from transformers import MarianForCausalLM, MarianTokenizer, Trainer, TrainingArguments
from datasets import load_dataset

# Załaduj model i tokenizer
model_name = "allegro/multislav-5lang"
model = MarianForCausalLM.from_pretrained(model_name)
tokenizer = MarianTokenizer.from_pretrained(model_name)

# Załaduj dane (przykład dla tłumaczenia z języka rumuńskiego na angielski)
dataset = load_dataset("wmt16", "ro-en")

# Przetwórz dane do formatu odpowiedniego dla modelu
def tokenize_function(examples):
    # Jeśli 'translation' to lista słowników, np. [{'en': 'text1', 'ro': 'text1_translated'}, ...]
    return tokenizer([example['en'] for example in examples['translation']], 
                     [example['ro'] for example in examples['translation']], 
                     truncation=True, padding='max_length', max_length=128)

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

# Skonfiguruj trenera
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=4,
    per_device_eval_batch_size=4,
    num_train_epochs=3,
    weight_decay=0.01,
)

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

# Trening modelu
trainer.train()