import os
import re
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, TrainingArguments, Trainer, DataCollatorForLanguageModeling
from datasets import Dataset

# Konfiguracja
os.environ["TOKENIZERS_PARALLELISM"] = "false"
MODEL_NAME = "gpt2"
SPECIAL_TOKENS = ["[CITATION_START]", "[CITATION_END]"]
TEXT_FILE_PATH = "./docs/kodekspracy.txt"  # Zmień na właściwą ścieżkę

def prepare_dataset_from_file(file_path):
    with open(file_path, 'r', encoding='utf-8') as f:
        text = f.read()

    # Wydziel artykuły za pomocą wyrażenia regularnego
    articles = re.findall(r'Art\.\s*\d+[a-z]*\..*?(?=\s*Art\.\s*\d+[a-z]*\.|\Z)', text, flags=re.DOTALL)
    
    formatted_articles = []
    for article in articles:
        # Usuń zbędne białe znaki
        article = ' '.join(article.strip().split())
        
        # Wydziel numer artykułu i treść
        art_match = re.match(r'Art\.\s*(\d+[a-z]*)\.?\s*(.*)', article, re.DOTALL)
        if art_match:
            art_number = art_match.group(1)
            art_text = art_match.group(2)
            
            # Podziel na paragrafy, jeśli istnieją
            paragraphs = re.split(r'(§\s*\d+\.)', art_text)
            if len(paragraphs) > 1:
                formatted_paragraphs = []
                for i in range(1, len(paragraphs), 2):
                    para_num = paragraphs[i].strip()
                    para_text = paragraphs[i+1].strip()
                    formatted_paragraphs.append(f"{para_num} {para_text}")
                formatted = f"[CITATION_START] Kodeks Pracy, Art. {art_number} [CITATION_END]\n" + "\n".join(formatted_paragraphs)
            else:
                formatted = f"[CITATION_START] Kodeks Pracy, Art. {art_number} [CITATION_END] {art_text}"
            
            formatted_articles.append({"text": formatted})
    
    return formatted_articles


def main():
    # Inicjalizacja tokenizera
    tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
    tokenizer.add_special_tokens({"additional_special_tokens": SPECIAL_TOKENS})
    tokenizer.pad_token = tokenizer.eos_token

    # Przygotowanie danych
    data = prepare_dataset_from_file(TEXT_FILE_PATH)
    dataset = Dataset.from_dict({"text": [d["text"] for d in data]})

    # Tokenizacja
    def tokenize_function(examples):
        tokenized = tokenizer(
            examples["text"],
            truncation=True,
            padding="max_length",
            max_length=256,  # Zwiększono dla dłuższych artykułów
            return_tensors="pt"
        )
        tokenized["labels"] = tokenized["input_ids"].clone()
        return tokenized

    tokenized_dataset = dataset.map(tokenize_function, batched=True)

    # Model i data collator
    model = AutoModelForCausalLM.from_pretrained(MODEL_NAME)
    model.resize_token_embeddings(len(tokenizer), mean_resizing=False)
    
    data_collator = DataCollatorForLanguageModeling(
        tokenizer=tokenizer,
        mlm=False
    )

    # Konfiguracja treningu
    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=5,  # Zwiększono liczbę epok
        per_device_train_batch_size=2,
        learning_rate=5e-5,
        logging_steps=10,
        report_to="none",
        save_strategy="no"
    )

    # Trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=tokenized_dataset,
        data_collator=data_collator
    )

    print("Rozpoczęcie treningu...")
    trainer.train()
    trainer.save_model("./trained_model/gpt")

if __name__ == "__main__":
    main()