x, commons.convert_pad_shape(padding))
        return x


import torch.nn as nn
from torch.nn.utils import remove_weight_norm, weight_norm


class Depthwise_Separable_Conv1D(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        dilation=1,
        bias=True,
        padding_mode="zeros",  # TODO: refine this type
        device=None,
        dtype=None,
    ):
        super().__init__()
        self.depth_conv = nn.Conv1d(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=kernel_size,
            groups=in_channels,
            stride=stride,
            padding=padding,
            dilation=dilation,
            bias=bias,
            padding_mode=padding_mode,
            device=device,
            dtype=dtype,
        )
        self.point_conv = nn.Conv1d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            bias=bias,
            device=device,
            dtype=dtype,
        )

    def forward(self, input):
        return self.point_conv(self.depth_conv(input))

    def weight_norm(self):
        self.depth_conv = weight_norm(self.depth_conv, name="weight")
        self.point_conv = weight_norm(self.point_conv, name="weight")

    def remove_weight_norm(self):
        self.depth_conv = remove_weight_norm(self.depth_conv, name="weight")
        self.point_conv = remove_weight_norm(self.point_conv, name="weight")


class Depthwise_Separable_TransposeConv1D(nn.Module):
    def __init__(
        self,
        in_channels,
        out_channels,
        kernel_size,
        stride=1,
        padding=0,
        output_padding=0,
        bias=True,
        dilation=1,
        padding_mode="zeros",  # TODO: refine this type
        device=None,
        dtype=None,
    ):
        super().__init__()
        self.depth_conv = nn.ConvTranspose1d(
            in_channels=in_channels,
            out_channels=in_channels,
            kernel_size=kernel_size,
            groups=in_channels,
            stride=stride,
            output_padding=output_padding,
            padding=padding,
            dilation=dilation,
            bias=bias,
            padding_mode=padding_mode,
            device=device,
            dtype=dtype,
        )
        self.point_conv = nn.Conv1d(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            bias=bias,
            device=device,
            dtype=dtype,
        )

    def forward(self, input):
        return self.point_conv(self.depth_conv(input))

    def weight_norm(self):
        self.depth_conv = weight_norm(self.depth_conv, name="weight")
        self.point_conv = weight_norm(self.point_conv, name="weight")

    def remove_weight_norm(self):
        remove_weight_norm(self.depth_conv, name="weight")
        remove_weight_norm(self.point_conv, name="weight")


def weight_norm_modules(module, name="weight", dim=0):
    if isinstance(module, Depthwise_Separable_Conv1D) or isinstance(module, Depthwise_Separable_TransposeConv1D):
        module.weight_norm()
        return module
    else:
        return weight_norm(module, name, dim)


def remove_weight_norm_modules(module, name="weight"):
    if isinstance(module, Depthwise_Separable_Conv1D) or isinstance(module, Depthwise_Separable_TransposeConv1D):
        module.remove_weight_norm()
    else:
        remove_weight_norm(module, name)


class FFT(nn.Module):
    def __init__(
        self,
        hidden_channels,
        filter_channels,
        n_heads,
        n_layers=1,
        kernel_size=1,
        p_dropout=0.0,
        proximal_bias=False,
        proximal_init=True,
        isflow=False,
        **kwargs,
    ):
        super().__init__()
        self.hidden_channels = hidden_channels
        self.filter_channels = filter_channels
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.kernel_size = kernel_size
        self.p_dropout = p_dropout
        self.proximal_bias = proximal_bias
        self.proximal_init = proximal_init
        if isflow:
            cond_layer = torch.nn.Conv1d(kwargs["gin_channels"], 2 * hidden_channels * n_layers, 1)
            self.cond_pre = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, 1)
            self.cond_layer = weight_norm_modules(cond_layer, name="weight")
            self.gin_channels = kwargs["gin_channels"]
        self.drop = nn.Dropout(p_dropout)
        self.self_attn_layers = nn.ModuleList()
        self.norm_layers_0 = nn.ModuleList()
        self.ffn_layers = nn.ModuleList()
        self.norm_layers_1 = nn.ModuleList()
        for i in range(self.n_layers):
            self.self_attn_layers.append(
                MultiHeadAttention(
                    hidden_channels,
                    hidden_channels,
                    n_heads,
                    p_dropout=p_dropout,
                    proximal_bias=proximal_bias,
                    proximal_init=proximal_init,
                )
            )
            self.norm_layers_0.append(LayerNorm(hidden_channels))
            self.ffn_layers.append(
                FFN(
                    hidden_channels,
                    hidden_channels,
                    filter_channels,
                    kernel_size,
                    p_dropout=p_dropout,
                    causal=True,
                )
            )
            self.norm_layers_1.append(LayerNorm(hidden_channels))

    def forward(self, x, x_mask, g=None):
        """
        x: decoder input
        h: encoder output
        """
        if g is not None:
            g = self.cond_layer(g)

        self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
        x = x * x_mask
        for i in range(self.n_layers):
            if g is not None:
                x = self.cond_pre(x)
                cond_offset = i * 2 * self.hidden_channels
                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
                x = commons.fused_add_tanh_sigmoid_multiply(x, g_l, torch.IntTensor([self.hidden_channels]))
            y = self.self_attn_layers[i](x, x, self_attn_mask)
            y = self.drop(y)
            x = self.norm_layers_0[i](x + y)

            y = self.ffn_layers[i](x, x_mask)
            y = self.drop(y)
            x = self.norm_layers_1[i](x + y)
        x = x * x_mask
        return x


class TransformerCouplingLayer(nn.Module):
    def __init__(
        self,
        channels,
        hidden_channels,
        kernel_size,
        n_layers,
        n_heads,
        p_dropout=0,
        filter_channels=0,
        mean_only=False,
        wn_sharing_parameter=None,
        gin_channels=0,
    ):
        assert channels % 2 == 0, "channels should be divisible by 2"
        super().__init__()
        self.channels = channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.n_layers = n_layers
        self.half_channels = channels // 2
        self.mean_only = mean_only

        self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
        self.enc = (
            Encoder(
                hidden_channels,
                filter_channels,
                n_heads,
                n_layers,
                kernel_size,
                p_dropout,
                isflow=True,
                gin_channels=gin_channels,
            )
            if wn_sharing_parameter is None
            else wn_sharing_parameter
        )
        self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
        self.post.weight.data.zero_()
        self.post.bias.data.zero_()

    def forward(self, x, x_mask, g=None, reverse=False):
        x0, x1 = torch.split(x, [self.half_channels] * 2, 1)
        h = self.pre(x0) * x_mask
        h = self.enc(h, x_mask, g=g)
        stats = self.post(h) * x_mask
        if not self.mean_only:
            m, logs = torch.split(stats, [self.half_channels] * 2, 1)
        else:
            m = stats
            logs = torch.zeros_like(m)

        if not reverse:
            x1 = m + x1 * torch.exp(logs) * x_mask
            x = torch.cat([x0, x1], 1)
            logdet = torch.sum(logs, [1, 2])
            return x, logdet
        else:
            x1 = (x1 - m) * torch.exp(-logs) * x_mask
            x = torch.cat([x0, x1], 1)
            return x
