import pickle import torch import transformers import gradio as gr # XLM model functions import transformers # Our model definition class MultilingualClipEdited(torch.nn.Module): def __init__(self, model_name, tokenizer_name, head_name, weights_dir='head_weights/', cache_dir=None,in_features=None,out_features=None): super().__init__() self.model_name = model_name self.tokenizer_name = tokenizer_name self.head_path = weights_dir + head_name self.tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_name, cache_dir=cache_dir) self.transformer = transformers.AutoModel.from_pretrained(model_name, cache_dir=cache_dir) self.clip_head = torch.nn.Linear(in_features=in_features, out_features=out_features) self._load_head() def forward(self, txt): txt_tok = self.tokenizer(txt, padding=True, return_tensors='pt') embs = self.transformer(**txt_tok)[0] att = txt_tok['attention_mask'] embs = (embs * att.unsqueeze(2)).sum(dim=1) / att.sum(dim=1)[:, None] return self.clip_head(embs) def _load_head(self): with open(self.head_path, 'rb') as f: lin_weights = pickle.loads(f.read()) self.clip_head.weight = torch.nn.Parameter(torch.tensor(lin_weights[0]).float().t()) self.clip_head.bias = torch.nn.Parameter(torch.tensor(lin_weights[1]).float()) AVAILABLE_MODELS = { 'bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M':{ 'model_name': 'Arabic-Clip/bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M', 'tokenizer_name': 'Arabic-Clip/bert-base-arabertv2-ViT-B-16-SigLIP-512-epoch-155-trained-2M', 'head_name': 'arabertv2-vit-B-16-siglibheads_of_the_model_arabertv2-ViT-B-16-SigLIP-512-155_.pickle' }, } def load_model(name, cache_dir=None,in_features=None,out_features=None): config = AVAILABLE_MODELS[name] return MultilingualClipEdited(**config, cache_dir=cache_dir, in_features= in_features, out_features=out_features)