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

class CustomDataCollator:
    def __init__(self, tokenizer, model):
        self.tokenizer = tokenizer
        self.model = model

    def __call__(self, batch):
        inputs = [item['input_text'] for item in batch]
        targets = [item['target_text'] for item in batch]
        model_inputs = self.tokenizer(inputs, padding=True, truncation=True, return_tensors="pt")
        with self.tokenizer.as_target_tokenizer():
            labels = self.tokenizer(targets, padding=True, truncation=True, return_tensors="pt")["input_ids"]
        labels[labels == self.tokenizer.pad_token_id] = -100
        decoder_input_ids = self.model.prepare_decoder_input_ids_from_labels(labels=labels)
        model_inputs["labels"] = labels
        model_inputs["decoder_input_ids"] = decoder_input_ids
        return model_inputs

class CustomNaiveStrategy(Naive):
    def __init__(self, model, optimizer, criterion, train_mb_size, eval_mb_size, device, collator):
        super().__init__(model, optimizer, criterion, train_mb_size, eval_mb_size, device)
        self.collator = collator

    def forward(self, x):
        return self.model(**x)

    def criterion(self, forward_results, y):
        return forward_results.loss

def preprocess_function(examples, tokenizer):
    inputs = [ex['input_text'] for ex in examples["translation"]]
    targets = [ex['target_text'] for ex in examples["translation"]]
    model_inputs = tokenizer(inputs, max_length=128, truncation=True)
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(targets, max_length=128, truncation=True)["input_ids"]
    model_inputs["labels"] = labels
    return model_inputs

def main():
    tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
    model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-fr")
    raw_dataset = load_dataset("wmt16", "de-en", split='train[:1%]')
    processed_dataset = raw_dataset.map(lambda examples: preprocess_function(examples, tokenizer), batched=True)
    train_dataset = AvalancheDataset(processed_dataset)
    scenario = GenericCLScenario(train_dataset=train_dataset, test_dataset=None, task_labels=[0]*len(train_dataset))

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    optimizer = torch.optim.Adam(model.parameters())
    collator = CustomDataCollator(tokenizer, model)
    strategy = CustomNaiveStrategy(model, optimizer, torch.nn.CrossEntropyLoss(), 16, 16, device, collator)

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

    for experience in scenario.train_stream:
        train_loader = DataLoader(experience.dataset, batch_size=16, collate_fn=collator, shuffle=True)
        strategy.train(train_loader, num_workers=4)
        strategy.eval(scenario.test_stream, num_workers=4)

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