codingutf8 2022 wenxiang zhongzhicheng ledellwu liuguang bowenzhang and 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 altclip model configuration import os from typing import union from configurationutils import pretrainedconfig from utils import logging logger logging getloggername altclippretrainedconfigarchivemap baaialtclip https huggingface cobaaialtclipresolvemainconfig json see all altclip models at https huggingface comodels filteraltclip class altcliptextconfigpretrainedconfig r this is the configuration class to store the configuration of a altcliptextmodel it is used to instantiate a altclip text 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 altclip baaialtcliphttps huggingface cobaaialtclip 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 250002 vocabulary size of the altclip model defines the number of different tokens that can be represented by the inputsids passed when calling altcliptextmodel hiddensize int optional defaults to 1024 dimensionality of the encoder layers and the pooler layer numhiddenlayers int optional defaults to 24 number of hidden layers in the transformer encoder numattentionheads int optional defaults to 16 number of attention heads for each attention layer in the transformer encoder intermediatesize int optional defaults to 4096 dimensionality of the intermediate often named feedforward layer in the transformer encoder hiddenact str or callable optional defaults to gelu the nonlinear activation function function or string in the encoder and pooler if string gelu relu silu and gelunew are supported hiddendropoutprob float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob float optional defaults to 0 1 the dropout ratio for the attention probabilities maxpositionembeddings int optional defaults to 514 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 typevocabsize int optional defaults to 1 the vocabulary size of the tokentypeids passed when calling altcliptextmodel initializerrange float optional defaults to 0 02 the standard deviation of the truncatednormalinitializer for initializing all weight matrices initializerfactor float optional defaults to 0 02 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing layernormeps float optional defaults to 1e05 the epsilon used by the layer normalization layers padtokenid int optional defaults to 1 the id of the padding token bostokenid int optional defaults to 0 the id of the beginningofsequence token eostokenid unionint listint optional defaults to 2 the id of the endofsequence token optionally use a list to set multiple endofsequence tokens positionembeddingtype str optional defaults to absolute type of position embedding choose one of absolute relativekey relativekeyquery for positional embeddings use absolute for more information on relativekey please refer to selfattention with relative position representations shaw et al https arxiv orgabs1803 02155 for more information on relativekeyquery please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv orgabs2009 13658 usecache bool optional defaults to true whether or not the model should return the last keyvalues attentions not used by all models only relevant if config isdecodertrue projectdim int optional defaults to 768 the dimentions of the teacher model before the mapping layer examples python from transformers import altcliptextmodel altcliptextconfig initializing a altcliptextconfig with baaialtclip style configuration configuration altcliptextconfig initializing a altcliptextmodel with random weights from the baaialtclip style configuration model altcliptextmodelconfiguration accessing the model configuration configuration model config modeltype altcliptextmodel def init self vocabsize250002 hiddensize1024 numhiddenlayers24 numattentionheads16 intermediatesize4096 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings514 typevocabsize1 initializerrange0 02 initializerfactor0 02 layernormeps1e05 padtokenid1 bostokenid0 eostokenid2 positionembeddingtypeabsolute usecachetrue projectdim768 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self hiddenact hiddenact self intermediatesize intermediatesize self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self maxpositionembeddings maxpositionembeddings self typevocabsize typevocabsize self initializerrange initializerrange self initializerfactor initializerfactor self layernormeps layernormeps self positionembeddingtype positionembeddingtype self usecache usecache self projectdim projectdim class altclipvisionconfigpretrainedconfig r this is the configuration class to store the configuration of a altclipmodel it is used to instantiate an altclip 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 altclip baaialtcliphttps huggingface cobaaialtclip 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 projectiondim int optional defaults to 512 dimentionality of text and vision projection layers 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 numchannels int optional defaults to 3 the number of input channels 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 and gelunew quickgelu are supported layernormeps float optional defaults to 1e05 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 0 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing example python from transformers import altclipvisionconfig altclipvisionmodel initializing a altclipvisionconfig with baaialtclip style configuration configuration altclipvisionconfig initializing a altclipvisionmodel with random weights from the baaialtclip style configuration model altclipvisionmodelconfiguration accessing the model configuration configuration model config modeltype altclipvisionmodel def init self hiddensize768 intermediatesize3072 projectiondim512 numhiddenlayers12 numattentionheads12 numchannels3 imagesize224 patchsize32 hiddenactquickgelu layernormeps1e5 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 kwargs super initkwargs self hiddensize hiddensize self intermediatesize intermediatesize self projectiondim projectiondim self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self numchannels numchannels self patchsize patchsize self imagesize imagesize self initializerrange initializerrange self initializerfactor initializerfactor self attentiondropout attentiondropout self layernormeps layernormeps self hiddenact hiddenact 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 altclipconfig if configdict getmodeltype altclip 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 altclipconfigpretrainedconfig r this is the configuration class to store the configuration of a altclipmodel it is used to instantiate an altclip 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 altclip baaialtcliphttps huggingface cobaaialtclip 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 altcliptextconfig visionconfig dict optional dictionary of configuration options used to initialize altclipvisionconfig projectiondim int optional defaults to 768 dimentionality of text and vision projection layers logitscaleinitvalue float optional defaults to 2 6592 the inital value of the logitscale paramter default is used as per the original clip implementation kwargs optional dictionary of keyword arguments example python from transformers import altclipconfig altclipmodel initializing a altclipconfig with baaialtclip style configuration configuration altclipconfig initializing a altclipmodel with random weights from the baaialtclip style configuration model altclipmodelconfiguration accessing the model configuration configuration model config we can also initialize a altclipconfig from a altcliptextconfig and a altclipvisionconfig initializing a altcliptext and altclipvision configuration configtext altcliptextconfig configvision altclipvisionconfig config altclipconfig fromtextvisionconfigsconfigtext configvision modeltype altclip def init self textconfignone visionconfignone projectiondim768 logitscaleinitvalue2 6592 kwargs 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 textconfigdict kwargs poptextconfigdict none visionconfigdict kwargs popvisionconfigdict none super initkwargs 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 if textconfigdict is not none if textconfig is none textconfig this is the complete result when using textconfigdict textconfigdict altcliptextconfigtextconfigdict todict give a warning if the values exist in both