[content] jasmine/genie.py (python)
        decode: bool,
        rngs: nnx.Rngs,
        dropout: float = 0.0,
        mask_limit: float = 0.0,
        diffusion_denoise_steps: int = 0,
    ):
        # --- Tokenizer ---
        self.in_dim = in_dim
        self.tokenizer_dim = tokenizer_dim
        self.tokenizer_ffn_dim = tokenizer_ffn_dim
        self.latent_patch_dim = latent_patch_dim
        self.num_patch_latents = num_patch_latents
        self.patch_size = patch_size
        self.tokenizer_num_blocks = tokenizer_num_blocks
        self.tokenizer_num_heads = tokenizer_num_heads
        # --- LAM ---
        self.lam_dim = lam_dim
        self.lam_ffn_dim = lam_ffn_dim
        self.latent_action_dim = latent_action_dim
        self.num_actions = num_actions
        self.lam_patch_size = lam_patch_size
        self.lam_num_blocks = lam_num_blocks
        self.lam_num_heads = lam_num_heads
        self.lam_co_train = lam_co_train
        self.use_gt_actions = use_gt_actions
        # --- Dynamics ---
        self.dyna_type = dyna_type
        self.dyna_dim = dyna_dim
        self.dyna_ffn_dim = dyna_ffn_dim
        self.dyna_num_blocks = dyna_num_blocks
        self.dyna_num_heads = dyna_num_heads
        self.param_dtype = param_dtype
        self.dtype = dtype
        self.use_flash_attention = use_flash_attention
        self.dropout = dropout
        self.mask_limit = mask_limit
        self.diffusion_denoise_steps = diffusion_denoise_steps
        self.decode = decode
        self.tokenizer_type = tokenizer_type
        if self.tokenizer_type == "vqvae":
            self.tokenizer = TokenizerVQVAE(
                in_dim=self.in_dim,
                model_dim=self.tokenizer_dim,
                ffn_dim=self.tokenizer_ffn_dim,
                latent_dim=self.latent_patch_dim,
                num_latents=self.num_patch_latents,
                patch_size=self.patch_size,
                num_blocks=self.tokenizer_num_blocks,
                num_heads=self.tokenizer_num_heads,
                dropout=0.0,
                codebook_dropout=0.0,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                rngs=rngs,
            )
        elif self.tokenizer_type == "mae":
            self.tokenizer = TokenizerMAE(
                in_dim=self.in_dim,
                model_dim=self.tokenizer_dim,
                ffn_dim=self.tokenizer_ffn_dim,
                latent_dim=self.latent_patch_dim,
                num_latents=self.num_patch_latents,
                patch_size=self.patch_size,
                num_blocks=self.tokenizer_num_blocks,
                num_heads=self.tokenizer_num_heads,
                dropout=0.0,
                max_mask_ratio=0.0,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                rngs=rngs,
            )
        else:
            raise ValueError(f"Invalid tokenizer type: {self.tokenizer_type}")
        if self.use_gt_actions:
            self.action_embed = nnx.Embed(
                self.num_actions, self.latent_action_dim, rngs=rngs
            )
            self.lam = None
        else:
            self.lam = LatentActionModel(
                in_dim=self.in_dim,
                model_dim=self.lam_dim,
                ffn_dim=self.lam_ffn_dim,
                latent_dim=self.latent_patch_dim,
                num_latents=self.num_actions,
                patch_size=self.lam_patch_size,
                num_blocks=self.lam_num_blocks,
                num_heads=self.lam_num_heads,
                dropout=0.0,
                codebook_dropout=0.0,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                rngs=rngs,
            )
            self.action_embed = None
        if self.dyna_type == "maskgit":
            self.dynamics = DynamicsMaskGIT(
                model_dim=self.dyna_dim,
                ffn_dim=self.dyna_ffn_dim,
                num_latents=self.num_patch_latents,
                latent_action_dim=self.latent_action_dim,
                num_blocks=self.dyna_num_blocks,
                num_heads=self.dyna_num_heads,
                dropout=self.dropout,
                mask_limit=self.mask_limit,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                rngs=rngs,
            )
        elif self.dyna_type == "causal":
            self.dynamics = DynamicsCausal(
                model_dim=self.dyna_dim,
                ffn_dim=self.dyna_ffn_dim,
                num_latents=self.num_patch_latents,
                latent_action_dim=self.latent_action_dim,
                num_blocks=self.dyna_num_blocks,
                num_heads=self.dyna_num_heads,
                dropout=self.dropout,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                decode=decode,
                rngs=rngs,
            )
        elif self.dyna_type == "diffusion":
            assert (
                self.diffusion_denoise_steps > 0
            ), "diffusion_denoise_steps must be greater than 0 when using the diffusion backend"
            self.dynamics = DynamicsDiffusion(
                input_dim=self.latent_patch_dim,
                model_dim=self.dyna_dim,
                ffn_dim=self.dyna_ffn_dim,
                out_dim=self.latent_patch_dim,
                num_latents=self.num_patch_latents,
                latent_action_dim=self.latent_action_dim,
                num_blocks=self.dyna_num_blocks,
                num_heads=self.dyna_num_heads,
                denoise_steps=self.diffusion_denoise_steps,
                dropout=self.dropout,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                decode=decode,
                rngs=rngs,
            )
        else:
            raise ValueError(f"Invalid dynamics type: {self.dyna_type}")

