mat_lit_dataset.py

import torch
from torch.utils.data import Dataset
from transformers import AutoTokenizer

class TransNARTextDataset(Dataset):
    def __init__(self, data_type, num_samples, max_length, vocab_size, device):
        self.data_type = data_type
        self.num_samples = num_samples
        self.max_length = max_length
        self.vocab_size = vocab_size
        self.device = device

        # Carregar o tokenizador pré-treinado
        if data_type == 'math':
            self.tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
        elif data_type == 'literature':
            self.tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased')
        else:
            raise ValueError("data_type must be 'math' or 'literature'")

        # Gerar dados de entrada e labels
        self.input_ids, self.attention_masks, self.labels = self.generate_data()

    def __len__(self):
        return self.num_samples

    def __getitem__(self, idx):
        return self.input_ids[idx], self.attention_masks[idx], self.labels[idx]

    def generate_data(self):
        input_ids = []
        attention_masks = []
        labels = []

        for _ in range(self.num_samples):
            if self.data_type == 'math':
                text = self.generate_math_text()
            else:
                text = self.generate_literature_text()

            # Tokenizar o texto
            encoded = self.tokenizer.encode_plus(
                text,
                max_length=self.max_length,
                pad_to_max_length=True,
                return_attention_mask=True,
                return_tensors='pt',
            )

            input_ids.append(encoded['input_ids'])
            attention_masks.append(encoded['attention_mask'])
            labels.append(self.generate_label(text))

        return torch.stack(input_ids).to(self.device), \
               torch.stack(attention_masks).to(self.device), \
               torch.stack(labels).to(self.device)

    def generate_math_text(self):
        # Gera texto matemático sintético
        pass

    def generate_literature_text(self):
        # Gera texto de literatura sintético
        pass

    def generate_label(self, text):
        # Gera label para o texto
        pass