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"""One mini-batch forward/backward for the ELF diffusion language model.
Each example in the batch independently picks the decoder (CE) or denoiser
(L2) branch via a Bernoulli draw at `decoder_prob`. A single forward consumes
a mixed input (decoder_z for decoder rows, denoiser_z for denoiser rows) and
both heads run; the CE / L2 losses are then masked to their respective rows
and combined with a single denominator. Self-conditioning + CFG guidance is
applied on the denoiser branch only.
"""

import contextlib
from typing import Dict, Tuple

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

from utils.train_utils import TrainState, ema_update
from utils.encoder_utils import encode_text
from utils.sampling_utils import (
    sample_cfg_scale, add_noise, sample_timesteps,
    net_out_to_v_x, restore_cond,
)


def _trainable_params(model: nn.Module):
    return [p for p in model.parameters() if p.requires_grad]


def train_step(
    state: TrainState,
    encoder: nn.Module,
    batch: Dict[str, torch.Tensor],
    config,
) -> Tuple[TrainState, Dict[str, float]]:
    """Perform a single training step."""
    device = next(state.model.parameters()).device
    dtype = next(state.model.parameters()).dtype
    use_bf16 = bool(getattr(config, "use_bf16", True)) and device.type == "cuda"
    t_eps = config.t_eps
    self_cond_prob = config.self_cond_prob
    latent_mean, latent_std = config.latent_mean, config.latent_std
    decoder_prob = config.decoder_prob
    decoder_noise_scale = config.decoder_noise_scale

    gen = state.dropout_generator

    # encoder_attention_mask: cond sees cond, x sees all
    input_ids = batch["input_ids"].to(device, non_blocking=True).long()
    encoder_attention_mask = batch["encoder_attention_mask"].to(device, dtype=torch.float32, non_blocking=True)
    cond_seq_mask = batch["cond_seq_mask"].to(device, dtype=torch.float32, non_blocking=True)
    attention_mask = batch["attention_mask"].to(device, dtype=torch.float32, non_blocking=True)
    label_drop_mask = batch.get("label_drop_mask",
                                torch.zeros((input_ids.shape[0],), dtype=torch.bool)).to(device, non_blocking=True)

    # Label drop before encoding: prevent target tokens from attending to
    # condition tokens so x0 is truly unconditional for dropped samples.
    if config.label_drop_prob > 0:
        drop = label_drop_mask.to(dtype=torch.float32).reshape(-1, 1, 1)  # (B, 1, 1)
        cond_mask = cond_seq_mask  # (B, S)
        # block_mask is 1 only at (non-cond row, cond col) — leaves cond↔cond unchanged
        block_mask = (1 - cond_mask).unsqueeze(-1) * cond_mask.unsqueeze(1)
        encoder_attention_mask = encoder_attention_mask * (1 - drop * block_mask)

    x0 = encode_text(
        input_ids=input_ids,
        attention_mask=encoder_attention_mask,
        encoder=encoder,
        latent_mean=latent_mean,
        latent_std=latent_std,
        use_bf16=use_bf16,
    ).to(dtype)

    batch_size, seq_length = x0.shape[0], x0.shape[1]

    t = sample_timesteps(
        batch_size,
        P_mean=config.denoiser_p_mean, P_std=config.denoiser_p_std,
        time_schedule=config.time_schedule,
        device=device, dtype=dtype,
    )

    noise = torch.randn(x0.shape, dtype=dtype, device=device)

    if config.pad_token == "pad":
        loss_mask = attention_mask
    else:
        loss_mask = torch.ones_like(attention_mask)
    loss_mask = loss_mask * (1 - cond_seq_mask)

    cond_seq_mask = cond_seq_mask.unsqueeze(-1)  # (B, S, 1)

    denoiser_z = add_noise(x0, noise, t, config, cond_seq_mask=cond_seq_mask)

    drop = label_drop_mask.unsqueeze(1)  # (B, 1)
    if config.label_drop_prob > 0:
        denoiser_z = torch.where(drop.unsqueeze(-1) & (cond_seq_mask > 0), torch.zeros_like(denoiser_z), denoiser_z)
        x0 = torch.where(drop.unsqueeze(-1) & (cond_seq_mask > 0), torch.zeros_like(x0), x0)

    decoder_targets = input_ids  # (B, S)

