modular_isaac.py

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from __future__ import annotations

import copy
import math
import re
from collections import defaultdict
from typing import Any, Callable, Optional, Sequence, Union

from PIL.Image import Image
import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import (
    AutoImageProcessor,
    AutoModel,
    AutoTokenizer,
    BatchFeature,
    PretrainedConfig,
    Qwen3Config,
    Qwen3ForCausalLM,
    Qwen3PreTrainedModel,
)
from transformers.configuration_utils import layer_type_validation

from transformers.cache_utils import DynamicCache, SlidingWindowCache, StaticCache
from transformers.generation.utils import GenerationMixin
from transformers.image_processing_utils_fast import (
    BaseImageProcessorFast,
    SizeDict,
    group_images_by_shape,
    reorder_images,
)
from transformers.image_utils import (
    ChannelDimension,
    PILImageResampling,
)
from transformers.modeling_attn_mask_utils import AttentionMaskConverter
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
from transformers.modeling_rope_utils import rope_config_validation
from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
from transformers.models.qwen2.tokenization_qwen2 import Qwen2Tokenizer
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import Qwen2_5_VLRotaryEmbedding
from transformers.models.qwen2_5_vl import modeling_qwen2_5_vl as qwen2_5_vl_modeling
from transformers.models.siglip2.configuration_siglip2 import Siglip2VisionConfig
from transformers.models.siglip2.modeling_siglip2 import (
    Siglip2Attention,
    Siglip2Encoder,
    Siglip2EncoderLayer,
    Siglip2VisionEmbeddings,
)
from transformers.masking_utils import create_masks_for_generate, eager_mask, packed_sequence_mask_function, sdpa_mask
from transformers.processing_utils import ImagesKwargs, ProcessorMixin, Unpack
from transformers.utils import auto_docstring, TensorType
from transformers.utils.generic import OutputRecorder, can_return_tuple, check_model_inputs

# Vision preprocessing constants
from transformers.utils.constants import IMAGENET_STANDARD_MEAN as VISION_MEAN
from transformers.utils.constants import IMAGENET_STANDARD_STD as VISION_STD
from transformers.utils.import_utils import is_torchdynamo_compiling

try:
    from perceptron.tensorstream.ops import (
        compute_mrope_pos_tensor,
        modality_mask,
        reconstruct_tensor_stream_from_compact_dict,
        tensor_stream_token_view,
    )
    from perceptron.tensorstream.ops import (
        slice as ts_slice,
    )
    from perceptron.tensorstream.tensorstream import (
        Event,
        Stream,
        TensorStream,
        TextType,
        VisionType,
        create_stream,
        group_streams,
    )
except ModuleNotFoundError as exc:  # pragma: no cover - import guard
    raise ModuleNotFoundError(
        "genesis.public.tensorstream is required for the Isaac HuggingFace integration. "
        "Ensure the TensorStream package is installed and on PYTHONPATH."
    ) from exc

# _ORIGINAL_ATTENTION_FUNCTIONS: dict[str, Callable[..., tuple[torch.Tensor, Optional[torch.Tensor]]]] = {}
# for _attn_name in ("flash_attention_2", "sdpa", "eager"):
#     if _attn_name in ALL_ATTENTION_FUNCTIONS:
#         _ORIGINAL_ATTENTION_FUNCTIONS[_attn_name] = ALL_ATTENTION_FUNCTIONS[_attn_name]


class IsaacVisionConfig(Siglip2VisionConfig):
    """Vision configuration for Isaac with Pixel Shuffle support.

    Extends Siglip2VisionConfig with additional fields for pixel shuffle.

    Args:
        pixel_shuffle_scale_factor (`int`, *optional*, defaults to 1):
            Spatial factor applied before pixel shuffle reduces the resolution.
        num_patches (`int`, *optional*, defaults to 256):
            Maximum number of learnable positional embeddings to initialize.
    """

    model_type = "isaac_vision"
    base_config_key = "vision_config"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        num_patches=256,
        patch_size=16,
        hidden_act="gelu_pytorch_tanh",
        layer_norm_eps=1e-6,
        attention_dropout=0.0,
        pixel_shuffle_scale_factor=1,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.attention_dropout = attention_dropout
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.num_patches = num_patches

        # Add our custom fields
        self.pixel_shuffle_scale_factor = pixel_shuffle_scale_factor

        # Ensure a sensible default attention backend
        if getattr(self, "_attn_implementation", None) is None:
            self._attn_implementation = "sdpa"


class IsaacImageProcessorFastKwargs(ImagesKwargs, total=False):
    patch_size: Optional[int]
    max_num_patches: Optional[int]
    min_num_patches: Optional[int]
    pixel_shuffle_scale: Optional[int]


@auto_docstring
class IsaacImageProcessorFast(BaseImageProcessorFast):
    MAX_PIXELS = 60_000_000  # 60‑megapixel ceiling ≈ 8200 × 7300 px
    r"""Fast torch-based image processor for Isaac vision inputs."""

    resample = PILImageResampling.BILINEAR
    model_input_names = ["patches", "token_grids"]
    valid_kwargs = IsaacImageProcessorFastKwargs
    unused_kwargs = ["size", "do_center_crop", "crop_size"]

    do_resize = True
    do_center_crop = False
    patch_size: Optional[int] = 16
    max_num_patches: Optional[int] = 256
    min_num_patches: Optional[int] = None
    pixel_shuffle_scale: Optional[int] = 1
    do_pad = False
    do_rescale = True
    do_normalize = True
    image_mean = list(VISION_MEAN)
    image_std = list(VISION_STD)
    do_convert_rgb = True
    disable_grouping = False
    size_divisor: Optional[int] = None

    def __init__(
        self,
        **kwargs: Unpack[IsaacImageProcessorFastKwargs],
    ) -> None:
        super().__init__(**kwargs)

        pixel_shuffle_scale = 1 if self.pixel_shuffle_scale is None else int(self.pixel_shuffle_scale)
        if pixel_shuffle_scale < 1:
            raise ValueError("`pixel_shuffle_scale` must be >= 1")
        self.pixel_shuffle_scale = pixel_shuffle_scale

    def _validate_preprocess_kwargs(self, **kwargs):
        # Allow callers to omit resize-related placeholders that BaseImageProcessorFast checks for.
        kwargs.pop("do_resize", None)
        kwargs.pop("size", None)
        kwargs.pop("do_center_crop", None)
        kwargs.pop("crop_size", None)
        kwargs.pop("disable_grouping", None)
        return super()._validate_preprocess_kwargs(**kwargs)

    def resize(
        self,
        image: torch.Tensor,
        size: SizeDict,
        interpolation: Optional[Any] = None,
        antialias: bool = True,
        **kwargs,
    ) -> torch.Tensor:
        if size.height is None or size.width is None:
            raise ValueError("IsaacImageProcessorFast requires explicit `height` and `width` when resizing.")

        resize_mode: Any = interpolation
        if hasattr(resize_mode, "value"):
            resize_mode = resize_mode.value
        elif hasattr(resize_mode, "name"):
            resize_mode = resize_mode.name.lower()
        elif resize_mode is None:
            resize_mode = "bilinear"

        if isinstance(resize_mode, str):
            mode_key = resize_mode.lower()
        else:
            mode_key = resize_mode

        resize_kwargs: dict[str, Any] = {}
        if mode_key in {"linear", "bilinear", "bicubic", "trilinear"}:
            resize_kwargs["align_corners"] = False

        return F.interpolate(
            image,
            size=(size.height, size.width),
            mode=resize_mode,
            **resize_kwargs,
        )

    def _preprocess(
        self,
        images: list[torch.Tensor],
        do_resize: bool,
        size: Optional[SizeDict],
        interpolation: Optional[Any],
        do_center_crop: bool,
        crop_size: Optional[SizeDict],
        do_rescale: Optional[bool],
        rescale_factor: Optional[float],
        do_normalize: Optional[bool],
        image_mean: Optional[Union[float, Sequence[float]]],
        image_std: Optional[Union[float, Sequence[float]]],
        disable_grouping: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        do_pad: Optional[bool] = None,
        pad_size: Optional[SizeDict] = None,
        *,
        patch_size: Optional[int] = None,
        max_num_patches: Optional[int] = None,
        min_num_patches: Optional[int] = None,
        pixel_shuffle_scale: Optional[int] = None,
        **kwargs,
    ) -> BatchFeature:
        if do_center_crop:
            raise ValueError("`do_center_crop` is not supported by IsaacImageProcessorFast.")
        if do_pad:
            raise ValueError("`do_pad` is not supported by IsaacImageProcessorFast.")

