import torch
from transformers import Trainer, TrainingArguments
from thinc.api import Config
from dataclasses import dataclass
from typing import List, Dict

@dataclass
class BatchEncoding:
    input_ids: List[List[int]]
    attention_mask: List[List[int]]

def create_transformer_tagger_model():
    pass

def create_transformer_tokenizer():
    pass

def create_transformer_model():
    pass

def convert_transformer_inputs():
    pass

def convert_transformer_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 = Config().from_str("")
    model_config = config["model"]
    optimizer_config = config["optimizer"]
    learning_rate = config["learning_rate"]
    training_params = config["training_params"]
    
    dataset = load_dataset()
    
    model = create_transformer_tagger_model()
    
    training_args = TrainingArguments(
        output_dir="./results",
        num_train_epochs=training_params["num_epochs"],
        per_device_train_batch_size=training_params["batch_size"],
        save_steps=10_000,
        save_total_limit=2,
    )
    
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=dataset,
    )
    
    trainer.train()

if __name__ == "__main__":
    main()