    def __call__(
        self,
        batch: Dict[str, jax.Array],
    ) -> Dict[str, jax.Array]:
        videos_BTHWC = batch["videos"]
        H, W = videos_BTHWC.shape[2:4]
        latent_actions_BTm11L = None
        action_embeddings_BTm11L = None
        if self.use_gt_actions:
            assert self.action_embed is not None
            action_indices_E = None
            action_embeddings_BT1L = self.action_embed(batch["actions"]).reshape(
                *batch["actions"].shape[:2], 1, self.latent_action_dim
            )
            action_embeddings_BTm11L = action_embeddings_BT1L[:, :-1]
        else:
            assert self.lam is not None
            lam_outputs = self.lam.vq_encode(videos_BTHWC, training=False)
            z_q_BTm11L = lam_outputs["z_q"]
            action_indices_E = lam_outputs["indices"]
            latent_actions_BTm11L = jax.lax.cond(
                self.lam_co_train,
                lambda: z_q_BTm11L,
                lambda: jax.lax.stop_gradient(z_q_BTm11L),
            )

        if self.dyna_type == "diffusion":
            rng, _rng = jax.random.split(batch["rng"])
            tokenizer_outputs = self.tokenizer.mask_and_encode(videos_BTHWC, rng)
            token_latents_BTNL = tokenizer_outputs["z"]
            token_latents_BTNL = jax.lax.stop_gradient(token_latents_BTNL)
            outputs = dict(
                token_latents=token_latents_BTNL,
                latent_actions=(
                    action_embeddings_BTm11L
                    if self.use_gt_actions
                    else latent_actions_BTm11L
                ),
                rng=rng,
            )
            x_pred, t = self.dynamics(outputs)
            outputs["x_pred"] = x_pred
            outputs["x_gt"] = token_latents_BTNL
            outputs["signal_level"] = t
            outputs["recon"] = self.tokenizer.decode(x_pred, video_hw=(H, W))
            return outputs