    # Per-example branching: each example independently picks decoder (CE) vs.
    # denoiser (L2) instead of one scalar bernoulli per step. Smooths training
    decoder_step_active = torch.bernoulli(
        torch.full((batch_size,), decoder_prob, dtype=torch.float32),
        generator=gen,
    ).to(device=device, dtype=dtype)  # (B,) — 1.0 = decoder mode, 0.0 = denoiser
    decoder_mask_B11 = decoder_step_active.view(-1, 1, 1)
    decoder_mask_B1 = decoder_step_active.view(-1, 1)

    # Decoder-branch input: logit-normal-noised latent (decoder_z) at t=1
    decoder_z_vals = (
        torch.randn((batch_size * seq_length,), dtype=dtype, device=device)
        * config.decoder_p_std + config.decoder_p_mean
    )
    decoder_lambda_t = torch.sigmoid(decoder_z_vals).reshape(batch_size, seq_length, 1)
    decoder_noise = torch.randn(x0.shape, dtype=dtype, device=device) * decoder_noise_scale
    decoder_z = decoder_lambda_t * x0 + (1 - decoder_lambda_t) * decoder_noise

    t_expanded = t.reshape(-1, 1, 1)
    v_target = (x0 - denoiser_z) / torch.clamp(1 - t_expanded, min=t_eps)

    if self_cond_prob > 0:
        use_self_cond_mask = (
            (torch.rand((batch_size,), dtype=dtype, device=device) < self_cond_prob)
            .reshape(-1, 1, 1).to(dtype)
        )
    else:
        use_self_cond_mask = None

    if config.num_self_cond_cfg_tokens > 0:
        self_cond_cfg_scale = sample_cfg_scale(
            batch_size,
            cfg_min=config.self_cond_cfg_min, cfg_max=config.self_cond_cfg_max,
            dtype=dtype, device=device,
        )
    else:
        self_cond_cfg_scale = None

    model = state.model

    def compute_shared_uncond(z, t_input, x_tokens):
        """Unconditional forward shared by self-cond-init and sc-cfg-uncond."""
        z_uncond = restore_cond(torch.zeros_like(z), x_tokens, cond_seq_mask)
        z_input_uncond = torch.cat([z, z_uncond], dim=-1)
        with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
            net_out_uncond = model(
                z_input_uncond, t_input,
                deterministic=True, self_cond_cfg_scale=self_cond_cfg_scale,
            )
        return net_out_uncond

    def get_sc_cond_and_uncond(z, t_input, cond_mask, x_tokens, shared_net_out_uncond):
        if config.self_cond_prob == 0:
            with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
                net_out_uncond = model(
                    z, t_input,
                    deterministic=True, self_cond_cfg_scale=self_cond_cfg_scale,
                )
            v_uncond, _ = net_out_to_v_x(net_out_uncond, z, t_input, t_eps)
            return v_uncond, v_uncond

        v_uncond, x_uncond = net_out_to_v_x(shared_net_out_uncond, z, t_input, t_eps)
        x_uncond = restore_cond(x_uncond, x_tokens, cond_mask)

        z_input_cond = torch.cat([z, x_uncond], dim=-1)
        with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
            net_out_cond = model(
                z_input_cond, t_input,
                deterministic=True, self_cond_cfg_scale=self_cond_cfg_scale,
            )
        v_cond, _ = net_out_to_v_x(net_out_cond, z, t_input, t_eps)
        return v_cond, v_uncond

