src/transformers/models/wavlm/modeling_wavlm.py/WavLMAttention/__init__
class WavLMAttention:    def __init__(
        self,
        embed_dim: int,
        num_heads: int,
        dropout: float = 0.0,
        num_buckets: int = 320,
        max_distance: int = 800,
        has_relative_position_bias: bool = True,
    ):
        super().__init__()
        self.embed_dim = embed_dim
        self.num_heads = num_heads
        self.dropout = dropout
        self.head_dim = embed_dim // num_heads

        if (self.head_dim * num_heads) != self.embed_dim:
            raise ValueError(
                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
                f" and `num_heads`: {num_heads})."
            )
        self.scaling = self.head_dim**-0.5

        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.out_proj = nn.Linear(embed_dim, embed_dim)

        self.num_buckets = num_buckets
        self.max_distance = max_distance

        self.gru_rel_pos_const = nn.Parameter(torch.ones(1, self.num_heads, 1, 1))
        self.gru_rel_pos_linear = nn.Linear(self.head_dim, 8)

        if has_relative_position_bias:
            self.rel_attn_embed = nn.Embedding(self.num_buckets, self.num_heads)