        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
        processed_patches_grouped: dict[tuple[int, ...], torch.Tensor] = {}
        token_grids_grouped: dict[tuple[int, ...], torch.Tensor] = {}
        virtual_dims_grouped: dict[tuple[int, ...], torch.Tensor] = {}
        real_dims_grouped: dict[tuple[int, ...], torch.Tensor] = {}

        for shape, stacked_images in grouped_images.items():
            if stacked_images.ndim != 4:
                raise ValueError("Expected batched channel-first image tensors.")

            batch_size, channels, original_height, original_width = stacked_images.shape

            if bool(self.do_convert_rgb) and channels == 1:
                stacked_images = stacked_images.repeat(1, 3, 1, 1)
                channels = 3

            if original_height * original_width > self.MAX_PIXELS:
                raise ValueError(f"Image (w={original_width}, h={original_height}) > MAX=`{self.MAX_PIXELS}`")

            target_height, target_width = get_image_size_for_max_num_patches(
                original_height,
                original_width,
                patch_size,
                max_num_patches,
                min_num_patches=min_num_patches,
                pixel_shuffle_scale=pixel_shuffle_scale,
            )

            if do_resize:
                resize_size = SizeDict(height=target_height, width=target_width)
                image_batch = self.resize(
                    image=stacked_images,
                    size=resize_size,
                    interpolation=interpolation,
                )
            else:
                if ((original_height % patch_size) != 0) or ((original_width % patch_size) != 0):
                    raise ValueError("Image dimensions must be divisible by patch_size when resize is disabled.")
                image_batch = stacked_images
                target_height, target_width = original_height, original_width

            if do_rescale:
                image_batch = self.rescale_and_normalize(
                    image_batch,
                    do_rescale=do_rescale,
                    rescale_factor=rescale_factor,
                    do_normalize=do_normalize,
                    image_mean=image_mean,
                    image_std=image_std,
                )

            nhwc_images = image_batch.permute(0, 2, 3, 1)
            nhwc_images = _compute_residual_p_frames(nhwc_images, is_p_frame=[False] * batch_size)

            patches = torch_extract_patches(nhwc_images.permute(0, 3, 1, 2), patch_size, patch_size)
            _, height_tokens, width_tokens, _ = patches.shape

            token_grid = (
                torch.tensor(
                    [height_tokens, width_tokens],
                    dtype=torch.long,
                    device=patches.device,
                )
                .unsqueeze(0)
                .repeat(batch_size, 1)
            )

            real_dim = (
                torch.tensor(
                    [1, height_tokens, width_tokens],
                    dtype=torch.long,
                    device=patches.device,
                )
                .unsqueeze(0)
                .repeat(batch_size, 1)
            )

            if (height_tokens % pixel_shuffle_scale) or (width_tokens % pixel_shuffle_scale):
                raise ValueError(
                    "Spatial dimensions must be divisible by pixel_shuffle_scale when pixel shuffle is enabled."
                )
            virtual_height = height_tokens // pixel_shuffle_scale
            virtual_width = width_tokens // pixel_shuffle_scale

            virtual_dim = (
                torch.tensor(
                    [1, virtual_height, virtual_width],
                    dtype=torch.long,
                    device=patches.device,
                )
                .unsqueeze(0)
                .repeat(batch_size, 1)
            )

            processed_patches_grouped[shape] = patches
            token_grids_grouped[shape] = token_grid
            virtual_dims_grouped[shape] = virtual_dim
            real_dims_grouped[shape] = real_dim

        patches_slices = reorder_images(processed_patches_grouped, grouped_images_index)
        token_grid_slices = reorder_images(token_grids_grouped, grouped_images_index)
        virtual_dim_slices = reorder_images(virtual_dims_grouped, grouped_images_index)
        real_dim_slices = reorder_images(real_dims_grouped, grouped_images_index)

        patches_tensor = torch.stack(patches_slices, dim=0)
        token_grids_tensor = torch.stack(token_grid_slices, dim=0)
        virtual_dims_tensor = torch.stack(virtual_dim_slices, dim=0)
        real_dims_tensor = torch.stack(real_dim_slices, dim=0)

        return BatchFeature(
            data={
                "patches": patches_tensor,
                "token_grids": token_grids_tensor,
                "virtual_pixel_size": virtual_dims_tensor,
                "real_pixel_size": real_dims_tensor,
            },
            tensor_type=return_tensors,
        )


def document_mask_function_from_cu_seqlens(cu_seqlens: Optional[torch.Tensor]) -> Optional[Callable]:
    """Return a mask function that blocks cross-document attention from packed ``cu_seqlens``.

    The returned callable matches the signature expected by ``masking_utils`` mask factories and
    yields ``True`` only when query/key positions belong to the same packed segment.
    """

    if cu_seqlens is None:
        return None

    if cu_seqlens.numel() < 2:
        return None

    seq_sizes = (cu_seqlens[1:] - cu_seqlens[:-1]).long()
    if seq_sizes.numel() == 0:
        return None

    total_tokens = int(seq_sizes.sum().item())
    seg_ids = torch.repeat_interleave(torch.arange(seq_sizes.numel(), device=cu_seqlens.device), seq_sizes)
    packed_sequence_mask = seg_ids.view(1, total_tokens)
    return packed_sequence_mask_function(packed_sequence_mask)


def create_document_attention_mask(
    config: PretrainedConfig,
    input_embeds: torch.Tensor,
    cu_seqlens: Optional[torch.Tensor],
) -> Optional[Union[torch.Tensor, Any]]:
    """Materialize a backend-specific block-diagonal attention mask.

    This uses the standard `masking_utils` mask interface (same mechanism as Llama4),
    so the returned object matches the selected attention backend (e.g. SDPA bool mask,
    eager additive mask, or flex `BlockMask`).
    """

    mask_function = document_mask_function_from_cu_seqlens(cu_seqlens)
    if mask_function is None:
        return None

    seq_len = input_embeds.shape[1]
    cache_position = torch.arange(seq_len, device=input_embeds.device, dtype=torch.long)

    mask_interface = ALL_MASK_ATTENTION_FUNCTIONS[config._attn_implementation]
    return mask_interface(
        batch_size=input_embeds.shape[0],
        cache_position=cache_position,
        kv_length=seq_len,
        kv_offset=0,
        mask_function=mask_function,
        attention_mask=None,
        allow_is_causal_skip=False,
        allow_is_bidirectional_skip=False,
        dtype=input_embeds.dtype,
        config=config,
        use_vmap=False,
    )


class IsaacVisionEmbeddings(nn.Module):
    """Adapter around SigLIP2 vision embeddings that consumes packed patch sequences."""

    def __init__(self, config: IsaacVisionConfig):
        super().__init__()
        self.config = config
        self.embed_dim = config.hidden_size
        self.patch_size = config.patch_size

        self.patch_embedding = nn.Linear(
            in_features=config.num_channels * self.patch_size * self.patch_size,
            out_features=self.embed_dim,
        )

        self.num_patches = config.num_patches
        self.position_embedding_size = int(self.num_patches**0.5)
        self.position_embedding = nn.Embedding(self.num_patches, self.embed_dim)

    def forward(self, seq_patches: torch.Tensor, spatial_shapes: torch.Tensor) -> torch.Tensor:
        packed_pixel_values, seq_lengths = self._pack_to_batch(seq_patches, spatial_shapes)
        if packed_pixel_values is None:
            return seq_patches.new_zeros((0, self.embed_dim))

        target_dtype = self.patch_embedding.weight.dtype
        patch_embeds = self.patch_embedding(packed_pixel_values.to(dtype=target_dtype))

        positional_embeddings = self.position_embedding.weight.reshape(
            self.position_embedding_size,
            self.position_embedding_size,
            -1,
        )
        resized_positional_embeddings = self.resize_positional_embeddings(
            positional_embeddings, spatial_shapes, max_length=packed_pixel_values.shape[1]
        )

        embeddings = patch_embeds + resized_positional_embeddings
        return self._unpack_from_batch(embeddings, seq_lengths)

    @staticmethod
    def resize_positional_embeddings(
        positional_embeddings: torch.Tensor,
        spatial_shapes: torch.LongTensor,
        max_length: int,
    ) -> torch.Tensor:
        """
        Resize positional embeddings to image-specific size and pad to a fixed size.

