```python
import torch
from torch.utils.data import DataLoader
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq
from avalanche.benchmarks import dataset_benchmark
from avalanche.models import SimpleMLP
from avalanche.training.strategies import Naive
from avalanche.training.plugins import EvaluationPlugin
from avalanche.evaluation.metrics import accuracy_metrics, loss_metrics
from datasets import load_dataset

class CustomDataCollator(DataCollatorForSeq2Seq):
    def __call__(self, features):
        labels = [feature['labels'] for feature in features]
        labels_with_padding = self.tokenizer.pad(labels, return_tensors="pt")
        features = super().__call__(features)
        features['labels'] = labels_with_padding['input_ids']
        return features

class HuggingfaceNaiveStrategy(Naive):
    def __init__(self, model, optimizer, criterion, train_mb_size, eval_mb_size, device, tokenizer, collate_fn):
        super().__init__(model, optimizer, criterion, train_mb_size, eval_mb_size, device)
        self.tokenizer = tokenizer
        self.collate_fn = collate_fn

    def forward(self, x):
        input_ids = x['input_ids'].to(self.device)
        attention_mask = x['attention_mask'].to(self.device)
        decoder_input_ids = x.get('decoder_input_ids', None)
        if decoder_input_ids is not None:
            decoder_input_ids = decoder_input_ids.to(self.device)
        outputs = self.model(input_ids, attention_mask=attention_mask, decoder_input_ids=decoder_input_ids)
        return outputs

    def criterion(self, outputs, targets, **kwargs):
        return outputs.loss

def preprocess_data(tokenizer, dataset):
    def tokenize_function(examples):
        model_inputs = tokenizer(examples["src"], max_length=128, truncation=True)
        with tokenizer.as_target_tokenizer():
            labels = tokenizer(examples["tgt"], max_length=128, truncation=True)
        model_inputs["labels"] = labels["input_ids"]
        return model_inputs
    dataset = dataset.map(tokenize_function, batched=True)
    return dataset

def main():
    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
    dataset = load_dataset("wmt16", "de-en", split='train[:1%]')

    dataset = preprocess_data(tokenizer, dataset)
    collate_fn = CustomDataCollator(tokenizer=tokenizer, model=model)

    train_dataset = dataset['train']
    test_dataset = dataset['test']

    train_loader = DataLoader(train_dataset, batch_size=16, collate_fn=collate_fn)
    test_loader = DataLoader(test_dataset, batch_size=16, collate_fn=collate_fn)

    optimizer = torch.optim.Adam(model.parameters())
    criterion = torch.nn.CrossEntropyLoss()

    cl_strategy = HuggingfaceNaiveStrategy(
        model=model,
        optimizer=optimizer,
        criterion=criterion,
        train_mb_size=16,
        eval_mb_size=16,
        device=torch.device("cuda" if torch.cuda.is_available() else "cpu"),
        tokenizer=tokenizer,
        collate_fn=collate_fn
    )

    eval_plugin = EvaluationPlugin(accuracy_metrics(epoch=True, experience=True), loss_metrics(epoch=True))

    scenario = dataset_benchmark(train_dataset=train_loader, test_dataset=test_loader)

    cl_strategy.train(scenario.train_stream[0])
    cl_strategy.eval(scenario.test_stream[0])

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