"""PureBit Transformer - Binary-level language model"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class Attention(nn.Module):
    def __init__(self, d, heads=8):
        super().__init__()
        self.heads = heads
        self.dk = d // heads
        self.q_proj = nn.Linear(d, d, bias=False)
        self.k_proj = nn.Linear(d, d, bias=False)
        self.v_proj = nn.Linear(d, d, bias=False)
        self.out_proj = nn.Linear(d, d, bias=False)
    
    def forward(self, x, mask=None):
        B, N, D = x.shape
        q = self.q_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
        k = self.k_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
        v = self.v_proj(x).view(B, N, self.heads, self.dk).transpose(1, 2)
        att = (q @ k.transpose(-1, -2)) / math.sqrt(self.dk)
        if mask is not None:
            att = att + mask
        att = F.softmax(att, dim=-1)
        out = (att @ v).transpose(1, 2).reshape(B, N, D)
        return self.out_proj(out)

class MLP(nn.Module):
    def __init__(self, d, mult=4):
        super().__init__()
        self.fc1 = nn.Linear(d, d * mult, bias=False)
        self.fc2 = nn.Linear(d * mult, d, bias=False)
    
    def forward(self, x):
        return self.fc2(F.gelu(self.fc1(x)))

class Block(nn.Module):
    def __init__(self, d, heads=8):
        super().__init__()
        self.ln1 = nn.LayerNorm(d)
        self.attn = Attention(d, heads)
        self.ln2 = nn.LayerNorm(d)
        self.mlp = MLP(d)
    
    def forward(self, x, mask):
        x = x + self.attn(self.ln1(x), mask)
        x = x + self.mlp(self.ln2(x))
        return x

class PureBitTransformer(nn.Module):
    """Transformer operating on raw binary bits (vocab_size=2)"""
    def __init__(self, d=256, layers=6, heads=8, ctx=4096):
        super().__init__()
        self.ctx = ctx
        self.emb = nn.Embedding(2, d)  # Binary: 0 or 1
        self.blocks = nn.ModuleList([Block(d, heads) for _ in range(layers)])
        self.ln = nn.LayerNorm(d)
        self.head = nn.Linear(d, 2, bias=False)
        self.head.weight = self.emb.weight  # Weight tying
    
    def forward(self, x):
        B, N = x.shape
        mask = torch.triu(torch.ones(N, N, device=x.device), 1) * -1e9
        h = self.emb(x)
        for b in self.blocks:
            h = b(h, mask)
        return self.head(self.ln(h))
    
    @torch.no_grad()
    def generate(self, bits, max_new=256, temp=0.8):
        """Generate new bits autoregressively"""
        x = torch.tensor(bits, device=next(self.parameters()).device).unsqueeze(0)
        for _ in range(max_new):
            logits = self(x[:, -self.ctx:])[:, -1, :] / temp
            next_bit = torch.multinomial(F.softmax(logits, -1), 1)
            x = torch.cat([x, next_bit], 1)
        return x[0].tolist()

def text_to_bits(text):
    """Convert UTF-8 text to list of bits"""
    bits = []
    for byte in text.encode('utf-8'):
        for i in range(7, -1, -1):
            bits.append((byte >> i) & 1)
    return bits

def bits_to_text(bits):
    """Convert list of bits back to UTF-8 text"""
    while len(bits) % 8 != 0:
        bits = bits + [0]
    bytes_out = []
    for i in range(0, len(bits), 8):
        byte = 0
        for j in range(8):
            byte = (byte << 1) | bits[i + j]
        bytes_out.append(byte)
    return bytes(bytes_out).decode('utf-8', errors='replace')

def load_model(checkpoint_path, device='cuda'):
    """Load model from checkpoint"""
    ckpt = torch.load(checkpoint_path, map_location=device)
    model = PureBitTransformer(d=256, layers=6, heads=8).to(device)
    model.load_state_dict(ckpt['model'])
    model.eval()
    return model, ckpt

if __name__ == "__main__":
    model = PureBitTransformer()
    params = sum(p.numel() for p in model.parameters())
    print(f"PureBit Transformer: {params:,} parameters")