from torch import nn
from GroupedQueryAttention import GroupedQueryAttention
from FeedForward import FeedForward

class TransformerBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.att = GroupedQueryAttention(
            d_in=cfg["emb_dim"],
            d_out=cfg["emb_dim"],
            num_heads=cfg["n_heads"],
            num_kv_groups=cfg["n_kv_groups"],
            dtype=cfg["dtype"]
        )
        self.ff = FeedForward(cfg)
        self.norm1 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])
        self.norm2 = nn.RMSNorm(cfg["emb_dim"], eps=1e-5, dtype=cfg["dtype"])

    def forward(self, x, mask, cos, sin):
        # Shortcut connection for attention block
        shortcut = x
        x = self.norm1(x)
        x = self.att(x, mask, cos, sin)  # Shape [batch_size, num_tokens, emb_size]
        x = x + shortcut  # Add the original input back

        # Shortcut connection for feed-forward block
        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = x + shortcut  # Add the original input back

        return x