        Args:
            positional_embeddings (`torch.Tensor`):
                Position embeddings of shape (height, width, embed_dim)
            spatial_shapes (`torch.LongTensor`):
                Spatial shapes of shape (batch_size, 2) to resize the positional embeddings to
            max_length (`int`):
                Maximum length of the positional embeddings to pad resized positional embeddings to

        Returns:
            `torch.Tensor`: Embeddings of shape (batch_size, max_length, embed_dim)
        """
        batch_size = spatial_shapes.shape[0]
        embed_dim = positional_embeddings.shape[-1]
        source_dtype = positional_embeddings.dtype

        resulted_positional_embeddings = torch.empty(
            (batch_size, max_length, embed_dim),
            device=positional_embeddings.device,
            dtype=source_dtype,
        )

        # (height, width, embed_dim) -> (1, embed_dim, height, width) for interpolation
        positional_embeddings = positional_embeddings.permute(2, 0, 1).unsqueeze(0)

        # Upcast to float32 on CPU because antialias is not supported for bfloat16/float16 on CPU
        if positional_embeddings.device.type == "cpu":
            positional_embeddings = positional_embeddings.to(torch.float32)

        for i in range(batch_size):
            # (1, dim, height, width) -> (1, dim, target_height, target_width)
            height, width = spatial_shapes[i]
            resized_embeddings = F.interpolate(
                positional_embeddings,
                size=(height, width),
                mode="bilinear",
                align_corners=False,
                antialias=True,
            )

            # (1, dim, target_height, target_width) -> (target_height * target_width, dim)
            resized_embeddings = resized_embeddings.reshape(embed_dim, height * width).transpose(0, 1)

            # Cast to original dtype
            resized_embeddings = resized_embeddings.to(source_dtype)

            resulted_positional_embeddings[i, : height * width] = resized_embeddings
            resulted_positional_embeddings[i, height * width :] = resized_embeddings[0]

        return resulted_positional_embeddings

    def _pack_to_batch(
        self,
        seq_patches: torch.Tensor,
        spatial_shapes: torch.Tensor,
    ) -> tuple[Optional[torch.Tensor], torch.Tensor]:
        if seq_patches.ndim != 2:
            raise ValueError("`seq_patches` is expected to be 2D (total_patches, patch_dim).")
        if spatial_shapes.ndim != 2 or spatial_shapes.size(-1) != 2:
            raise ValueError("`spatial_shapes` must have shape (num_images, 2) with (height_tokens, width_tokens).")

        seq_lengths = spatial_shapes.long().prod(dim=-1)
        total_patches = int(seq_lengths.sum().item())
        if total_patches != seq_patches.size(0):
            raise ValueError(
                "Mismatch between packed patches and spatial shapes: got "
                f"{seq_patches.size(0)} patches but spatial shapes imply {total_patches}."
            )

        batch_size = spatial_shapes.size(0)
        if batch_size == 0:
            return None, seq_lengths

        max_length = int(seq_lengths.max().item())
        patch_dim = seq_patches.size(-1)
        device = seq_patches.device

        packed_pixel_values = seq_patches.new_zeros((batch_size, max_length, patch_dim), device=device)

        start = 0
        for batch_idx, length in enumerate(seq_lengths.tolist()):
            if length == 0:
                continue
            end = start + length
            packed_pixel_values[batch_idx, :length] = seq_patches[start:end]
            start = end

        return packed_pixel_values, seq_lengths

    def _unpack_from_batch(self, embeddings: torch.Tensor, seq_lengths: torch.Tensor) -> torch.Tensor:
        output_chunks: list[torch.Tensor] = []
        for batch_idx, length in enumerate(seq_lengths.tolist()):
            if length == 0:
                continue
            output_chunks.append(embeddings[batch_idx, :length])

        if not output_chunks:
            return embeddings.new_zeros((0, embeddings.size(-1)))

        return torch.cat(output_chunks, dim=0)


class IsaacVisionAttention(Siglip2Attention):
    """Custom attention that supports variable-length sequences with flash attention."""

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        output_attentions: bool = False,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        **kwargs,
    ):
        kwargs.pop("output_hidden_states", None)
        kwargs.pop("return_dict", None)

        batch_size, seq_length, embed_dim = hidden_states.shape
        queries = self.q_proj(hidden_states)
        keys = self.k_proj(hidden_states)
        values = self.v_proj(hidden_states)

        queries = queries.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
        keys = keys.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)
        values = values.view(batch_size, seq_length, self.num_heads, self.head_dim).transpose(1, 2)

        attn_impl = self.config._attn_implementation
        attention_interface: Callable = ALL_ATTENTION_FUNCTIONS["sdpa"]
        if attn_impl != "sdpa":
            attention_interface = ALL_ATTENTION_FUNCTIONS[attn_impl]

        dropout = 0.0 if not self.training else self.dropout
        attention_kwargs: dict[str, Any] = {
            "is_causal": False,
            "scaling": self.scale,
            "dropout": dropout,
        }

        supports_varlen = cu_seqlens is not None and attn_impl in {
            "flash_attention_2",
            "flash_attention_3",
            "flex_attention",
            "paged|flash_attention_2",
            "paged|flash_attention_3",
        }

        if output_attentions and attn_impl == "eager":
            attention_kwargs["output_attentions"] = True

        if supports_varlen:
            if max_seqlen is not None:
                max_q = max_k = int(max_seqlen)
            elif cu_seqlens.numel() >= 2:
                lengths = cu_seqlens[1:] - cu_seqlens[:-1]
                max_q = max_k = lengths.max() if lengths.numel() > 0 else seq_length
            else:
                max_q = max_k = seq_length

            attention_kwargs.update(
                {
                    "cu_seq_lens_q": cu_seqlens,
                    "cu_seq_lens_k": cu_seqlens,
                    "max_length_q": max_q,
                    "max_length_k": max_k,
                }
            )

        attn_output, attn_weights = attention_interface(
            self,
            queries,
            keys,
            values,
            attention_mask,
            **attention_kwargs,
        )

        attn_output = attn_output.reshape(batch_size, seq_length, embed_dim).contiguous()

        # Align projection inputs with parameter dtype to avoid mixed-dtype matmul errors
        out_proj_dtype = self.out_proj.weight.dtype
        if attn_output.dtype != out_proj_dtype:
            attn_output = attn_output.to(out_proj_dtype)

        attn_output = self.out_proj(attn_output)
        if attn_output.dtype != hidden_states.dtype:
            attn_output = attn_output.to(hidden_states.dtype)

        return attn_output, attn_weights


class IsaacVisionEncoderLayer(Siglip2EncoderLayer):
    """Isaac vision encoder layer with variable-length attention."""

    def __init__(self, config: IsaacVisionConfig):
        super().__init__(config)
        self.self_attn = IsaacVisionAttention(config)

    def forward(
        self,
        hidden_states: torch.Tensor,
        attention_mask: Optional[torch.Tensor] = None,
        cu_seqlens: Optional[torch.Tensor] = None,
        max_seqlen: Optional[int] = None,
        output_attentions: bool = False,
        **kwargs: Unpack[TransformersKwargs],
    ):
        r"""
        cu_seqlens (`torch.Tensor`, *optional*):
            Prefix-sum tensor whose length equals the number of documents + 1. The difference between successive
            entries gives each document's token count and enables block-diagonal attention masking for packed batches.
        max_seqlen (`int`, *optional*):
            Maximum document length referenced by `cu_seqlens`. Passed to FlashAttention so it can size temporary
            buffers for packed variable-length attention.
        """
        # Run attention directly so variable-length metadata reaches FlashAttention.
        residual = hidden_states
        hidden_states = self.layer_norm1(hidden_states)
        attn_output, _ = self.self_attn(
            hidden_states,
            attention_mask=attention_mask,
            cu_seqlens=cu_seqlens,
            max_seqlen=max_seqlen,
            **kwargs,
        )
        hidden_states = residual + attn_output

        residual = hidden_states
        hidden_states = self.layer_norm2(hidden_states)
        hidden_states = self.mlp(hidden_states)
        hidden_states = residual + hidden_states

        return hidden_states


class IsaacVisionEncoder(Siglip2Encoder):
    """Encoder using Isaac encoder layers with variable-length attention support."""

    def __init__(self, config: IsaacVisionConfig):
        super().__init__(config)
        self.layers = nn.ModuleList([IsaacVisionEncoderLayer(config) for _ in range(config.num_hidden_layers)])

    @can_return_tuple
    @check_model_inputs
    def forward(
        self,
        inputs_embeds,
        attention_mask: Optional[torch.Tensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ):
        hidden_states = inputs_embeds
        for encoder_layer in self.layers:
            hidden_states = encoder_layer(
                hidden_states,
                attention_mask,
                **kwargs,
            )
        return BaseModelOutput(last_hidden_state=hidden_states)


def create_pixel_shuffle_index_map(
    seq_sizes: torch.Tensor,
    token_grids: torch.Tensor,
    scale_factor: int = 1,
    device: Optional[torch.device] = None,
) -> torch.Tensor:
    """
    Build a gather-index map that tells us, for every *output* token after
    pixel-shuffle, which `scale_factor**2` *input* tokens are being merged.

