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

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

# Load the dataset and take only 1000 samples
logger.info("Loading dataset...")
dataset = load_dataset("fddemarco/pushshift-reddit-comments", split="train")
dataset = dataset.shuffle(seed=42)
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
def prepare_data(example):
    return {"input_text": example["body"], "target_text": example["body"]}

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-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)

def tokenize_function(examples):
    model_inputs = tokenizer(examples["input_text"], max_length=256, truncation=True, padding="max_length")
    labels = tokenizer(examples["target_text"], max_length=256, truncation=True, padding="max_length")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

logger.info("Tokenizing dataset...")
tokenized_dataset = {split: data.map(tokenize_function, 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=3,  # Reduced from 5 to 3
    per_device_train_batch_size=32,  # Increased from 16 to 32
    per_device_eval_batch_size=32,
    warmup_steps=2000,  # Increased from 1000 to 2000
    weight_decay=0.01,
    logging_dir="./logs",
    logging_steps=500,  # Increased from 100 to 500
    evaluation_strategy="steps",
    eval_steps=2000,  # Increased from 500 to 2000
    save_steps=2000,  # Increased from 500 to 2000
    use_cpu=False,
    load_best_model_at_end=True,
    metric_for_best_model="eval_loss",
    greater_is_better=False,
    fp16=True,
    gradient_accumulation_steps=2,  # Reduced from 4 to 2
    predict_with_generate=True,
    generation_max_length=256,
    generation_num_beams=4,
)

optimizer = torch.optim.AdamW(model.parameters(), lr=3e-5)  # Slightly lower learning rate
scheduler = get_linear_schedule_with_warmup(
    optimizer,
    num_warmup_steps=500,
    num_training_steps=len(tokenized_dataset["train"]) * training_args.num_train_epochs
)

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

# Train the model
logger.info("Starting training...")
trainer.train()
logger.info("Training completed.")

# Log final results
logger.info("Evaluating model...")
results = trainer.evaluate()
logger.info(f"Final evaluation results: {results}")

# Save the model and tokenizer to the Hugging Face Hub
logger.info("Saving model and tokenizer to Hugging Face Hub...")
model_name = "umut-bozdag/humanize_model"
trainer.push_to_hub(model_name)
tokenizer.push_to_hub(model_name)
logger.info(f"Model and tokenizer saved successfully as '{model_name}'")