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"""
EO1Vision processor for `eo_pi_internvl`.
This is the InternVL-backbone EO1 processor with a Pi05-style action prompt:
- We keep a *single* `<|action_pad|>` as a placeholder suffix token in text prompts.
- The action expert consumes *continuous* action tokens (length=`action_chunk_size`) internally, so we do not need to
repeat `<|action_pad|>` by chunk size in the text (this also keeps AR loss extensible).
"""
from __future__ import annotations
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.video_utils import VideoInput
from eo_internvl.model.processing_eo1_internvl import (
DEFAULT_ACTION_TOKEN,
EO1VisionProcessor as _BaseEO1VisionProcessor,
EO1VisionProcessorKwargs,
RobotInput,
)
class EO1VisionProcessor(_BaseEO1VisionProcessor):
def expand_action_prompt(self, chunk_size: int) -> str:
# Pi05-style: keep a single placeholder token in text; the model builds the full continuous action block.
return DEFAULT_ACTION_TOKEN
def __call__(
self,
images: ImageInput = None,
text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None,
videos: VideoInput = None,
states: RobotInput = None,
actions: RobotInput = None,
**kwargs: Unpack[EO1VisionProcessorKwargs],
) -> BatchFeature:
# Force action-token expansion length to 1 (no-op), regardless of robot_config / caller.
text_kwargs = kwargs.get("text_kwargs") or {}
text_kwargs = dict(text_kwargs)
text_kwargs["noise_token_num"] = 1
kwargs["text_kwargs"] = text_kwargs
return super().__call__(images=images, text=text, videos=videos, states=states, actions=actions, **kwargs)
EO1VisionProcessor.register_for_auto_class()
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