    Args
    ----
    seq_sizes     : (num_images,)  - #patches in each image (row-major order)
    token_grids   : (num_images,2) - (height, width) for every image
    scale_factor  : spatial down-scale factor (≥2)
    device        : (optional) overrides `seq_sizes.device`

    Returns
    -------
    gather_idx : (new_total_seq_len, scale_factor**2) int64 tensor.
                 gather_idx[i, j] is the *flat* index into the *original*
                 packed sequence for the j-th sub-patch that forms the
                 i-th output token.
    """
    if device is None:
        device = seq_sizes.device

    scale_factor = int(scale_factor)
    if scale_factor < 2:
        raise ValueError("`scale_factor` must be ≥ 2")

    # Safety: all spatial dims must be divisible by the scale factor
    # Cannot run under torch compile fullgraph mode hence
    if not is_torchdynamo_compiling():
        if not ((token_grids[:, 0] % scale_factor == 0).all() and (token_grids[:, 1] % scale_factor == 0).all()):
            raise AssertionError(
                "Every (H,W) in `token_grids` must be divisible by "
                f"scale_factor={scale_factor}, got {token_grids.tolist()}"
            )

    gather_chunks: list[torch.Tensor] = []
    tok_offset = 0

    for seq_len, (h, w) in zip(seq_sizes.tolist(), token_grids.tolist(), strict=False):
        # Build the (H, W) grid of flat indices for this image
        grid = torch.arange(seq_len, device=device, dtype=torch.int64) + tok_offset
        grid = grid.view(h, w)  # (H, W)

        # -------- identical ordering to your fixed-res routine --------
        # Step 1: split width into blocks of scale_factor
        grid = grid.view(h, w // scale_factor, scale_factor)  # (H, W/scale_factor, scale_factor)
        # Step 2: now split height into blocks of scale_factor
        grid = grid.view(h // scale_factor, scale_factor, w // scale_factor, scale_factor)
        # (H/scale_factor, scale_factor, W/scale_factor, scale_factor)
        # Step 3: final permutation to (H/scale_factor, W/scale_factor, scale_factor, scale_factor)
        grid = grid.permute(0, 2, 1, 3).contiguous()  # (H/scale_factor, W/scale_factor, scale_factor, scale_factor)
        # Step 4: each (scale_factor, scale_factor) block forms one output token
        gather_chunks.append(grid.reshape(-1, scale_factor * scale_factor))
        # (H*W / scale_factor**2, scale_factor**2)

        tok_offset += seq_len

    # Concatenate over all images in the packed batch
    gather_idx = torch.cat(gather_chunks, dim=0)  # (Σ_i HᵢWᵢ/scale_factor**2, scale_factor**2)
    return gather_idx


def pixel_shuffle_varlen(
    x: torch.Tensor,
    token_grids: torch.Tensor,
    scale_factor: int = 1,
) -> torch.Tensor:
    r"""Apply pixel shuffle to a packed vision sequence without unpacking per image.

    Args:
        x (`torch.Tensor`):
            Concatenated vision embeddings. Accepts `(seq_len, hidden_size)` or `(1, seq_len, hidden_size)` shapes
            produced by stacking image patches.
        token_grids (`torch.Tensor`):
            Integer tensor of shape `(num_images, 2)` whose rows give the `(height, width)` patch grid sizes
            corresponding to each image segment inside `x`.
        scale_factor (`int`, *optional*, defaults to 1):
            Spatial down-sampling factor specific to pixel shuffle. Values greater than one merge `scale_factor**2` neighboring patches into a
            single embedding channel-group.

    Returns:
        `torch.Tensor`: Pixel-shuffled embeddings with shape matching the input convention:
        `(seq_len, hidden_size * scale_factor**2)` when the input was 2D, or `(1, seq_len, hidden_size * scale_factor**2)`
        if the singleton batch dimension was present.

    Raises:
        ValueError: If more than one batch item is provided.
    """
    return_with_batch_dim = x.dim() == 3
    if return_with_batch_dim:
        if x.size(0) != 1:
            raise AssertionError("Packed sequence is expected to have batch_size == 1")
        embeddings = x.squeeze(0)  # (seq, embed)
    else:
        embeddings = x  # (seq, embed)

    embed_dim = embeddings.size(-1)
    scale_factor = int(scale_factor)

    # Calculate seq_sizes from token_grids
    seq_sizes = torch.prod(token_grids, dim=-1)

    # Build index map and gather in one go
    gather_idx = create_pixel_shuffle_index_map(
        seq_sizes=seq_sizes,
        token_grids=token_grids,
        scale_factor=scale_factor,
        device=embeddings.device,
    )  # (new_seq, scale_factor**2)

    # Gather → (new_seq, scale_factor**2, embed_dim)
    gathered = embeddings[gather_idx]  # fancy indexing keeps gradient

    # Merge the scale_factor**2 group dimension into channels to finish the shuffle
    out = gathered.reshape(gathered.size(0), embed_dim * scale_factor * scale_factor)

    # Restore batch dimension if needed
    if return_with_batch_dim:
        out = out.unsqueeze(0)
    return out


class IsaacVisionTransformer(nn.Module):
    _supports_sdpa = True

    def __init__(self, config: IsaacVisionConfig):
        super().__init__()
        self.config = config
        self.embeddings = IsaacVisionEmbeddings(config)
        self.encoder = IsaacVisionEncoder(config)
        self.post_layernorm = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
        self.pixel_shuffle_scale_factor = config.pixel_shuffle_scale_factor

    def forward(self, packed_seq_patches: tuple[torch.Tensor, torch.Tensor]):
        seq_patches, token_grids = packed_seq_patches
        seq_sizes = torch.prod(token_grids, dim=-1)

        # Get embeddings from packed sequence
        hidden_states = self.embeddings(seq_patches, token_grids)

        # Add a pseudo batch dimension for the encoder
        hidden_states = hidden_states.unsqueeze(0)

        # Generate cumulative sequence lengths for variable-length attention
        cu_seqlens = torch.zeros(seq_sizes.size(0) + 1, dtype=torch.int32, device=hidden_states.device)
        cu_seqlens[1:] = seq_sizes.cumsum(0)

        attention_mask = create_document_attention_mask(self.config, hidden_states, cu_seqlens)

        # Pass through encoder with variable-length attention parameters
        encoder_outputs = self.encoder(
            inputs_embeds=hidden_states,
            attention_mask=attention_mask,
            cu_seqlens=cu_seqlens,
        )
        hidden_states = encoder_outputs.last_hidden_state

        # Apply final layer normalization
        hidden_states = self.post_layernorm(hidden_states)

        hidden_states = pixel_shuffle_varlen(
            x=hidden_states,
            token_grids=token_grids,
            scale_factor=self.pixel_shuffle_scale_factor,
        )
        # Remove the pseudo batch dimension we added earlier
        hidden_states = hidden_states.squeeze(0)

        # Return the full sequence of embeddings
        return hidden_states


class IsaacMultiModalProjector(nn.Module):
    def __init__(self, config: IsaacConfig):
        super().__init__()
        self.vision_hidden_size = config.vision_config.hidden_size * (
            config.vision_config.pixel_shuffle_scale_factor**2
        )
        self.backbone_hidden_size = config.hidden_size
        self.linear_1 = nn.Linear(self.vision_hidden_size, 4 * self.vision_hidden_size, bias=False)
        self.silu = nn.SiLU()
        self.linear_2 = nn.Linear(4 * self.vision_hidden_size, self.backbone_hidden_size, bias=False)

    def forward(self, image_features):
        hidden_states = self.linear_1(image_features)
        hidden_states = self.silu(hidden_states)
        hidden_states = self.linear_2(hidden_states)
        return hidden_states


class IsaacVisionEmbedding(nn.Module):
    """Vision embedding wrapper exposing tower and projector."""

    _supports_sdpa = True

    def __init__(self, config: IsaacConfig):
        super().__init__()
        vision_cfg = config.vision_config

        self.vision_tower = IsaacVisionTransformer(vision_cfg)
        self.multimodal_projector = IsaacMultiModalProjector(config)

    def forward(self, vision_tokens: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
        hidden_states = self.vision_tower(vision_tokens)
        return self.multimodal_projector(hidden_states)


def get_scaled_image_size(
    scale: float,
    original_size: int,
    patch_size: int,
    pixel_shuffle_scale: int,
) -> int:
    scaled_size = scale * original_size
    divisor = patch_size * pixel_shuffle_scale
    scaled_size = math.ceil(scaled_size / divisor) * divisor
    scaled_size = max(divisor, scaled_size)
    return int(scaled_size)


def get_image_size_for_max_num_patches(
    image_height: int,
    image_width: int,
    patch_size: int,
    max_num_patches: int,
    min_num_patches: Optional[int] = None,
    eps: float = 1e-5,
    pixel_shuffle_scale: int = 1,
) -> tuple[int, int]:
    r"""Compute a target resolution whose patch grid satisfies patching parametrization.

    Args:
        image_height (`int`):
            Height in pixels of the source image prior to any resizing.
        image_width (`int`):
            Width in pixels of the source image prior to any resizing.
        patch_size (`int`):
            Size of the square patch used by the vision encoder.
        max_num_patches (`int`):
            Upper bound on `(height / patch_size) * (width / patch_size)` after resizing.
        min_num_patches (`int`, *optional*):
            Lower bound on the number of patches. When provided the image will be scaled up if necessary.
        eps (`float`, *optional*, defaults to 1e-5):
            Convergence tolerance for the internal binary search to determing the target dimensions.
        pixel_shuffle_scale (`int`, *optional*, defaults to 1):
            Additional stride multiplier applied when pixel shuffle later reduces spatial resolution.

