This is the starting code, if any parts are missing please fill them with the appropriate code: import torch
import torch.nn as nn
from transformers import BertTokenizer, GPT2Tokenizer, XLNetTokenizer
import sys
from PyQt5 import QtWidgets, QtGui
import torch
from torch.utils.data import DataLoader, Dataset
from transformers import BertTokenizer
import sys
from PyQt5 import QtWidgets, QtGui

# Step 1: Define the Language Model Configuration
def define_language_model():
layer_type_choices = [‘LSTM’, ‘GRU’, ‘Transformer’]
print(“Available layer types: LSTM, GRU, Transformer”)

number_of_layers = int(input(“Enter the number of layers: “))
layers_configuration = []
for i in range(number_of_layers):
while True:
layer_type = input(f"Enter the type of layer {i+1}: “).strip()
if layer_type in layer_type_choices:
layer_size = int(input(f"Enter the size for layer {i+1}: “))
layers_configuration.append((layer_type, layer_size))
break
else:
print(“Invalid layer type. Please choose from LSTM, GRU, Transformer.”)

return layers_configuration

# Step 2: Define the Tokenizer Choice
def choose_tokenizer():
tokenizer_choices = {
‘bert’: BertTokenizer,
‘gpt2’: GPT2Tokenizer,
‘xlnet’: XLNetTokenizer
}
print(“Available tokenizers: ‘bert’, ‘gpt2’, ‘xlnet’”)
while True:
choice = input(“Choose a tokenizer: “).strip().lower()
if choice in tokenizer_choices:
tokenizer = tokenizer_choices[choice].from_pretrained(f”{choice}-base-cased”)
return tokenizer
else:
print(“Invalid choice. Please select a tokenizer from ‘bert’, ‘gpt2’, ‘xlnet’.”)

# Example usage:
model_config = define_language_model()
tokenizer = choose_tokenizer()

# The configurations can then be used to build the actual model and set up the tokenizer.
# This would involve defining a PyTorch model class and initializing it with the given configurations.

print(“Model configuration and tokenizer setup are now complete.”)

# Example custom dataset (can be replaced with your specific data handling logic)
class TextDataset(Dataset):
def init(self, texts, labels, tokenizer, max_length):
self.texts = texts
self.labels = labels
self.tokenizer = tokenizer
self.max_length = max_length

def len(self):
return len(self.texts)

def getitem(self, idx):
text = self.texts[idx]
label = self.labels[idx]
encoding = self.tokenizer.encode_plus(
text,
add_special_tokens=True,
max_length=self.max_length,
return_token_type_ids=False,
padding=‘max_length’,
truncation=True,
return_attention_mask=True,
return_tensors=‘pt’,
)
return {
‘text’: text,
‘input_ids’: encoding[‘input_ids’].flatten(),
‘attention_mask’: encoding[‘attention_mask’].flatten(),
‘labels’: torch.tensor(label, dtype=torch.long)
}

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

def initUI(self):
self.setGeometry(300, 300, 400, 300)
self.setWindowTitle(‘Training Configuration’)
self.setWindowIcon(QtGui.QIcon(‘app_icon.png’)) # Put your app icon here

layout = QtWidgets.QVBoxLayout()

# Data Type Selection
data_type_label = QtWidgets.QLabel(‘Select data type:’, self)
layout.addWidget(data_type_label)
self.data_type_combo = QtWidgets.QComboBox()
self.data_type_combo.addItems([‘Text’, ‘Image’, ‘Audio’])
layout.addWidget(self.data_type_combo)

# Epochs Input
epochs_label = QtWidgets.QLabel(‘Number of epochs:’, self)
layout.addWidget(epochs_label)
self.epochs_spinbox = QtWidgets.QSpinBox()
self.epochs_spinbox.setMinimum(1)
self.epochs_spinbox.setMaximum(1000)
layout.addWidget(self.epochs_spinbox)

# Batch Size Input
batch_size_label = QtWidgets.QLabel(‘Batch size:’, self)
layout.addWidget(batch_size_label)
self.batch_size_spinbox = QtWidgets.QSpinBox()
self.batch_size_spinbox.setMinimum(1)
self.batch_size_spinbox.setMaximum(256)
layout.addWidget(self.batch_size_spinbox)

# Max Sequence Length Input
max_length_label = QtWidgets.QLabel(‘Max sequence length:’, self)
layout.addWidget(max_length_label)
self.max_length_spinbox = QtWidgets.QSpinBox()
self.max_length_spinbox.setMinimum(1)
layout.addWidget(self.max_length_spinbox)

# Submit Button
submit_button = QtWidgets.QPushButton(‘Start Training’, self)
submit_button.clicked.connect(self.start_training)
layout.addWidget(submit_button)

self.setLayout(layout)

def start_training(self):
data_type = self.data_type_combo.currentText()
epochs = self.epochs_spinbox.value()
batch_size = self.batch_size_spinbox.value()
max_length = self.max_length_spinbox.value()

# Placeholder logic for starting training
print(‘Starting training with the following configuration:’)
print(‘Data Type:’, data_type)
print(‘Number of Epochs:’, epochs)
print(‘Batch Size:’, batch_size)
print(‘Max Sequence Length:’, max_length)

# This is where you would start the training process using the provided configuration.

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’)
self.setWindowIcon(QtGui.QIcon(‘app_icon.png’)) # Put your app icon here

vbox = QtWidgets.QVBoxLayout()

# Model Layer Configuration
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 Selection
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
submit_button = QtWidgets.QPushButton(‘Generate Model’, self)
submit_button.clicked.connect(self.generate_model)
vbox.addWidget(submit_button)

self.setLayout(vbox)

# Apply custom style
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):
layers = []
for i in range(self.layer_list_widget.count()):
item_text = self.layer_list_widget.item(i).text()
layer_info = item_text.split(’ - Size: ')
layers.append((layer_info[0], int(layer_info[1])))
tokenizer = self.tokenizer_combo.currentText()

# Placeholder logic for model generation
print(‘Generating model with layers:’, layers)
print(‘Using tokenizer:’, tokenizer)

# This is where you would pass the layer configurations and tokenizer
# to your model creation logic.

def main():
app = QtWidgets.QApplication(sys.argv)
ex = LanguageModelConfigurator()
ex.show()
sys.exit(app.exec_())

if name == “main”:
main()