#!/usr/bin/env python3
"""Hybrid diffusion scaffold for continuous + discrete HAI features.

Continuous: Gaussian diffusion (DDPM-style).
Discrete: mask-based diffusion (predict original token).
"""

import math
from typing import List, Tuple

import torch
import torch.nn as nn
import torch.nn.functional as F


def cosine_beta_schedule(timesteps: int, s: float = 0.008) -> torch.Tensor:
    steps = timesteps + 1
    x = torch.linspace(0, timesteps, steps)
    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * math.pi * 0.5) ** 2
    alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
    betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
    return torch.clip(betas, 1e-5, 0.999)


def q_sample_continuous(x0: torch.Tensor, t: torch.Tensor, alphas_cumprod: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
    """Add Gaussian noise to continuous features at timestep t."""
    noise = torch.randn_like(x0)
    a_bar = alphas_cumprod[t].view(-1, 1, 1)
    xt = torch.sqrt(a_bar) * x0 + torch.sqrt(1.0 - a_bar) * noise
    return xt, noise


def q_sample_discrete(
    x0: torch.Tensor,
    t: torch.Tensor,
    mask_tokens: torch.Tensor,
    max_t: int,
) -> Tuple[torch.Tensor, torch.Tensor]:
    """Randomly mask discrete tokens with a linear schedule over t."""
    bsz = x0.size(0)
    p = t.float() / float(max_t)
    p = p.view(bsz, 1, 1)
    mask = torch.rand_like(x0.float()) < p
    x_masked = x0.clone()
    for i in range(x0.size(2)):
        x_masked[:, :, i][mask[:, :, i]] = mask_tokens[i]
    return x_masked, mask


class SinusoidalTimeEmbedding(nn.Module):
    def __init__(self, dim: int):
        super().__init__()
        self.dim = dim

    def forward(self, t: torch.Tensor) -> torch.Tensor:
        half = self.dim // 2
        freqs = torch.exp(-math.log(10000) * torch.arange(0, half, device=t.device) / half)
        args = t.float().unsqueeze(1) * freqs.unsqueeze(0)
        emb = torch.cat([torch.sin(args), torch.cos(args)], dim=1)
        if self.dim % 2 == 1:
            emb = F.pad(emb, (0, 1))
        return emb


class HybridDiffusionModel(nn.Module):
    def __init__(
        self,
        cont_dim: int,
        disc_vocab_sizes: List[int],
        time_dim: int = 64,
        hidden_dim: int = 256,
    ):
        super().__init__()
        self.cont_dim = cont_dim
        self.disc_vocab_sizes = disc_vocab_sizes

        self.time_embed = SinusoidalTimeEmbedding(time_dim)

        self.disc_embeds = nn.ModuleList([
            nn.Embedding(vocab_size + 1, min(32, vocab_size * 2))
            for vocab_size in disc_vocab_sizes
        ])
        disc_embed_dim = sum(e.embedding_dim for e in self.disc_embeds)

        self.cont_proj = nn.Linear(cont_dim, cont_dim)
        self.in_proj = nn.Linear(cont_dim + disc_embed_dim + time_dim, hidden_dim)
        self.backbone = nn.GRU(hidden_dim, hidden_dim, batch_first=True)

        self.cont_head = nn.Linear(hidden_dim, cont_dim)
        self.disc_heads = nn.ModuleList([
            nn.Linear(hidden_dim, vocab_size)
            for vocab_size in disc_vocab_sizes
        ])

    def forward(self, x_cont: torch.Tensor, x_disc: torch.Tensor, t: torch.Tensor):
        """x_cont: (B,T,Cc), x_disc: (B,T,Cd) with integer tokens."""
        time_emb = self.time_embed(t)
        time_emb = time_emb.unsqueeze(1).expand(-1, x_cont.size(1), -1)

        disc_embs = []
        for i, emb in enumerate(self.disc_embeds):
            disc_embs.append(emb(x_disc[:, :, i]))
        disc_feat = torch.cat(disc_embs, dim=-1)

        cont_feat = self.cont_proj(x_cont)
        feat = torch.cat([cont_feat, disc_feat, time_emb], dim=-1)
        feat = self.in_proj(feat)

        out, _ = self.backbone(feat)

        eps_pred = self.cont_head(out)
        logits = [head(out) for head in self.disc_heads]
        return eps_pred, logits