print("Starting training process...")
from datasets import load_dataset
from transformers import (
    AutoModelForSeq2SeqLM,
    AutoTokenizer,
    Trainer,
    DataCollatorForSeq2Seq
)
from training_config import training_args

# Load dataset
print("Loading dataset...")
dataset = load_dataset("health360/Healix-Shot", split=f"train[:100000]")

# Initialize model and tokenizer
print("Initializing model and tokenizer...")
model_name = "google/flan-t5-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)

def tokenize_function(examples):
    return tokenizer(
        examples['text'],
        padding="max_length",
        truncation=True,
        max_length=512,
        return_attention_mask=True
    )

# Process dataset
print("Processing dataset...")
train_test_split = dataset.train_test_split(test_size=0.1)
tokenized_train = train_test_split['train'].map(
    tokenize_function, 
    batched=True, 
    remove_columns=dataset.column_names
)
tokenized_eval = train_test_split['test'].map(
    tokenize_function, 
    batched=True, 
    remove_columns=dataset.column_names
)

# Initialize trainer
print("Initializing trainer...")
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_train,
    eval_dataset=tokenized_eval,
    data_collator=DataCollatorForSeq2Seq(tokenizer=tokenizer, model=model)
)

# Train and save
print("Starting the training...")
trainer.train()
print("Training complete, saving model...")
model.push_to_hub("MjolnirThor/flan-t5-custom-handler")
tokenizer.push_to_hub("MjolnirThor/flan-t5-custom-handler")
print("Model saved successfully!")