Review this code: import sys
import torch
from PyQt5 import QtWidgets, QtGui, QtCore
from transformers import BertTokenizer, GPT2Tokenizer, XLNetTokenizer
import sys
import torch
import torch.nn as nn
from PyQt5 import QtWidgets
from transformers import BertTokenizer, GPT2Tokenizer, XLNetTokenizer

class RNNModel(nn.Module):
def init(self, input_size, hidden_sizes, layer_types):
super(RNNModel, self).init()
layers = []
for i, layer_type in enumerate(layer_types):
if i == 0:
input_dim = input_size
else:
input_dim = hidden_sizes[i-1]

if layer_type == ‘LSTM’:
layers.append(nn.LSTM(input_dim, hidden_sizes[i], batch_first=True))
elif layer_type == ‘GRU’:
layers.append(nn.GRU(input_dim, hidden_sizes[i], batch_first=True))
elif layer_type == ‘Transformer’:
# Assuming the use of nn.TransformerEncoderLayer for simplicity
transformer_layer = nn.TransformerEncoderLayer(
d_model=input_dim,
nhead=1, # This would depend on your specific requirements
)
layers.append(nn.TransformerEncoder(transformer_layer, num_layers=1))

# This should be replaced with the actual sequential logic you want
self.rnn_layers = nn.ModuleList(layers)

# Add output layer if needed, make sure to match the dimensions
# self.output_layer = nn.Linear(hidden_sizes[-1], num_classes)

def forward(self, x):
# Implement the forward pass, chaining together the layers
for layer in self.rnn_layers:
x, _ = layer(x)
# Output layer if it was added
# x = self.output_layer(x)
return x
# Custom Dataset class remains unchanged (except for print statements removed)
class TextDataset(torch.utils.data.Dataset):
# …


class LanguageModelConfigurator(QtWidgets.QWidget):
def init(self):
super().init()
self.initUI()

def initUI(self):
# Set window properties
self.setGeometry(300, 300, 300, 200)
self.setWindowTitle(‘Language Model Configuration’)

vbox = QtWidgets.QVBoxLayout()
self.layer_list_widget = QtWidgets.QListWidget()
vbox.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)
vbox.addLayout(hbox_layers)

tokenizer_label = QtWidgets.QLabel(‘Choose a tokenizer:’, self)
vbox.addWidget(tokenizer_label)

self.tokenizer_combo = QtWidgets.QComboBox()
self.tokenizer_combo.addItems([‘bert-base-cased’, ‘gpt2-base-cased’, ‘xlnet-base-cased’])
vbox.addWidget(self.tokenizer_combo)

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;
}
“””)

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 generate_model(self):
# Get layer configurations from the UI
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)

# Get the tokenizer based on user selection
tokenizer_class = {
‘bert-base-cased’: BertTokenizer,
‘gpt2-base-cased’: GPT2Tokenizer,
‘xlnet-base-cased’: XLNetTokenizer
}[self.tokenizer_combo.currentText()]
tokenizer = tokenizer_class.from_pretrained(self.tokenizer_combo.currentText())

# Assume input size for RNN is the size of token embeddings
input_size = tokenizer.model_max_length

# Instantiate the model with the selected configuration
model = RNNModel(input_size, hidden_sizes, layer_types)

# Print the generated model
print(model)

class MainWindow(QtWidgets.QMainWindow):
def init(self):
super().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()