    def get_sc_guided_v(z, t_input, base_v_target, x_tokens, shared_net_out_uncond):
        """v target with self-conditioning guidance."""
        v_cond, v_uncond = get_sc_cond_and_uncond(
            z, t_input, cond_mask=cond_seq_mask, x_tokens=x_tokens,
            shared_net_out_uncond=shared_net_out_uncond,
        )
        sc_w = self_cond_cfg_scale.reshape(batch_size, 1, 1)
        sc_guidance = (1 - 1 / sc_w) * (v_cond - v_uncond)
        sc_guidance = torch.where(use_self_cond_mask.bool(), sc_guidance, torch.zeros_like(sc_guidance))
        return (base_v_target + sc_guidance).detach()

    def get_v_target(z, t_input, base_v_target, x_tokens, shared_net_out_uncond):
        """Compute final v target with self-conditioning guidance."""
        if config.num_self_cond_cfg_tokens > 0 and config.self_cond_prob > 0:
            return get_sc_guided_v(
                z, t_input, base_v_target=base_v_target, x_tokens=x_tokens,
                shared_net_out_uncond=shared_net_out_uncond,
            )
        return base_v_target

    model.train()

    # Per-example branching: build a mixed input (decoder_z for decoder-mode
    # rows, denoiser_z for denoiser-mode rows). One forward computes both
    # heads; we mask CE / L2 losses to their respective rows. 
    denoiser_t = t
    decoder_t = torch.ones_like(t)
    t_mixed = decoder_step_active * decoder_t + (1.0 - decoder_step_active) * t  # (B,)
    z_mixed = decoder_mask_B11 * decoder_z + (1.0 - decoder_mask_B11) * denoiser_z

    # Self-cond shared forward (run on denoiser_z / t — only relevant for
    # denoiser-mode rows; decoder-mode rows zero out the self-cond half below).
    if self_cond_prob > 0 or config.num_self_cond_cfg_tokens > 0:
        shared_net_out_uncond = compute_shared_uncond(denoiser_z, denoiser_t, x0)
    else:
        shared_net_out_uncond = None

    if config.self_cond_prob > 0:
        _, x_pred_init = net_out_to_v_x(shared_net_out_uncond, denoiser_z, denoiser_t, t_eps)
        x_pred_init = restore_cond(x_pred_init, x0, cond_seq_mask)
        x_pred_cond = x_pred_init * use_self_cond_mask.to(dtype)
        x_pred_cond = restore_cond(x_pred_cond, x0, cond_seq_mask)
        # Zero the self-cond half for decoder-mode rows (matches the old
        # `cat([decoder_z, zeros], -1)` decoder-branch input).
        sc_half = x_pred_cond * (1.0 - decoder_mask_B11)
        model_input = torch.cat([z_mixed, sc_half], dim=-1)
    else:
        model_input = z_mixed

    with torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
        net_out, decoder_logits = model(
            model_input, t_mixed,
            deterministic=False,
            self_cond_cfg_scale=self_cond_cfg_scale,
            decoder_step_active=decoder_step_active,  # (B,) tensor
        )

    # CE per-token (used on decoder-mode rows).
    log_probs = F.log_softmax(decoder_logits.to(torch.float32), dim=-1)
    ce_per_token = -log_probs.gather(-1, decoder_targets.unsqueeze(-1)).squeeze(-1)

    # L2 per-token (used on denoiser-mode rows). v_pred is extracted with
    # (denoiser_z, t) — meaningful only for denoiser rows; decoder rows are
    # masked out below.
    v_pred, _ = net_out_to_v_x(net_out, denoiser_z, denoiser_t, t_eps)
    v_final_target = get_v_target(
        denoiser_z, denoiser_t, base_v_target=v_target, x_tokens=x0,
        shared_net_out_uncond=shared_net_out_uncond,
    )
    l2_per_token = ((v_pred - v_final_target) ** 2).mean(dim=-1)

