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 groupvit model configuration import os from collections import ordereddict from typing import typechecking any mapping optional union from configurationutils import pretrainedconfig from onnx import onnxconfig from utils import logging if typechecking from processingutils import processormixin from utils import tensortype logger logging getloggername groupvitpretrainedconfigarchivemap nvidiagroupvitgccyfcc https huggingface convidiagroupvitgccyfccresolvemainconfig json class groupvittextconfigpretrainedconfig r this is the configuration class to store the configuration of a groupvittextmodel it is used to instantiate an groupvit 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 groupvit nvidiagroupvitgccyfcchttps huggingface convidiagroupvitgccyfcc 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 groupvit text model defines the number of different tokens that can be represented by the inputsids passed when calling groupvitmodel hiddensize int optional defaults to 256 dimensionality of the encoder layers and the pooler layer intermediatesize int optional defaults to 1024 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 4 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 dropout float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler 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 groupvittextconfig groupvittextmodel initializing a groupvittextmodel with nvidiagroupvitgccyfcc style configuration configuration groupvittextconfig model groupvittextmodelconfiguration accessing the model configuration configuration model config modeltype groupvittextmodel def init self vocabsize49408 hiddensize256 intermediatesize1024 numhiddenlayers12 numattentionheads4 maxpositionembeddings77 hiddenactquickgelu layernormeps1e5 dropout0 0 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 padtokenid1 bostokenid49406 eostokenid49407 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self hiddensize hiddensize self intermediatesize intermediatesize self dropout dropout 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 groupvitconfig if configdict getmodeltype groupvit 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 groupvitvisionconfigpretrainedconfig r this is the configuration class to store the configuration of a groupvitvisionmodel it is used to instantiate an groupvit 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 groupvit nvidiagroupvitgccyfcchttps huggingface convidiagroupvitgccyfcc 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 384 dimensionality of the encoder layers and the pooler layer intermediatesize int optional defaults to 1536 dimensionality of the intermediate i e feedforward layer in the transformer encoder depths listint optional defaults to 6 3 3 the number of layers in each encoder block numgrouptokens listint optional defaults to 64 8 0 the number of group tokens for each stage numoutputgroups listint optional defaults to 64 8 8 the number of output groups for each stage 0 means no group numattentionheads int optional defaults to 6 number of attention heads for each attention layer in the transformer encoder imagesize int optional defaults to 224 the size resolution of each image patchsize int optional defaults to 16 the size resolution of each patch hiddenact str or function optional defaults to gelu 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 dropout float optional defaults to 0 0 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler 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 groupvitvisionconfig groupvitvisionmodel initializing a groupvitvisionmodel with nvidiagroupvitgccyfcc style configuration configuration groupvitvisionconfig model groupvitvisionmodelconfiguration accessing the model configuration configuration model config modeltype groupvitvisionmodel def init self hiddensize384 intermediatesize1536 depths6 3 3 numhiddenlayers12 numgrouptokens64 8 0 numoutputgroups64 8 8 numattentionheads6 imagesize224 patchsize16 numchannels3 hiddenactgelu layernormeps1e5 dropout0 0 attentiondropout0 0 initializerrange0 02 initializerfactor1 0 assigneps1 0 assignmlpratio0 5 4 kwargs super initkwargs self hiddensize hiddensize self intermediatesize intermediatesize self depths depths if numhiddenlayers sumdepths logger warning fmanually setting numhiddenlayers to numhiddenlayers but we expect numhiddenlayers f sumdepth sumdepths self numhiddenlayers numhiddenlayers self numgrouptokens numgrouptokens self numoutputgroups numoutputgroups self numattentionheads numattentionheads self imagesize imagesize self patchsize patchsize self numchannels numchannels self hiddenact hiddenact self layernormeps layernormeps self dropout dropout self attentiondropout attentiondropout self initializerrange initializerrange self initializerfactor initializerfactor self assigneps assigneps self assignmlpratio assignmlpratio 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 groupvitconfig if configdict getmodeltype groupvit 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 groupvitconfigpretrainedconfig r groupvitconfig is the configuration class to store the configuration of a groupvitmodel it is used to instantiate a groupvit 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 groupvit nvidiagroupvitgccyfcchttps huggingface convidiagroupvitgccyfcc 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 groupvittextconfig visionconfig dict optional dictionary of