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from transformers import Qwen2Config
from univa.models.configuration_univa_vision_tower import UnivaVisionTowerConfig
from univa.models.configuration_univa_denoise_tower import UnivaDenoiseTowerConfig
from typing import Optional
class UnivaConfig(Qwen2Config):
model_type = "univa"
sub_configs = {
"vision_tower": UnivaVisionTowerConfig,
"denoise_tower": UnivaDenoiseTowerConfig,
}
def __init__(
self,
vision_tower: UnivaVisionTowerConfig = None,
denoise_tower: UnivaDenoiseTowerConfig = None,
image_token_length: Optional[int] = None,
shortcut_image_embeds: bool = False,
shortcut_image_embeds_scale: float = 0.5,
shortcut_projector_type: Optional[str] = "mlp2x_gelu",
**kwargs,
):
super().__init__(**kwargs)
self.image_token_length = image_token_length
self.shortcut_image_embeds = shortcut_image_embeds
self.shortcut_image_embeds_scale = shortcut_image_embeds_scale
if not shortcut_image_embeds:
shortcut_projector_type = None
if isinstance(vision_tower, dict):
vision_tower["shortcut_projector_type"] = shortcut_projector_type
self.vision_tower = UnivaVisionTowerConfig(**vision_tower)
elif vision_tower is None:
self.vision_tower = UnivaVisionTowerConfig(
shortcut_projector_type=shortcut_projector_type
)
else:
self.vision_tower = vision_tower
print(denoise_tower)
if isinstance(denoise_tower, dict):
denoise_tower["input_hidden_size"] = self.hidden_size
self.denoise_tower = UnivaDenoiseTowerConfig(**denoise_tower)
elif denoise_tower is None:
self.denoise_tower = UnivaDenoiseTowerConfig(
input_hidden_size=self.hidden_size
)
else:
self.denoise_tower = denoise_tower
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