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 visualbert model configuration from configurationutils import pretrainedconfig from utils import logging logger logging getloggername visualbertpretrainedconfigarchivemap uclanlpvisualbertvqa https huggingface couclanlpvisualbertvqaresolvemainconfig json uclanlpvisualbertvqapre https huggingface couclanlpvisualbertvqapreresolvemainconfig json uclanlpvisualbertvqacocopre https huggingface couclanlpvisualbertvqacocopreresolvemainconfig json uclanlpvisualbertvcr https huggingface couclanlpvisualbertvcrresolvemainconfig json uclanlpvisualbertvcrpre https huggingface couclanlpvisualbertvcrpreresolvemainconfig json uclanlpvisualbertvcrcocopre https huggingface couclanlpvisualbertvcrcocopreresolvemainconfig json uclanlpvisualbertnlvr2 https huggingface couclanlpvisualbertnlvr2resolvemainconfig json uclanlpvisualbertnlvr2pre https huggingface couclanlpvisualbertnlvr2preresolvemainconfig json uclanlpvisualbertnlvr2cocopre https huggingface couclanlpvisualbertnlvr2cocopreresolvemainconfig json see all visualbert models at https huggingface comodels filtervisualbert class visualbertconfigpretrainedconfig r this is the configuration class to store the configuration of a visualbertmodel it is used to instantiate an visualbert 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 visualbert uclanlpvisualbertvqacocoprehttps huggingface couclanlpvisualbertvqacocopre 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 visualbert model defines the number of different tokens that can be represented by the inputsids passed when calling visualbertmodel vocabulary size of the model defines the different tokens that can be represented by the inputsids passed to the forward method of visualbertmodel hiddensize int optional defaults to 768 dimensionality of the encoder layers and the pooler layer visualembeddingdim int optional defaults to 512 dimensionality of the visual embeddings to be passed to the model 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 1 the dropout probabilitiy 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 512 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 2 the vocabulary size of the tokentypeids passed when calling visualbertmodel 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 bypasstransformer bool optional defaults to false whether or not the model should bypass the transformer for the visual embeddings if set to true the model directly concatenates the visual embeddings from visualbertembeddings with text output from transformers and then pass it to a selfattention layer specialvisualinitialize bool optional defaults to true whether or not the visual token type and position type embedding weights should be initialized the same as the textual token type and positive type embeddings when set to true the weights of the textual token type and position type embeddings are copied to the respective visual embedding layers example python from transformers import visualbertconfig visualbertmodel initializing a visualbert visualbertvqacocopre style configuration configuration visualbertconfig frompretraineduclanlpvisualbertvqacocopre initializing a model with random weights from the visualbertvqacocopre style configuration model visualbertmodelconfiguration accessing the model configuration configuration model config modeltype visualbert def init self vocabsize30522 hiddensize768 visualembeddingdim512 numhiddenlayers12 numattentionheads12 intermediatesize3072 hiddenactgelu hiddendropoutprob0 1 attentionprobsdropoutprob0 1 maxpositionembeddings512 typevocabsize2 initializerrange0 02 layernormeps1e12 bypasstransformerfalse specialvisualinitializetrue padtokenid1 bostokenid0 eostokenid2 kwargs super initpadtokenidpadtokenid bostokenidbostokenid eostokenideostokenid kwargs self vocabsize vocabsize self maxpositionembeddings maxpositionembeddings self hiddensize hiddensize self visualembeddingdim visualembeddingdim self numhiddenlayers numhiddenlayers self numattentionheads numattentionheads self intermediatesize intermediatesize self hiddenact hiddenact self hiddendropoutprob hiddendropoutprob self attentionprobsdropoutprob attentionprobsdropoutprob self initializerrange initializerrange self typevocabsize typevocabsize self layernormeps layernormeps self bypasstransformer bypasstransformer self specialvisualinitialize specialvisualinitialize 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 visualbert model configuration see all visualbert models at https huggingface co models filter visual_bert this is the configuration class to store the configuration of a visualbertmodel it is used to instantiate an visualbert 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 visualbert uclanlp visualbert vqa coco pre https huggingface co uclanlp visualbert vqa coco pre 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 visualbert model defines the number of different tokens that can be represented by the inputs_ids passed when calling visualbertmodel vocabulary size of the model defines the different tokens that can be represented by the inputs_ids passed to the forward method of visualbertmodel hidden_size int optional defaults to 768 dimensionality of the encoder layers and the pooler layer visual_embedding_dim int optional defaults to 512 dimensionality of the visual embeddings to be passed to the model 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 1 the dropout probabilitiy 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 512 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 2 the vocabulary size of the token_type_ids passed when calling visualbertmodel 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 bypass_transformer bool optional defaults to false whether or not the model should bypass the transformer for the visual embeddings if set to true the model directly concatenates the visual embeddings from visualbertembeddings with text output from transformers and then pass it to a self attention layer special_visual_initialize bool optional defaults to true whether or not the visual token type and position type embedding weights should be initialized the same as the textual token type and positive type embeddings when set to true the weights of the textual token type and position type embeddings are copied to the respective visual embedding layers example python from transformers import visualbertconfig visualbertmodel initializing a visualbert visualbert vqa coco pre style configuration configuration visualbertconfig from_pretrained uclanlp visualbert vqa coco pre initializing a model with random weights from the visualbert vqa coco pre style configuration model visualbertmodel configuration accessing the model configuration configuration model config
from ...configuration_utils import PretrainedConfig
from ...utils import logging


logger = logging.get_logger(__name__)

VISUAL_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP = {
    "uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
    "uclanlp/visualbert-vqa-pre": "https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json",
    "uclanlp/visualbert-vqa-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-vcr": "https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json",
    "uclanlp/visualbert-vcr-pre": "https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json",
    "uclanlp/visualbert-vcr-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json"
    ),
    "uclanlp/visualbert-nlvr2": "https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-pre": "https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json",
    "uclanlp/visualbert-nlvr2-coco-pre": (
        "https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json"
    ),
    
}


class VisualBertConfig(PretrainedConfig):
    r

    model_type = "visual_bert"

    def __init__(
        self,
        vocab_size=30522,
        hidden_size=768,
        visual_embedding_dim=512,
        num_hidden_layers=12,
        num_attention_heads=12,
        intermediate_size=3072,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        max_position_embeddings=512,
        type_vocab_size=2,
        initializer_range=0.02,
        layer_norm_eps=1e-12,
        bypass_transformer=False,
        special_visual_initialize=True,
        pad_token_id=1,
        bos_token_id=0,
        eos_token_id=2,
        **kwargs,
    ):
        super().__init__(pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)

        self.vocab_size = vocab_size
        self.max_position_embeddings = max_position_embeddings
        self.hidden_size = hidden_size
        self.visual_embedding_dim = visual_embedding_dim
        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.type_vocab_size = type_vocab_size
        self.layer_norm_eps = layer_norm_eps
        self.bypass_transformer = bypass_transformer
        self.special_visual_initialize = special_visual_initialize