    Returns:
        `tuple[int, int]`: Height and width (in pixels) that are multiples of `patch_size * pixel_shuffle_scale`
        and respect both the maximum and optional minimum patch-count constraints.
    """

    # Ensure divisibility
    divisor = patch_size * pixel_shuffle_scale
    adjusted_height = math.ceil(image_height / divisor) * divisor
    adjusted_height = max(divisor, adjusted_height)
    adjusted_width = math.ceil(image_width / divisor) * divisor
    adjusted_width = max(divisor, adjusted_width)

    num_patches = (adjusted_height / patch_size) * (adjusted_width / patch_size)

    if min_num_patches is not None and num_patches < min_num_patches:
        # Scale up
        scale_min, scale_max = 1.0, 100.0
        while (scale_max - scale_min) >= eps:
            scale = (scale_min + scale_max) / 2
            target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale)
            target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale)
            num_patches = (target_height / patch_size) * (target_width / patch_size)
            if num_patches >= min_num_patches:
                scale_max = scale
            else:
                scale_min = scale
        scale = scale_max
        target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale)
        target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale)
        return target_height, target_width
    elif num_patches <= max_num_patches:
        return adjusted_height, adjusted_width
    else:
        # Scale down
        scale_min, scale_max = eps / 10, 1.0
        while (scale_max - scale_min) >= eps:
            scale = (scale_min + scale_max) / 2
            target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale)
            target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale)
            num_patches = (target_height / patch_size) * (target_width / patch_size)
            if num_patches <= max_num_patches:
                scale_min = scale
            else:
                scale_max = scale
        scale = scale_min
        target_height = get_scaled_image_size(scale, image_height, patch_size, pixel_shuffle_scale)
        target_width = get_scaled_image_size(scale, image_width, patch_size, pixel_shuffle_scale)
        return target_height, target_width


class IsaacConfig(PretrainedConfig):
    """Configuration class for Isaac multimodal model.

    This configuration corresponds to checkpoints such as
    [Perceptron/isaac-base](https://huggingface.co/Perceptron/isaac-base).
    """

    model_type = "isaac"
    sub_configs = {"vision_config": IsaacVisionConfig, "text_config": Qwen3Config}
    image_processor_type = "IsaacImageProcessor"

    def __init__(
        self,
        vision_config: Optional[IsaacVisionConfig] = None,
        text_config: Optional[Union[Qwen3Config, dict]] = None,
        vision_rescale_factor: float = 1 / 255,
        max_sequence_length: int = 16384,
        vision_token: str = "<image>",
        **kwargs,
    ):
        attn_implementation = kwargs.get("attn_implementation")

        if isinstance(text_config, dict):
            self.text_config = self.sub_configs["text_config"](**text_config)
        elif isinstance(text_config, Qwen3Config):
            self.text_config = text_config
        elif text_config is None:
            self.text_config = self.sub_configs["text_config"]()

        # Seed RoPE parameters before base init so the shared mixin can standardize/validate them.
        self.rope_parameters = getattr(self.text_config, "rope_parameters", None)
        self.layer_types = getattr(self.text_config, "layer_types", None)

        super().__init__(**kwargs)

        # Keep rope parameters aligned between the composite and text sub-configs.
        self.text_config.rope_parameters = self.rope_parameters

        # Mirror frequently accessed Qwen3 attributes at the composite config level
        self.vocab_size = self.text_config.vocab_size
        self.hidden_size = self.text_config.hidden_size
        self.num_hidden_layers = self.text_config.num_hidden_layers
        self.num_attention_heads = self.text_config.num_attention_heads
        self.head_dim = self.text_config.head_dim
        self.hidden_act = self.text_config.hidden_act
        self.use_cache = self.text_config.use_cache
        self.rope_theta = self.rope_parameters["rope_theta"]

        self.layer_types = getattr(self.text_config, "layer_types", None)
        layer_type_validation(self.layer_types, self.num_hidden_layers)

        # Handle vision config - either dict or IsaacVisionConfig instance
        if isinstance(vision_config, dict):
            self.vision_config = self.sub_configs["vision_config"](**vision_config)
        elif isinstance(vision_config, IsaacVisionConfig):
            self.vision_config = vision_config
        elif vision_config is None:
            self.vision_config = self.sub_configs["vision_config"]()

        # Propagate user-requested attention backend to the vision sub-config when provided.
        if attn_implementation is not None:
            if isinstance(attn_implementation, dict):
                vision_attn = attn_implementation.get("vision_config", attn_implementation.get("", None))
            else:
                vision_attn = attn_implementation
            if vision_attn is not None:
                self.vision_config._attn_implementation = vision_attn

        # Vision normalization parameters
        self.vision_rescale_factor = float(vision_rescale_factor)

        # Processing parameters
        self.max_sequence_length = max_sequence_length
        self.vision_token = vision_token

    def to_dict(self):
        output = super().to_dict()
        # Ensure nested configs round-trip through dict serialization
        if hasattr(self, "text_config") and self.text_config is not None:
            output["text_config"] = self.text_config.to_dict()
        if hasattr(self, "vision_config") and self.vision_config is not None:
            output["vision_config"] = self.vision_config.to_dict()
        return output


# ============================================================================
# Processor Components
# ============================================================================


def create_text_event(tokenizer: AutoTokenizer, text: str, time: float = 0.0) -> Event:
    r"""Wrap a text into an `Event` compatible with the multimodal TensorStream.

    Args:
        tokenizer (`AutoTokenizer`):
            Tokenizer used to convert text into model vocabulary ids.
        text (`str`):
            Plain-text fragment to encode.
        time (`float`, *optional*, defaults to 0.0):
            Timeline coordinate associated with the event. Both start and end times use the same value because text
            segments are instantaneous in the scheduler.

    Returns:
        `Event`: Event carrying a `(num_tokens, 1)` tensor of token ids with matching
        metadata so that downstream processors can compute modality-specific embeddings.
    """
    tokens = tokenizer.encode(text, add_special_tokens=False, return_tensors="pt").squeeze(0)

    # Calculate dimensions for the event
    num_tokens = len(tokens)
    dims_virtual = [num_tokens, 1]  # [sequence_length, 1]
    dims_real = dims_virtual.copy()

    # Ensure tokens has the right shape for tensor_stream_token_view
    # It expects a 2D tensor where sum(dim=-1) gives the token IDs
    if tokens.dim() == 1:
        tokens = tokens.unsqueeze(-1)

    return Event(
        data=tokens,
        type=TextType.text,
        time=(time, time),
        dims_virtual=dims_virtual,
        dims_real=dims_real,
        idx_range=(0, num_tokens),
    )


# ============================================================================
# Processor
# ============================================================================


class IsaacProcessor(ProcessorMixin):
    attributes = ["image_processor", "tokenizer"]
    image_processor_class = ("IsaacImageProcessorFast",)
    tokenizer_class = ("Qwen2Tokenizer",)

    def __init__(
        self,
        image_processor,
        tokenizer,
        *,
        vision_token: str = "<image>",
        max_sequence_length: int = 16384,
        rescale_factor: Optional[float] = None,
        config: Optional[Union[IsaacConfig, dict]] = None,
    ) -> None:
        if tokenizer is None:
            raise ValueError("`tokenizer` must be provided to initialize IsaacProcessor.")

        if isinstance(config, dict):
            config = IsaacConfig(**config)

        if config is not None:
            max_sequence_length = config.max_sequence_length
            vision_token = config.vision_token
            rescale_factor = config.vision_rescale_factor

        resolved_rescale_factor = float(rescale_factor) if rescale_factor is not None else float(1 / 255)

        if config is not None:
            config.vision_rescale_factor = resolved_rescale_factor

        self.image_processor = image_processor

        super().__init__(image_processor, tokenizer)
        self.current_processor = self.image_processor
        self.config = config

        # Mirror tokenizer chat template so ProcessorMixin.apply_chat_template works.
        self.chat_template = getattr(self.tokenizer, "chat_template", None)

        self.vision_token = vision_token
        self.max_sequence_length = max_sequence_length

    def build_event_stream_simple(
        self,
        text: str,
        images: Optional[list[Image]] = None,
    ) -> Stream:
        events = []
        # Process text and images
        # Find all occurrences of vision token

        pattern = re.escape(self.vision_token)
        parts = re.split(f"({pattern})", text)  # Keep the delimiter in the result

        image_idx = 0
        for current_time, part in enumerate(parts):
            if part == self.vision_token:
                # Replace vision token with image event
                if images is None or image_idx >= len(images):
                    raise ValueError("Encountered vision token without a corresponding image.")

                features = self.image_processor(
                    images=images[image_idx],
                    return_tensors=TensorType.PYTORCH,
                )

                patches = features["patches"][0]  # (H_tokens, W_tokens, embed)
                virtual_dims = features["virtual_pixel_size"][0].tolist()
                real_dims = features["real_pixel_size"][0].tolist()

                vision_event = Event(
                    data=patches.reshape(-1, patches.shape[-1]),
                    type=VisionType.image,
                    time=(current_time, current_time),
                    dims_virtual=virtual_dims,
                    dims_real=real_dims,
                    idx_range=(0, math.prod(virtual_dims)),
                )
                events.append(vision_event)
                image_idx += 1
            elif part:  # Non-empty text part
                # tokens = self.text_processor.tokenize(part, add_special_tokens=False)
                text_event = create_text_event(self.tokenizer, part, time=current_time)
                events.append(text_event)

