from transformers import GPT2Tokenizer, GPT2LMHeadModel, Trainer, TrainingArguments
from datasets import load_dataset

# Load the text dataset from the specified file.
dataset = load_dataset("text", data_files="training.txt")

# Initialize the GPT-2 tokenizer.
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
# Set the tokenizer's pad token to the EOS token.
tokenizer.pad_token = tokenizer.eos_token

# Define a function to tokenize the dataset and prepare labels.
def tokenize_function(examples):
    # Tokenize the text to input_ids, attention_mask
    tokenized_inputs = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=512)
    # Prepare labels: labels are the same as input_ids for language modeling
    tokenized_inputs["labels"] = tokenized_inputs["input_ids"].copy()
    return tokenized_inputs

# Tokenize the entire dataset.
tokenized_datasets = dataset.map(tokenize_function, batched=True)
# Remove the 'text' column as it's no longer needed after tokenization.
tokenized_datasets = tokenized_datasets.remove_columns(["text"])
# Set the format of the dataset to PyTorch tensors.
tokenized_datasets.set_format(type="torch", columns=["input_ids", "attention_mask", "labels"])

# Load the GPT-2 model.
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Define training arguments.
training_args = TrainingArguments(
    output_dir="./output",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
)

# Initialize the Trainer with the training dataset including labels.
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
)

# Start the training process.
trainer.train()

# Save the fine-tuned model and tokenizer.
model.save_pretrained("fine_tuned_gpt2_model")
tokenizer.save_pretrained("fine_tuned_gpt2_model")