codingutf8 2022 the huggingface inc team 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 orglicenseslicense2 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 xclip model configuration import os from typing import union from configurationutils import pretrainedconfig from utils import logging logger logging getloggername xclippretrainedconfigarchivemap microsoftxclipbasepatch32 https huggingface comicrosoftxclipbasepatch32resolvemainconfig json class xcliptextconfigpretrainedconfig r this is the configuration class to store the configuration of a xclipmodel it is used to instantiate an xclip model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the xclip microsoftxclipbasepatch32https huggingface comicrosoftxclipbasepatch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocabsize int optional defaults to 49408 vocabulary size of the xclip text model defines the number of different tokens that can be represented by the inputsids passed when calling xclipmodel hiddensize int optional defaults to 512 dimensionality of the encoder layers and the pooler layer intermediatesize int optional defaults to 2048 dimensionality of the intermediate i e feedforward layer in the transformer encoder numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 8 number of attention heads for each attention layer in the transformer encoder maxpositionembeddings int optional defaults to 77 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 512 or 1024 or 2048 hiddenact str or function optional defaults to quickgelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu and gelunew quickgelu are supported layernormeps float optional defaults to 1e5 the epsilon used by the layer normalization layers attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices initializerfactor float optional defaults to 1 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing example python from transformers import xcliptextmodel xcliptextconfig initializing a xcliptextmodel with microsoftxclipbasepatch32 style configuration configuration xcliptextconfig initializing a xcliptextconfig from the microsoftxclipbasepatch32 style configuration model xcliptextmodelconfiguration accessing the model configuration configuration model config modeltype xcliptextmodel def init self vocabsize49408 hiddensize512 intermediatesize2048 numhiddenlayers12 numattentionheads8 maxpositionembeddings77 hiddenactquickgelu layernormeps1e5 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 padtokenid1 bostokenid0 eostokenid2 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self intermediatesize intermediatesize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self maxpositionembeddings maxpositionembeddings self layernormeps layernormeps self hiddenact hiddenact self initializerrange initializerrange self initializerfactor initializerfactor self attentiondropout attentiondropout classmethod def frompretrainedcls pretrainedmodelnameorpath unionstr os pathlike kwargs pretrainedconfig cls settokeninkwargskwargs configdict kwargs cls getconfigdictpretrainedmodelnameorpath kwargs get the text config dict if we are loading from xclipconfig if configdict getmodeltype xclip configdict configdicttextconfig if modeltype in configdict and hasattrcls modeltype and configdictmodeltype cls modeltype logger warning fyou are using a model of type configdict modeltype to instantiate a model of type fcls modeltype this is not supported for all configurations of models and can yield errors return cls fromdictconfigdict kwargs class xclipvisionconfigpretrainedconfig r this is the configuration class to store the configuration of a xclipmodel it is used to instantiate an xclip model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the xclip microsoftxclipbasepatch32https huggingface comicrosoftxclipbasepatch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args hiddensize int optional defaults to 768 dimensionality of the encoder layers and the pooler layer intermediatesize int optional defaults to 3072 dimensionality of the intermediate i e feedforward layer in the transformer encoder numhiddenlayers int optional defaults to 12 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder mithiddensize int optional defaults to 512 dimensionality of the encoder layers of the multiframe integration transformer mit mitintermediatesize int optional defaults to 2048 dimensionality of the intermediate i e feedforward layer in the multiframe integration transformer mit mitnumhiddenlayers int optional defaults to 1 number of hidden layers in the multiframe integration transformer mit mitnumattentionheads int optional defaults to 8 number of attention heads for each attention layer in the multiframe integration transformer mit imagesize int optional defaults to 224 the size resolution of each image patchsize int optional defaults to 32 the size resolution of each patch hiddenact str or function optional defaults to quickgelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu selu gelunew and quickgelu are supported layernormeps float optional defaults to 1e5 the epsilon used by the layer normalization layers attentiondropout float optional defaults to 0 0 the dropout ratio for the attention probabilities initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices initializerfactor float optional defaults to 1 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing droppathrate float optional defaults to 0 0 stochastic depth rate example python from transformers import xclipvisionmodel xclipvisionconfig initializing a xclipvisionmodel with microsoftxclipbasepatch32 style configuration configuration xclipvisionconfig initializing a xclipvisionmodel model from the microsoftxclipbasepatch32 style configuration model xclipvisionmodelconfiguration accessing the model configuration configuration model config modeltype xclipvisionmodel def init self hiddensize768 intermediatesize3072 numhiddenlayers12 numattentionheads12 mithiddensize512 mitintermediatesize2048 mitnumhiddenlayers1 mitnumattentionheads8 numchannels3 imagesize224 patchsize32 numframes8 hiddenactquickgelu layernormeps1e5 