#!/usr/bin/env python3
"""Sampling stub for hybrid diffusion (continuous + discrete)."""

import json
import math
import os
from pathlib import Path

import torch
import torch.nn.functional as F

from data_utils import load_split
from hybrid_diffusion import HybridDiffusionModel, cosine_beta_schedule
from platform_utils import resolve_device, safe_path, ensure_dir

BASE_DIR = Path(__file__).resolve().parent
SPLIT_PATH = BASE_DIR / "feature_split.json"
VOCAB_PATH = BASE_DIR / "results" / "disc_vocab.json"
MODEL_PATH = BASE_DIR / "results" / "model.pt"

# 使用 platform_utils 中的 resolve_device 函数


DEVICE = resolve_device("auto")
TIMESTEPS = 200
SEQ_LEN = 64
BATCH_SIZE = 2


def load_vocab():
    with open(str(VOCAB_PATH), "r", encoding="utf-8") as f:
        return json.load(f)["vocab"]


def main():
    split = load_split(str(SPLIT_PATH))
    time_col = split.get("time_column", "time")
    cont_cols = [c for c in split["continuous"] if c != time_col]
    disc_cols = [c for c in split["discrete"] if not c.startswith("attack") and c != time_col]

    vocab = load_vocab()
    vocab_sizes = [len(vocab[c]) for c in disc_cols]

    print("device", DEVICE)
    model = HybridDiffusionModel(cont_dim=len(cont_cols), disc_vocab_sizes=vocab_sizes).to(DEVICE)
    if MODEL_PATH.exists():
        model.load_state_dict(torch.load(str(MODEL_PATH), map_location=DEVICE, weights_only=True))
    model.eval()

    betas = cosine_beta_schedule(TIMESTEPS).to(DEVICE)
    alphas = 1.0 - betas
    alphas_cumprod = torch.cumprod(alphas, dim=0)

    x_cont = torch.randn(BATCH_SIZE, SEQ_LEN, len(cont_cols), device=DEVICE)
    x_disc = torch.full((BATCH_SIZE, SEQ_LEN, len(disc_cols)), 0, device=DEVICE, dtype=torch.long)
    mask_tokens = torch.tensor(vocab_sizes, device=DEVICE)

    # Initialize discrete with mask tokens
    for i in range(len(disc_cols)):
        x_disc[:, :, i] = mask_tokens[i]

    for t in reversed(range(TIMESTEPS)):
        t_batch = torch.full((BATCH_SIZE,), t, device=DEVICE, dtype=torch.long)
        eps_pred, logits = model(x_cont, x_disc, t_batch)

        # Continuous reverse step (DDPM): x_{t-1} mean
        a_t = alphas[t]
        a_bar_t = alphas_cumprod[t]
        coef1 = 1.0 / torch.sqrt(a_t)
        coef2 = (1 - a_t) / torch.sqrt(1 - a_bar_t)
        mean = coef1 * (x_cont - coef2 * eps_pred)
        if t > 0:
            noise = torch.randn_like(x_cont)
            x_cont = mean + torch.sqrt(betas[t]) * noise
        else:
            x_cont = mean

        # Discrete: fill masked positions by sampling logits
        for i, logit in enumerate(logits):
            if t == 0:
                probs = F.softmax(logit, dim=-1)
                x_disc[:, :, i] = torch.argmax(probs, dim=-1)
            else:
                mask = x_disc[:, :, i] == mask_tokens[i]
                if mask.any():
                    probs = F.softmax(logit, dim=-1)
                    sampled = torch.multinomial(probs.view(-1, probs.size(-1)), 1).view(BATCH_SIZE, SEQ_LEN)
                    x_disc[:, :, i][mask] = sampled[mask]

    print("sampled_cont_shape", tuple(x_cont.shape))
    print("sampled_disc_shape", tuple(x_disc.shape))


if __name__ == "__main__":
    main()