  Here is a possible implementation of the instructions:
```
import torch
from torch.utils.data import Dataset, DataLoader
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments
from avalanche.models import Naive
from avalanche.training import SupervisedTraining
from avalanche.evaluation import EvaluationPlugin

class HuggingfaceDataCollator:
    def __init__(self, tokenizer, max_length):
        self.tokenizer = tokenizer
        self.max_length = max_length

    def __call__(self, batch):
        # Prepare labels
        labels = torch.tensor([example['label'] for example in batch])

        # Prepare decoder input ids
        decoder_input_ids = torch.tensor([example['decoder_input_ids'] for example in batch])

        # Prepare attention masks
        attention_mask = torch.tensor([example['attention_mask'] for example in batch])

        # Prepare token type ids
        token_type_ids = torch.tensor([example['token_type_ids'] for example in batch])

        # Prepare special tokens mask
        special_tokens_mask = torch.tensor([example['special_tokens_mask'] for example in batch])

        # Prepare padding
        padding = torch.tensor([example['padding'] for example in batch])

        # Prepare batch
        batch = {
            'input_ids': torch.tensor([example['input_ids'] for example in batch]),
            'labels': labels,
            'decoder_input_ids': decoder_input_ids,
            'attention_mask': attention_mask,
            'token_type_ids': token_type_ids,
            'special_tokens_mask': special_tokens_mask,
            'padding': padding
        }

        return batch

class HuggingfaceNaiveTraining(Naive):
    def __init__(self, model, criterion, optimizer, scheduler, device, tokenizer, max_length):
        super().__init__(model, criterion, optimizer, scheduler, device)
        self.tokenizer = tokenizer
        self.max_length = max_length

    def forward(self, batch):
        # Encode input ids
        input_ids = batch['input_ids']
        attention_mask = batch['attention_mask']
        token_type_ids = batch['token_type_ids']
        special_tokens_mask = batch['special_tokens_mask']
        padding = batch['padding']

        # Encode input ids
        encoder_output = self.model.encode(input_ids, attention_mask, token_type_ids, special_tokens_mask, padding)

        # Decode input ids
        decoder_input_ids = batch['decoder_input_ids']
        decoder_attention_mask = batch['decoder_attention_mask']
        decoder_token_type_ids = batch['decoder_token_type_ids']
        decoder_special_tokens_mask = batch['decoder_special_tokens_mask']
        decoder_padding = batch['decoder_padding']

        # Decode input ids
        decoder_output = self.model.decode(decoder_input_ids, decoder_attention_mask, decoder_token_type_ids, decoder_special_tokens_mask, decoder_padding)

        # Compute loss
        loss = self.criterion(decoder_output, batch['labels'])

        return loss

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

        return loss

def main():
    # Load tokenizer and dataset
    tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
    dataset = ...

    # Preprocess dataset
    dataset = dataset.map(lambda example: tokenizer.encode_plus(example['text'], max_length=512, padding='max_length', truncation=True, return_attention_mask=True, return_tensors='pt'))

    # Create sequence-to-sequence model
    model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased', num_labels=8)

    # Set up continual learning scenario
    scenario = ...

    # Create custom data collator
    data_collator = HuggingfaceDataCollator(tokenizer, max_length=512)

    # Create custom naive training strategy
    training_strategy = HuggingfaceNaiveTraining(model, criterion=torch.nn.CrossEntropyLoss(), optimizer=torch.optim.AdamW(model.parameters(), lr=1e-5), scheduler=torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=10), device=torch.device('cuda'), tokenizer=tokenizer, max_length=512)

    # Train and evaluate model
    training_strategy.train(dataset, scenario, data_collator=data_collator, evaluation_plugin=EvaluationPlugin(accuracy_fn=torch.nn.functional.accuracy))

if __name__ == '__main__':
    main()
```
Note that this is just a possible implementation and may need to be modified to fit the specific needs of your project. Additionally, this code assumes that you have already installed the necessary dependencies, including `transformers`, `torch`, and `avalanche`.