src/transformers/models/layoutlmv2/modeling_layoutlmv2.py/LayoutLMv2SelfAttention/__init__
class LayoutLMv2SelfAttention:    def __init__(self, config):
        super().__init__()
        if config.hidden_size % config.num_attention_heads != 0 and not hasattr(config, "embedding_size"):
            raise ValueError(
                f"The hidden size ({config.hidden_size}) is not a multiple of the number of attention "
                f"heads ({config.num_attention_heads})"
            )
        self.fast_qkv = config.fast_qkv
        self.num_attention_heads = config.num_attention_heads
        self.attention_head_size = int(config.hidden_size / config.num_attention_heads)
        self.all_head_size = self.num_attention_heads * self.attention_head_size

        self.has_relative_attention_bias = config.has_relative_attention_bias
        self.has_spatial_attention_bias = config.has_spatial_attention_bias

        if config.fast_qkv:
            self.qkv_linear = nn.Linear(config.hidden_size, 3 * self.all_head_size, bias=False)
            self.q_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
            self.v_bias = nn.Parameter(torch.zeros(1, 1, self.all_head_size))
        else:
            self.query = nn.Linear(config.hidden_size, self.all_head_size)
            self.key = nn.Linear(config.hidden_size, self.all_head_size)
            self.value = nn.Linear(config.hidden_size, self.all_head_size)

        self.dropout = nn.Dropout(config.attention_probs_dropout_prob)