    # Masks: each position is "alive" for exactly one branch.
    loss_mask_f = loss_mask.to(ce_per_token.dtype)
    ce_mask = loss_mask_f * decoder_mask_B1
    l2_mask = loss_mask_f * (1.0 - decoder_mask_B1)

    # Combined loss with a single denominator. In expectation this is
    # decoder_prob * mean_CE + (1 - decoder_prob) * mean_L2.
    total_sum = (ce_per_token * ce_mask).sum() + (l2_per_token * l2_mask).sum()
    loss = total_sum / torch.clamp(loss_mask_f.sum(), min=1.0)

    # Per-branch metrics: mean per-token within each branch.
    ce_loss_val = ((ce_per_token * ce_mask).sum()
                   / torch.clamp(ce_mask.sum(), min=1.0)).detach()
    l2_loss_val = ((l2_per_token * l2_mask).sum()
                   / torch.clamp(l2_mask.sum(), min=1.0)).detach()

    accum_steps = max(config.grad_accum_steps, 1)
    state.step += 1
    is_optimizer_step = (state.step % accum_steps) == 0

    sync_ctx = model.no_sync() if (not is_optimizer_step and hasattr(model, 'no_sync')) else contextlib.nullcontext()
    with sync_ctx:
        (loss / accum_steps).backward()

    if is_optimizer_step:
        torch.nn.utils.clip_grad_norm_(_trainable_params(model), max_norm=1.0)
        state.optimizer.step()
        if state.lr_scheduler is not None:
            state.lr_scheduler.step()
        ema_update(state.ema_params1, state.model, config.ema_decay1)
        state.optimizer.zero_grad(set_to_none=True)

    metrics = {
        "loss": loss.detach(),
        "l2_loss": l2_loss_val,
        "ce_loss": ce_loss_val,
    }
    return state, metrics

	"""One mini-batch forward/backward for the ELF diffusion language model.

	Each example in the batch independently picks the decoder (CE) or denoiser
	(L2) branch via a Bernoulli draw at `decoder_prob`. A single forward consumes
	a mixed input (decoder_z for decoder rows, denoiser_z for denoiser rows) and
	both heads run; the CE / L2 losses are then masked to their respective rows
	and combined with a single denominator. Self-conditioning + CFG guidance is
	applied on the denoiser branch only.
	"""

	import contextlib
	from typing import Dict, Tuple

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

	from utils.train_utils import TrainState, ema_update
	from utils.encoder_utils import encode_text
	from utils.sampling_utils import (
	sample_cfg_scale, add_noise, sample_timesteps,
	net_out_to_v_x, restore_cond,
	)


	def _trainable_params(model: nn.Module):
	return [p for p in model.parameters() if p.requires_grad]


	def train_step(
	state: TrainState,
	encoder: nn.Module,
	batch: Dict[str, torch.Tensor],
	config,
	) -> Tuple[TrainState, Dict[str, float]]:
	"""Perform a single training step."""
	device = next(state.model.parameters()).device
	dtype = next(state.model.parameters()).dtype
	use_bf16 = bool(getattr(config, "use_bf16", True)) and device.type == "cuda"
	t_eps = config.t_eps
	self_cond_prob = config.self_cond_prob
	latent_mean, latent_std = config.latent_mean, config.latent_std
	decoder_prob = config.decoder_prob
	decoder_noise_scale = config.decoder_noise_scale

	gen = state.dropout_generator

	# encoder_attention_mask: cond sees cond, x sees all
	input_ids = batch["input_ids"].to(device, non_blocking=True).long()
	encoder_attention_mask = batch["encoder_attention_mask"].to(device, dtype=torch.float32, non_blocking=True)
	cond_seq_mask = batch["cond_seq_mask"].to(device, dtype=torch.float32, non_blocking=True)
	attention_mask = batch["attention_mask"].to(device, dtype=torch.float32, non_blocking=True)
	label_drop_mask = batch.get("label_drop_mask",
	torch.zeros((input_ids.shape[0],), dtype=torch.bool)).to(device, non_blocking=True)