        tokenizer_outputs = self.tokenizer.vq_encode(videos_BTHWC, training=False)
        token_indices_BTN = tokenizer_outputs["indices"]
        outputs = dict(
            video_tokens=jax.lax.stop_gradient(token_indices_BTN),
            latent_actions=(
                action_embeddings_BTm11L
                if self.use_gt_actions
                else latent_actions_BTm11L
            ),
        )
        outputs["mask_rng"] = batch["rng"]
        dyna_logits_BTNV, dyna_mask = self.dynamics(outputs)
        outputs["token_logits"] = dyna_logits_BTNV
        outputs["mask"] = dyna_mask
        mle_indices_BTN = jnp.argmax(outputs["token_logits"], axis=-1)
        outputs["recon"] = self.tokenizer.decode(mle_indices_BTN, (H, W))
        if action_indices_E is not None:
            outputs["lam_indices"] = action_indices_E
        return outputs

    def sample(
        self,
        batch: Dict[str, jax.Array],
        seq_len: int,
        temperature: float = 1,
        sample_argmax: bool = False,
        maskgit_steps: int = 25,
        diffusion_steps: int = 64,
        diffusion_corrupt_context_factor: float = 0.1,
    ) -> tuple[jax.Array, jax.Array]:
        if self.dyna_type == "maskgit":
            return self.sample_maskgit(
                batch, seq_len, maskgit_steps, temperature, sample_argmax
            )
        elif self.dyna_type == "causal":
            return self.sample_causal(batch, seq_len, temperature, sample_argmax)
        elif self.dyna_type == "diffusion":
            return self.sample_diffusion(
                batch, seq_len, diffusion_steps, diffusion_corrupt_context_factor
            )
        else:
            raise ValueError(f"Dynamics model type unknown: {self.dyna_type}")

    def sample_maskgit(
        self,
        batch: Dict[str, jax.Array],
        seq_len: int,
        steps: int = 25,
        temperature: float = 1,
        sample_argmax: bool = False,
    ) -> tuple[jax.Array, jax.Array]:
        """
        Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.

        - Input frames are tokenized once.
        - Future frames are generated autoregressively in token space.
        - All frames are detokenized in a single pass.

        Note:
        - For interactive or step-wise sampling, detokenization should occur after each action.
        - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.
        - Temporal causal structure is preserved by
            a) reapplying the mask before each decoding step.
            b) a temporal causal mask is applied within each ST-transformer block.

        Dimension keys:
            B: batch size
            T: number of input (conditioning) frames
            N: number of patches per frame
            M: model dimension
            S: sequence length
            H: height
            W: width
            E: B * (S - 1)
            P: S * N
        """
        assert isinstance(self.dynamics, DynamicsMaskGIT)
        # --- Encode videos and actions ---
        videos_BTHWC = batch["videos"]
        tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)
        token_idxs_BTN = tokenizer_out["indices"]
        B, T, N = token_idxs_BTN.shape
        pad_shape = (B, seq_len - T, N)
        pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)
        token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)
        init_logits_BSNV = jnp.zeros(
            shape=(*token_idxs_BSN.shape, self.num_patch_latents)
        )
        if self.use_gt_actions:
            assert self.action_embed is not None
            latent_actions_BT1L = self.action_embed(batch["actions"]).reshape(
                *batch["actions"].shape[:2], 1, self.latent_action_dim
            )
            latent_actions_BTm11L = latent_actions_BT1L[:, :-1]
            action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)
        else:
            assert self.lam is not None
            latent_actions_E = batch["latent_actions"]
            action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)

        # --- Extract submodule state ---
        dynamics_state = nnx.state(self.dynamics)

        @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
        def maskgit_step_fn(
            carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],
            step: jax.Array,
        ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:
            rng, token_idxs_BSN, logits_BSNV, mask_BSN, action_tokens_EL = carry
            S, N = token_idxs_BSN.shape[1:]
            L = action_tokens_EL.shape[-1]

            # We need to reconstruct the submodule inside scan body to prevent trace context mismatches
            dynamics_maskgit = DynamicsMaskGIT(
                model_dim=self.dyna_dim,
                ffn_dim=self.dyna_ffn_dim,
                num_latents=self.num_patch_latents,
                latent_action_dim=self.latent_action_dim,
                num_blocks=self.dyna_num_blocks,
                num_heads=self.dyna_num_heads,
                dropout=self.dropout,
                mask_limit=self.mask_limit,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                rngs=nnx.Rngs(0),
            )
            nnx.update(dynamics_maskgit, dynamics_state)

