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import os
from transformers.configuration_utils import PretrainedConfig
from transformers import SiglipVisionConfig
from typing import Literal, Optional, Union
class UnivaVisionTowerConfig(PretrainedConfig):
model_type = "univa_vision_tower"
def __init__(
self,
vision_tower_type: Literal["siglip"] = "siglip",
vision_tower_config: Optional[Union[str, dict]] = None,
mm_projector_type: str = "mlp2x_gelu",
feature_select_layer: int = -1,
output_hidden_size: int = 1152,
shortcut_projector_type: Optional[str] = None,
**kwargs,
):
super().__init__(**kwargs)
self.vision_tower_type = vision_tower_type
self.mm_projector_type = mm_projector_type
self.feature_select_layer = feature_select_layer
self.output_hidden_size = output_hidden_size
self.shortcut_projector_type = shortcut_projector_type
if vision_tower_type == "siglip":
config_cls = SiglipVisionConfig
else:
raise ValueError(f"Unknown vision tower type: {vision_tower_type}")
if vision_tower_config is not None:
if isinstance(vision_tower_config, str):
if not os.path.exists(vision_tower_config):
raise FileNotFoundError(
f"Vision tower config file not found: {vision_tower_config}"
)
self.vision_tower_config = config_cls.from_pretrained(
vision_tower_config
)
elif isinstance(vision_tower_config, dict):
self.vision_tower_config = config_cls(**vision_tower_config)
else:
self.vision_tower_config = config_cls()
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