#!/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"
CONFIG_PATH = BASE_DIR / "results" / "config_used.json"

# 使用 platform_utils 中的 resolve_device 函数


DEVICE = resolve_device("auto")
TIMESTEPS = 200
SEQ_LEN = 64
BATCH_SIZE = 2
CLIP_K = 5.0


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


def main():
    cfg = {}
    if CONFIG_PATH.exists():
        with open(str(CONFIG_PATH), "r", encoding="utf-8") as f:
            cfg = json.load(f)
    timesteps = int(cfg.get("timesteps", TIMESTEPS))
    seq_len = int(cfg.get("sample_seq_len", cfg.get("seq_len", SEQ_LEN)))
    batch_size = int(cfg.get("sample_batch_size", cfg.get("batch_size", BATCH_SIZE)))
    clip_k = float(cfg.get("clip_k", CLIP_K))
    use_condition = bool(cfg.get("use_condition")) and cfg.get("condition_type") == "file_id"
    cond_dim = int(cfg.get("cond_dim", 32))
    use_tanh_eps = bool(cfg.get("use_tanh_eps", False))
    eps_scale = float(cfg.get("eps_scale", 1.0))

    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)
    cond_vocab_size = 0
    if use_condition:
        data_glob = cfg.get("data_glob")
        if data_glob:
            base = Path(data_glob).parent
            pat = Path(data_glob).name
            cond_vocab_size = len(sorted(base.glob(pat)))
    model = HybridDiffusionModel(
        cont_dim=len(cont_cols),
        disc_vocab_sizes=vocab_sizes,
        cond_vocab_size=cond_vocab_size,
        cond_dim=cond_dim,
        use_tanh_eps=use_tanh_eps,
        eps_scale=eps_scale,
    ).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]

    cond = None
    if use_condition:
        if cond_vocab_size <= 0:
            raise SystemExit("use_condition enabled but no files matched data_glob")
        cond = torch.randint(0, cond_vocab_size, (batch_size,), device=DEVICE, dtype=torch.long)

    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, cond)

        # 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
        if clip_k > 0:
            x_cont = torch.clamp(x_cont, -clip_k, clip_k)

        # 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()