 Here is a simplified version of the requested code. 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 TranslationBenchmark
from avalanche.datasets import TabularDataset
from avalanche.utils.data import DataCollator
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from torch.utils.data import DataLoader

class TranslationDataCollator(DataCollator):
    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 = [input_ids[i][:, -1] for i in range(len(input_ids))]
        return {'input_ids': torch.cat(input_ids, dim=0), 'attention_mask': torch.cat([torch.ones_like(input_ids[0])] * len(input_ids), dim=0), 'decoder_input_ids': torch.cat(decoder_input_ids, dim=0), 'target_ids': torch.cat(target_ids, dim=0)}

class TranslationStrategy(avalanche.training.strategy.NaiveStrategy):
    def __init__(self, model, batch_size, tokenizer):
        super().__init__(model)
        self.batch_size = batch_size
        self.tokenizer = tokenizer

    def forward(self, batch):
        input_ids = batch['input_ids'].squeeze(1)
        attention_mask = batch['attention_mask'].squeeze(1)
        decoder_input_ids = batch['decoder_input_ids'].squeeze(1)
        target_ids = batch['target_ids'].squeeze(1)

        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_ids):
        return loss

def main():
    tokenizer = AutoTokenizer.from_pretrained('t5-base')
    dataset = TabularDataset(path='path_to_your_dataset', target_column='target', source_column='input')
    preprocessed_dataset = dataset.transform(lambda example: {'input': example['input'], 'target': example['target']})

    model = AutoModelForSeq2SeqLM.from_pretrained('t5-base')
    strategy = TranslationStrategy(model, batch_size=8, tokenizer=tokenizer)

    benchmark = TranslationBenchmark(preprocessed_dataset, strategy)
    benchmark.fit(5)
    benchmark.evaluate(5)

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

This code loads a T5 model, prepares a custom data collator, creates a training strategy for machine translation tasks, and sets up a continual learning scenario with the Avalanche library. The model is then trained and evaluated on the continual learning benchmark. You should replace `'path_to_your_dataset'` with the path to your dataset.