import copy
import math
import numpy as np
import torch
from torch import nn
from torch.nn import functional as F

import commons
import modules
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=None,
        block_length=None,
        **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.block_length = block_length

        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,
                    window_size=window_size,
                    p_dropout=p_dropout,
                    block_length=block_length,
                )
            )
            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)
        for i in range(self.n_layers):
            x = x * x_mask
            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 CouplingBlock(nn.Module):
    def __init__(
        self,
        in_channels,
        hidden_channels,
        kernel_size,
        dilation_rate,
        n_layers,
        gin_channels=0,
        p_dropout=0,
        sigmoid_scale=False,
    ):
        super().__init__()
        self.in_channels = in_channels
        self.hidden_channels = hidden_channels
        self.kernel_size = kernel_size
        self.dilation_rate = dilation_rate
        self.n_layers = n_layers
        self.gin_channels = gin_channels
        self.p_dropout = p_dropout
        self.sigmoid_scale = sigmoid_scale

        start = torch.nn.Conv1d(in_channels // 2, hidden_channels, 1)
        start = torch.nn.utils.weight_norm(start)
        self.start = start
        # Initializing last layer to 0 makes the affine coupling layers
        # do nothing at first.  It helps to stabilze training.
        end = torch.nn.Conv1d(hidden_channels, in_channels, 1)
        end.weight.data.zero_()
        end.bias.data.zero_()
        self.end = end

        self.wn = modules.WN(
            in_channels,
            hidden_channels,
            kernel_size,
            dilation_rate,
            n_layers,
            gin_channels,
            p_dropout,
        )

    def forward(self, x, x_mask=None, reverse=False, g=None, **kwargs):
        b, c, t = x.size()
        if x_mask is None:
            x_mask = 1
        x_0, x_1 = x[:, : self.in_channels // 2], x[:, self.in_channels // 2 :]

        x = self.start(x_0) * x_mask
        x = self.wn(x, x_mask, g)
        out = self.end(x)

        z_0 = x_0
        m = out[:, : self.in_channels // 2, :]
        logs = out[:, self.in_channels // 2 :, :]
        if self.sigmoid_scale:
            logs = torch.log(1e-6 + torch.sigmoid(logs + 2))

        if reverse:
            z_1 = (x_1 - m) * torch.exp(-logs) * x_mask
            logdet = None
        else:
            z_1 = (m + torch.exp(logs) * x_1) * x_mask
            logdet = torch.sum(logs * x_mask, [1, 2])

        z = torch.cat([z_0, z_1], 1)
        return z, logdet

    def store_inverse(self):
        self.wn.remove_weight_norm()


class MultiHeadAttention(nn.Module):
    def __init__(
        self,
        channels,
        out_channels,
        n_heads,
        window_size=None,
        heads_share=True,
        p_dropout=0.0,
        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.window_size = window_size
        self.heads_share = heads_share
        self.block_length = block_length
        self.proximal_bias = proximal_bias
        self.p_dropout = p_dropout
        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)
        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
            )
        self.conv_o = nn.Conv1d(channels, out_channels, 1)
        self.drop = nn.Dropout(p_dropout)

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

    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, key.transpose(-2, -1)) / math.sqrt(self.k_channels)
        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, key_relative_embeddings)
            rel_logits = self._relative_position_to_absolute_position(rel_logits)
            scores_local = rel_logits / math.sqrt(self.k_channels)
            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:
                block_mask = (
                    torch.ones_like(scores)
                    .triu(-self.block_length)
                    .tril(self.block_length)
                )
                scores = scores * block_mask + -1e4 * (1 - block_mask)
        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,
    ):
        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.conv_1 = nn.Conv1d(
            in_channels, filter_channels, kernel_size, padding=kernel_size // 2
        )
        self.conv_2 = nn.Conv1d(
            filter_channels, out_channels, kernel_size, padding=kernel_size // 2
        )
        self.drop = nn.Dropout(p_dropout)

    def forward(self, x, x_mask):
        x = self.conv_1(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(x * x_mask)
        return x * x_mask