Fix any bugs in this code and return the fixed code: import sys
import torch
import torch.nn as nn
from PyQt5 import QtWidgets, QtCore
from PyQt5.QtWidgets import QMessageBox, QFileDialog
from transformers import BertTokenizer, GPT2Tokenizer, XLNetTokenizer
import pandas as pd
import torch.optim as optim


# Define the neural network model.
class RNNModel(nn.Module):
    def __init__(self, input_size, hidden_sizes, layer_types):
        super(RNNModel, self).__init__()
        self.layers = nn.ModuleList()
        for i, layer_type in enumerate(layer_types):
            input_dim = input_size if i == 0 else hidden_sizes[i - 1]
            if layer_type == 'LSTM':
                self.layers.append(nn.LSTM(input_dim, hidden_sizes[i], batch_first=True))
            elif layer_type == 'GRU':
                self.layers.append(nn.GRU(input_dim, hidden_sizes[i], batch_first=True))
            elif layer_type == 'Transformer':
                transformer_layer = nn.TransformerEncoderLayer(d_model=input_dim, nhead=1)
                self.layers.append(nn.TransformerEncoder(transformer_layer, num_layers=1))
            else:
                raise ValueError(f"Unrecognized layer type: {layer_type}")

    def forward(self, x):
        for layer in self.layers:
            if isinstance(layer, nn.TransformerEncoder):
                x = layer(x)
            else:
                x, _ = layer(x)
        return x
    

# Define the custom GUI widget for configuring the language model.
class LanguageModelConfigurator(QtWidgets.QWidget):
    def __init__(self):
        super(LanguageModelConfigurator, self).__init__()
        self.model = None
        self.train_data_loader = None
        self.data_filepath = None
        self.initUI()

    def initUI(self):
        self.setGeometry(300, 300, 300, 200)
        self.setWindowTitle('Language Model Configuration')
        self.seq_length_spinbox = QtWidgets.QSpinBox()
        self.seq_length_spinbox.setMinimum(1)
        self.seq_length_spinbox.setMaximum(512)
        self.seq_length_spinbox.setValue(128)

        vbox = QtWidgets.QVBoxLayout(self)

        seq_length_layout = QtWidgets.QHBoxLayout()
        seq_length_label = QtWidgets.QLabel("Sequence Length:", self)
        seq_length_layout.addWidget(seq_length_label)
        seq_length_layout.addWidget(self.seq_length_spinbox)

        vbox.addLayout(seq_length_layout)

        layer_config_group = QtWidgets.QGroupBox("Layer Configuration")
        layer_config_layout = QtWidgets.QVBoxLayout(layer_config_group)

        self.layer_list_widget = QtWidgets.QListWidget()
        layer_config_layout.addWidget(self.layer_list_widget)

        hbox_layers = QtWidgets.QHBoxLayout()
        self.layer_type_combo = QtWidgets.QComboBox()
        self.layer_type_combo.addItems(['LSTM', 'GRU', 'Transformer'])
        self.layer_size_spinbox = QtWidgets.QSpinBox()
        self.layer_size_spinbox.setMinimum(1)
        self.layer_size_spinbox.setMaximum(1024)

        hbox_layers.addWidget(self.layer_type_combo)
        hbox_layers.addWidget(self.layer_size_spinbox)

        add_layer_button = QtWidgets.QPushButton('Add Layer')
        add_layer_button.clicked.connect(self.add_layer)
        hbox_layers.addWidget(add_layer_button)

        layer_config_layout.addLayout(hbox_layers)
        vbox.addWidget(layer_config_group)

        tokenizer_config_group = QtWidgets.QGroupBox("Tokenizer Configuration")
        tokenizer_config_layout = QtWidgets.QVBoxLayout(tokenizer_config_group)

        tokenizer_label = QtWidgets.QLabel('Choose a tokenizer:', self)
        tokenizer_config_layout.addWidget(tokenizer_label)

        self.tokenizer_combo = QtWidgets.QComboBox()
        self.tokenizer_combo.addItems([
            'bert-base-cased',
            'gpt2-medium',
            'xlnet-base-cased'
        ])
        tokenizer_config_layout.addWidget(self.tokenizer_combo)

        self.status_label = QtWidgets.QLabel("")

        vbox.addWidget(tokenizer_config_group)
        vbox.addWidget(self.status_label)

        submit_button = QtWidgets.QPushButton('Generate Model', self)
        submit_button.clicked.connect(self.generate_model)
        vbox.addWidget(submit_button)

        self.setLayout(vbox)
        self.setStyleSheet("""
            QWidget {
                font-size: 14px;
            }
            QPushButton {
                background-color: #007BFF;
                border-style: none;
                padding: 6px 12px;
                color: white;
                border-radius: 4px;
            }
            QPushButton:hover {
                background-color: #0056b3;
            }
            QLabel {
                padding-bottom: 4px;
            }
        """)

        # Data file line edit and browse button
        self.data_file_line_edit = QtWidgets.QLineEdit(self)
        browse_button = QtWidgets.QPushButton('Browse Data File', self)
        browse_button.clicked.connect(self.browse_data_file)

        data_file_layout = QtWidgets.QHBoxLayout()
        data_file_layout.addWidget(self.data_file_line_edit)
        data_file_layout.addWidget(browse_button)

        vbox.addLayout(data_file_layout)

        # Epochs spinbox
        num_epochs_layout = QtWidgets.QHBoxLayout()
        num_epochs_label = QtWidgets.QLabel("Number of Epochs:", self)
        self.num_epochs_spinbox = QtWidgets.QSpinBox()
        self.num_epochs_spinbox.setMinimum(1)
        self.num_epochs_spinbox.setMaximum(100)
        num_epochs_layout.addWidget(num_epochs_label)
        num_epochs_layout.addWidget(self.num_epochs_spinbox)

        vbox.addLayout(num_epochs_layout)