	# Label drop before encoding: prevent target tokens from attending to
	# condition tokens so x0 is truly unconditional for dropped samples.
	if config.label_drop_prob > 0:
	drop = label_drop_mask.to(dtype=torch.float32).reshape(-1, 1, 1) # (B, 1, 1)
	cond_mask = cond_seq_mask # (B, S)
	# block_mask is 1 only at (non-cond row, cond col) — leaves cond↔cond unchanged
	block_mask = (1 - cond_mask).unsqueeze(-1) * cond_mask.unsqueeze(1)
	encoder_attention_mask = encoder_attention_mask * (1 - drop * block_mask)

	x0 = encode_text(
	input_ids=input_ids,
	attention_mask=encoder_attention_mask,
	encoder=encoder,
	latent_mean=latent_mean,
	latent_std=latent_std,
	use_bf16=use_bf16,
	).to(dtype)

	batch_size, seq_length = x0.shape[0], x0.shape[1]

	t = sample_timesteps(
	batch_size,
	P_mean=config.denoiser_p_mean, P_std=config.denoiser_p_std,
	time_schedule=config.time_schedule,
	device=device, dtype=dtype,
	)

	noise = torch.randn(x0.shape, dtype=dtype, device=device)

	if config.pad_token == "pad":
	loss_mask = attention_mask
	else:
	loss_mask = torch.ones_like(attention_mask)
	loss_mask = loss_mask * (1 - cond_seq_mask)

	cond_seq_mask = cond_seq_mask.unsqueeze(-1) # (B, S, 1)

	denoiser_z = add_noise(x0, noise, t, config, cond_seq_mask=cond_seq_mask)

	drop = label_drop_mask.unsqueeze(1) # (B, 1)
	if config.label_drop_prob > 0:
	denoiser_z = torch.where(drop.unsqueeze(-1) & (cond_seq_mask > 0), torch.zeros_like(denoiser_z), denoiser_z)
	x0 = torch.where(drop.unsqueeze(-1) & (cond_seq_mask > 0), torch.zeros_like(x0), x0)

	decoder_targets = input_ids # (B, S)

	# Per-example branching: each example independently picks decoder (CE) vs.
	# denoiser (L2) instead of one scalar bernoulli per step. Smooths training
	decoder_step_active = torch.bernoulli(
	torch.full((batch_size,), decoder_prob, dtype=torch.float32),
	generator=gen,
	).to(device=device, dtype=dtype) # (B,) — 1.0 = decoder mode, 0.0 = denoiser
	decoder_mask_B11 = decoder_step_active.view(-1, 1, 1)
	decoder_mask_B1 = decoder_step_active.view(-1, 1)

	# Decoder-branch input: logit-normal-noised latent (decoder_z) at t=1
	decoder_z_vals = (
	torch.randn((batch_size * seq_length,), dtype=dtype, device=device)
	* config.decoder_p_std + config.decoder_p_mean
	)
	decoder_lambda_t = torch.sigmoid(decoder_z_vals).reshape(batch_size, seq_length, 1)
	decoder_noise = torch.randn(x0.shape, dtype=dtype, device=device) * decoder_noise_scale
	decoder_z = decoder_lambda_t * x0 + (1 - decoder_lambda_t) * decoder_noise

	t_expanded = t.reshape(-1, 1, 1)
	v_target = (x0 - denoiser_z) / torch.clamp(1 - t_expanded, min=t_eps)

	if self_cond_prob > 0:
	use_self_cond_mask = (
	(torch.rand((batch_size,), dtype=dtype, device=device) < self_cond_prob)
	.reshape(-1, 1, 1).to(dtype)
	)
	else:
	use_self_cond_mask = None

