import torch
from transformers import Trainer, TrainingArguments
from transformers import Thinc
from dataclasses import dataclass

@dataclass
class BatchEncoding:
    input_ids: torch.Tensor
    attention_mask: torch.Tensor

def create_transformer_tagger_model():
    pass

def create_transformer_tokenizer():
    pass

def create_transformer_model():
    pass

def convert_inputs():
    pass

def convert_outputs():
    pass

def evaluate_sequences():
    pass

def group_sequences_into_minibatches():
    pass

def main():
    if torch.cuda.is_available():
        device = torch.device("cuda")
    else:
        device = torch.device("cpu")

    config = {
        "model_name": "bert-base-uncased",
        "optimizer": "AdamW",
        "learning_rate": 5e-5,
        "num_epochs": 3
    }

    dataset = load_dataset()

    tokenizer = create_transformer_tokenizer(config["model_name"])
    model = create_transformer_model(config["model_name"])

    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=config["num_epochs"],
        per_device_train_batch_size=8,
        save_steps=10_000,
        save_total_limit=2
    )

    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset["train"],
        eval_dataset=dataset["validation"]
    )

    trainer.train()

if __name__ == "__main__":
    main()