            # --- Construct + encode video ---
            vid_embed_BSNM = dynamics_maskgit.patch_embed(token_idxs_BSN)
            mask_token_111M = dynamics_maskgit.mask_token.value
            mask_expanded_BSN1 = mask_BSN[..., None]
            vid_embed_BSNM = jnp.where(
                mask_expanded_BSN1, mask_token_111M, vid_embed_BSNM
            )

            # --- Predict transition ---
            action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))
            act_embed_BSm1M = dynamics_maskgit.action_up(action_tokens_BSm1L)
            act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))
            act_embed_BS1M = jnp.reshape(
                act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])
            )
            vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)
            unmasked_ratio = jnp.cos(jnp.pi * (step + 1) / (steps * 2))
            step_temp = temperature * (1.0 - unmasked_ratio)
            final_logits_BSNp1V = (
                dynamics_maskgit.transformer(vid_embed_BSNp1M) / step_temp
            )
            final_logits_BSNV = final_logits_BSNp1V[:, :, 1:]

            # --- Sample new tokens for final frame ---
            if sample_argmax:
                sampled_token_idxs_BSN = jnp.argmax(final_logits_BSNV, axis=-1)
            else:
                rng, _rng = jax.random.split(rng)
                sampled_token_idxs_BSN = jax.random.categorical(_rng, final_logits_BSNV)
            gather_fn = jax.vmap(jax.vmap(jax.vmap(lambda x, y: x[y])))
            final_token_probs_BSN = gather_fn(
                jax.nn.softmax(final_logits_BSNV), sampled_token_idxs_BSN
            )
            final_token_probs_BSN += ~mask_BSN
            # Update masked tokens and logits only
            token_idxs_BSN = jnp.where(mask_BSN, sampled_token_idxs_BSN, token_idxs_BSN)
            logits_BSNV = jnp.where(
                jnp.expand_dims(mask_BSN, -1), final_logits_BSNV, logits_BSNV
            )

            # --- Update mask ---
            num_unmasked_tokens = jnp.round(N * (1.0 - unmasked_ratio)).astype(int)
            final_token_probs_flat_BP = einops.rearrange(
                final_token_probs_BSN, "b s n -> b (s n)"
            )
            idx_mask_P = (
                jnp.arange(final_token_probs_flat_BP.shape[-1])
                <= N - num_unmasked_tokens
            )
            sorted_idxs_BP = jnp.argsort(final_token_probs_flat_BP, axis=-1)
            mask_update_fn = jax.vmap(lambda msk, ids: msk.at[ids].set(idx_mask_P))
            mask_flat_BP = einops.rearrange(mask_BSN, "b s n -> b (s n)")
            new_mask_flat_BP = mask_update_fn(mask_flat_BP, sorted_idxs_BP)
            new_mask_BSN = einops.rearrange(new_mask_flat_BP, "b (s n) -> b s n", n=N)

            new_carry = (
                rng,
                token_idxs_BSN,
                logits_BSNV,
                new_mask_BSN,
                action_tokens_EL,
            )
            return new_carry

        @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
        def generation_step_fn(
            carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array
        ) -> tuple[jax.Array, jax.Array, jax.Array]:
            rng, current_token_idxs_BSN, current_logits_BSNV = carry
            rng, step_rng = jax.random.split(rng)

            # Mask current frame (i.e., t == step_t)
            mask_S = jnp.arange(seq_len) == step_t
            mask_BSN = jnp.broadcast_to(mask_S[None, :, None], (B, seq_len, N)).astype(
                bool
            )
            masked_token_idxs_BSN = current_token_idxs_BSN * ~mask_BSN
            masked_logits_BSNV = current_logits_BSNV * jnp.expand_dims(~mask_BSN, -1)

