"""
SOVYN-85M 모델 아키텍처
https://huggingface.co/SOVYN/SOVYN-85M
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
import math


class ModelConfig:
    vocab_size: int = 16384
    context_length: int = 512
    embed_dim: int = 768
    num_heads: int = 12
    num_layers: int = 12
    dropout: float = 0.1
    bias: bool = False


class CausalSelfAttention(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.num_heads = cfg.num_heads
        self.head_dim = cfg.embed_dim // cfg.num_heads
        self.embed_dim = cfg.embed_dim
        self.qkv = nn.Linear(cfg.embed_dim, 3 * cfg.embed_dim, bias=cfg.bias)
        self.proj = nn.Linear(cfg.embed_dim, cfg.embed_dim, bias=cfg.bias)
        self.resid_drop = nn.Dropout(cfg.dropout)
        self.dropout_p = cfg.dropout

    def forward(self, x):
        B, T, C = x.shape
        qkv = self.qkv(x).reshape(B, T, 3, self.num_heads, self.head_dim)
        qkv = qkv.permute(2, 0, 3, 1, 4)
        q, k, v = qkv.unbind(0)
        out = F.scaled_dot_product_attention(
            q, k, v, is_causal=True,
            dropout_p=self.dropout_p if self.training else 0.0,
        )
        out = out.transpose(1, 2).reshape(B, T, C)
        return self.resid_drop(self.proj(out))


class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        hidden = 4 * cfg.embed_dim
        self.fc1 = nn.Linear(cfg.embed_dim, hidden, bias=cfg.bias)
        self.fc2 = nn.Linear(hidden, cfg.embed_dim, bias=cfg.bias)
        self.drop = nn.Dropout(cfg.dropout)

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


class Block(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.ln1 = nn.LayerNorm(cfg.embed_dim)
        self.attn = CausalSelfAttention(cfg)
        self.ln2 = nn.LayerNorm(cfg.embed_dim)
        self.ffn = FeedForward(cfg)

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


class SOVYN85M(nn.Module):
    def __init__(self, cfg=None):
        super().__init__()
        if cfg is None:
            cfg = ModelConfig()
        self.cfg = cfg
        self.tok_emb = nn.Embedding(cfg.vocab_size, cfg.embed_dim)
        self.pos_emb = nn.Embedding(cfg.context_length, cfg.embed_dim)
        self.drop = nn.Dropout(cfg.dropout)
        self.blocks = nn.ModuleList([Block(cfg) for _ in range(cfg.num_layers)])
        self.ln_f = nn.LayerNorm(cfg.embed_dim)
        self.head = nn.Linear(cfg.embed_dim, cfg.vocab_size, bias=False)
        self.head.weight = self.tok_emb.weight

    @property
    def num_params(self):
        return sum(p.numel() for p in self.parameters()) - self.tok_emb.weight.numel()

    def forward(self, idx, targets=None):
        B, T = idx.shape
        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=0)
        return logits, loss

    @torch.no_grad()
    def generate(self, idx, max_new_tokens=200, temperature=0.8, top_k=50):
        self.eval()
        for _ in range(max_new_tokens):
            ctx = idx[:, -self.cfg.context_length:]
            logits, _ = self(ctx)
            logits = logits[:, -1, :] / max(temperature, 1e-8)
            if top_k > 0:
                v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
                logits[logits < v[:, -1:]] = float('-inf')
            probs = F.softmax(logits, dim=-1)
            nxt = torch.multinomial(probs, 1)
            idx = torch.cat([idx, nxt], dim=1)
            if nxt.item() == 2:  # EOS
                break
        return idx