[content] train_tokenizer.py (python)
    val_iterator: Optional[grain.DataLoaderIterator],
    restore_step: Optional[int] = None,
) -> tuple[int, nnx.Optimizer, grain.DataLoaderIterator, grain.DataLoaderIterator]:
    step = 0
    if restore_step is None:
        restore_step = checkpoint_manager.latest_step()
    if args.restore_ckpt:
        abstract_optimizer = nnx.eval_shape(lambda: optimizer)
        abstract_optimizer_state = nnx.state(abstract_optimizer)
        if val_iterator:
            restore_args = ocp.args.Composite(
                model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
                train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator),  # type: ignore
                val_dataloader_state=grain.checkpoint.CheckpointRestore(val_iterator),  # type: ignore
            )
        else:
            restore_args = ocp.args.Composite(
                model_state=ocp.args.PyTreeRestore(abstract_optimizer_state),  # type: ignore
                train_dataloader_state=grain.checkpoint.CheckpointRestore(train_iterator),  # type: ignore
            )
        restored = checkpoint_manager.restore(restore_step, args=restore_args)
        restored_optimizer_state = restored["model_state"]
        nnx.update(optimizer, restored_optimizer_state)
        train_iterator = restored["train_dataloader_state"]
        if val_iterator:
            val_iterator = restored["val_dataloader_state"]
        step = restore_step or 0
        print(f"Restored dataloader and model state from step {step}")
    return step, optimizer, train_iterator, val_iterator


def main(args: Args) -> None:
    jax.distributed.initialize()
    num_devices = jax.device_count()
    if num_devices == 0:
        raise ValueError("No JAX devices found.")
    print(f"Running on {num_devices} devices.")

    if args.batch_size % num_devices != 0:
        raise ValueError(
            f"Global batch size {args.batch_size} must be divisible by "
            f"number of devices {num_devices}."
        )

    rng = jax.random.key(args.seed)

    # --- Initialize model ---
    tokenizer, rng = build_model(args, rng)

    _, params, _ = nnx.split(tokenizer, nnx.Param, ...)
    param_counts = count_parameters_by_component(params)

    if args.log and jax.process_index() == 0:
        wandb_init_kwargs = {
            "entity": args.entity,
            "project": args.project,
            "name": args.name,
            "tags": args.tags,
            "group": "debug",
            "config": args,
        }

        if args.wandb_id:
            wandb_init_kwargs.update(
                {
                    "id": args.wandb_id,
                    "resume": "allow",
                }
            )
        wandb.init(**wandb_init_kwargs)

        wandb.config.update({"model_param_count": param_counts})

    print("Parameter counts:")
    print(param_counts)

    # --- Initialize optimizer ---
    optimizer, lr_schedule = build_optimizer(tokenizer, args)
    del tokenizer

    # FIXME: switch to create_hybrid_device_mesh for runs spanning multiple nodes
    _, replicated_sharding, videos_sharding = build_mesh_and_sharding(num_devices)

    shard_optimizer_states(optimizer, replicated_sharding)

    # --- Initialize checkpoint manager ---
    checkpoint_manager = build_checkpoint_manager(args)

    # --- Create DataLoaderIterator from dataloader ---
    train_iterator = build_dataloader(args, args.data_dir)
    val_iterator = None
    if args.val_data_dir:
        val_iterator = build_dataloader(args, args.val_data_dir)

    # --- Restore checkpoint ---
    step, optimizer, train_iterator, val_iterator = restore_checkpoint_if_needed(
        args, checkpoint_manager, optimizer, train_iterator, val_iterator
    )

    # --- Define loss and train step (close over args) ---
    def tokenizer_loss_fn(
        model: TokenizerVQVAE, inputs: dict, training: bool = False
    ) -> tuple[jax.Array, tuple[jax.Array, dict]]:
        gt = jnp.asarray(inputs["videos"], dtype=jnp.float32) / 255.0
        inputs["videos"] = gt.astype(args.dtype)
        outputs = model(inputs, training=training)
        outputs["recon"] = outputs["recon"].astype(jnp.float32)
        mse = jnp.square(gt - outputs["recon"]).mean()
        q_loss = jnp.square(jax.lax.stop_gradient(outputs["emb"]) - outputs["z"]).mean()
        commitment_loss = jnp.square(
            outputs["emb"] - jax.lax.stop_gradient(outputs["z"])
        ).mean()
        loss = mse + q_loss + args.vq_beta * commitment_loss

        gt_clipped = gt.clip(0, 1).reshape(-1, *gt.shape[2:])
        recon = outputs["recon"].clip(0, 1).reshape(-1, *outputs["recon"].shape[2:])
        psnr = jnp.asarray(pix.psnr(gt_clipped, recon)).mean()
        ssim = jnp.asarray(pix.ssim(gt_clipped, recon)).mean()
        _, index_counts = jnp.unique_counts(
            jnp.ravel(outputs["indices"]), size=args.num_latents, fill_value=0
        )
        codebook_usage = (index_counts != 0).mean()
        metrics = dict(
            loss=loss,
            mse=mse,
            q_loss=q_loss,
            commitment_loss=commitment_loss,
            psnr=psnr,
            ssim=ssim,
            codebook_usage=codebook_usage,
        )
        return loss, (outputs["recon"], metrics)

