  Here is the code you requested:
```
from avalanche.benchmarks.classic import CelebA
from avalanche.evaluation.metrics import accuracy
from avalanche.models import Naive
from avalanche.training.plugins import EvaluationPlugin
from avalanche.training.strategies import Naive
from avalanche.training.utils import get_layers_and_params
from avalanche.training.utils import get_paths_and_targets
from avalanche.training.utils import declare_state_and_memory
from avalanche.training.utils import test_flatten_and_reindex
from avalanche.training.utils import filter_bn_and_brn
from avalanche.training.utils import grouped_and_ordered_indexes
from avalanche.training.utils import _download_and_extract_archive
from avalanche.training.utils import test_avalanche_dataset_multiple_outputs_and_float_y
from avalanche.training.utils import _check_and_adapt_user_stream_def
from avalanche.training.utils import test_custom_streams_name_and_length
from avalanche.training.utils import test_set_random_seeds_and_align
from avalanche.training.utils import generate_code
from avalanche.training.utils import _get_experience_and_load_if_needed

# Load the tokenizer and dataset
tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
dataset = CelebA(phase_and_task='train', download=True)

# Preprocess the dataset
dataset = dataset.map(lambda x: tokenizer(x['text'], truncation=True, padding=True))

# Create a sequence-to-sequence model
model = Naive(
    input_dim=dataset.input_dim,
    output_dim=dataset.output_dim,
    hidden_size=128,
    num_layers=2,
    dropout=0.1,
    bidirectional=True
)

# Create a custom data collator class that pads labels and prepares decoder input ids
class CustomDataCollator:
    def __init__(self, tokenizer, max_length):
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __call__(self, batch):
        # Pad labels
        labels = batch['labels']
        labels = torch.tensor(labels)
        labels = labels.unsqueeze(1)
        labels = labels.repeat(1, self.max_length)

        # Prepare decoder input ids
        input_ids = batch['input_ids']
        input_ids = torch.tensor(input_ids)
        input_ids = input_ids.unsqueeze(1)
        input_ids = input_ids.repeat(1, self.max_length)

        # Create a dictionary with the padded labels and decoder input ids
        output = {
            'labels': labels,
            'input_ids': input_ids
        }

        return output

# Create a custom training strategy that handles Huggingface minibatches and adapts the forward and criterion methods for machine translation tasks
class CustomTrainingStrategy(Naive):
    def __init__(self, model, optimizer, criterion, device, data_collator, max_length):
        super().__init__(model, optimizer, criterion, device, data_collator)
        self.max_length = max_length

    def forward(self, batch):
        # Prepare the input and output sequences
        input_ids = batch['input_ids']
        labels = batch['labels']

        # Encode the input sequence
        encoder_output = self.model.encoder(input_ids)

        # Prepare the decoder input
        decoder_input = torch.tensor(labels)
        decoder_input = decoder_input.unsqueeze(1)
        decoder_input = decoder_input.repeat(1, self.max_length)

        # Decode the output sequence
        output = self.model.decoder(decoder_input, encoder_output)

        return output

    def criterion(self, output, labels):
        # Calculate the loss
        loss = self.criterion(output, labels)

        return loss

# Set up a continual learning scenario with the Avalanche library
scenario = ContinualLearningScenario(
    dataset=dataset,
    model=model,
    optimizer=optimizer,
    criterion=criterion,
    device=device,
    data_collator=CustomDataCollator(tokenizer, max_length=128),
    training_strategy=CustomTrainingStrategy(model, optimizer, criterion, device, data_collator, max_length=128),
    metrics=accuracy,
    plugins=[EvaluationPlugin()]
)

# Train and evaluate the model on the continual learning benchmark
scenario.train(num_experiences=10, num_epochs=10)
scenario.evaluate(num_experiences=10)
```