	if config.num_self_cond_cfg_tokens > 0:
	self_cond_cfg_scale = sample_cfg_scale(
	batch_size,
	cfg_min=config.self_cond_cfg_min, cfg_max=config.self_cond_cfg_max,
	dtype=dtype, device=device,
	)
	else:
	self_cond_cfg_scale = None

	model = state.model

	def compute_shared_uncond(z, t_input, x_tokens):
	"""Unconditional forward shared by self-cond-init and sc-cfg-uncond."""
	z_uncond = restore_cond(torch.zeros_like(z), x_tokens, cond_seq_mask)
	z_input_uncond = torch.cat([z, z_uncond], dim=-1)
	with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
	net_out_uncond = model(
	z_input_uncond, t_input,
	deterministic=True, self_cond_cfg_scale=self_cond_cfg_scale,
	)
	return net_out_uncond

	def get_sc_cond_and_uncond(z, t_input, cond_mask, x_tokens, shared_net_out_uncond):
	if config.self_cond_prob == 0:
	with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
	net_out_uncond = model(
	z, t_input,
	deterministic=True, self_cond_cfg_scale=self_cond_cfg_scale,
	)
	v_uncond, _ = net_out_to_v_x(net_out_uncond, z, t_input, t_eps)
	return v_uncond, v_uncond

	v_uncond, x_uncond = net_out_to_v_x(shared_net_out_uncond, z, t_input, t_eps)
	x_uncond = restore_cond(x_uncond, x_tokens, cond_mask)

	z_input_cond = torch.cat([z, x_uncond], dim=-1)
	with torch.no_grad(), torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
	net_out_cond = model(
	z_input_cond, t_input,
	deterministic=True, self_cond_cfg_scale=self_cond_cfg_scale,
	)
	v_cond, _ = net_out_to_v_x(net_out_cond, z, t_input, t_eps)
	return v_cond, v_uncond

	def get_sc_guided_v(z, t_input, base_v_target, x_tokens, shared_net_out_uncond):
	"""v target with self-conditioning guidance."""
	v_cond, v_uncond = get_sc_cond_and_uncond(
	z, t_input, cond_mask=cond_seq_mask, x_tokens=x_tokens,
	shared_net_out_uncond=shared_net_out_uncond,
	)
	sc_w = self_cond_cfg_scale.reshape(batch_size, 1, 1)
	sc_guidance = (1 - 1 / sc_w) * (v_cond - v_uncond)
	sc_guidance = torch.where(use_self_cond_mask.bool(), sc_guidance, torch.zeros_like(sc_guidance))
	return (base_v_target + sc_guidance).detach()

	def get_v_target(z, t_input, base_v_target, x_tokens, shared_net_out_uncond):
	"""Compute final v target with self-conditioning guidance."""
	if config.num_self_cond_cfg_tokens > 0 and config.self_cond_prob > 0:
	return get_sc_guided_v(
	z, t_input, base_v_target=base_v_target, x_tokens=x_tokens,
	shared_net_out_uncond=shared_net_out_uncond,
	)
	return base_v_target

	model.train()

	# Per-example branching: build a mixed input (decoder_z for decoder-mode
	# rows, denoiser_z for denoiser-mode rows). One forward computes both
	# heads; we mask CE / L2 losses to their respective rows.
	denoiser_t = t
	decoder_t = torch.ones_like(t)
	t_mixed = decoder_step_active * decoder_t + (1.0 - decoder_step_active) * t # (B,)
	z_mixed = decoder_mask_B11 * decoder_z + (1.0 - decoder_mask_B11) * denoiser_z

