import os
import sys
import torch

now_dir = os.getcwd()
sys.path.append(now_dir)

from .commons import fused_add_tanh_sigmoid_multiply

class WaveNet(torch.nn.Module):
    def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
        super(WaveNet, self).__init__()
        assert kernel_size % 2 == 1

        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.in_layers = torch.nn.ModuleList()
        self.res_skip_layers = torch.nn.ModuleList()
        self.drop = torch.nn.Dropout(p_dropout)

        if gin_channels != 0:
            cond_layer = torch.nn.Conv1d(gin_channels, 2 * hidden_channels * n_layers, 1)
            self.cond_layer = torch.nn.utils.parametrizations.weight_norm(cond_layer, name="weight")

        dilations = [dilation_rate**i for i in range(n_layers)]
        paddings = [(kernel_size * d - d) // 2 for d in dilations]

        for i in range(n_layers):
            in_layer = torch.nn.Conv1d(hidden_channels, 2 * hidden_channels, kernel_size, dilation=dilations[i], padding=paddings[i])
            in_layer = torch.nn.utils.parametrizations.weight_norm(in_layer, name="weight")

            self.in_layers.append(in_layer)

            res_skip_channels = (hidden_channels if i == n_layers - 1 else 2 * hidden_channels)
            res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
            res_skip_layer = torch.nn.utils.parametrizations.weight_norm(res_skip_layer, name="weight")
            
            self.res_skip_layers.append(res_skip_layer)

    def forward(self, x, x_mask, g=None, **kwargs):
        output = torch.zeros_like(x)
        n_channels_tensor = torch.IntTensor([self.hidden_channels])

        if g is not None: g = self.cond_layer(g)

        for i in range(self.n_layers):
            x_in = self.in_layers[i](x)

            if g is not None:
                cond_offset = i * 2 * self.hidden_channels
                g_l = g[:, cond_offset : cond_offset + 2 * self.hidden_channels, :]
            else: g_l = torch.zeros_like(x_in)

            acts = fused_add_tanh_sigmoid_multiply(x_in, g_l, n_channels_tensor)
            acts = self.drop(acts)

            res_skip_acts = self.res_skip_layers[i](acts)

            if i < self.n_layers - 1:
                res_acts = res_skip_acts[:, : self.hidden_channels, :]
                x = (x + res_acts) * x_mask
                output = output + res_skip_acts[:, self.hidden_channels :, :]
            else: output = output + res_skip_acts

        return output * x_mask

    def remove_weight_norm(self):
        if self.gin_channels != 0: torch.nn.utils.remove_weight_norm(self.cond_layer)

        for l in self.in_layers:
            torch.nn.utils.remove_weight_norm(l)

        for l in self.res_skip_layers:
            torch.nn.utils.remove_weight_norm(l)