from datasets import load_dataset, DatasetDict
from transformers import AutoTokenizer, T5ForConditionalGeneration, Seq2SeqTrainingArguments, Seq2SeqTrainer
from transformers import EarlyStoppingCallback
from transformers.integrations import TensorBoardCallback
import logging

# Set up logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

def generate_formal_text(text):
    # Implement formal text generation here
    return text  # Placeholder

def prepare_data(example):
    example["formal_text"] = generate_formal_text(example["text"])
    return example

def tokenize_function(examples, tokenizer):
    model_inputs = tokenizer(examples["formal_text"], max_length=128, truncation=True, padding="max_length")
    labels = tokenizer(examples["text"], max_length=128, truncation=True, padding="max_length")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

def main():
    # Load the dataset and take only 10000 samples
    logger.info("Loading dataset...")
    dataset = load_dataset("LucasChu/reddit_comments")
    dataset = dataset.shuffle(seed=42)
    dataset["train"] = dataset["train"].select(range(10000))
    logger.info("Dataset loaded, shuffled, and truncated to 10,000 samples.")

    # Split the train dataset into train and test
    train_testvalid = dataset["train"].train_test_split(test_size=0.2, seed=42)
    test_valid = train_testvalid["test"].train_test_split(test_size=0.5, seed=42)

    dataset = DatasetDict({
        "train": train_testvalid["train"],
        "test": test_valid["test"],
        "validation": test_valid["train"]
    })

    # Prepare the dataset
    logger.info("Preparing dataset...")
    processed_dataset = {split: data.map(prepare_data) for split, data in dataset.items()}
    logger.info("Dataset prepared.")

    # Tokenize the dataset
    model_name = "t5-small"
    tokenizer = AutoTokenizer.from_pretrained(model_name)

    logger.info("Tokenizing dataset...")
    tokenized_dataset = {split: data.map(lambda examples: tokenize_function(examples, tokenizer), batched=True) for split, data in processed_dataset.items()}
    logger.info("Dataset tokenized.")

    # Check available splits in the dataset
    available_splits = list(tokenized_dataset.keys())
    logger.info(f"Available splits in the dataset: {available_splits}")

    # Set up the model and trainer
    logger.info("Setting up model and trainer...")
    model = T5ForConditionalGeneration.from_pretrained(model_name)

    training_args = Seq2SeqTrainingArguments(
        output_dir="./results",
        num_train_epochs=1,
        per_device_train_batch_size=16,
        warmup_steps=100,
        weight_decay=0.01,
        logging_dir="./logs",
        logging_steps=100,
        evaluation_strategy="steps" if "test" in available_splits else "no",
        eval_steps=500,
        save_steps=1000,
        load_best_model_at_end=True,
        metric_for_best_model="eval_loss",
        greater_is_better=False
    )

    trainer = Seq2SeqTrainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset["train"],
        eval_dataset=tokenized_dataset.get("test"),
        tokenizer=tokenizer,
        callbacks=[EarlyStoppingCallback(early_stopping_patience=3), TensorBoardCallback()]
    )
    logger.info("Model and trainer set up.")

    # Return the trainer object
    return trainer

if __name__ == "__main__":
    main()