- Re-sort _toctree.yml to place Molmo2 after mllama alphabetically
- Add None guard in test_video_processor_from_dict_with_kwargs to skip
  when fast_video_processing_class is not defined

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Molmo2TextModel is an internal sub-component used by Molmo2Model and
Molmo2ForConditionalGeneration and is tested implicitly through those.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

requests is not part of the standard library and caused ImportError in
minimal environments (e.g. HuggingFace Jobs). Use urllib.request instead.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Molmo2's processor has several behaviors that are incompatible with the
default ProcessorTesterMixin assumptions:
- Chat template enforces strict user/assistant alternation (no system role)
- Processor inserts BOS token, shifting sequence length by 1
- Image processor patchifies output, so rescale_factor passthrough fails
- Video processor requires FPS metadata not provided by base tests
- Hub processor_config.json contains auto_map not preserved in save/load

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

Add @auto_docstring(checkpoint="allenai/Molmo2-8B") decorator to
Molmo2TextConfig and Molmo2Config with custom_args for documenting
non-standard parameters. This fixes check_config_docstrings CI check.

Co-Authored-By: Claude Sonnet 4.6 <[email protected]>

… date

Add parameter docstrings to Molmo2TextConfig and Molmo2Config __init__
methods so @strict-wrapped classes pass config docstring CI checks.
Update model doc date to 2026-03-28.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

Move top-level `import torch` and `import torchvision.transforms` behind
`is_torch_available()` / `is_torchvision_available()` guards in both
image and video processors to prevent ModuleNotFoundError when
torchvision is not installed.

Also skip test_kwargs_overrides_default_image_processor_kwargs since
Molmo2's patchifying image processor doesn't support rescale_factor
passthrough.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

Convert all absolute imports (from transformers.xxx) to relative imports
(from ...xxx) in image_processing, video_processing, and processing
modules to match the convention used by all other in-library models.

Remove register_for_auto_class() calls which are only needed for custom
hub models and were causing dynamic_module_utils to incorrectly scan
local files for relative imports during save_pretrained.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

…n_available

The processor's top-level imports from image_processing_molmo2 and
video_processing_molmo2 pull in PILImageResampling which requires PIL.
Guard these imports with is_vision_available() so `from transformers
import *` works when only torch is installed (no PIL/torchvision).

Co-Authored-By: Claude Opus 4.6 <[email protected]>

…L imports

Move Molmo2ImagesKwargs and Molmo2VideosKwargs definitions directly into
processing_molmo2.py instead of importing them from image/video processor
modules which require PIL. Also remove Molmo2ImageProcessor/VideoProcessor
type hints from __init__ to avoid NameError when vision is unavailable.

Co-Authored-By: Claude Opus 4.6 <[email protected]>

Addresses two of guarin's image-processor review comments (the image_num_crops
device fix was done separately by the maintainer):

1. do_rescale / float images: resize_and_normalize_image raised on any non-uint8
   input and gated rescale on uint8, so float32 images were rejected and never
   rescaled. Route rescale/normalize through the base TorchvisionBackend
   rescale_and_normalize, which honors do_rescale/do_normalize for any dtype (we
   trust the user's do_rescale for their float scale) and builds the mean/std on
   the input device. The uint8 path is numerically unchanged.

2. device placement: resize_idx / patch_idx / image_grid were created on CPU
   while pixel_values live on the input image's device. Create them on
   image_chw.device so the returned tensors are device-consistent.

The group-images-by-shape batching from the same review is a larger refactor,
handled separately.

make fix-repo side-effect: the 'remove unnecessary slicing' change was applied to
modular_molmo2.py but modeling_molmo2.py still had
enumerate(self.blocks[: self.config.num_hidden_layers]). Regenerated so the
decoder loop matches the modular (self.blocks already holds exactly
num_hidden_layers layers).

Implements guarin's optimization: flatten the images, group_images_by_shape,
process each unique shape as one [N,C,H,W] batch (tiling + all patch/pooling
indices are shape-dependent only, computed once; resize/unfold vectorized over
N), then reorder_images back to the original order. Semantically identical to the
former per-image loop (when grouping is disabled each group is N=1).

build_resized_image / _build_overlapping_crops are now batch-native; the video
_build_frame_patches passes a 1-frame batch.

`get_placeholder_mask` returns a [batch, seq, 1] mask, but Molmo2 adds the
image features onto the placeholder-token embeddings via
`inputs_embeds[special_image_mask] + image_features`. Direct boolean indexing
does not broadcast, so a [batch, seq, 1] mask cannot reach the hidden dim and
raises `IndexError: shape of the mask [.., .., 1] at index 2 does not match the
indexed tensor [.., .., hidden]` on every image/video input (text-only inputs
skip this path, which is why they worked).

Expand the mask to the hidden dim before the masked_scatter, restoring the
residual-add semantics of the original Molmo2 implementation. Fixes 29 of the
fast Molmo2ModelTest failures (test_model, save/load, all generate tests).

`token_type_ids_mask_function` only clamped `kv_idx` against the length of
`mm_token_type_ids` (which covers just the original prompt), but indexed
`token_type_ids[batch_idx, q_idx]` directly. When generation verifies several
new tokens in one forward (e.g. assisted/speculative decoding), the query
length exceeds the prompt length and `q_idx` runs out of bounds:
`IndexError: index N is out of bounds for dimension 1 with size N`.

Newly generated positions are always text, never image patches, so clamp the
`q_idx` lookup the same way as `kv_idx` and treat out-of-range positions as
token-type 0. Fixes test_assisted_decoding_sample and
test_assisted_decoding_matches_greedy_search. Fast Molmo2ModelTest is now fully
green (129 passed, 119 skipped).