 Here is a simplified version of the code that follows your instructions. Please note that this is a basic example and might need adjustments to fit your specific needs. Also, you need to have the Avalanche library and Hugging Face Transformers installed.

```python
from avalanche.benchmarks import CelebA
from avalanche.dataset import AvalancheDataset
from avalanche.metrics import Accuracy
from avalanche.models.torch import Naive
from avalanche.utils import flatten_datasets_and_reindex, grouped_and_ordered_indexes
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch

class MachineTranslationDataCollator(object):
    def __init__(self, tokenizer):
        self.tokenizer = tokenizer

    def __call__(self, samples):
        input_ids = [self.tokenizer(sample['input'], padding=True, truncation=True, return_tensors="pt")['input_ids'] for sample in samples]
        target_ids = [self.tokenizer(sample['target'], padding=True, truncation=True, return_tensors="pt")['input_ids'] for sample in samples]
        decoder_input_ids = torch.cat([torch.zeros((len(input_ids[i]), 1), dtype=torch.long) for i in range(len(input_ids))], dim=0)
        decoder_input_ids = torch.cat([decoder_input_ids, torch.cat(input_ids, dim=0)], dim=1)
        return {'input_ids': torch.cat(input_ids, dim=0), 'attention_mask': torch.cat([torch.ones_like(input_ids[i]) for i in range(len(input_ids))], dim=0), 'decoder_input_ids': decoder_input_ids, 'target_ids': torch.cat(target_ids, dim=0)}

class MachineTranslationTrainer(Naive):
    def forward(self, batch):
        input_ids = batch['input_ids']
        attention_mask = batch['attention_mask']
        decoder_input_ids = batch['decoder_input_ids']
        target_ids = batch['target_ids']
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids, labels=target_ids)
        loss = outputs.loss
        logits = outputs.logits
        return loss, logits

    def criterion(self, loss, logits, target):
        return loss

def main():
    tokenizer = AutoTokenizer.from_pretrained('t5-base')
    dataset = AvalancheDataset(CelebA(num_classes=10), tokenizer, 'input', 'target')
    preprocessed_dataset = MachineTranslationDataCollator(tokenizer)(dataset)
    model = AutoModelForSeq2SeqLM.from_pretrained('t5-base')
    trainer = MachineTranslationTrainer(model)
    state = trainer.initialize_state(preprocessed_dataset)
    avalanche_dataset = flatten_datasets_and_reindex(preprocessed_dataset, grouped_and_ordered_indexes(preprocessed_dataset))
    check_model_and_optimizer(trainer.model, trainer.optimizer)

    for phase, task in phase_and_task(avalanche_dataset):
        phase_state = declare_state_and_memory(state, phase)
        for experience in phase:
            phase_state = trainer.train_on_batch(experience)
            _update_metrics_and_loggers(trainer, phase_state, experience)
        state = phase_state

    test_flatten_and_reindex(avalanche_dataset)
    test_set_random_seeds_and_align(avalanche_dataset)

if __name__ == "__main__":
    main()
```

This code loads a T5 model from Hugging Face, preprocesses the CelebA dataset for machine translation tasks, and sets up a continual learning scenario with the Avalanche library. The MachineTranslationDataCollator class pads labels and prepares decoder input ids, and the MachineTranslationTrainer class modifies the forward and criterion methods for machine translation tasks. The main function initializes the model, preprocesses the dataset, and trains the model on the continual learning benchmark.