import torch
from transformers import T5ForConditionalGeneration, T5Tokenizer, Trainer, TrainingArguments
from datasets import load_dataset
import os

os.environ["HF_HOME"] = "/app/hf_cache"
os.environ["HF_DATASETS_CACHE"] = "/app/hf_cache"
os.environ["TRANSFORMERS_CACHE"] = "/app/hf_cache"

# Load dataset (Replace this with your dataset)
dataset = load_dataset("tatsu-lab/alpaca")  # Example alternative dataset

# Load model and tokenizer
model_name = "t5-large"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name)

# Tokenization function
def tokenize_function(examples):
    inputs = [ex["input"] for ex in examples]
    targets = [ex["output"] for ex in examples]
    model_inputs = tokenizer(inputs, max_length=512, truncation=True, padding="max_length")
    labels = tokenizer(targets, max_length=512, truncation=True, padding="max_length")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

# Apply tokenization
tokenized_datasets = dataset.map(tokenize_function, batched=True)

# Training arguments
training_args = TrainingArguments(
    output_dir="./t5-finetuned",
    per_device_train_batch_size=2,  # Smaller batch to avoid memory errors
    per_device_eval_batch_size=2,  # Smaller eval batch
    save_total_limit=1,  # Keep only 1 checkpoint
    num_train_epochs=1,  # Quick test with 1 epoch
    logging_steps=50,  # More frequent logging
    evaluation_strategy="epoch",  # Evaluate only at the end of the epoch
    save_strategy="epoch",  # Save only at the end of the epoch
    push_to_hub=False  # Avoid pushing test model to Hugging Face Hub
)

# Trainer setup
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["test"],
)

# Train the model
trainer.train()

# Save and push model to Hugging Face Hub
trainer.push_to_hub("your-hf-username/t5-cover-letter")