# from https://github.com/jaywalnut310/vits
import math
import torch
from torch import nn
from torch.nn import functional as F

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., 
        window_size=4, 
        **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))

    def forward(self, x, x_mask):
        attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
        x = x * x_mask
        for i in range(self.n_layers):
            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., 
        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., 
        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) #[b,h,t_t,c], d=h*c
        #key = key.view(
        #    b, 
        #    self.n_heads, 
        #    self.k_channels, 
        #    t_s
        #).transpose(2, 3) #[b,h,t_s,c]
        #value = value.view(
        #    b, 
        #    self.n_heads, 
        #    self.k_channels,
        #    t_s
        #).transpose(2, 3) #[b,h,t_s,c]
        #scores = torch.matmul(
        #    query / math.sqrt(self.k_channels), key.transpose(-2, -1)
        #) #[b,h,t_t,t_s]
        query = query.view(
            b, 
            self.n_heads, 
            self.k_channels,
            t_t
        ) #[b,h,c,t_t]
        key = key.view(
            b, 
            self.n_heads, 
            self.k_channels, 
            t_s
        ) #[b,h,c,t_s]
        value = value.view(
            b, 
            self.n_heads, 
            self.k_channels,
            t_s
        ) #[b,h,c,t_s]
        scores = torch.einsum('bhdt,bhds -> bhts', query / math.sqrt(self.k_channels), key) #[b,h,t_t,t_s]
        #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
        #    ) #[b,h,t_t,d],[h or 1,e,d] ->[b,h,t_t,e]
        #    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, h, t_t, t_s]
        #p_attn = self.drop(p_attn)
        #output = torch.matmul(p_attn, value) # [b,h,t_t,t_s],[b,h,t_s,c] -> [b,h,t_t,c]
        #if self.window_size is not None:
        #    relative_weights = self._absolute_position_to_relative_position(p_attn) #[b, h, t_t, 2*t_t-1]
        #    value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) #[h or 1, 2*t_t-1, c]
        #    output = output + \
        #        self._matmul_with_relative_values(
        #            relative_weights, value_relative_embeddings) # [b, h, t_t, 2*t_t-1],[h or 1, 2*t_t-1, c] -> [b, h, t_t, c]
        #output = output.transpose(2, 3).contiguous().view(b, d, t_t)  # [b, n_h, t_t, c] -> [b,h,c,t_t] -> [b, d, t_t]
        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 = torch.einsum('bhdt,hed->bhte',
                query / math.sqrt(self.k_channels), key_relative_embeddings
            ) #[b,h,c,t_t],[h or 1,e,c] ->[b,h,t_t,e]
            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, h, t_t, t_s]
        p_attn = self.drop(p_attn)
        output = torch.einsum('bhcs,bhts->bhct', value , p_attn) # [b,h,c,t_s],[b,h,t_t,t_s] -> [b,h,c,t_t]
        if self.window_size is not None:
            relative_weights = self._absolute_position_to_relative_position(p_attn) #[b, h, t_t, 2*t_t-1]
            value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s) #[h or 1, 2*t_t-1, c]
            output = output + \
                torch.einsum('bhte,hec->bhct',
                    relative_weights, value_relative_embeddings) # [b, h, t_t, 2*t_t-1],[h or 1, 2*t_t-1, c] -> [b, h, c, t_t]
        output = output.view(b, d, t_t)  # [b, h, c, t_t] -> [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))
        ret = torch.einsum('bhld,hmd -> bhlm', x, y)
        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., 
        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
