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import os
from typing import Union
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class InternVisionConfig(PretrainedConfig):
model_type = 'intern_vit_6b'
def __init__(
self,
num_channels=3,
patch_size=14,
image_size=224,
qkv_bias=False,
hidden_size=3200,
num_attention_heads=25,
intermediate_size=12800,
qk_normalization=True,
num_hidden_layers=48,
use_flash_attn=True,
hidden_act='gelu',
norm_type='rms_norm',
layer_norm_eps=1e-6,
dropout=0.0,
drop_path_rate=0.0,
attention_dropout=0.0,
initializer_range=0.02,
initializer_factor=0.1,
**kwargs,
):
super().__init__(**kwargs)
self.hidden_size = hidden_size
self.intermediate_size = intermediate_size
self.dropout = dropout
self.drop_path_rate = drop_path_rate
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.num_channels = num_channels
self.patch_size = patch_size
self.image_size = image_size
self.initializer_range = initializer_range
self.initializer_factor = initializer_factor
self.attention_dropout = attention_dropout
self.layer_norm_eps = layer_norm_eps
self.hidden_act = hidden_act
self.norm_type = norm_type
self.qkv_bias = qkv_bias
self.qk_normalization = qk_normalization
self.use_flash_attn = use_flash_attn
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> 'PretrainedConfig':
config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
if 'vision_config' in config_dict:
config_dict = config_dict['vision_config']
if 'model_type' in config_dict and hasattr(cls, 'model_type') and config_dict['model_type'] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'
)
return cls.from_dict(config_dict, **kwargs)
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