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# coding=utf-8
# Copyright 2025 HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from transformers.configuration_utils import PretrainedConfig
from transformers import AutoConfig
class InternS1VisionConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InternS1VisionModel`]. It is used to instantiate an InternS1VisionModel
model according to the specified arguments, defining the model architecture.
Args:
hidden_size (`int`, *optional*, defaults to 1024):
Dimensionality of the encoder layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_attention_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
attention_bias (`bool`, *optional*, defaults to `False`):
Whether to add a bias to the queries, keys and values.
use_qk_norm (`bool`, *optional*, defaults to `False`):
Whether to apply normalization to the queries and keys before the attention operation.
intermediate_size (`int`, *optional*, defaults to 4096):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
attention_dropout (`float`, *optional*, defaults to 0.0):
Dropout probability for attention weights.
projection_dropout (`float`, *optional*, defaults to 0.0):
Dropout probability for the projection layer.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
norm_type (`str`, *optional*, defaults to `"layer_norm"`):
The type of normalization to use in the encoder. Can be `"layer_norm"` or `"rms_norm"`.
layer_norm_eps (`float`, *optional*, defaults to 1e-06):
The epsilon used by the layer normalization layers.
image_size (`int` or `list[int]`, *optional*, defaults to `[448, 448]`):
The size (resolution) of each image.
patch_size (`int` or `list[int]`, *optional*, defaults to `[14, 14]`):
The size (resolution) of each patch.
num_channels (`int`, *optional*, defaults to 3):
The number of input channels.
use_mask_token (`bool`, *optional*, defaults to `False`):
Whether to use a mask token for masked image modeling.
use_absolute_position_embeddings (`bool`, *optional*, defaults to `True`):
Whether to use BERT-style absolute position embeddings.
layer_scale_init_value (`float`, *optional*, defaults to 0.1):
Scale to use in the self-attention layers. 0.1 for base, 1e-5 for large. Set 0 to disable layer scale.
use_mean_pooling (`bool`, *optional*, defaults to `True`):
Whether to mean pool the final hidden states of the patches instead of using the final hidden state of the
CLS token, before applying the classification head.
Example:
```python
>>> from transformers import InternS1VisionConfig, InternS1VisionModel
>>> # Initializing a InternS1VisionModel
>>> configuration = InternS1VisionConfig()
>>> # Initializing a model (with random weights) from configuration
>>> model = InternS1VisionModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "interns1_vision"
base_config_key = "vision_config"
def __init__(
self,
hidden_size=1024,
num_hidden_layers=24,
num_attention_heads=16,
attention_bias=False,
use_qk_norm=False,
intermediate_size=4096,
hidden_act="gelu",
hidden_dropout_prob=0.0,
attention_dropout=0.0,
projection_dropout=0.0,
drop_path_rate=0.0,
initializer_range=0.02,
norm_type="layer_norm",
layer_norm_eps=1e-06,
image_size=[448, 448],
patch_size=[14, 14],
num_channels=3,
use_mask_token=False,
use_absolute_position_embeddings=True,
layer_scale_init_value=0.1,
use_mean_pooling=True,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.attention_bias = attention_bias
self.use_qk_norm = use_qk_norm
self.intermediate_size = intermediate_size
self.hidden_act = hidden_act
self.hidden_dropout_prob = hidden_dropout_prob
self.attention_dropout = attention_dropout
self.projection_dropout = projection_dropout
self.initializer_range = initializer_range
self.norm_type = norm_type
self.layer_norm_eps = layer_norm_eps
self.drop_path_rate = drop_path_rate
image_size = image_size if isinstance(image_size, (list, tuple)) else (image_size, image_size)
patch_size = patch_size if isinstance(patch_size, (list, tuple)) else (patch_size, patch_size)
self.image_size = image_size
self.patch_size = patch_size
self.num_channels = num_channels
self.use_mask_token = use_mask_token
self.use_absolute_position_embeddings = use_absolute_position_embeddings
self.layer_scale_init_value = layer_scale_init_value
self.use_mean_pooling = use_mean_pooling
class InternS1Config(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`InternS1ForConditionalGeneration`]. It is used to instantiate a
InternS1 model according to the specified arguments, defining the model architecture.
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
documentation from [`PretrainedConfig`] for more information.
Args:
vision_config (`Union[AutoConfig, dict]`, *optional*, defaults to `InternVisonConfig`):
The config object or dictionary of the vision backbone.
text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `Qwen2Config`):
The config object or dictionary of the text backbone.
image_token_id (`int`, *optional*, defaults to 151667):
The image token index to encode the image prompt.
image_seq_length (`int`, *optional*, defaults to 256):
Number of image tokens to use per image patch.
downsample_ratio (`float`, *optional*, defaults to 0.5):
Factor by which to downsample the image.
projector_hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
The non-linear activation function (function or string) in the projector.
vision_feature_layer (`int`, *optional*, defaults to -1):
The index of the layer to use as the image features.
vision_feature_select_strategy (`str`, *optional*, defaults to `"default"`):
The feature selection strategy used to select the vision feature from the vision backbone.
Can be one of `"default"` or `"full"`.
```python
>>> from transformers import InternS1ForConditionalGeneration, InternS1Config
>>> # Initializing a InternS1 style configuration
>>> configuration = InternS1Config()
>>> # Initializing a model (with random weights) from configuration
>>> model = InternS1ForConditionalGeneration(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "interns1"
sub_configs = {"text_config": AutoConfig, "vision_config": InternS1VisionConfig}
def __init__(
self,
vision_config=None,
text_config=None,
image_token_id=151667,
image_seq_length=256,
downsample_ratio=0.5,
projector_hidden_act="gelu",
vision_feature_layer=-1,
vision_feature_select_strategy="default",
**kwargs,
):
from transformers import CONFIG_MAPPING
self.image_token_id = image_token_id
self.image_seq_length = image_seq_length
self.downsample_ratio = downsample_ratio
self.projector_hidden_act = projector_hidden_act
self.vision_feature_layer = vision_feature_layer
self.vision_feature_select_strategy = vision_feature_select_strategy
if isinstance(vision_config, dict):
self.vision_config = InternS1VisionConfig(**vision_config)
elif isinstance(vision_config, InternS1VisionConfig):
self.vision_config = vision_config
elif vision_config is None:
self.vision_config = InternS1VisionConfig()
if isinstance(text_config, dict):
text_config["model_type"] = text_config["model_type"] if "model_type" in text_config else "qwen3"
text_config = CONFIG_MAPPING[text_config["model_type"]](**text_config)
elif text_config is None:
text_config = CONFIG_MAPPING["qwen3"]()
self.text_config = text_config
super().__init__(**kwargs)
__all__ = ["InternS1VisionConfig", "InternS1Config"]