            # --- Initialize and run MaskGIT loop ---
            init_carry_maskgit = (
                step_rng,
                masked_token_idxs_BSN,
                masked_logits_BSNV,
                mask_BSN,
                action_tokens_EL,
            )
            final_carry_maskgit = maskgit_step_fn(init_carry_maskgit, jnp.arange(steps))
            updated_token_idxs_BSN = final_carry_maskgit[1]
            updated_logits_BSNV = final_carry_maskgit[2]
            new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)
            return new_carry

        # --- Run the autoregressive generation using jax.lax.scan ---
        initial_carry = (batch["rng"], token_idxs_BSN, init_logits_BSNV)
        timesteps_to_scan = jnp.arange(T, seq_len)
        final_carry = generation_step_fn(initial_carry, timesteps_to_scan)
        final_token_idxs_BSN = final_carry[1]
        final_logits_BSNV = final_carry[2]

        # --- Decode all tokens at once at the end ---
        H, W = batch["videos"].shape[2:4]
        final_frames_BSHWC = self.tokenizer.decode(
            final_token_idxs_BSN,
            video_hw=(H, W),
        )
        return final_frames_BSHWC, final_logits_BSNV

    def sample_causal(
        self,
        batch: Dict[str, jax.Array],
        seq_len: int,
        temperature: float = 1,
        sample_argmax: bool = False,
    ) -> tuple[jax.Array, jax.Array]:
        """
        Autoregressively samples up to `seq_len` future frames, following Figure 8 of the paper.

        - Input frames are tokenized once.
        - Future frames are generated autoregressively in token space.
        - All frames are detokenized in a single pass.

        Note:
        - For interactive or step-wise sampling, detokenization should occur after each action.
        - To maintain consistent tensor shapes across timesteps, all current and future frames are decoded at every step.
        - Temporal causal structure is preserved by
            a) reapplying the mask before each decoding step.
            b) a temporal causal mask is applied within each ST-transformer block.

        Dimension keys:
            B: batch size
            T: number of input (conditioning) frames
            N: number of patches per frame
            M: model dimension
            S: sequence length
            H: height
            W: width
            E: B * (S - 1)
        """
        assert isinstance(self.dynamics, DynamicsCausal)
        # --- Encode videos and actions ---
        videos_BTHWC = batch["videos"]
        tokenizer_out = self.tokenizer.vq_encode(videos_BTHWC, training=False)
        token_idxs_BTN = tokenizer_out["indices"]
        B, T, N = token_idxs_BTN.shape
        pad_shape = (B, seq_len - T, N)
        pad = jnp.zeros(pad_shape, dtype=token_idxs_BTN.dtype)
        token_idxs_BSN = jnp.concatenate([token_idxs_BTN, pad], axis=1)
        logits_BSNV = jnp.zeros((*token_idxs_BSN.shape, self.num_patch_latents))
        dynamics_state = nnx.state(self.dynamics)

        if self.use_gt_actions:
            assert self.action_embed is not None
            latent_actions_BT1L = self.action_embed(batch["actions"]).reshape(
                *batch["actions"].shape[:2], 1, self.latent_action_dim
            )
            latent_actions_BTm11L = latent_actions_BT1L[:, :-1]
            action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)
        else:
            assert self.lam is not None
            latent_actions_E = batch["latent_actions"]
            action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)

        @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
        def causal_step_fn(
            carry: tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array],
            step_n: jax.Array,
        ) -> tuple[jax.Array, jax.Array, jax.Array, jax.Array, jax.Array]:
            rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t = carry
            S, N = token_idxs_BSN.shape[1:]
            L = action_tokens_EL.shape[-1]

