图文匹配（ITM）demo 运行报错，

[guanhdrmq]

from VLE import VLEForITM, VLEProcessor, VLEForITMPipeline from PIL import Image

model_dir = "./pretrained/vle-base" itm_text = ["a photo of a cat.", "a photo of dogs."] itm_images = Image.open("pics/dogs.png")

print("Init ITM model") model = VLEForITM.from_pretrained(model_dir) vle_processor = VLEProcessor.from_pretrained(model_dir)

print("init ITM pipeline") itm_pipeline = VLEForITMPipeline(model=model, device='cpu', vle_processor=vle_processor)

itm_pred = itm_pipeline([{"image": itm_images, "text": itm_text[0]}, {"image": itm_images, "text": itm_text[1]}])

for t, pred in zip(itm_text,itm_pred): print(t,pred)

==================================== Init ITM model init ITM pipeline Traceback (most recent call last): File "D:\PycharmProjects\vle_attack\test.py", line 16, in itm_pred = itm_pipeline([{"image": itm_images, "text": itm_text[0]}, File "D:\PycharmProjects\vle_attack\VLE\pipeline_vle.py", line 206, in call results = super().call(inputs, kwargs) File "D:\anaconda3\envs\tinyllava\lib\site-packages\transformers\pipelines\base.py", line 1143, in call outputs = list(final_iterator) File "D:\anaconda3\envs\tinyllava\lib\site-packages\transformers\pipelines\pt_utils.py", line 124, in next item = next(self.iterator) File "D:\anaconda3\envs\tinyllava\lib\site-packages\transformers\pipelines\pt_utils.py", line 124, in next item = next(self.iterator) File "D:\anaconda3\envs\tinyllava\lib\site-packages\torch\utils\data\dataloader.py", line 631, in next data = self._next_data() File "D:\anaconda3\envs\tinyllava\lib\site-packages\torch\utils\data\dataloader.py", line 675, in _next_data data = self._dataset_fetcher.fetch(index) # may raise StopIteration File "D:\anaconda3\envs\tinyllava\lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\anaconda3\envs\tinyllava\lib\site-packages\torch\utils\data_utils\fetch.py", line 51, in data = [self.dataset[idx] for idx in possibly_batched_index] File "D:\anaconda3\envs\tinyllava\lib\site-packages\transformers\pipelines\pt_utils.py", line 19, in getitem processed = self.process(item, self.params) File "D:\PycharmProjects\vle_attack\VLE\pipeline_vle.py", line 210, in preprocess model_inputs = self.vle_processor(text=inputs['text'], images=inputs['image'], return_tensors="pt", padding=True) File "D:\PycharmProjects\vle_attack\VLE\processing_vle.py", line 102, in call encoding = self.tokenizer(text, max_length=25, return_tensors=return_tensors, padding="max_length", truncation=True, **kwargs) TypeError: DebertaV2Tokenizer(name_or_path='./pretrained/vle-large', vocab_size=128000, model_max_length=1000000000000000019884624838656, is_fast=False, padding_side='right', truncation_side='right', special_tokens={'bos_token': '[CLS]', 'eos_token': '[SEP]', 'unk_token': '[UNK]', 'sep_token': '[SEP]', 'pad_token': '[PAD]', 'cls_token': '[CLS]', 'mask_token': '[MASK]'}, clean_up_tokenization_spaces=True), added_tokens_decoder={ 0: AddedToken("[PAD]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), 1: AddedToken("[CLS]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), 2: AddedToken("[SEP]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), 3: AddedToken("[UNK]", rstrip=False, lstrip=False, single_word=False, normalized=True, special=True), 128000: AddedToken("[MASK]", rstrip=False, lstrip=False, single_word=False, normalized=False, special=True), } got multiple values for keyword argument 'padding'

1请问demo输入是不是不对？测试Patch分类（PBC）demo可以执行。 2 图文匹配模型是否可以执行NLVR任务或者visual spatial reasoning任务? 谢谢