import torch.nn as nn
import torch

class CausalSelfAttention(nn.Module):
    def __init__(self, embed_dim):
        super().__init__()

        self.embed_dim = embed_dim

        # Query | Key | Value
        self.query = nn.Linear(
            embed_dim,
            embed_dim
        )

        self.key = nn.Linear(
            embed_dim,
            embed_dim
        )

        self.value = nn.Linear(
            embed_dim,
            embed_dim
        )

        self.out = nn.Linear(embed_dim, embed_dim)

    def forward(self, x):
        batch_size, seq_len, embed_dim = x.shape

        Q = self.query(x)
        K = self.key(x)
        V = self.value(x)

        scores = Q @ K.transpose(-2, -1)

        scores = scores / (embed_dim ** 0.5)

        # Causal Mask
        mask = torch.triu(
            torch.ones(seq_len, seq_len, device=x.device),
            diagonal=1
        ).bool()

        scores = scores.masked_fill(mask, float("-inf"))

        attention_weight = torch.softmax(scores, dim=-1)

        output = attention_weight @ V

        output = self.out(output)

        return output