            # We need to reconstruct the submodule inside scan body to prevent trace context mismatches
            dynamics_causal = DynamicsCausal(
                model_dim=self.dyna_dim,
                ffn_dim=self.dyna_ffn_dim,
                num_latents=self.num_patch_latents,
                latent_action_dim=self.latent_action_dim,
                num_blocks=self.dyna_num_blocks,
                num_heads=self.dyna_num_heads,
                dropout=self.dropout,
                param_dtype=self.param_dtype,
                dtype=self.dtype,
                use_flash_attention=self.use_flash_attention,
                decode=self.decode,
                rngs=nnx.Rngs(0),
            )
            nnx.update(dynamics_causal, dynamics_state)

            # --- Construct + encode video ---
            vid_embed_BSNM = dynamics_causal.patch_embed(token_idxs_BSN)

            # --- Predict transition ---
            action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, S - 1, L))
            act_embed_BSm1M = dynamics_causal.action_up(action_tokens_BSm1L)
            act_embed_BSM = jnp.pad(act_embed_BSm1M, ((0, 0), (1, 0), (0, 0)))
            act_embed_BS1M = jnp.reshape(
                act_embed_BSM, (B, S, 1, act_embed_BSM.shape[-1])
            )
            vid_embed_BSNp1M = jnp.concatenate([act_embed_BS1M, vid_embed_BSNM], axis=2)
            final_logits_BTNp1V = (
                dynamics_causal.transformer(vid_embed_BSNp1M, (step_t, step_n))
                / temperature
            )
            final_logits_BV = final_logits_BTNp1V[:, step_t, step_n, :]

            # --- Sample new tokens for final frame ---
            if sample_argmax:
                sampled_token_idxs_B = jnp.argmax(final_logits_BV, axis=-1)
            else:
                rng, _rng = jax.random.split(rng)
                sampled_token_idxs_B = jax.random.categorical(_rng, final_logits_BV)
            # Update next tokens only
            token_idxs_BSN = token_idxs_BSN.at[:, step_t, step_n].set(
                sampled_token_idxs_B
            )
            logits_BSNV = logits_BSNV.at[:, step_t, step_n].set(final_logits_BV)

            new_carry = (rng, token_idxs_BSN, logits_BSNV, action_tokens_EL, step_t)
            return new_carry

        @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
        def generation_step_fn(
            carry: tuple[jax.Array, jax.Array, jax.Array], step_t: jax.Array
        ) -> tuple[jax.Array, jax.Array, jax.Array]:
            rng, current_token_idxs_BSN, current_logits_BSNV = carry
            rng, step_rng = jax.random.split(rng)

            # --- Initialize and run causal loop ---
            init_carry_causal = (
                step_rng,
                current_token_idxs_BSN,
                current_logits_BSNV,
                action_tokens_EL,
                step_t,
            )
            final_carry_causal = causal_step_fn(init_carry_causal, jnp.arange(N))
            updated_token_idxs_BSN = final_carry_causal[1]
            updated_logits_BSNV = final_carry_causal[2]
            new_carry = (rng, updated_token_idxs_BSN, updated_logits_BSNV)
            return new_carry

        # --- Run the autoregressive generation using jax.lax.scan ---
        initial_carry = (batch["rng"], token_idxs_BSN, logits_BSNV)
        timesteps_to_scan = jnp.arange(T, seq_len)
        final_carry = generation_step_fn(initial_carry, timesteps_to_scan)
        final_token_idxs_BSN = final_carry[1]
        final_logits_BSNV = final_carry[2]

