import torch
import torch.nn as nn
import torch.nn.functional as F

class ChannelLastConv1d(nn.Conv1d):

    def forward(self, x: torch.Tensor) -> torch.Tensor:
        x = x.permute(0, 2, 1)
        x = super().forward(x)
        x = x.permute(0, 2, 1)
        return x


class ConvMLP(nn.Module):

    def __init__(
        self,
        dim: int,
        hidden_dim: int,
        multiple_of: int = 256,
        kernel_size: int = 3,
        padding: int = 1,
    ):
        """
        Initialize the FeedForward module.

        Args:
            dim (int): Input dimension.
            hidden_dim (int): Hidden dimension of the feedforward layer.
            multiple_of (int): Value to ensure hidden dimension is a multiple of this value.

        Attributes:
            w1 (ColumnParallelLinear): Linear transformation for the first layer.
            w2 (RowParallelLinear): Linear transformation for the second layer.
            w3 (ColumnParallelLinear): Linear transformation for the third layer.

        """
        super().__init__()
        hidden_dim = int(2 * hidden_dim / 3)
        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)

        self.w1 = ChannelLastConv1d(dim, hidden_dim, bias=False, kernel_size=kernel_size, padding=padding)
        self.w2 = ChannelLastConv1d(hidden_dim, dim, bias=False, kernel_size=kernel_size, padding=padding)
        self.w3 = ChannelLastConv1d(dim, hidden_dim, bias=False, kernel_size=kernel_size, padding=padding)

    def forward(self, x):
        return self.w2(F.silu(self.w1(x)) * self.w3(x))