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']