"""GPT-2 style transformer (BPE). Pre-norm, GELU, weight-tied head, causal attention.
Trained fully from scratch."""
import math, torch, torch.nn as nn
from torch.nn import functional as F

class CausalSelfAttention(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        assert cfg['n_embd'] % cfg['n_head'] == 0
        self.c_attn = nn.Linear(cfg['n_embd'], 3 * cfg['n_embd'])
        self.c_proj = nn.Linear(cfg['n_embd'], cfg['n_embd'])
        self.n_head = cfg['n_head']; self.n_embd = cfg['n_embd']
        self.drop = nn.Dropout(cfg['dropout'])
        self.resid_drop = nn.Dropout(cfg['dropout'])
        self.register_buffer('mask', torch.tril(torch.ones(cfg['block_size'], cfg['block_size']))
                             .view(1, 1, cfg['block_size'], cfg['block_size']))

    def forward(self, x):
        B, T, C = x.size()
        q, k, v = self.c_attn(x).split(self.n_embd, dim=2)
        hs = C // self.n_head
        q = q.view(B, T, self.n_head, hs).transpose(1, 2)
        k = k.view(B, T, self.n_head, hs).transpose(1, 2)
        v = v.view(B, T, self.n_head, hs).transpose(1, 2)
        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(hs))
        att = att.masked_fill(self.mask[:, :, :T, :T] == 0, float('-inf'))
        att = self.drop(F.softmax(att, dim=-1))
        y = (att @ v).transpose(1, 2).contiguous().view(B, T, C)
        return self.resid_drop(self.c_proj(y))

class MLP(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.c_fc = nn.Linear(cfg['n_embd'], 4 * cfg['n_embd'])
        self.c_proj = nn.Linear(4 * cfg['n_embd'], cfg['n_embd'])
        self.drop = nn.Dropout(cfg['dropout'])
    def forward(self, x):
        return self.drop(self.c_proj(F.gelu(self.c_fc(x))))

class Block(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.ln1 = nn.LayerNorm(cfg['n_embd']); self.attn = CausalSelfAttention(cfg)
        self.ln2 = nn.LayerNorm(cfg['n_embd']); self.mlp = MLP(cfg)
    def forward(self, x):
        x = x + self.attn(self.ln1(x))
        x = x + self.mlp(self.ln2(x))
        return x

class GPT2(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.cfg = cfg; self.block_size = cfg['block_size']
        self.wte = nn.Embedding(cfg['vocab_size'], cfg['n_embd'])
        self.wpe = nn.Embedding(cfg['block_size'], cfg['n_embd'])
        self.drop = nn.Dropout(cfg['dropout'])
        self.blocks = nn.ModuleList([Block(cfg) for _ in range(cfg['n_layer'])])
        self.ln_f = nn.LayerNorm(cfg['n_embd'])
        self.head = nn.Linear(cfg['n_embd'], cfg['vocab_size'], bias=False)
        self.wte.weight = self.head.weight  # weight tying
        self.apply(self._init)
        for pn, p in self.named_parameters():
            if pn.endswith('c_proj.weight'):
                nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * cfg['n_layer']))

    def _init(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 forward(self, idx, targets=None):
        B, T = idx.size()
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        x = self.drop(self.wte(idx) + self.wpe(pos))
        for b in self.blocks: x = b(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=0.8, top_k=50, eot_id=None):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -self.block_size:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :] / temperature
            if top_k:
                v, _ = torch.topk(logits, top_k)
                logits[logits < v[:, [-1]]] = float('-inf')
            probs = F.softmax(logits, dim=-1)
            nxt = torch.multinomial(probs, 1)
            if eot_id is not None and nxt.item() == eot_id:
                break
            idx = torch.cat((idx, nxt), dim=1)
        return idx