codingutf8 2021 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 clip model configuration import os from collections import ordereddict from typing import typechecking any mapping optional union if typechecking from processingutils import processormixin from utils import tensortype from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging logger logging getloggername clippretrainedconfigarchivemap openaiclipvitbasepatch32 https huggingface coopenaiclipvitbasepatch32resolvemainconfig json see all clip models at https huggingface comodels filterclip class cliptextconfigpretrainedconfig r this is the configuration class to store the configuration of a cliptextmodel it is used to instantiate a clip text encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the clip openaiclipvitbasepatch32https huggingface coopenaiclipvitbasepatch32 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 clip text model defines the number of different tokens that can be represented by the inputsids passed when calling clipmodel 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 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 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 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 padtokenid int optional defaults to 1 padding token id bostokenid int optional defaults to 49406 beginning of stream token id eostokenid int optional defaults to 49407 end of stream token id example python from transformers import cliptextconfig cliptextmodel initializing a cliptextconfig with openaiclipvitbasepatch32 style configuration configuration cliptextconfig initializing a cliptextmodel with random weights from the openaiclipvitbasepatch32 style configuration model cliptextmodelconfiguration accessing the model configuration configuration model config modeltype cliptextmodel def init self vocabsize49408 hiddensize512 intermediatesize2048 projectiondim512 numhiddenlayers12 numattentionheads8 maxpositionembeddings77 hiddenactquickgelu layernormeps1e5 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 this differs from cliptokenizer s default and from openaiclip see https github comhuggingfacetransformerspull24773issuecomment1632287538 padtokenid1 bostokenid49406 eostokenid49407 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self intermediatesize intermediatesize self projectiondim projectiondim 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 clipconfig if configdict getmodeltype clip 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 clipvisionconfigpretrainedconfig r this is the configuration class to store the configuration of a clipvisionmodel it is used to instantiate a clip vision encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the clip openaiclipvitbasepatch32https huggingface coopenaiclipvitbasepatch32 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 clipvisionconfig clipvisionmodel initializing a clipvisionconfig with openaiclipvitbasepatch32 style configuration configuration clipvisionconfig initializing a clipvisionmodel with random weights from the openaiclipvitbasepatch32 style configuration model clipvisionmodelconfiguration accessing the model configuration configuration model config modeltype clipvisionmodel 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 clipconfig if configdict getmodeltype clip 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 clipconfigpretrainedconfig r clipconfig is the configuration class to store the configuration of a clipmodel it is used to instantiate a 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 clip openaiclipvitbasepatch32https huggingface coopenaiclipvitbasepatch32 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 cliptextconfig visionconfig dict optional dictionary of configuration options used to initialize clipvisionconfig projectiondim int optional defaults to 512 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 clipconfig clipmodel initializing a clipconfig with openaiclipvitbasepatch32 style configuration configuration clipconfig initializing a clipmodel with random weights from the openaiclipvitbasepatch32 style configuration model clipmodelconfiguration accessing the model configuration configuration model config we can also initialize a clipconfig from a cliptextconfig and a clipvisionconfig from transformers import cliptextconfig clipvisionconfig initializing a cliptext and clipvision configuration configtext cliptextconfig configvision clipvisionconfig config clipconfig fromtextvisionconfigsconfigtext configvision modeltype clip def init self textconfignone visionconfignone projectiondim512 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 cliptextconfigtextconfigdict 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 cliptextconfig 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 clipvisionconfigvisionconfigdict 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 clipvisionconfig 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 cliptextconfig with default values if visionconfig is none visionconfig logger infovisionconfig is none initializing the clipvisionconfig with default values self textconfig cliptextconfigtextconfig self visionconfig clipvisionconfigvisionconfig self projectiondim projectiondim self logitscaleinitvalue logitscaleinitvalue self initializerfactor 1 0 classmethod def fromtextvisionconfigscls textconfig cliptextconfig visionconfig clipvisionconfig kwargs r instantiate a clipconfig or a derived class from clip text model configuration and clip vision model configuration returns clipconfig an instance of a configuration object coding utf 8 2021 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 clip model configuration see all clip models at https huggingface co models filter clip this is the configuration class to store the configuration of a cliptextmodel it is used to instantiate a clip text encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the text encoder of the clip openai clip vit base patch32 https huggingface co openai clip vit 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 clip text model defines the number of different tokens that can be represented by the inputs_ids passed when calling clipmodel 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 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 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 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 pad_token_id int optional defaults to 1 padding token id bos_token_id int optional defaults to 49406 beginning of stream token id eos_token_id int optional defaults to 49407 end of stream token id example python from transformers import cliptextconfig cliptextmodel initializing a cliptextconfig with openai clip vit base patch32 style configuration configuration cliptextconfig initializing a cliptextmodel with random weights from the openai clip vit base patch32 style configuration model cliptextmodel configuration accessing the model configuration configuration model config this differs from cliptokenizer s default and from openai clip see https github com huggingface transformers pull 24773 issuecomment 1632287538 get the text config dict if we are loading from clipconfig this is the configuration class to store the configuration of a clipvisionmodel it is used to instantiate a clip vision encoder according to the specified arguments defining the model architecture instantiating a configuration with the defaults will yield a similar configuration to that of the vision encoder of the clip openai clip vit base patch32 https huggingface co openai clip vit 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 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 clipvisionconfig clipvisionmodel initializing a clipvisionconfig with openai clip vit base patch32 style configuration configuration clipvisionconfig initializing a clipvisionmodel with random weights from the openai clip vit base patch32 style configuration model clipvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from clipconfig clipconfig is the configuration class to store the configuration of a clipmodel it is used to instantiate a 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 clip openai clip vit base patch32 https huggingface co openai clip vit 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 cliptextconfig vision_config dict optional dictionary of configuration options used to initialize clipvisionconfig projection_dim int optional defaults to 512 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 clipconfig clipmodel initializing a clipconfig with openai clip vit base patch32 style configuration configuration clipconfig initializing a clipmodel with random weights from the openai clip vit base patch32 style configuration model clipmodel configuration accessing the model configuration configuration model config we can also initialize a clipconfig from a cliptextconfig and a clipvisionconfig from transformers import cliptextconfig clipvisionconfig initializing a cliptext and clipvision configuration config_text cliptextconfig config_vision clipvisionconfig config clipconfig 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 clipconfig or a derived class from clip text model configuration and clip vision model configuration returns clipconfig an instance of a configuration object
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union