	# Self-cond shared forward (run on denoiser_z / t — only relevant for
	# denoiser-mode rows; decoder-mode rows zero out the self-cond half below).
	if self_cond_prob > 0 or config.num_self_cond_cfg_tokens > 0:
	shared_net_out_uncond = compute_shared_uncond(denoiser_z, denoiser_t, x0)
	else:
	shared_net_out_uncond = None

	if config.self_cond_prob > 0:
	_, x_pred_init = net_out_to_v_x(shared_net_out_uncond, denoiser_z, denoiser_t, t_eps)
	x_pred_init = restore_cond(x_pred_init, x0, cond_seq_mask)
	x_pred_cond = x_pred_init * use_self_cond_mask.to(dtype)
	x_pred_cond = restore_cond(x_pred_cond, x0, cond_seq_mask)
	# Zero the self-cond half for decoder-mode rows (matches the old
	# `cat([decoder_z, zeros], -1)` decoder-branch input).
	sc_half = x_pred_cond * (1.0 - decoder_mask_B11)
	model_input = torch.cat([z_mixed, sc_half], dim=-1)
	else:
	model_input = z_mixed

	with torch.amp.autocast('cuda', dtype=torch.bfloat16, enabled=use_bf16):
	net_out, decoder_logits = model(
	model_input, t_mixed,
	deterministic=False,
	self_cond_cfg_scale=self_cond_cfg_scale,
	decoder_step_active=decoder_step_active, # (B,) tensor
	)

	# CE per-token (used on decoder-mode rows).
	log_probs = F.log_softmax(decoder_logits.to(torch.float32), dim=-1)
	ce_per_token = -log_probs.gather(-1, decoder_targets.unsqueeze(-1)).squeeze(-1)

	# L2 per-token (used on denoiser-mode rows). v_pred is extracted with
	# (denoiser_z, t) — meaningful only for denoiser rows; decoder rows are
	# masked out below.
	v_pred, _ = net_out_to_v_x(net_out, denoiser_z, denoiser_t, t_eps)
	v_final_target = get_v_target(
	denoiser_z, denoiser_t, base_v_target=v_target, x_tokens=x0,
	shared_net_out_uncond=shared_net_out_uncond,
	)
	l2_per_token = ((v_pred - v_final_target) ** 2).mean(dim=-1)

	# Masks: each position is "alive" for exactly one branch.
	loss_mask_f = loss_mask.to(ce_per_token.dtype)
	ce_mask = loss_mask_f * decoder_mask_B1
	l2_mask = loss_mask_f * (1.0 - decoder_mask_B1)

	# Combined loss with a single denominator. In expectation this is
	# decoder_prob * mean_CE + (1 - decoder_prob) * mean_L2.
	total_sum = (ce_per_token * ce_mask).sum() + (l2_per_token * l2_mask).sum()
	loss = total_sum / torch.clamp(loss_mask_f.sum(), min=1.0)

	# Per-branch metrics: mean per-token within each branch.
	ce_loss_val = ((ce_per_token * ce_mask).sum()
	/ torch.clamp(ce_mask.sum(), min=1.0)).detach()
	l2_loss_val = ((l2_per_token * l2_mask).sum()
	/ torch.clamp(l2_mask.sum(), min=1.0)).detach()

	accum_steps = max(config.grad_accum_steps, 1)
	state.step += 1
	is_optimizer_step = (state.step % accum_steps) == 0

	sync_ctx = model.no_sync() if (not is_optimizer_step and hasattr(model, 'no_sync')) else contextlib.nullcontext()
	with sync_ctx:
	(loss / accum_steps).backward()

	if is_optimizer_step:
	torch.nn.utils.clip_grad_norm_(_trainable_params(model), max_norm=1.0)
	state.optimizer.step()
	if state.lr_scheduler is not None:
	state.lr_scheduler.step()
	ema_update(state.ema_params1, state.model, config.ema_decay1)
	state.optimizer.zero_grad(set_to_none=True)

	metrics = {
	"loss": loss.detach(),
	"l2_loss": l2_loss_val,
	"ce_loss": ce_loss_val,
	}
	return state, metrics