        # --- Decode all tokens at once at the end ---
        H, W = batch["videos"].shape[2:4]
        final_frames_BSHWC = self.tokenizer.decode(
            final_token_idxs_BSN,
            video_hw=(H, W),
        )
        return final_frames_BSHWC, final_logits_BSNV

    def sample_diffusion(
        self,
        batch: Dict[str, jax.Array],
        seq_len: int,
        diffusion_steps: int,
        diffusion_corrupt_context_factor: float = 0.1,
    ) -> tuple[jax.Array, jax.Array]:
        assert isinstance(self.dynamics, DynamicsDiffusion)
        # --- Encode videos and actions ---
        videos_BTHWC = batch["videos"]
        H, W = videos_BTHWC.shape[2:4]
        rng, _rng_mask, _rng_noise_full = jax.random.split(batch["rng"], 3)
        tokenizer_outputs = self.tokenizer.mask_and_encode(videos_BTHWC, _rng_mask)
        token_latents_BTNL = tokenizer_outputs["z"]
        B, T, N, L = token_latents_BTNL.shape
        pad_shape = (B, seq_len - T, N, L)
        pad = jax.random.normal(_rng_noise_full, pad_shape)
        token_latents_BSNL = jnp.concatenate([token_latents_BTNL, pad], axis=1)

        if self.use_gt_actions:
            assert self.action_embed is not None
            latent_actions_BT1L = self.action_embed(batch["actions"]).reshape(
                *batch["actions"].shape[:2], 1, self.latent_action_dim
            )
            latent_actions_BTm11L = latent_actions_BT1L[:, :-1]
            action_tokens_EL = latent_actions_BTm11L.reshape(-1, self.latent_action_dim)
        else:
            assert self.lam is not None
            latent_actions_E = batch["latent_actions"]
            action_tokens_EL = self.lam.vq.get_codes(latent_actions_E)

        action_tokens_BSm1L = jnp.reshape(action_tokens_EL, (B, seq_len - 1, -1))
        act_embed_BSL = jnp.pad(action_tokens_BSm1L, ((0, 0), (1, 0), (0, 0)))
        act_embed_BSM = self.dynamics.action_up(act_embed_BSL)

        t_corrupt_context = 1 - diffusion_corrupt_context_factor
        t_corrupt_context = jnp.argmin(
            jnp.abs(jnp.arange(diffusion_steps) / diffusion_steps - t_corrupt_context)
        )
        t_corrupt_context = t_corrupt_context / diffusion_steps

        @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
        def denoise_step_fn(carry, denoise_step: jax.Array) -> jax.Array:
            tok_latents_BSNL, frame_i, rng = carry
            rng, _rng_noise_context = jax.random.split(rng)

            t = denoise_step / diffusion_steps

            # corrupt the context frames like in Dreamer 4 section 3.2
            t_vector = jnp.ones((B, seq_len)) * t_corrupt_context
            t_vector = t_vector.at[:, frame_i].set(t)

            noise_context_BSNL = jax.random.normal(
                _rng_noise_context, (B, seq_len, N, L)
            )
            currupted_latents_BSNL = (
                tok_latents_BSNL * t_corrupt_context
                + (1 - t_corrupt_context) * noise_context_BSNL
            )

            # Create a mask for the frames to update
            frame_mask = jnp.arange(seq_len) < frame_i
            frame_mask = frame_mask.reshape(1, -1, 1, 1)  # Broadcast to BSNL shape
            # Update only the frames that should be corrupted
            corrupted_tok_latents_BSNL = jnp.where(
                frame_mask, currupted_latents_BSNL, tok_latents_BSNL
            )

            dt_flow = jnp.log2(diffusion_steps).astype(jnp.int32)
            dt_base = jnp.ones((B, seq_len), dtype=jnp.int32) * dt_flow  # Smallest dt.

            tok_lat_pred_BSNL = self.dynamics.diffusion_transformer(
                corrupted_tok_latents_BSNL, t_vector, dt_base, act_embed_BSM
            )
            tok_latents_BSNL = tok_latents_BSNL.at[:, frame_i].set(
                tok_lat_pred_BSNL[:, frame_i]
            )
            new_carry = (tok_latents_BSNL, frame_i, rng)
            return new_carry

