"""
Inference script for nano GPT.

Usage:
    python generate.py --prompt "ROMEO:" --length 500 --temperature 0.8

Loads best.pt (saved by train_standalone.py) and generates text.
"""

import argparse
import torch
import torch.nn as nn
from torch.nn import functional as F
from dataclasses import dataclass


@dataclass
class GPTConfig:
    block_size: int = 256
    vocab_size: int = 65
    n_layer: int = 4
    n_head: int = 4
    n_embd: int = 256
    dropout: float = 0.0


class CausalSelfAttention(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        assert config.n_embd % config.n_head == 0
        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
        self.n_head = config.n_head
        self.n_embd = config.n_embd
        self.register_buffer(
            "bias",
            torch.tril(torch.ones(config.block_size, config.block_size))
                 .view(1, 1, config.block_size, config.block_size)
        )

    def forward(self, x):
        B, T, C = x.size()
        qkv = self.c_attn(x)
        q, k, v = qkv.split(self.n_embd, dim=2)
        head_size = C // self.n_head
        q = q.view(B, T, self.n_head, head_size).transpose(1, 2)
        k = k.view(B, T, self.n_head, head_size).transpose(1, 2)
        v = v.view(B, T, self.n_head, head_size).transpose(1, 2)
        att = (q @ k.transpose(-2, -1)) * (1.0 / (head_size ** 0.5))
        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float("-inf"))
        att = F.softmax(att, dim=-1)
        y = att @ v
        y = y.transpose(1, 2).contiguous().view(B, T, C)
        y = self.c_proj(y)
        return y


class MLP(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.c_fc   = nn.Linear(config.n_embd, 4 * config.n_embd)
        self.gelu   = nn.GELU()
        self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd)
        self.dropout = nn.Dropout(config.dropout)

    def forward(self, x):
        x = self.c_fc(x)
        x = self.gelu(x)
        x = self.c_proj(x)
        x = self.dropout(x)
        return x


class Block(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.ln_1 = nn.LayerNorm(config.n_embd)
        self.attn = CausalSelfAttention(config)
        self.ln_2 = nn.LayerNorm(config.n_embd)
        self.mlp  = MLP(config)

    def forward(self, x):
        x = x + self.attn(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class GPT(nn.Module):
    def __init__(self, config: GPTConfig):
        super().__init__()
        self.config = config
        self.transformer = nn.ModuleDict({
            "wte": nn.Embedding(config.vocab_size, config.n_embd),
            "wpe": nn.Embedding(config.block_size, config.n_embd),
            "h":   nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
            "ln_f": nn.LayerNorm(config.n_embd),
        })
        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
        self.transformer.wte.weight = self.lm_head.weight
        self.apply(self._init_weights)

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

    def forward(self, idx, targets=None):
        B, T = idx.size()
        assert T <= self.config.block_size
        pos = torch.arange(0, T, dtype=torch.long, device=idx.device)
        x = self.transformer.wte(idx) + self.transformer.wpe(pos)
        for block in self.transformer.h:
            x = block(x)
        x = self.transformer.ln_f(x)
        logits = self.lm_head(x)
        loss = None
        if targets is not None:
            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        return logits, loss

    def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
        for _ in range(max_new_tokens):
            idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
            logits, _ = self(idx_cond)
            logits = logits[:, -1, :]
            if top_k is not None:
                v, _ = torch.topk(logits, top_k, dim=-1)
                logits[logits < v[:, [-1]]] = float("-inf")
            probs = F.softmax(logits / temperature, dim=-1)
            idx_next = torch.multinomial(probs, num_samples=1)
            idx = torch.cat((idx, idx_next), dim=1)
        return idx


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--checkpoint", default="best.pt", help="Path to checkpoint")
    parser.add_argument("--prompt", default="\n", help="Starting text")
    parser.add_argument("--length", type=int, default=500, help="Tokens to generate")
    parser.add_argument("--temperature", type=float, default=1.0, help="Sampling temperature")
    parser.add_argument("--top_k", type=int, default=40, help="Top-k sampling")
    parser.add_argument("--seed", type=int, default=1337, help="Random seed")
    args = parser.parse_args()

    torch.manual_seed(args.seed)
    device = "cuda" if torch.cuda.is_available() else "cpu"

    # Load checkpoint
    ckpt = torch.load(args.checkpoint, map_location=device, weights_only=False)
    config = ckpt["config"]
    stoi = ckpt["stoi"]
    itos = ckpt["itos"]

    # Build model and load weights
    model = GPT(config)
    model.load_state_dict(ckpt["model_state_dict"])
    model.to(device)
    model.eval()

    # Encode prompt
    encode = lambda s: [stoi[c] for c in s]
    decode = lambda l: "".join([itos[i] for i in l])

    context = torch.tensor(encode(args.prompt), dtype=torch.long, device=device).unsqueeze(0)

    # Generate
    with torch.no_grad():
        generated = model.generate(context, args.length, temperature=args.temperature, top_k=args.top_k)

    print(decode(generated[0].tolist()))


if __name__ == "__main__":
    main()