class SinusoidalPositionalEmbedding(nn.Module):
    """Apply positional information to a sequence of embeddings.

    Takes in a sequence of embeddings with shape (batch_size, seq_length, embed_dim) and adds positional embeddings to
    them

    Args:
        embed_dim: (int): Dimension of the positional embedding.
        max_seq_length: Maximum sequence length to apply positional embeddings

    """

    def __init__(self, embed_dim: int, max_seq_length: int = 32):
        super().__init__()
        position = torch.arange(max_seq_length).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, embed_dim, 2) * (-math.log(10000.0) / embed_dim))
        pe = torch.zeros(1, max_seq_length, embed_dim)
        pe[0, :, 0::2] = torch.sin(position * div_term)
        pe[0, :, 1::2] = torch.cos(position * div_term)
        self.register_buffer("pe", pe)

    def forward(self, x):
        _, seq_length, _ = x.shape
        x = x + self.pe[:, :seq_length]
        return x
/Users/nielsrogge/Documents/python_projecten/diffusers/src/diffusers/models/embeddings.py