import torch
from transformers import (
    AutoModelForCausalLM,
    AutoTokenizer,
    TrainingArguments,
    Trainer,
    DataCollatorForLanguageModeling
)
from datasets import load_dataset
import os

def train():
    # Load model and tokenizer
    model_name = "microsoft/phi-2"
    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
    model = AutoModelForCausalLM.from_pretrained(model_name, device_map="cpu", trust_remote_code=True)

    # Add padding token if missing
    if tokenizer.pad_token is None:
        tokenizer.pad_token = tokenizer.eos_token

    # Load dataset (update paths as needed)
    dataset = load_dataset(
        "csv",
        data_files={
            "train": "eswardivi/medical_qa",
            "validation": "eswardivi/medical_qa"
        }
    )

    # Tokenization function
    def tokenize_function(examples):
        return tokenizer(
            examples["text"],
            padding="max_length",
            truncation=True,
            max_length=256,
            return_tensors="pt",
        )

    # Preprocess dataset
    tokenized_dataset = dataset.map(
        tokenize_function,
        batched=True,
        remove_columns=["text"]
    )

    # Data collator
    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer,
        mlm=False
    )

    # Training arguments
    training_args = TrainingArguments(
        output_dir="./phi2-cpu-results",
        overwrite_output_dir=True,
        per_device_train_batch_size=2,
        per_device_eval_batch_size=2,
        num_train_epochs=3,
        logging_dir="./logs",
        logging_steps=100,
        evaluation_strategy="epoch",
        save_strategy="epoch",
        fp16=False,
        report_to="none",
    )

    # Initialize Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset["train"],
        eval_dataset=tokenized_dataset["validation"],
        data_collator=data_collator,
    )

    # Start training
    print("Starting training...")
    trainer.train()

    # Save model
    trainer.save_model("./phi2-trained-model")
    tokenizer.save_pretrained("./phi2-trained-model")
    print("Training complete! Model saved.")

if __name__ == "__main__":
    train()