textconfigdict and textconfig but being different for key value in textconfigdict items if key in textconfig and value textconfigkey and key not in transformersversion if specified in textconfigdict if key in textconfigdict message fkey is found in both textconfigdict and textconfig but with different values f the value textconfigdictkey will be used instead if inferred from default argument values just to be super careful else message ftextconfigdict is provided which will be used to initialize altcliptextconfig the f value textconfigkey will be overriden logger warningmessage update all values in textconfig with the ones in textconfigdict textconfig updatetextconfigdict if visionconfigdict is not none if visionconfig is none visionconfig this is the complete result when using visionconfigdict visionconfigdict altclipvisionconfigvisionconfigdict todict convert keys to string instead of integer if id2label in visionconfigdict visionconfigdictid2label strkey value for key value in visionconfigdictid2label items give a warning if the values exist in both visionconfigdict and visionconfig but being different for key value in visionconfigdict items if key in visionconfig and value visionconfigkey and key not in transformersversion if specified in visionconfigdict if key in visionconfigdict message fkey is found in both visionconfigdict and visionconfig but with different f values the value visionconfigdictkey will be used instead if inferred from default argument values just to be super careful else message fvisionconfigdict is provided which will be used to initialize altclipvisionconfig f the value visionconfigkey will be overriden logger warningmessage update all values in visionconfig with the ones in visionconfigdict visionconfig updatevisionconfigdict if textconfig is none textconfig logger infotextconfig is none initializing the altcliptextconfig with default values if visionconfig is none visionconfig logger infovisionconfig is none initializing the altclipvisionconfig with default values self textconfig altcliptextconfigtextconfig self visionconfig altclipvisionconfigvisionconfig self projectiondim projectiondim self logitscaleinitvalue logitscaleinitvalue self initializerfactor 1 0 classmethod def fromtextvisionconfigscls textconfig altcliptextconfig visionconfig altclipvisionconfig kwargs r instantiate a altclipconfig or a derived class from altclip text model configuration and altclip vision model configuration returns altclipconfig an instance of a configuration object coding utf 8 2022 wenxiang zhongzhicheng ledellwu liuguang bowenzhang and 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 altclip model configuration see all altclip models at https huggingface co models filter altclip this is the configuration class to store the configuration of a altcliptextmodel it is used to instantiate a altclip text 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 altclip baai altclip https huggingface co baai altclip 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 250002 vocabulary size of the altclip model defines the number of different tokens that can be represented by the inputs_ids passed when calling altcliptextmodel 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 intermediate_size int optional defaults to 4096 dimensionality of the intermediate often named feed forward layer in the transformer encoder hidden_act str or callable optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu silu and gelu_new are supported hidden_dropout_prob float optional defaults to 0 1 the dropout probability for all fully connected layers in the embeddings encoder and pooler attention_probs_dropout_prob float optional defaults to 0 1 the dropout ratio for the attention probabilities max_position_embeddings int optional defaults to 514 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 type_vocab_size int optional defaults to 1 the vocabulary size of the token_type_ids passed when calling altcliptextmodel 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 0 02 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing layer_norm_eps float optional defaults to 1e 05 the epsilon used by the layer normalization layers pad_token_id int optional defaults to 1 the id of the padding token bos_token_id int optional defaults to 0 the id of the beginning of sequence token eos_token_id union int list int optional defaults to 2 the id of the end of sequence token optionally use a list to set multiple end of sequence tokens position_embedding_type str optional defaults to absolute type of position embedding choose one of absolute relative_key relative_key_query for positional embeddings use absolute for more information on relative_key please refer to self attention with relative position representations shaw et al https arxiv org abs 1803 02155 for more information on relative_key_query please refer to method 4 in improve transformer models with better relative position embeddings huang et al https arxiv org abs 2009 13658 use_cache bool optional defaults to true whether or not the model should return the last key values attentions not used by all models only relevant if config is_decoder true project_dim int optional defaults to 768 the dimentions of the teacher model before the mapping layer examples python from transformers import altcliptextmodel altcliptextconfig initializing a altcliptextconfig with baai altclip style configuration configuration altcliptextconfig initializing a altcliptextmodel with random weights from the baai altclip style configuration model altcliptextmodel configuration accessing the model configuration configuration model config this is the configuration class to store the configuration of a altclipmodel it is used to instantiate an altclip 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 altclip baai altclip https huggingface co baai altclip 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 projection_dim int optional defaults to 512 dimentionality of text and vision projection layers 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 num_channels int optional defaults to 3 the number of input channels 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 and gelu_new quick_gelu are supported layer_norm_eps float optional defaults to 1e 05 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 0 a factor for initializing all weight matrices should be kept to 1 used internally for initialization testing example python from transformers import altclipvisionconfig altclipvisionmodel initializing a altclipvisionconfig with baai altclip style configuration configuration altclipvisionconfig initializing a altclipvisionmodel with random weights from the baai altclip style configuration model altclipvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from altclipconfig this is the configuration class to store the configuration of a altclipmodel it is used to instantiate an altclip 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 altclip baai altclip https huggingface co baai altclip 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 altcliptextconfig vision_config dict optional dictionary of configuration options used to initialize altclipvisionconfig projection_dim int optional defaults to 768 dimentionality of text and vision projection layers logit_scale_init_value float optional defaults to 2 6592 the inital value of the logit_scale paramter default is used as per the original clip implementation kwargs optional dictionary of keyword arguments example python from transformers import altclipconfig altclipmodel initializing a altclipconfig with baai altclip style configuration configuration altclipconfig initializing a altclipmodel with random weights from the baai altclip style configuration model altclipmodel configuration accessing the model configuration configuration model config we can also initialize a altclipconfig from a altcliptextconfig and a altclipvisionconfig initializing a altcliptext and altclipvision configuration config_text altcliptextconfig config_vision altclipvisionconfig config altclipconfig from_text_vision_configs config_text config_vision 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 altclipconfig or a derived class from altclip text model configuration and altclip vision model configuration returns altclipconfig 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__)

ALTCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "BAAI/AltCLIP": "https://huggingface.co/BAAI/AltCLIP/resolve/main/config.json",
    
}


class AltCLIPTextConfig(PretrainedConfig):
    r

    model_type = "altclip_text_model"

    def __init__(
        self,
        vocab_size=250002,
        hidden_size=1024,
        num_hidden_layers=24,
        num_attention_heads=16,
        intermediate_size=4096,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=514,
        type_vocab_size=1,
        initializer_range=0.02,
        initializer_factor=0.02,
        layer_norm_eps=1e-05,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        position_embedding_type="absolute",
        use_cache=True,
        project_dim=768,
        **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.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.hidden_act = hidden_act
        self.intermediate_size = intermediate_size
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.max_position_embeddings = max_position_embeddings
        self.type_vocab_size = type_vocab_size
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
        self.layer_norm_eps = layer_norm_eps
        self.position_embedding_type = position_embedding_type
        self.use_cache = use_cache
        self.project_dim = project_dim


class AltCLIPVisionConfig(PretrainedConfig):
    r

    model_type = "altclip_vision_model"

    def __init__(
        self,
        hidden_size=768,
        intermediate_size=3072,
        projection_dim=512,
        num_hidden_layers=12,
        num_attention_heads=12,
        num_channels=3,
        image_size=224,
        patch_size=32,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.projection_dim = projection_dim
        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

    @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") == "altclip":
            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 AltCLIPConfig(PretrainedConfig):
    r

    model_type = "altclip"

    def __init__(
        self, text_config=None, vision_config=None, projection_dim=768, 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 = AltCLIPTextConfig(**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 `AltCLIPTextConfig`. 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 = AltCLIPVisionConfig(**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 `AltCLIPVisionConfig`. "
                            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 `AltCLIPTextConfig` with default values.")

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

        self.text_config = AltCLIPTextConfig(**text_config)
        self.vision_config = AltCLIPVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.initializer_factor = 1.0

    @classmethod
    def from_text_vision_configs(cls, text_config: AltCLIPTextConfig, vision_config: AltCLIPVisionConfig, **kwargs):
        r

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