"""
Nano-SLM: a tiny decoder-only transformer (~1M params).

Architecture is intentionally minimal so every line is readable.
Mirrors the standard GPT recipe: token + position embeddings, N stacked
(causal self-attention -> MLP) blocks with pre-LayerNorm and residuals,
final LayerNorm, and a tied LM head.
"""
import math
import torch
import torch.nn as nn
import torch.nn.functional as F


class CausalSelfAttention(nn.Module):
    """Multi-head causal self-attention. Uses fused QKV and PyTorch's SDPA."""

    def __init__(self, d_model, n_heads, dropout=0.1):
        super().__init__()
        assert d_model % n_heads == 0
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        # one big linear that produces Q, K, V at once
        self.qkv = nn.Linear(d_model, 3 * d_model, bias=False)
        self.proj = nn.Linear(d_model, d_model, bias=False)
        self.attn_dropout_p = dropout
        self.resid_dropout = nn.Dropout(dropout)

    def forward(self, x):
        B, T, C = x.shape
        q, k, v = self.qkv(x).split(C, dim=-1)
        # reshape to (B, n_heads, T, head_dim)
        q = q.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
        k = k.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
        v = v.view(B, T, self.n_heads, self.head_dim).transpose(1, 2)
        # Flash/SDPA: causal mask + scaling handled internally
        y = F.scaled_dot_product_attention(
            q, k, v,
            is_causal=True,
            dropout_p=self.attn_dropout_p if self.training else 0.0,
        )
        y = y.transpose(1, 2).contiguous().view(B, T, C)
        return self.resid_dropout(self.proj(y))


class MLP(nn.Module):
    """Position-wise feed-forward (GELU)."""

    def __init__(self, d_model, ffn_dim, dropout=0.1):
        super().__init__()
        self.fc1 = nn.Linear(d_model, ffn_dim, bias=False)
        self.fc2 = nn.Linear(ffn_dim, d_model, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
        return self.dropout(self.fc2(F.gelu(self.fc1(x))))


class Block(nn.Module):
    """Pre-LN transformer block: x = x + attn(LN(x)); x = x + mlp(LN(x))."""

    def __init__(self, d_model, n_heads, ffn_dim, dropout=0.1):
        super().__init__()
        self.ln1 = nn.LayerNorm(d_model)
        self.attn = CausalSelfAttention(d_model, n_heads, dropout)
        self.ln2 = nn.LayerNorm(d_model)
        self.mlp = MLP(d_model, ffn_dim, dropout)

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


class NanoSLM(nn.Module):
    def __init__(
        self,
        vocab_size=4096,
        d_model=128,
        n_heads=4,
        n_layers=4,
        ffn_dim=512,
        ctx_len=256,
        dropout=0.1,
    ):
        super().__init__()
        self.ctx_len = ctx_len
        self.tok_emb = nn.Embedding(vocab_size, d_model)
        self.pos_emb = nn.Embedding(ctx_len, d_model)
        self.drop = nn.Dropout(dropout)
        self.blocks = nn.ModuleList(
            [Block(d_model, n_heads, ffn_dim, dropout) for _ in range(n_layers)]
        )
        self.ln_f = nn.LayerNorm(d_model)
        self.head = nn.Linear(d_model, vocab_size, bias=False)
        # weight tying: input embedding and output projection share weights.
        # saves a lot of params at small vocab sizes and usually helps quality.
        self.head.weight = self.tok_emb.weight

        self.apply(self._init_weights)
        # scaled init for residual projections (GPT-2 trick)
        for name, p in self.named_parameters():
            if name.endswith("proj.weight") or name.endswith("fc2.weight"):
                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * n_layers))

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            nn.init.normal_(m.weight, mean=0.0, std=0.02)
            if m.bias is not None:
                nn.init.zeros_(m.bias)
        elif isinstance(m, nn.Embedding):
            nn.init.normal_(m.weight, mean=0.0, std=0.02)

    def num_params(self, non_embedding=False):
        n = sum(p.numel() for p in self.parameters())
        if non_embedding:
            n -= self.tok_emb.weight.numel()
            n -= self.pos_emb.weight.numel()
        return n

    def forward(self, idx, targets=None):
        B, T = idx.shape
        assert T <= self.ctx_len, f"sequence length {T} > ctx_len {self.ctx_len}"
        pos = torch.arange(T, device=idx.device)
        x = self.drop(self.tok_emb(idx) + self.pos_emb(pos))
        for block in self.blocks:
            x = block(x)
        x = self.ln_f(x)
        logits = self.head(x)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(
                logits.view(-1, logits.size(-1)),
                targets.view(-1),
                ignore_index=-100,
            )
        return logits, loss

    @torch.no_grad()
    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        """Autoregressive sampling. Slow on purpose: no KV cache (a great upgrade later)."""
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.ctx_len:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / temperature
            if top_k is not None:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, [-1]]] = -float("inf")
            probs = F.softmax(logits, dim=-1)
            next_tok = torch.multinomial(probs, num_samples=1)
            idx = torch.cat([idx, next_tok], dim=1)
        return idx