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 droppathrate0 0 kwargs super initkwargs self hiddensize hiddensize self intermediatesize intermediatesize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self mithiddensize mithiddensize self mitintermediatesize mitintermediatesize self mitnumhiddenlayers mitnumhiddenlayers self mitnumattentionheads mitnumattentionheads self numchannels numchannels self patchsize patchsize self numframes numframes self imagesize imagesize self initializerrange initializerrange self initializerfactor initializerfactor self attentiondropout attentiondropout self layernormeps layernormeps self hiddenact hiddenact self droppathrate droppathrate classmethod def frompretrainedcls pretrainedmodelnameorpath unionstr os pathlike kwargs pretrainedconfig cls settokeninkwargskwargs configdict kwargs cls getconfigdictpretrainedmodelnameorpath kwargs get the vision config dict if we are loading from xclipconfig if configdict getmodeltype xclip configdict configdictvisionconfig if modeltype in configdict and hasattrcls modeltype and configdictmodeltype cls modeltype logger warning fyou are using a model of type configdict modeltype to instantiate a model of type fcls modeltype this is not supported for all configurations of models and can yield errors return cls fromdictconfigdict kwargs class xclipconfigpretrainedconfig r xclipconfig is the configuration class to store the configuration of a xclipmodel it is used to instantiate xclip model according to the specified arguments defining the text model and vision model configs instantiating a configuration with the defaults will yield a similar configuration to that of the xclip microsoftxclipbasepatch32https huggingface comicrosoftxclipbasepatch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args textconfig dict optional dictionary of configuration options used to initialize xcliptextconfig visionconfig dict optional dictionary of configuration options used to initialize xclipvisionconfig projectiondim int optional defaults to 512 dimentionality of text and vision projection layers promptlayers int optional defaults to 2 number of layers in the video specific prompt generator promptalpha float optional defaults to 0 1 alpha value to use in the video specific prompt generator prompthiddenact str or function optional defaults to quickgelu the nonlinear activation function function or string in the video specific prompt generator if string gelu relu selu and gelunew quickgelu are supported promptnumattentionheads int optional defaults to 8 number of attention heads in the crossattention of the video specific prompt generator promptattentiondropout float optional defaults to 0 0 the dropout probability for the attention layers in the video specific prompt generator promptprojectiondropout float optional defaults to 0 0 the dropout probability for the projection layers in the video specific prompt generator logitscaleinitvalue float optional defaults to 2 6592 the inital value of the logitscale parameter default is used as per the original xclip implementation kwargs optional dictionary of keyword arguments if configdict exist we use them for the backward compatibility we pop out these 2 attributes before calling super init to avoid them being saved which causes a lot of confusion instead of simply assigning textvisionconfigdict to textvisionconfig we use the values in textvisionconfigdict to update the values in textvisionconfig the values should be same in most cases but we don t want to break anything regarding configdict that existed before commit 8827e1b2 this is the complete result when using textconfigdict give a warning if the values exist in both textconfigdict and textconfig but being different if specified in textconfigdict if inferred from default argument values just to be super careful update all values in textconfig with the ones in textconfigdict this is the complete result when using visionconfigdict convert keys to string instead of integer give a warning if the values exist in both visionconfigdict and visionconfig but being different if specified in visionconfigdict if inferred from default argument values just to be super careful update all values in visionconfig with the ones in visionconfigdict return clstextconfigtextconfig todict visionconfigvisionconfig todict kwargs coding utf 8 2022 the huggingface inc team 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 x clip model configuration this is the configuration class to store the configuration of a xclipmodel it is used to instantiate an x clip model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the x clip microsoft xclip base patch32 https huggingface co microsoft xclip base patch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args vocab_size int optional defaults to 49408 vocabulary size of the x clip text model defines the number of different tokens that can be represented by the inputs_ids passed when calling xclipmodel hidden_size int optional defaults to 512 dimensionality of the encoder layers and the pooler layer intermediate_size int optional defaults to 2048 dimensionality of the intermediate i e feed forward layer in the transformer encoder num_hidden_layers int optional defaults to 12 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 8 number of attention heads for each attention layer in the transformer encoder max_position_embeddings int optional defaults to 77 the maximum sequence length that this model might ever be used with typically set this to something large just in case e g 512 or 1024 or 2048 hidden_act str or function optional defaults to quick_gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new quick_gelu are supported layer_norm_eps float optional defaults to 1e 5 the epsilon used by the layer normalization layers attention_dropout float optional defaults to 0 0 the dropout ratio for the attention probabilities initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices initializer_factor float optional defaults to 1 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing example python from transformers