 ```python
from avalanche.benchmarks.classification import CelebA
from avalanche.training.strategies import Naive
from avalanche.models import HFModel
from avalanche.datasets import AvalancheDataset
from avalanche.training.templates import Sequential
from avalanche.training.utils import get_learning_rate
from avalanche.evaluation.metrics import AverageScore
from avalanche.logging import TextLogger
from avalanche.training.callbacks import Callback
from transformers import (
    AutoTokenizer,
    AutoModelForSeq2SeqLM,
    Seq2SeqTrainingArguments,
    Seq2SeqTrainer,
)
import torch

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

    def collate(self, samples):
        input_sequences, labels = zip(*samples)
        input_ids = self.tokenizer(input_sequences, padding=True, truncation=True, return_tensors="pt").input_ids
        labels = self.tokenizer(labels, padding=True, truncation=True, return_tensors="pt").input_ids
        max_input_length = input_ids.shape[1]
        max_target_length = labels.shape[1]

        # Pad decoder input ids
        decoder_input_ids = torch.zeros((len(input_ids), max_target_length), dtype=torch.long)
        for i, ids in enumerate(input_ids):
            decoder_input_ids[i, :ids.shape[1]] = ids[0, 1:]

        return {"input_ids": input_ids, "decoder_input_ids": decoder_input_ids, "labels": labels}

class CustomNaiveStrategy(Naive):
    def __init__(self, model: HFModel, data_collator: CustomDataCollator, criterion, optimizer, **kwargs):
        super().__init__(model=model, criterion=criterion, optimizer=optimizer, **kwargs)
        self.data_collator = data_collator

    def forward(self, batch):
        input_ids = batch["input_ids"]
        decoder_input_ids = batch["decoder_input_ids"]
        labels = batch["labels"]

        # Perform forward pass
        outputs = self.model(
            input_ids,
            attention_mask=None,
            decoder_input_ids=decoder_input_ids,
            labels=labels,
        )

        return outputs.loss, outputs.logits

    def criterion(self, outputs, batch):
        return self.criterion_fn(outputs, batch["labels"])

def main():
    tokenizer = AutoTokenizer.from_pretrained("t5-base")
    model = AutoModelForSeq2SeqLM.from_pretrained("t5-base")

    # Prepare dataset
    dataset = CelebA(phase_and_task="train", download=True)
    dataset = AvalancheDataset(dataset, transform=tokenizer, target_transform=tokenizer)

    # Preprocess dataset
    data_collator = CustomDataCollator(tokenizer)
    preprocessed_dataset = dataset.map(data_collator, batched=True)

    # Create a sequence-to-sequence model
    model = HFModel(model, optimizer=None, criterion=torch.nn.CrossEntropyLoss())

    # Set up a continual learning scenario with the Avalanche library
    strategy = CustomNaiveStrategy(
        model=model,
        data_collator=data_collator,
        criterion=torch.nn.CrossEntropyLoss(),
        optimizer=torch.optim.AdamW(model.parameters(), lr=1e-4),
        batch_sampler=None,
        train_mb_sampler=None,
        valid_mb_sampler=None,
        lr_scheduler=None,
        device="cuda",
        callbacks=[],
    )

    # Train and evaluate the model
    scenario = Sequential(
        streams=[
            {
                "name": "stream_1",
                "num_examples": len(dataset),
            }
        ],
        max_length=10,
        evaluation_strategy="end",
        model=model,
        strategy=strategy,
        logger=TextLogger(),
        metrics=[AverageScore()],
    )

    scenario.train()
    scenario.eval()

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