Seq2SeqTrainer Error
I have the following code:encoder_model = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")

decoder_model = BertModel.from_pretrained("bert-base-chinese")

model = VisionEncoderDecoderModel(encoder=encoder_model, decoder=decoder_model)

model.config.decoder.is_decoder = True

model.config.decoder.add_cross_attention = True

model.config.decoder_start_token_id = processor.tokenizer.cls_token_id

model.config.pad_token_id = processor.tokenizer.pad_token_id

model.config.vocab_size = model.config.decoder.vocab_size

model.config.eos_token_id = processor.tokenizer.sep_token_id

model.config.max_length = 64

model.config.early_stopping = True

model.config.no_repeat_ngram_size = 3

model.config.length_penalty = 2.0

model.config.num_beams = 4

training_args = Seq2SeqTrainingArguments(
    output_dir="./",
    evaluation_strategy="steps",
    learning_rate=1e-4,  # Decreased learning rate
    per_device_train_batch_size=10,  # Decreased batch size
    per_device_eval_batch_size=10,  # Decreased batch size
    weight_decay=0.01,
    save_total_limit=3,
    num_train_epochs=5,  # Decreased number of epochs
    logging_dir="./logs",
    logging_steps=500,  
    save_steps=1000,  
    save_strategy="steps",
    predict_with_generate=True,
    max_steps=100,
    gradient_accumulation_steps=2
)

cer_metric = load_metric("cer")

def compute_metrics(pred):
    labels_ids = pred.label_ids
    decoder_logits = pred.predictions.decoder_logits

    if isinstance(decoder_logits, tuple):
        # If using beam search decoding, select the first beam
        decoder_logits = decoder_logits[0]

    pred_ids = decoder_logits.argmax(-1)
    pred_str = processor.batch_decode(pred_ids, skip_special_tokens=True)

    labels_ids[labels_ids == -100] = processor.tokenizer.pad_token_id
    label_str = processor.batch_decode(labels_ids, skip_special_tokens=True)

    cer = cer_metric.compute(predictions=pred_str, references=label_str)

    return {"cer": cer}and I’m trying to train my model as follows:os.environ["TOKENIZERS_PARALLELISM"] = "false"

optimizer = TorchAdamW(model.parameters(), lr=training_args.learning_rate)
scheduler = get_linear_schedule_with_warmup(
    optimizer,
    num_warmup_steps=500,
    num_training_steps=len(train_dataset) * training_args.num_train_epochs
)

# Create the trainer
trainer = Seq2SeqTrainer(
    model=model,
    tokenizer=processor.tokenizer,
    args=training_args,
    compute_metrics=compute_metrics,
    train_dataset=train_dataset,
    eval_dataset=test_dataset,
    data_collator=default_data_collator,
    optimizers=(optimizer, scheduler) 
)

# Train the model
trainer.train()but I getAttributeError: ‘BaseModelOutputWithPoolingAndCrossAttentions’ object has no attribute ‘logits’There doesn’t seem to be a lot of info online on how to debug this error, any help would be greatly appreciated!!