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 vilt model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername viltpretrainedconfigarchivemap dandelinviltb32mlm https huggingface codandelinviltb32mlmblobmainconfig json class viltconfigpretrainedconfig r this is the configuration class to store the configuration of a viltmodel it is used to instantiate an vilt 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 vilt dandelinviltb32mlmhttps huggingface codandelinviltb32mlm 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 30522 vocabulary size of the text part of the model defines the number of different tokens that can be represented by the inputsids passed when calling viltmodel typevocabsize int optional defaults to 2 the vocabulary size of the tokentypeids passed when calling viltmodel this is used when encoding text modalitytypevocabsize int optional defaults to 2 the vocabulary size of the modalities passed when calling viltmodel this is used after concatening the embeddings of the text and image modalities maxpositionembeddings int optional defaults to 40 the maximum sequence length that this model might ever be used with hiddensize int optional defaults to 768 dimensionality of the encoder layers and the pooler layer 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 intermediatesize int optional defaults to 3072 dimensionality of the intermediate i e feedforward layer in the transformer encoder 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 are supported hiddendropoutprob float optional defaults to 0 0 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler attentionprobsdropoutprob 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 layernormeps float optional defaults to 1e12 the epsilon used by the layer normalization layers imagesize int optional defaults to 384 the size resolution of each image patchsize int optional defaults to 32 the size resolution of each patch numchannels int optional defaults to 3 the number of input channels qkvbias bool optional defaults to true whether to add a bias to the queries keys and values maximagelength int optional defaults to 1 the maximum number of patches to take as input for the transformer encoder if set to a positive integer the encoder will sample maximagelength patches at maximum if set to 1 will not be taken into account numimages int optional defaults to 1 the number of images to use for natural language visual reasoning if set to a positive integer will be used by viltforimagesandtextclassification for defining the classifier head example python from transformers import viltmodel viltconfig initializing a vilt dandelinviltb32mlm style configuration configuration viltconfig initializing a model from the dandelinviltb32mlm style configuration model viltmodelconfiguration accessing the model configuration configuration model config modeltype vilt def init self vocabsize30522 typevocabsize2 modalitytypevocabsize2 maxpositionembeddings40 hiddensize768 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 0 attentionprobsdropoutprob0 0 initializerrange0 02 layernormeps1e12 imagesize384 patchsize32 numchannels3 qkvbiastrue maximagelength1 tiewordembeddingsfalse numimages1 kwargs super inittiewordembeddingstiewordembeddings kwargs self vocabsize vocabsize self typevocabsize typevocabsize self modalitytypevocabsize modalitytypevocabsize self maxpositionembeddings maxpositionembeddings self hiddensize hiddensize self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self layernormeps layernormeps self imagesize imagesize self patchsize patchsize self numchannels numchannels self qkvbias qkvbias self maximagelength maximagelength self numimages numimages 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 vilt model configuration this is the configuration class to store the configuration of a viltmodel it is used to instantiate an vilt 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 vilt dandelin vilt b32 mlm https huggingface co dandelin vilt b32 mlm 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 30522 vocabulary size of the text part of the model defines the number of different tokens that can be represented by the inputs_ids passed when calling viltmodel type_vocab_size int optional defaults to 2 the vocabulary size of the token_type_ids passed when calling viltmodel this is used when encoding text modality_type_vocab_size int optional defaults to 2 the vocabulary size of the modalities passed when calling viltmodel this is used after concatening the embeddings of the text and image modalities max_position_embeddings int optional defaults to 40 the maximum sequence length that this model might ever be used with hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer 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 intermediate_size int optional defaults to 3072 dimensionality of the intermediate i e feed forward layer in the transformer encoder 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 are supported hidden_dropout_prob float optional defaults to 0 0 the dropout probabilitiy for all fully connected layers in the embeddings encoder and pooler attention_probs_dropout_prob 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 layer_norm_eps float optional defaults to 1e 12 the epsilon used by the layer normalization layers image_size int optional defaults to 384 the size resolution of each image patch_size int optional defaults to 32 the size resolution of each patch num_channels int optional defaults to 3 the number of input channels qkv_bias bool optional defaults to true whether to add a bias to the queries keys and values max_image_length int optional defaults to 1 the maximum number of patches to take as input for the transformer encoder if set to a positive integer the encoder will sample max_image_length patches at maximum if set to 1 will not be taken into account num_images int optional defaults to 1 the number of images to use for natural language visual reasoning if set to a positive integer will be used by viltforimagesandtextclassification for defining the classifier head example python from transformers import viltmodel viltconfig initializing a vilt dandelin vilt b32 mlm style configuration configuration viltconfig initializing a model from the dandelin vilt b32 mlm style configuration model viltmodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

VILT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "dandelin/vilt-b32-mlm": "https://huggingface.co/dandelin/vilt-b32-mlm/blob/main/config.json"
}


class ViltConfig(PretrainedConfig):
    r

    model_type = "vilt"

    def __init__(
        self,
        vocab_size=30522,
        type_vocab_size=2,
        modality_type_vocab_size=2,
        max_position_embeddings=40,
        hidden_size=768,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.0,
        attention_probs_dropout_prob=0.0,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        image_size=384,
        patch_size=32,
        num_channels=3,
        qkv_bias=True,
        max_image_length=-1,
        tie_word_embeddings=False,
        num_images=-1,
        **kwargs,
    ):
        super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)

        self.vocab_size = vocab_size
        self.type_vocab_size = type_vocab_size
        self.modality_type_vocab_size = modality_type_vocab_size
        self.max_position_embeddings = max_position_embeddings

        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.initializer_range = initializer_range
        self.layer_norm_eps = layer_norm_eps

        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.qkv_bias = qkv_bias
        self.max_image_length = max_image_length
        self.num_images = num_images