        @nnx.scan(in_axes=(nnx.Carry, 0), out_axes=nnx.Carry)
        def autoregressive_step_fn(carry, frame_t: jax.Array) -> jax.Array:
            tok_latents_BSNL, rng = carry
            rng, _rng_noise_context = jax.random.split(rng)

            carry_denoise = (tok_latents_BSNL, frame_t, _rng_noise_context)
            final_carry_denoise = denoise_step_fn(
                carry_denoise, jnp.arange(diffusion_steps)
            )
            tok_latents_BSNL = final_carry_denoise[0]
            new_carry = (tok_latents_BSNL, rng)
            return new_carry

        rng, _rng_sample = jax.random.split(rng)
        initial_carry = (token_latents_BSNL, _rng_sample)
        final_carry = autoregressive_step_fn(initial_carry, jnp.arange(T, seq_len))
        final_token_latents_BSNL = final_carry[0]

        final_videos_BSHWC = self.tokenizer.decode(
            final_token_latents_BSNL, video_hw=(H, W)
        )

        return final_videos_BSHWC, None  # TODO return something meaningful?

    def vq_encode(self, batch: Dict[str, jax.Array], training: bool) -> jax.Array:
        # --- Preprocess videos ---
        assert self.lam is not None
        video_BTHWC = batch["videos"]
        lam_output: Dict[str, jax.Array] = self.lam.vq_encode(
            video_BTHWC, training=training
        )
        lam_indices_E = lam_output["indices"]
        return lam_indices_E


# FIXME (f.srambical): add conversion script for old checkpoints
def restore_genie_components(
    optimizer: nnx.ModelAndOptimizer,
    sharding: jax.sharding.NamedSharding,
    rng: jax.Array,
    args,
) -> nnx.ModelAndOptimizer:
    """Restore pre-trained Genie components"""
    rng_tokenizer, rng_lam = jax.random.split(rng)
    rngs_tokenizer = nnx.Rngs(rng_tokenizer)
    rngs_lam = nnx.Rngs(rng_lam)

    tx = optimizer.tx
    model = optimizer.model
    handler_registry = ocp.handlers.DefaultCheckpointHandlerRegistry()
    handler_registry.add(
        "model_state", ocp.args.PyTreeRestore, ocp.handlers.PyTreeCheckpointHandler
    )

    checkpoint_options = ocp.CheckpointManagerOptions(
        step_format_fixed_length=6,
    )
    tokenizer_checkpoint_manager = ocp.CheckpointManager(
        directory=args.tokenizer_checkpoint,
        options=checkpoint_options,
        handler_registry=handler_registry,
    )
    if args.tokenizer_type == "vqvae":
        dummy_tokenizer = TokenizerVQVAE(
            in_dim=args.image_channels,
            model_dim=args.tokenizer_dim,
            ffn_dim=args.tokenizer_ffn_dim,
            latent_dim=args.latent_patch_dim,
            num_latents=args.num_patch_latents,
            patch_size=args.patch_size,
            num_blocks=args.tokenizer_num_blocks,
            num_heads=args.tokenizer_num_heads,
            dropout=args.dropout,
            codebook_dropout=args.dropout,
            param_dtype=args.param_dtype,
            dtype=args.dtype,
            use_flash_attention=args.use_flash_attention,
            rngs=rngs_tokenizer,
        )
    elif args.tokenizer_type == "mae":
        dummy_tokenizer = TokenizerMAE(
            in_dim=args.image_channels,
            model_dim=args.tokenizer_dim,
            ffn_dim=args.tokenizer_ffn_dim,
            latent_dim=args.latent_patch_dim,
            num_latents=args.num_patch_latents,
            patch_size=args.patch_size,
            num_blocks=args.tokenizer_num_blocks,
            num_heads=args.tokenizer_num_heads,
            dropout=args.dropout,
            max_mask_ratio=0.0,
            param_dtype=args.param_dtype,
            dtype=args.dtype,
            use_flash_attention=args.use_flash_attention,
            rngs=rngs_tokenizer,
        )
    else:
        raise ValueError(f"Invalid tokenizer type: {args.tokenizer_type}")