        # Create stream without scheduling (events already in order)
        return create_stream(events, priority=[TextType.text, VisionType.image], schedule=True)

    def __call__(
        self,
        text: Union[str, list[str]],
        images: Optional[Union[Image, list[Image]]] = None,
        return_tensors: Optional[Union[str, TensorType]] = TensorType.PYTORCH,
        **kwargs,
    ) -> BatchFeature:
        """
        Process text and images into TensorStream format.
        Args:
            text: Input text or list of texts with vision tokens
            images: PIL image or list of images (optional)
            return_tensors: Format for output tensors

        Returns:
            BatchFeature with input_ids and tensor_stream
        """
        # Normalize inputs to lists
        if isinstance(text, str):
            texts = [text]
        else:
            texts = text

        if images is not None:
            if isinstance(images, Image):
                images_list = [images]
            else:
                images_list = images
        else:
            images_list = None

        if len(texts) != 1:
            raise ValueError("IsaacProcessor currently supports batch_size=1")
        if images_list is not None:
            # Count vision tokens in text to validate image count
            vision_token_count = texts[0].count(self.vision_token)
            if vision_token_count != len(images_list):
                raise ValueError(
                    f"Number of {self.vision_token} tokens in text ({vision_token_count}) "
                    f"must match number of images ({len(images_list)})"
                )

        # Build event stream
        stream = self.build_event_stream_simple(
            text=texts[0],
            images=images_list,
        )

        # Create TensorStream
        tensor_stream = TensorStream([stream])

        # Slice to max length if needed
        _, T = tensor_stream.shape
        if T > self.max_sequence_length:
            tensor_stream = ts_slice(tensor_stream, start=T - self.max_sequence_length, end=T)

        # Get token view
        tokens = tensor_stream_token_view(tensor_stream)
        if return_tensors in (TensorType.PYTORCH, "pt"):
            input_ids = torch.as_tensor(tokens, dtype=torch.long)
        else:
            input_ids = tokens

        data = {
            "input_ids": input_ids,
            "tensor_stream": tensor_stream,
        }

        return BatchFeature(data=data)


# ============================================================================
# Model
# ============================================================================


def compute_position_ids_input_ids(input_ids: torch.Tensor) -> torch.Tensor:
    r"""Create 3D positional indices for token input.

    Args:
        input_ids (`torch.Tensor`):
            Tensor of shape `(batch_size, seq_len)` containing token ids.

    Returns:
        `torch.Tensor`: Positional indices with shape `(batch_size, seq_len, 3)` where each channel duplicates the
        1D position so it can be consumed by the 3-axis MRoPE rotary embedding.
    """
    batch_size, seq_length = input_ids.shape
    position_ids = torch.arange(seq_length, device=input_ids.device)
    position_ids = position_ids.view(1, -1).expand(batch_size, -1)
    position_ids = position_ids.unsqueeze(2).expand(-1, -1, 3)  # Add 3D for MRoPE
    return position_ids


class IsaacRotaryEmbedding(qwen2_5_vl_modeling.Qwen2_5_VLRotaryEmbedding):
    EXTRA_ROPE_KEYS = {"mrope_section", "mrope_interleaved"}

    def __init__(self, config: IsaacConfig, device=None):
        rope_source_cfg = config.get_text_config() if hasattr(config, "get_text_config") else config
        rope_scaling = getattr(rope_source_cfg, "rope_scaling", None) or {}

        sanitized_scaling = {k: v for k, v in rope_scaling.items() if k not in self.EXTRA_ROPE_KEYS}
        config_for_rope = copy.copy(rope_source_cfg)
        config_for_rope.rope_scaling = sanitized_scaling if sanitized_scaling else None

        init_device = device if device is not None and getattr(device, "type", None) != "meta" else None
        super().__init__(config_for_rope, device=init_device)

        rotary_half_dim = self.inv_freq.shape[0]
        self.mrope_section = self._resolve_mrope_section(rope_scaling.get("mrope_section"), rotary_half_dim)
        self.hidden_size = getattr(rope_source_cfg, "hidden_size", None) or config.hidden_size

    @staticmethod
    def _resolve_mrope_section(section: Optional[list[int]], rotary_half_dim: int) -> list[int]:
        if section is None:
            weights = (2, 1, 1)
            base = [rotary_half_dim * w // sum(weights) for w in weights]
            base[0] += rotary_half_dim - sum(base)
            return base

        section = [int(v) for v in section]
        if len(section) != 3:
            raise ValueError("`mrope_section` must contain exactly three elements (temporal, height, width)")
        if sum(section) != rotary_half_dim:
            raise ValueError(
                f"`mrope_section` must sum to the rotary half-dimension ({rotary_half_dim}). Received {section}."
            )
        return section

    def _combine_axes(self, tensor: torch.Tensor) -> torch.Tensor:
        split_sections = tuple(self.mrope_section * 2)
        chunks = tensor.split(split_sections, dim=-1)
        return torch.cat([chunk[i % 3] for i, chunk in enumerate(chunks)], dim=-1)

    def forward(
        self,
        position_ids: torch.Tensor,
        modality_tensor: torch.Tensor,
        hidden_states: Optional[torch.Tensor] = None,
    ) -> tuple[torch.Tensor, torch.Tensor]:
        if position_ids.ndim != 3 or position_ids.size(-1) != 3:
            raise ValueError("`position_ids` must have shape (batch, seq_len, 3) for MRoPE")
        if modality_tensor.shape != position_ids.shape[:2]:
            raise ValueError("`modality_tensor` must align with the first two dims of `position_ids`")

        if hidden_states is None:
            batch, seq_len, _ = position_ids.shape
            hidden_states = torch.zeros(
                batch,
                seq_len,
                self.hidden_size,
                dtype=torch.float32,
                device=position_ids.device,
            )

        with torch.no_grad():
            pos = position_ids.clone()
            image_value = VisionType.image.value if VisionType is not None else 1
            not_spatial = modality_tensor != image_value
            if not_spatial.any():
                data_1d = pos[not_spatial][..., 0].unsqueeze(-1)
                pos[not_spatial] = data_1d.expand(-1, pos.shape[-1])

            pos_axes = pos.permute(2, 0, 1).contiguous()

        cos_axes, sin_axes = super().forward(hidden_states, pos_axes)

        cos_axes = cos_axes.to(hidden_states.dtype)
        sin_axes = sin_axes.to(hidden_states.dtype)

        cos_combined = self._combine_axes(cos_axes)
        sin_combined = self._combine_axes(sin_axes)

        return cos_combined, sin_combined


class IsaacModel(Qwen3PreTrainedModel):
    supports_gradient_checkpointing = True
    _can_compile_fullgraph = False
    _supports_flex_attn = False
    _can_record_outputs = {"attentions": OutputRecorder(IsaacVisionAttention, index=1)}
    # Expose tied-weights mapping even if empty for base model tests.
    all_tied_weights_keys: dict[str, str] = {}

    def __init__(self, config: IsaacConfig):
        Qwen3PreTrainedModel.__init__(self, config)

        text_cfg_source = config.text_config
        text_cfg = copy.deepcopy(text_cfg_source)
        self.text_model = AutoModel.from_config(text_cfg)
        # Ensure downstream callers observe the composed config
        self.text_model.config = config

        self.rotary_emb = IsaacRotaryEmbedding(config, device=self.device)

        if config.vision_config is None:
            raise ValueError("IsaacConfig should always have vision_config")

        self.vision_embedding = IsaacVisionEmbedding(config)
        self.vision_embedding._supports_sdpa = True

        # Dispatch table for TensorStream balanced embedding (text + vision)
        self.embed_fns = {
            TextType: self.embed_text_tokens,
            VisionType: self.embed_vision,
        }

        # Keep track of config attributes that downstream utilities may query directly on the model.
        self.max_sequence_length = config.max_sequence_length
        self.vision_rescale_factor = config.vision_rescale_factor
        self.vision_token = config.vision_token

        # Initialize weights and parallel plans (including tp_plan from the text model)
        self.post_init()

        # Respect config-specified gradient checkpointing
        if getattr(config, "gradient_checkpointing", False):
            self.gradient_checkpointing_enable()

    def get_input_embeddings(self) -> nn.Module:
        return self.text_model.get_input_embeddings()

    def set_input_embeddings(self, value: nn.Module) -> None:
        self.text_model.set_input_embeddings(value)
        vocab_size = getattr(value, "num_embeddings", None)
        if vocab_size is not None:
            self.config.vocab_size = vocab_size
            if hasattr(self.config, "text_config"):
                self.config.text_config.vocab_size = vocab_size
            self.text_model.config.vocab_size = vocab_size

    @property
    def embed_tokens(self) -> nn.Module:
        return self.text_model.embed_tokens

    @embed_tokens.setter
    def embed_tokens(self, value: nn.Module) -> None:
        self.text_model.embed_tokens = value

    @property
    def vision_model(self) -> nn.Module:
        return self.vision_embedding.vision_tower