    @nnx.jit(donate_argnums=0)
    def train_step(
        optimizer: nnx.Optimizer, inputs: dict
    ) -> tuple[jax.Array, jax.Array, dict]:
        def loss_fn(model: TokenizerVQVAE) -> tuple[jax.Array, tuple[jax.Array, dict]]:
            model.train()
            return tokenizer_loss_fn(model, inputs, training=True)

        (loss, (recon, metrics)), grads = nnx.value_and_grad(loss_fn, has_aux=True)(
            optimizer.model
        )
        optimizer.update(grads)
        if args.log_gradients:
            metrics["encoder_gradients_std/"] = jax.tree.map(
                lambda x: x.std(), grads["params"]["encoder"]
            )
            metrics["vq_gradients_std/"] = jax.tree.map(
                lambda x: x.std(), grads["params"]["vq"]
            )
            metrics["decoder_gradients_std/"] = jax.tree.map(
                lambda x: x.std(), grads["params"]["decoder"]
            )
        return loss, recon, metrics

    @nnx.jit
    def val_step(
        tokenizer: TokenizerVQVAE, inputs: dict
    ) -> tuple[jax.Array, jax.Array, dict]:
        tokenizer.eval()
        (loss, (recon, metrics)) = tokenizer_loss_fn(tokenizer, inputs, training=False)
        return loss, recon, metrics

    def calculate_validation_metrics(val_dataloader, tokenizer):
        step = 0
        loss_per_step = []
        metrics_per_step = []
        batch = None
        recon = None
        for batch in val_dataloader:
            loss, recon, metrics = val_step(tokenizer, batch)
            loss_per_step.append(loss)
            metrics_per_step.append(metrics)
            step += 1
            if step > args.val_steps:
                break

        if step < args.val_steps:
            print(
                f"Warning: Your validation dataset is too small to make val_steps many steps. Made {step} steps, expected {args.val_steps}"
            )

        val_loss = np.mean(loss_per_step)
        val_metrics = {
            f"val_{key}": np.mean([float(m[key]) for m in metrics_per_step])
            for key in metrics_per_step[0].keys()
        }
        val_metrics["val_loss"] = val_loss
        return val_metrics, batch, recon

    # --- TRAIN LOOP ---
    dataloader_train = (
        {
            "videos": jax.make_array_from_process_local_data(
                videos_sharding, elem["videos"]
            ),
        }
        for elem in train_iterator
    )
    dataloader_val = None
    if val_iterator:
        dataloader_val = (
            {
                "videos": jax.make_array_from_process_local_data(
                    videos_sharding, elem["videos"]
                ),
            }
            for elem in val_iterator
        )
    if jax.process_index() == 0:
        first_batch = next(dataloader_train)
        compiled = train_step.lower(optimizer, first_batch).compile()
        print_compiled_memory_stats(compiled.memory_analysis())
        print_compiled_cost_analysis(compiled.cost_analysis())
        # Do not skip the first batch during training
        dataloader_train = itertools.chain([first_batch], dataloader_train)
    print(f"Starting training from step {step}...")
    first_step = step
    while step < args.num_steps:
        for batch in dataloader_train:
            # --- Train step ---
            loss, recon, metrics = train_step(optimizer, batch)
            if step == first_step:
                print_mem_stats("After params initialized")
            step += 1

            # --- Validation loss ---
            val_results = {}
            if dataloader_val and step % args.val_interval == 0:
                print("Calculating validation metrics...")
                val_metrics, val_gt_batch, val_recon = calculate_validation_metrics(
                    dataloader_val, optimizer.model
                )
                print(f"Step {step}, validation loss: {val_metrics['val_loss']}")
                val_results = {
                    "metrics": val_metrics,
                    "gt_batch": val_gt_batch,
                    "recon": val_recon,
                }

            # --- Logging ---
            if args.log:
                if step % args.log_interval == 0 and jax.process_index() == 0:
                    log_dict = {"loss": loss, "step": step, **metrics}
                    if val_results:
                        log_dict.update(val_results["metrics"])
                    wandb.log(log_dict)
                if step % args.log_image_interval == 0:
                    gt_seq = batch["videos"][0].astype(jnp.float32) / 255.0