import xcliptextmodel xcliptextconfig initializing a xcliptextmodel with microsoft xclip base patch32 style configuration configuration xcliptextconfig initializing a xcliptextconfig from the microsoft xclip base patch32 style configuration model xcliptextmodel configuration accessing the model configuration configuration model config get the text config dict if we are loading from xclipconfig this is the configuration class to store the configuration of a xclipmodel it is used to instantiate an x clip model according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the x clip microsoft xclip base patch32 https huggingface co microsoft xclip base patch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder num_hidden_layers int optional defaults to 12 number of hidden layers in the transformer encoder num_attention_heads int optional defaults to 12 number of attention heads for each attention layer in the transformer encoder mit_hidden_size int optional defaults to 512 dimensionality of the encoder layers of the multiframe integration transformer mit mit_intermediate_size int optional defaults to 2048 dimensionality of the intermediate i e feed forward layer in the multiframe integration transformer mit mit_num_hidden_layers int optional defaults to 1 number of hidden layers in the multiframe integration transformer mit mit_num_attention_heads int optional defaults to 8 number of attention heads for each attention layer in the multiframe integration transformer mit image_size int optional defaults to 224 the size resolution of each image patch_size int optional defaults to 32 the size resolution of each patch hidden_act str or function optional defaults to quick_gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu gelu_new and quick_gelu are supported layer_norm_eps float optional defaults to 1e 5 the epsilon used by the layer normalization layers attention_dropout float optional defaults to 0 0 the dropout ratio for the attention probabilities initializer_range float optional defaults to 0 02 the standard deviation of the truncated_normal_initializer for initializing all weight matrices initializer_factor float optional defaults to 1 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing drop_path_rate float optional defaults to 0 0 stochastic depth rate example python from transformers import xclipvisionmodel xclipvisionconfig initializing a xclipvisionmodel with microsoft xclip base patch32 style configuration configuration xclipvisionconfig initializing a xclipvisionmodel model from the microsoft xclip base patch32 style configuration model xclipvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from xclipconfig xclipconfig is the configuration class to store the configuration of a xclipmodel it is used to instantiate x clip model according to the specified arguments defining the text model and vision model configs instantiating a configuration with the defaults will yield a similar configuration to that of the x clip microsoft xclip base patch32 https huggingface co microsoft xclip base patch32 architecture configuration objects inherit from pretrainedconfig and can be used to control the model outputs read the documentation from pretrainedconfig for more information args text_config dict optional dictionary of configuration options used to initialize xcliptextconfig vision_config dict optional dictionary of configuration options used to initialize xclipvisionconfig projection_dim int optional defaults to 512 dimentionality of text and vision projection layers prompt_layers int optional defaults to 2 number of layers in the video specific prompt generator prompt_alpha float optional defaults to 0 1 alpha value to use in the video specific prompt generator prompt_hidden_act str or function optional defaults to quick_gelu the non linear activation function function or string in the video specific prompt generator if string gelu relu selu and gelu_new quick_gelu are supported prompt_num_attention_heads int optional defaults to 8 number of attention heads in the cross attention of the video specific prompt generator prompt_attention_dropout float optional defaults to 0 0 the dropout probability for the attention layers in the video specific prompt generator prompt_projection_dropout float optional defaults to 0 0 the dropout probability for the projection layers in the video specific prompt generator logit_scale_init_value float optional defaults to 2 6592 the inital value of the logit_scale parameter default is used as per the original xclip implementation kwargs optional dictionary of keyword arguments if _config_dict exist we use them for the backward compatibility we pop out these 2 attributes before calling super __init__ to avoid them being saved which causes a lot of confusion instead of simply assigning text vision _config_dict to text vision _config we use the values in text vision _config_dict to update the values in text vision _config the values should be same in most cases but we don t want to break anything regarding _config_dict that existed before commit 8827e1b2 this is the complete result when using text_config_dict give a warning if the values exist in both _text_config_dict and text_config but being different if specified in text_config_dict if inferred from default argument values just to be super careful update all values in text_config with the ones in _text_config_dict this is the complete result when using vision_config_dict convert keys to string instead of integer give a warning if the values exist in both _vision_config_dict and vision_config but being different if specified in vision_config_dict if inferred from default argument values just to be super careful update all values in vision_config with the ones in _vision_config_dict instantiate a xclipconfig or a derived class from xclip text model configuration and xclip vision model configuration returns xclipconfig an instance of a configuration object
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "microsoft/xclip-base-patch32": "https://huggingface.co/microsoft/xclip-base-patch32/resolve/main/config.json",
}