    @property
    def vision_model(self) -> nn.Module:
        return self.vision_embedding.vision_tower

    @property
    def vision_tower(self) -> nn.Module:
        return self.vision_embedding.vision_tower

    def embed_text_tokens(self, token_ids: torch.Tensor) -> torch.Tensor:
        """Embed text tokens, squeezing singleton dimensions."""
        # Text events are shaped as (..., 1); squeeze the singleton index dim
        h = self.text_model.embed_tokens(token_ids)
        if h.dim() >= 2 and h.size(-2) == 1:
            h = h[..., 0, :]
        return h

    def embed_vision(self, vision_tokens: tuple[torch.Tensor, torch.Tensor]) -> torch.Tensor:
        """Embed vision tokens using the vision encoder."""
        # vision tokens is (seq_patches, token_grids)
        return self.vision_embedding(vision_tokens)

    def embed_stream(self, tensor_stream: TensorStream) -> torch.Tensor:
        """
        Embed each modality stream independently, preserving the original TensorStream
        structure.
        """
        flat_stream = tensor_stream.flat_stream()
        per_modality_stream = group_streams(flat_stream, group_fn=lambda ev: ev.type, schedule=False)
        per_modality_compact_stream = {k: v.compact() for k, v in per_modality_stream.items()}

        # Collect per-event grids for vision tokens (H, W like dims sans time)
        token_grids = defaultdict(list)
        for stream in tensor_stream.streams:
            for event in stream:
                token_grids[event.type].append(event.dims(virtual=False))

        embedded_compact = {}
        for stream_type, modality_payload_tensor in per_modality_compact_stream.items():
            if stream_type.modality == VisionType:
                # Build a (N_events, 2) grid tensor with spatial dims only
                grids = token_grids.get(stream_type, [])
                if len(grids) == 0:
                    input_tensor = modality_payload_tensor
                else:
                    token_grids_tensor = torch.tensor(grids, dtype=torch.long, device=tensor_stream.device)[:, 1:]
                    input_tensor = (modality_payload_tensor, token_grids_tensor)
                embedded_compact[stream_type] = self.embed_fns[stream_type.modality](input_tensor)
            else:
                embedded_compact[stream_type] = self.embed_fns[stream_type.modality](modality_payload_tensor)

        # Reconstruct a TensorStream with embedded payloads and compact
        embedded_ts = reconstruct_tensor_stream_from_compact_dict(tensor_stream, embedded_compact)
        h = embedded_ts.compact()  # (B, T, D)
        return h

    @staticmethod
    def compute_position_ids_input_ids(input_ids: torch.Tensor) -> torch.Tensor:
        return compute_position_ids_input_ids(input_ids)

    def _prepare_position_and_modality(
        self,
        position_ids: Optional[torch.LongTensor],
        modality_tensor: Optional[torch.LongTensor],
        tensor_stream: Optional[TensorStream],
        inputs_embeds: torch.Tensor,
        cache_position: torch.LongTensor,
    ) -> tuple[torch.LongTensor, torch.LongTensor, torch.LongTensor, torch.Tensor, torch.Tensor]:
        text_value = TextType.text.value if TextType is not None else 0
        batch_size, seq_len = inputs_embeds.shape[:2]

        if modality_tensor is None:
            if tensor_stream is not None:
                modality_tensor = modality_mask(tensor_stream)
            else:
                modality_tensor = torch.full(
                    (batch_size, seq_len), text_value, device=inputs_embeds.device, dtype=torch.long
                )
        else:
            modality_tensor = modality_tensor.to(device=inputs_embeds.device, dtype=torch.long)
            expected_shape = (batch_size, seq_len)
            if modality_tensor.shape != torch.Size(expected_shape):
                raise ValueError(
                    f"modality_tensor must have shape (batch_size, seq_len) {expected_shape}, "
                    f"but got {tuple(modality_tensor.shape)}"
                )

        if position_ids is None:
            if tensor_stream is not None:
                position_ids = compute_mrope_pos_tensor(tensor_stream)  # (B,L,3)
            else:
                position_ids = cache_position.view(1, -1).expand(modality_tensor.shape[0], -1)

        if position_ids.ndim == 2:
            position_ids = position_ids.to(device=inputs_embeds.device)
            position_ids = position_ids.unsqueeze(-1).expand(-1, -1, 3)

        if position_ids.shape[1] != seq_len:
            start_positions = position_ids[:, :1, 0]
            position_ids = torch.arange(seq_len, device=inputs_embeds.device).view(1, -1)
            position_ids = position_ids + start_positions
            position_ids = position_ids.unsqueeze(-1).expand(-1, -1, 3)

        cos, sin = self.rotary_emb(
            position_ids,
            modality_tensor,
            hidden_states=inputs_embeds,
        )

        decoder_position_ids = position_ids[..., 0] if position_ids.ndim == 3 else position_ids
        return position_ids, modality_tensor, decoder_position_ids, cos, sin

    @auto_docstring
    @check_model_inputs
    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        tensor_stream: Optional[TensorStream] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        modality_tensor: Optional[torch.LongTensor] = None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | BaseModelOutputWithPast:
        """
        Forward pass with MRoPE position embeddings.

        Computes position embeddings once and passes them through all layers.

        Args:
            tensor_stream (`TensorStream`, *optional*):
                Packed multimodal stream of text and vision events to embed directly. Mutually exclusive with
                `input_ids` and `inputs_embeds`. When provided, the method derives `position_ids` and `modality_tensor`
                if they are not supplied.
            modality_tensor (`torch.LongTensor`, *optional*):
                Modality identifiers aligned with the embedded sequence, shaped `(batch_size, seq_len)` and containing
                values from `TextType`/`VisionType`. Automatically built from `tensor_stream` or `input_ids` when
                omitted.
        """

        output_attentions = kwargs.pop("output_attentions", None)

        # Get inputs
        if tensor_stream is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both tensor_stream and inputs_embeds")
        if tensor_stream is None and input_ids is not None and inputs_embeds is not None:
            raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time")

        # Resolve the input source (TensorStream takes precedence over token ids).
        if tensor_stream is not None:
            inputs_embeds = self.embed_stream(tensor_stream)
        elif input_ids is not None:
            inputs_embeds = self.text_model.embed_tokens(input_ids)
        elif inputs_embeds is None:
            raise ValueError("You have to specify either tensor_stream, input_ids or inputs_embeds")

        batch_size, seq_len = inputs_embeds.shape[:2]

        # Ensure cache exists when requested
        if use_cache and past_key_values is None:
            cache_config = self.config.get_text_config() if hasattr(self.config, "get_text_config") else self.config
            past_key_values = DynamicCache(config=cache_config)

        if cache_position is None:
            past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
            cache_position = torch.arange(past_seen_tokens, past_seen_tokens + seq_len, device=inputs_embeds.device)

        if attention_mask is None:
            attention_mask = torch.ones((batch_size, seq_len), device=inputs_embeds.device, dtype=torch.long)

        position_ids, modality_tensor, decoder_position_ids, cos, sin = self._prepare_position_and_modality(
            position_ids=position_ids,
            modality_tensor=modality_tensor,
            tensor_stream=tensor_stream,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
        )

        # Prepare attention mask
        if not isinstance(attention_mask, dict):
            attention_mask = create_masks_for_generate(
                config=self.config,
                input_embeds=inputs_embeds,
                attention_mask=attention_mask,
                cache_position=cache_position,
                past_key_values=past_key_values,
                position_ids=decoder_position_ids,
            )

        is_attention_mask_dict = isinstance(attention_mask, dict)

        # Initialize hidden states
        hidden_states = inputs_embeds
        all_attentions = [] if output_attentions else None

        for decoder_layer in self.text_model.layers:
            layer_attention_mask = (
                attention_mask[decoder_layer.attention_type] if is_attention_mask_dict else attention_mask
            )
            layer_outputs = decoder_layer(
                hidden_states,
                attention_mask=layer_attention_mask,
                position_ids=decoder_position_ids,
                past_key_values=past_key_values,
                use_cache=use_cache,
                cache_position=cache_position,
                position_embeddings=(cos, sin),
                output_attentions=output_attentions,
                **kwargs,
            )

            layer_outputs_is_tuple = isinstance(layer_outputs, tuple)
            hidden_states = layer_outputs[0] if layer_outputs_is_tuple else layer_outputs
            if output_attentions and layer_outputs_is_tuple:
                all_attentions.append(layer_outputs[1])

        # Final layer norm
        hidden_states = self.text_model.norm(hidden_states)

        return BaseModelOutputWithPast(
            last_hidden_state=hidden_states,
            past_key_values=past_key_values,
            hidden_states=(hidden_states,),
            attentions=tuple(all_attentions) if output_attentions else None,
        )


class IsaacForConditionalGeneration(Qwen3ForCausalLM, GenerationMixin):
    """Isaac multimodal model for conditional generation."""

