# models/gpt.py

import torch
import torch.nn as nn


class CausalSelfAttention(nn.Module):
    def __init__(self, embed_dim, num_heads, dropout):
        super().__init__()
        self.attn = nn.MultiheadAttention(
            embed_dim, num_heads,
            dropout=dropout,
            batch_first=True,
            bias=True
        )

    def forward(self, x):
        B, T, _ = x.size()
        # Create causal mask: (T, T) with float('-inf') for future positions
        mask = torch.triu(torch.ones(T, T, device=x.device), diagonal=1)
        mask = mask.masked_fill(mask == 1, float('-inf'))

        # Pass it in as attn_mask
        return self.attn(x, x, x, attn_mask=mask)[0]


class GPTBlock(nn.Module):
    def __init__(self, embed_dim, num_heads, dropout):
        super().__init__()
        self.ln1 = nn.LayerNorm(embed_dim)
        self.attn = CausalSelfAttention(embed_dim, num_heads, dropout)
        self.ln2 = nn.LayerNorm(embed_dim)
        self.ff = nn.Sequential(
            nn.Linear(embed_dim, 4 * embed_dim),
            nn.GELU(),
            nn.Linear(4 * embed_dim, embed_dim),
            nn.Dropout(dropout)
        )

    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = x + self.ff(self.ln2(x))
        return x


class GPT(nn.Module):
    def __init__(self, vocab_size, context_size, embed_dim, num_heads, num_layers, dropout=0.1):
        super().__init__()
        self.token_emb = nn.Embedding(vocab_size, embed_dim)
        self.pos_emb = nn.Parameter(torch.zeros(1, context_size, embed_dim))
        self.drop = nn.Dropout(dropout)
        self.blocks = nn.ModuleList([
            GPTBlock(embed_dim, num_heads, dropout)
            for _ in range(num_layers)
        ])
        self.ln_f = nn.LayerNorm(embed_dim)
        self.head = nn.Linear(embed_dim, vocab_size)

    def forward(self, x):
        B, T = x.size()
        x = self.token_emb(x) + self.pos_emb[:, :T, :]
        x = self.drop(x)
        for block in self.blocks:
            x = block(x)
        x = self.ln_f(x)
        return self.head(x)