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from typing import Any, Optional, List, Union
from transformers import Qwen3Config
from transformers.configuration_utils import PretrainedConfig
__all__ = ["Siglip2NavitConfig", "Ovis2_5_Config"]
class Siglip2NavitConfig(PretrainedConfig):
"""This is the configuration class to store the configuration of an [`AIMv2Model`].
Instantiating a configuration with the defaults will yield a similar configuration
to that of the [apple/aimv2-large-patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224).
Args:
hidden_size: Dimension of the hidden representations.
intermediate_size: Dimension of the SwiGLU representations.
num_hidden_layers: Number of hidden layers in the Transformer.
num_attention_heads: Number of attention heads for each attention layer
in the Transformer.
num_channels: Number of input channels.
image_size: Image size.
patch_size: Patch size.
rms_norm_eps: Epsilon value used for the RMS normalization layer.
attention_dropout: Dropout ratio for attention probabilities.
projection_dropout: Dropout ratio for the projection layer after the attention.
qkv_bias: Whether to add a bias to the queries, keys and values.
use_bias: Whether to add a bias in the feed-forward and projection layers.
kwargs: Keyword arguments for the [`PretrainedConfig`].
"""
model_type: str = "siglip2_navit"
def __init__(
self,
hidden_size: int = 1024,
intermediate_size: int = 4096,
num_hidden_layers: int = 24,
num_attention_heads: int = 16,
num_channels: int = 3,
num_patches: int = -1,
image_size: int = 512,
patch_size: int = 16,
hidden_act: str="gelu_pytorch_tanh",
layer_norm_eps: float = 1e-6,
attention_dropout: float = 0.0,
hidden_stride: int = 2,
window_size: int = 112,
fullatt_block_indexes: Optional[list] = None,
temporal_patch_size: int = 1,
preserve_original_pe: bool = True,
use_rope: bool = True,
**kwargs: Any,
):
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.num_patches = num_patches
self.patch_size = patch_size
self.image_size = image_size
self.hidden_act = hidden_act
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_stride = hidden_stride
self.window_size = window_size
self.fullatt_block_indexes = fullatt_block_indexes
self.temporal_patch_size = temporal_patch_size
self.preserve_original_pe = preserve_original_pe
self.use_rope = use_rope
class Ovis2_5_Config(PretrainedConfig):
model_type = "ovis2_5"
sub_configs = dict(llm_config=Qwen3Config, vit_config=Siglip2NavitConfig)
def __init__(self,
llm_config: Optional[Union[Qwen3Config, dict]] = None,
vit_config: Optional[Union[Siglip2NavitConfig, dict]] = None,
visual_vocab_size=65536,
hidden_size=None,
**kwargs
):
super().__init__(**kwargs)
if isinstance(llm_config, dict):
llm_config = Qwen3Config(**llm_config)
self.llm_config = llm_config
if isinstance(vit_config, dict):
vit_config = Siglip2NavitConfig(**vit_config)
self.vit_config = vit_config
self.visual_vocab_size = visual_vocab_size
self.hidden_size = hidden_size
if kwargs.get('attn_implementation'):
self.llm_config._attn_implementation = kwargs['attn_implementation']
self.vit_config._attn_implementation = kwargs['attn_implementation']
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