class XCLIPTextConfig(PretrainedConfig):
    r

    model_type = "xclip_text_model"

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        num_hidden_layers=12,
        num_attention_heads=8,
        max_position_embeddings=77,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        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.max_position_embeddings = max_position_embeddings
        self.layer_norm_eps = layer_norm_eps
        self.hidden_act = hidden_act
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
        self.attention_dropout = attention_dropout

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        
        if config_dict.get("model_type") == "xclip":
            config_dict = config_dict["text_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)


class XCLIPVisionConfig(PretrainedConfig):
    r

    model_type = "xclip_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        num_hidden_layers=12,
        num_attention_heads=12,
        mit_hidden_size=512,
        mit_intermediate_size=2048,
        mit_num_hidden_layers=1,
        mit_num_attention_heads=8,
        num_channels=3,
        image_size=224,
        patch_size=32,
        num_frames=8,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        drop_path_rate=0.0,
        **kwargs,
    ):
        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.mit_hidden_size = mit_hidden_size
        self.mit_intermediate_size = mit_intermediate_size
        self.mit_num_hidden_layers = mit_num_hidden_layers
        self.mit_num_attention_heads = mit_num_attention_heads
        self.num_channels = num_channels
        self.patch_size = patch_size
        self.num_frames = num_frames
        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.drop_path_rate = drop_path_rate

    @classmethod
    def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
        cls._set_token_in_kwargs(kwargs)

        config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)

        
        if config_dict.get("model_type") == "xclip":
            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)


class XCLIPConfig(PretrainedConfig):
    r

    model_type = "xclip"

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=512,
        prompt_layers=2,
        prompt_alpha=0.1,
        prompt_hidden_act="quick_gelu",
        prompt_num_attention_heads=8,
        prompt_attention_dropout=0.0,
        prompt_projection_dropout=0.0,
        logit_scale_init_value=2.6592,
        **kwargs,
    ):
        
        
        
        text_config_dict = kwargs.pop("text_config_dict", None)
        vision_config_dict = kwargs.pop("vision_config_dict", None)

        super().__init__(**kwargs)

        
        
        
        if text_config_dict is not None:
            if text_config is None:
                text_config = {}

            
            _text_config_dict = XCLIPTextConfig(**text_config_dict).to_dict()

            
            for key, value in _text_config_dict.items():
                if key in text_config and value != text_config[key] and key not in ["transformers_version"]:
                    
                    if key in text_config_dict:
                        message = (
                            f"`{key}` is found in both `text_config_dict` and `text_config` but with different values. "
                            f'The value `text_config_dict["{key}"]` will be used instead.'
                        )
                    
                    else:
                        message = (
                            f"`text_config_dict` is provided which will be used to initialize `XCLIPTextConfig`. The "
                            f'value `text_config["{key}"]` will be overriden.'
                        )
                    logger.warning(message)

            
            text_config.update(_text_config_dict)

        if vision_config_dict is not None:
            if vision_config is None:
                vision_config = {}

            
            _vision_config_dict = XCLIPVisionConfig(**vision_config_dict).to_dict()
            
            if "id2label" in _vision_config_dict:
                _vision_config_dict["id2label"] = {
                    str(key): value for key, value in _vision_config_dict["id2label"].items()
                }

            
            for key, value in _vision_config_dict.items():
                if key in vision_config and value != vision_config[key] and key not in ["transformers_version"]:
                    
                    if key in vision_config_dict:
                        message = (
                            f"`{key}` is found in both `vision_config_dict` and `vision_config` but with different "
                            f'values. The value `vision_config_dict["{key}"]` will be used instead.'
                        )
                    
                    else:
                        message = (
                            f"`vision_config_dict` is provided which will be used to initialize `XCLIPVisionConfig`. "
                            f'The value `vision_config["{key}"]` will be overriden.'
                        )
                    logger.warning(message)

            
            vision_config.update(_vision_config_dict)

        if text_config is None:
            text_config = {}
            logger.info("`text_config` is `None`. Initializing the `XCLIPTextConfig` with default values.")

        if vision_config is None:
            vision_config = {}
            logger.info("`vision_config` is `None`. initializing the `XCLIPVisionConfig` with default values.")

        self.text_config = XCLIPTextConfig(**text_config)
        self.vision_config = XCLIPVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.prompt_layers = prompt_layers
        self.prompt_alpha = prompt_alpha
        self.prompt_hidden_act = prompt_hidden_act
        self.prompt_num_attention_heads = prompt_num_attention_heads
        self.prompt_attention_dropout = prompt_attention_dropout
        self.prompt_projection_dropout = prompt_projection_dropout
        self.logit_scale_init_value = logit_scale_init_value
        self.initializer_factor = 1.0

    @classmethod
    def from_text_vision_configs(cls, text_config: XCLIPTextConfig, vision_config: XCLIPVisionConfig, **kwargs):
        r

        return cls(text_config=text_config.to_dict(), vision_config=vision_config.to_dict(), **kwargs)