        # Training button layout
        training_layout = QtWidgets.QHBoxLayout()
        self.train_button = QtWidgets.QPushButton('Train Model', self)
        self.train_button.clicked.connect(self.on_train_click)
        training_layout.addWidget(self.train_button)

        vbox.addLayout(training_layout)


    def browse_data_file(self):
        filepath, _ = QFileDialog.getOpenFileName(self, "Open Data File", "", "Data Files (*.csv *.txt)")
        if filepath:
            self.data_file_line_edit.setText(filepath)
            self.data_filepath = filepath

    def load_data(self):
        if self.data_filepath:
            try:
               if self.data_filepath.endswith('.csv'):
                  data_df = pd.read_csv(self.data_filepath)
                  print("CSV data loaded successfully!")
                  return data_df
               elif self.data_filepath.endswith('.txt'):
                with open(self.data_filepath, 'r', encoding='utf-8') as file:
                    text_data = file.read()
                print("Text data loaded successfully!")
                return text_data
               else:
                self.showMessage("Error", "Unsupported file type selected.")
                return None
            except Exception as e:
              self.showMessage("Error", f"Failed to load data: {e}")
            return None
        else:
         self.showMessage("Error", "No data file selected.")
        return None

    def create_data_loader(self, data):
        if isinstance(data, pd.DataFrame):
        # Convert DataFrame to tensors
          inputs = torch.tensor(data['input'].values)
          labels = torch.tensor(data['label'].values)
          dataset = torch.utils.data.TensorDataset(inputs, labels)
        elif isinstance(data, str):
        # Tokenize text data and convert to tensors
          tokenizer_name = self.tokenizer_combo.currentText()
        tokenizer_class = {
            'bert-base-cased': BertTokenizer,
            'gpt2-medium': GPT2Tokenizer,
            'xlnet-base-cased': XLNetTokenizer
        }.get(tokenizer_name)
        tokenizer = tokenizer_class.from_pretrained(tokenizer_name)
        encoded_data = tokenizer.encode_plus(
            data,
            add_special_tokens=True,
            padding='max_length',
            truncation=True,
            max_length=self.seq_length_spinbox.value(),
            return_tensors='pt'
        )
        inputs = encoded_data['input_ids']
        labels = encoded_data['input_ids']  # Just for demonstration, replace with actual labels
        dataset = torch.utils.data.TensorDataset(inputs, labels)
        else:
        return None

    # Create data loader
    batch_size = 32  # Adjust batch size as needed
    data_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=True)
        return data_loader

    def add_layer(self):
        layer_type = self.layer_type_combo.currentText()
        layer_size = self.layer_size_spinbox.value()
        self.layer_list_widget.addItem(f'{layer_type} - Size: {layer_size}')

    def showMessage(self, title, message):
        QMessageBox.information(self, title, message)

    def generate_model(self):
        self.status_label.setText("Generating model…")

        layers = [
            (item.text().split(' - Size: ')[0], int(item.text().split(' - Size: ')[1]))
            for item in self.layer_list_widget.findItems('*', QtCore.Qt.MatchWildcard)
        ]
        layer_types, hidden_sizes = zip(*layers) if layers else ([], [])

        tokenizer_name = self.tokenizer_combo.currentText()
        tokenizer_class = {
            'bert-base-cased': BertTokenizer,
            'gpt2-medium': GPT2Tokenizer,
            'xlnet-base-cased': XLNetTokenizer
        }.get(tokenizer_name)
        if not tokenizer_class:
            self.showMessage("Error", "Unsupported tokenizer selected.")
            return

        self.status_label.setText("Model generated successfully!")
        tokenizer = tokenizer_class.from_pretrained(tokenizer_name)
        input_size = self.seq_length_spinbox.value()

        try:
            self.model = RNNModel(input_size, list(hidden_sizes), list(layer_types))
            self.status_label.setText("Model generated successfully!")
        except Exception as e:
            self.status_label.setText("Model generation failed!")
            self.showMessage("Error", f"Failed to generate model: {e}")
            self.model = None

    def train_model(self, model, train_data_loader):
        num_epochs = self.num_epochs_spinbox.value()
        criterion = nn.CrossEntropyLoss()
        optimizer = optim.Adam(model.parameters(), lr=0.001)

        model.train()
        for epoch in range(num_epochs):
            for batch in train_data_loader:
                inputs, labels = batch
                
                optimizer.zero_grad()
                outputs = model(inputs)
                loss = criterion(outputs, labels)
                loss.backward()
                optimizer.step()

            print(f'Epoch {epoch+1}/{num_epochs}, Loss: {loss.item()}')
                  
    def on_train_click(self):
        if not hasattr(self, 'model'):
            self.showMessage("Error", "Model not defined.")
            return
        if self.train_data_loader is None:
            self.showMessage("Error", "Training data not loaded.")
            return
        
        self.train_model(self.model, self.train_data_loader)


class MainWindow(QtWidgets.QMainWindow):
    def __init__(self):
        super(MainWindow, self).__init__()
        self.initUI()

    def initUI(self):
        self.setWindowTitle('Model Training')
        self.language_model_configurator = LanguageModelConfigurator()
        self.setCentralWidget(self.language_model_configurator)
        self.show()


def main():
    app = QtWidgets.QApplication(sys.argv)
    main_window = MainWindow()
    sys.exit(app.exec_())


if __name__ == "__main__":
    main()