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


class ConvRNNF0Predictor(nn.Module):
    def __init__(self,
                 num_class: int = 1,
                 in_channels: int = 80,
                 cond_channels: int = 512
                 ):
        super().__init__()

        self.num_class = num_class
        self.condnet = nn.Sequential(
            weight_norm(
                nn.Conv1d(in_channels, cond_channels, kernel_size=3, padding=1)
            ),
            nn.ELU(),
            weight_norm(
                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
            ),
            nn.ELU(),
            weight_norm(
                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
            ),
            nn.ELU(),
            weight_norm(
                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
            ),
            nn.ELU(),
            weight_norm(
                nn.Conv1d(cond_channels, cond_channels, kernel_size=3, padding=1)
            ),
            nn.ELU(),
        )
        self.classifier = nn.Linear(in_features=cond_channels, out_features=self.num_class)

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = self.condnet(x)
        x = x.transpose(1, 2)
        return torch.abs(self.classifier(x).squeeze(-1))