if TYPE_CHECKING:
    from ...processing_utils import ProcessorMixin
    from ...utils import TensorType

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


logger = logging.get_logger(__name__)

CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "openai/clip-vit-base-patch32": "https://huggingface.co/openai/clip-vit-base-patch32/resolve/main/config.json",
    
}


class CLIPTextConfig(PretrainedConfig):
    r

    model_type = "clip_text_model"

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=512,
        intermediate_size=2048,
        projection_dim=512,
        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=49406,
        eos_token_id=49407,
        **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.projection_dim = projection_dim
        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") == "clip":
            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 CLIPVisionConfig(PretrainedConfig):
    r

    model_type = "clip_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") == "clip":
            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 CLIPConfig(PretrainedConfig):
    r

    model_type = "clip"

    def __init__(
        self, text_config=None, vision_config=None, projection_dim=512, 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 = CLIPTextConfig(**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 `CLIPTextConfig`. 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 = CLIPVisionConfig(**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 `CLIPVisionConfig`. "
                            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 `CLIPTextConfig` with default values.")

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

        self.text_config = CLIPTextConfig(**text_config)
        self.vision_config = CLIPVisionConfig(**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: CLIPTextConfig, vision_config: CLIPVisionConfig, **kwargs):
        r

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


class CLIPOnnxConfig(OnnxConfig):
    @property
    def inputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("input_ids", {0: "batch", 1: "sequence"}),
                ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
                ("attention_mask", {0: "batch", 1: "sequence"}),
            ]
        )

    @property
    def outputs(self) -> Mapping[str, Mapping[int, str]]:
        return OrderedDict(
            [
                ("logits_per_image", {0: "batch"}),
                ("logits_per_text", {0: "batch"}),
                ("text_embeds", {0: "batch"}),
                ("image_embeds", {0: "batch"}),
            ]
        )

    @property
    def atol_for_validation(self) -> float:
        return 1e-4

    def generate_dummy_inputs(
        self,
        processor: "ProcessorMixin",
        batch_size: int = -1,
        seq_length: int = -1,
        framework: Optional["TensorType"] = None,
    ) -> Mapping[str, Any]:
        text_input_dict = super().generate_dummy_inputs(
            processor.tokenizer, batch_size=batch_size, seq_length=seq_length, framework=framework
        )
        image_input_dict = super().generate_dummy_inputs(
            processor.image_processor, batch_size=batch_size, framework=framework
        )
        return {**text_input_dict, **image_input_dict}

    @property
    def default_onnx_opset(self) -> int:
        return 14