    config_class = IsaacConfig
    _can_compile_fullgraph = False
    _tied_weights_keys = {"lm_head.weight": "model.text_model.embed_tokens.weight"}
    all_tied_weights_keys: dict[str, str] = {"lm_head.weight": "model.text_model.embed_tokens.weight"}

    def __init__(self, config: IsaacConfig):
        super().__init__(config)
        self.model = IsaacModel(config)  # Use our custom model
        self.vocab_size = config.vocab_size
        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
        # Tracks rotary position offsets computed during a full forward pass so decode steps can reuse them.
        self.rope_deltas = None

    def forward(
        self,
        input_ids: Optional[torch.LongTensor] = None,
        tensor_stream: Optional[TensorStream] = None,
        attention_mask: Optional[torch.Tensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        labels: Optional[torch.LongTensor] = None,
        use_cache: Optional[bool] = None,
        cache_position: Optional[torch.LongTensor] = None,
        **kwargs: Unpack[TransformersKwargs],
    ) -> tuple | CausalLMOutputWithPast:
        r"""
        Forward pass for conditional generation supporting both standard inputs and TensorStream.

        tensor_stream (`TensorStream`, *optional*):
            Packed multimodal stream (text, vision, audio tokens) that already encodes spatial metadata. When provided,
            the model derives embeddings, modality masks, and 3D rotary coordinates directly from the stream instead of
            `input_ids`.
        """

        output_attentions = kwargs.pop("output_attentions", None)

        # Don't compute embeddings here - let the inner model handle it
        if tensor_stream is not None:
            input_ids = None
        if input_ids is None and inputs_embeds is None and tensor_stream is None:
            raise ValueError("Either input_ids, inputs_embeds, or tensor_stream must be provided.")

        # Record rope deltas on prefill when TensorStream is provided; leave position_ids building to IsaacModel.
        if position_ids is None and tensor_stream is not None:
            position_ids, self.rope_deltas = self.get_rope_index(input_ids, tensor_stream, attention_mask)
        elif position_ids is None and cache_position is not None and self.rope_deltas is not None:
            # Decode continuation after TensorStream prefill: advance positions using cached rope offsets.
            if input_ids is not None:
                base_position_ids = compute_position_ids_input_ids(input_ids)
            else:
                if inputs_embeds is None:
                    raise ValueError("inputs_embeds must be provided when input_ids is None during decode")
                batch_size, seq_len = inputs_embeds.shape[:2]
                dummy_ids = torch.zeros((batch_size, seq_len), device=inputs_embeds.device, dtype=torch.long)
                base_position_ids = compute_position_ids_input_ids(dummy_ids)

            rope_delta = (cache_position[0] + self.rope_deltas).to(base_position_ids.device)
            if not isinstance(rope_delta, int):
                rope_delta = rope_delta.repeat_interleave(base_position_ids.shape[0] // rope_delta.shape[0], dim=0)
            position_ids = base_position_ids.add(rope_delta)

        outputs = self.model(
            input_ids=input_ids,
            tensor_stream=tensor_stream,
            attention_mask=attention_mask,
            position_ids=position_ids,
            modality_tensor=None,
            past_key_values=past_key_values,
            inputs_embeds=inputs_embeds,
            use_cache=use_cache,
            output_attentions=output_attentions,
            cache_position=cache_position,
            **kwargs,
        )

        hidden_states = outputs[0]
        logits = self.lm_head(hidden_states)

        loss = None
        if labels is not None:
            loss = self.loss_function(logits=logits, labels=labels, vocab_size=self.config.vocab_size)

        return CausalLMOutputWithPast(
            loss=loss,
            logits=logits,
            past_key_values=outputs.past_key_values,
            hidden_states=outputs.hidden_states,
            attentions=outputs.attentions if output_attentions else None,
        )

    def set_input_embeddings(self, value: nn.Module) -> None:
        self.model.set_input_embeddings(value)
        vocab_size = getattr(value, "num_embeddings", None)
        if vocab_size is not None:
            self.config.vocab_size = vocab_size
            self.model.config.vocab_size = vocab_size
            if hasattr(self.model, "text_model"):
                self.model.text_model.config.vocab_size = vocab_size
            if self.lm_head.weight.shape[0] != vocab_size:
                self.lm_head = nn.Linear(self.config.hidden_size, vocab_size, bias=False)
            if hasattr(self.model, "embed_tokens"):
                self.lm_head.weight = self.model.text_model.embed_tokens.weight

    def get_rope_index(
        self,
        input_ids: Optional[torch.Tensor],
        tensor_stream: Optional[TensorStream],
        attention_mask: Optional[torch.Tensor],
    ) -> tuple[torch.Tensor, torch.Tensor]:
        """Compute MRoPE position ids from a TensorStream (or 1D fallback).

        Returns (position_ids, rope_deltas). position_ids is (B,L,3) for MRoPE.
        rope_deltas is (B,1) used to advance positions in decode.
        """
        # tensor_stream present: compute 3D coords
        if tensor_stream is None and input_ids is None:
            raise ValueError("`tensor_stream` or `input_ids` must be provided to compute rope indices")

        if tensor_stream is not None:
            pos_3d = compute_mrope_pos_tensor(tensor_stream)  # (B,L,3)
        else:
            pos_3d = compute_position_ids_input_ids(input_ids)
        B, L, _ = pos_3d.shape

        # Max position per batch across the 3 planes and sequence dimension: (B,)
        m_per_batch = pos_3d.amax(dim=(1, 2))

        # Sequence lengths per batch: (B,)
        if attention_mask is None:
            seq_lens = torch.full_like(m_per_batch, L)
        else:
            seq_lens = attention_mask.eq(1).sum(dim=-1).to(dtype=m_per_batch.dtype, device=m_per_batch.device)

        rope_deltas = (m_per_batch + 1 - seq_lens).to(dtype=pos_3d.dtype).unsqueeze(1)
        return pos_3d, rope_deltas

    def prepare_inputs_for_generation(
        self,
        input_ids: torch.LongTensor,
        past_key_values: Optional[list[torch.FloatTensor]] = None,
        attention_mask: Optional[torch.Tensor] = None,
        inputs_embeds: Optional[torch.FloatTensor] = None,
        tensor_stream: Optional[TensorStream] = None,
        cache_position: Optional[torch.LongTensor] = None,
        position_ids: Optional[torch.LongTensor] = None,
        use_cache: bool = True,
        **kwargs,
    ) -> dict[str, Any]:
        """
        Prepare inputs for generation, handling TensorStream inputs properly.
        """
        if cache_position is None:
            seq_length = None
            device = None
            if input_ids is not None:
                seq_length = input_ids.shape[1]
                device = input_ids.device
            elif inputs_embeds is not None:
                seq_length = inputs_embeds.shape[1]
                device = inputs_embeds.device
            elif tensor_stream is not None:
                _, seq_length = tensor_stream.shape
                device = tensor_stream.device
            if seq_length is not None:
                # prepare_inputs_for_generation may be invoked outside `generate`, so synthesize the
                # same cache positions that GenerationMixin would have created during prefill.
                cache_position = torch.arange(seq_length, dtype=torch.long, device=device)

        # Call parent preparation
        model_inputs = super().prepare_inputs_for_generation(
            input_ids,
            past_key_values=past_key_values,
            attention_mask=attention_mask,
            inputs_embeds=inputs_embeds,
            cache_position=cache_position,
            position_ids=position_ids,
            use_cache=use_cache,
            **kwargs,
        )

        cache_position = model_inputs.get("cache_position", cache_position)

        # Handle TensorStream only for the prefill step
        first_step = cache_position is None or cache_position[0] == 0
        if tensor_stream is not None and first_step:
            model_inputs["tensor_stream"] = tensor_stream
            # Let forward rebuild MRoPE coordinates from the TensorStream
            model_inputs["position_ids"] = None
        else:
            model_inputs["tensor_stream"] = None

        # TensorStream decode path: preserve rotary offsets from prefill; let forward rebuild positions
        if tensor_stream is not None and not first_step and self.rope_deltas is not None:
            model_inputs["position_ids"] = None
            return model_inputs

        return model_inputs

    @classmethod
    def can_generate(cls) -> bool:
        return True


def _compute_residual_p_frames(frames: torch.Tensor, is_p_frame: list[bool]) -> torch.Tensor:
    """Compute residuals for P-frames to stay in sync with the training pipeline."""
    if not any(is_p_frame):
        return frames

    frame_indices = torch.arange(len(is_p_frame), device=frames.device)
    i_frame_mask = torch.tensor([not flag for flag in is_p_frame], device=frames.device)
    last_i_indices = torch.cummax((i_frame_mask * (1 + frame_indices)), dim=0).values.long() - 1
    p_indices = frame_indices[torch.tensor(is_p_frame, device=frames.device)]
    frames[p_indices] = frames[p_indices] - frames[last_i_indices[p_indices]]
    return frames


__all__ = [
    "IsaacConfig",
    "IsaacModel",
    "IsaacPreTrainedModel",  # noqa: F822
    "IsaacForConditionalGeneration",
    "IsaacImageProcessorFast",
    "IsaacProcessor",
]