configuration options used to initialize groupvitvisionconfig projectiondim int optional defaults to 256 dimentionality of text and vision projection layers projectionintermediatedim int optional defaults to 4096 dimentionality of intermediate layer of text and vision projection layers logitscaleinitvalue float optional defaults to 2 6592 the inital value of the logitscale parameter default is used as per the original groupvit 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 class groupvitonnxconfigonnxconfig property def inputsself mappingstr mappingint str return ordereddict inputids 0 batch 1 sequence pixelvalues 0 batch 1 numchannels 2 height 3 width attentionmask 0 batch 1 sequence property def outputsself mappingstr mappingint str return ordereddict logitsperimage 0 batch logitspertext 0 batch textembeds 0 batch imageembeds 0 batch property def atolforvalidationself float return 1e4 def generatedummyinputs self processor processormixin batchsize int 1 seqlength int 1 framework optionaltensortype none mappingstr any textinputdict super generatedummyinputs processor tokenizer batchsizebatchsize seqlengthseqlength frameworkframework imageinputdict super generatedummyinputs processor imageprocessor batchsizebatchsize frameworkframework return textinputdict imageinputdict property def defaultonnxopsetself int return 14 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 groupvit model configuration this is the configuration class to store the configuration of a groupvittextmodel it is used to instantiate an groupvit 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 groupvit nvidia groupvit gcc yfcc https huggingface co nvidia groupvit gcc yfcc 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 groupvit text model defines the number of different tokens that can be represented by the inputs_ids passed when calling groupvitmodel hidden_size int optional defaults to 256 dimensionality of the encoder layers and the pooler layer intermediate_size int optional defaults to 1024 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 4 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 dropout float optional defaults to 0 0 the dropout probability for all fully connected layers in the embeddings encoder and pooler 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 groupvittextconfig groupvittextmodel initializing a groupvittextmodel with nvidia groupvit gcc yfcc style configuration configuration groupvittextconfig model groupvittextmodel configuration accessing the model configuration configuration model config get the text config dict if we are loading from groupvitconfig this is the configuration class to store the configuration of a groupvitvisionmodel it is used to instantiate an groupvit 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 groupvit nvidia groupvit gcc yfcc https huggingface co nvidia groupvit gcc yfcc 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 384 dimensionality of the encoder layers and the pooler layer intermediate_size int optional defaults to 1536 dimensionality of the intermediate i e feed forward layer in the transformer encoder depths list int optional defaults to 6 3 3 the number of layers in each encoder block num_group_tokens list int optional defaults to 64 8 0 the number of group tokens for each stage num_output_groups list int optional defaults to 64 8 8 the number of output groups for each stage 0 means no group num_attention_heads int optional defaults to 6 number of attention heads for each attention layer in the transformer encoder image_size int optional defaults to 224 the size resolution of each image patch_size int optional defaults to 16 the size resolution of each patch hidden_act str or function optional defaults to gelu the non linear activation function function or string in the encoder and pooler if string gelu relu selu and gelu_new quick_gelu are supported layer_norm_eps float optional defaults to 1e 5 the epsilon used by the layer normalization layers dropout float optional defaults to 0 0 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler 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 groupvitvisionconfig groupvitvisionmodel initializing a groupvitvisionmodel with nvidia groupvit gcc yfcc style configuration configuration groupvitvisionconfig model groupvitvisionmodel configuration accessing the model configuration configuration model config get the vision config dict if we are loading from groupvitconfig groupvitconfig is the configuration class to store the configuration of a groupvitmodel it is used to instantiate a groupvit 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 groupvit nvidia groupvit gcc yfcc https huggingface co nvidia groupvit gcc yfcc 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 groupvittextconfig vision_config dict optional dictionary of configuration options used to initialize groupvitvisionconfig projection_dim int optional defaults to 256 dimentionality of text and vision projection layers projection_intermediate_dim int optional defaults to 4096 dimentionality of intermediate layer of text and vision projection layers 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 groupvit 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 groupvitconfig or a derived class from groupvit text model configuration and groupvit vision model configuration returns groupvitconfig an instance of a configuration object
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

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


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


logger = logging.get_logger(__name__)

GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "nvidia/groupvit-gcc-yfcc": "https://huggingface.co/nvidia/groupvit-gcc-yfcc/resolve/main/config.json",
}


class GroupViTTextConfig(PretrainedConfig):
    r

    model_type = "groupvit_text_model"

    def __init__(
        self,
        vocab_size=49408,
        hidden_size=256,
        intermediate_size=1024,
        num_hidden_layers=12,
        num_attention_heads=4,
        max_position_embeddings=77,
        hidden_act="quick_gelu",
        layer_norm_eps=1e-5,
        dropout=0.0,
        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.dropout = dropout
        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") == "groupvit":
            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 GroupViTVisionConfig(PretrainedConfig):
    r

    model_type = "groupvit_vision_model"

    def __init__(
        self,
        hidden_size=384,
        intermediate_size=1536,
        depths=[6, 3, 3],
        num_hidden_layers=12,
        num_group_tokens=[64, 8, 0],
        num_output_groups=[64, 8, 8],
        num_attention_heads=6,
        image_size=224,
        patch_size=16,
        num_channels=3,
        hidden_act="gelu",
        layer_norm_eps=1e-5,
        dropout=0.0,
        attention_dropout=0.0,
        initializer_range=0.02,
        initializer_factor=1.0,
        assign_eps=1.0,
        assign_mlp_ratio=[0.5, 4],
        **kwargs,
    ):
        super().__init__(**kwargs)

        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.depths = depths
        if num_hidden_layers != sum(depths):
            logger.warning(
                f"Manually setting num_hidden_layers to {num_hidden_layers}, but we expect num_hidden_layers ="
                f" sum(depth) = {sum(depths)}"
            )
        self.num_hidden_layers = num_hidden_layers
        self.num_group_tokens = num_group_tokens
        self.num_output_groups = num_output_groups
        self.num_attention_heads = num_attention_heads
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.hidden_act = hidden_act
        self.layer_norm_eps = layer_norm_eps
        self.dropout = dropout
        self.attention_dropout = attention_dropout
        self.initializer_range = initializer_range
        self.initializer_factor = initializer_factor
        self.assign_eps = assign_eps
        self.assign_mlp_ratio = assign_mlp_ratio

    @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") == "groupvit":
            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 GroupViTConfig(PretrainedConfig):
    r

    model_type = "groupvit"

    def __init__(
        self,
        text_config=None,
        vision_config=None,
        projection_dim=256,
        projection_intermediate_dim=4096,
        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 = GroupViTTextConfig(**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 `GroupViTTextConfig`. "
                            f'The 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 = GroupViTVisionConfig(**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 `GroupViTVisionConfig`."
                            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 `GroupViTTextConfig` with default values.")

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

        self.text_config = GroupViTTextConfig(**text_config)
        self.vision_config = GroupViTVisionConfig(**vision_config)

        self.projection_dim = projection_dim
        self.projection_intermediate_dim = projection_intermediate_dim
        self.logit_scale_init_value = logit_scale_init_value
        self.initializer_range = 0.02
        self.initializer_factor = 1.0
        self.output_segmentation = False

    @classmethod
    def from_text_vision_configs(cls, text_config: GroupViTTextConfig, vision_config: GroupViTVisionConfig, **kwargs):
        r

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


class GroupViTOnnxConfig(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