import os

import torch
from peft import set_peft_model_state_dict
from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
from transformers.trainer_utils import PREFIX_CHECKPOINT_DIR


class SavePeftModelCallback(TrainerCallback):
    def on_save(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        checkpoint_folder = os.path.join(args.output_dir, f"{PREFIX_CHECKPOINT_DIR}-{state.global_step}")

        kwargs["model"].save_pretrained(checkpoint_folder)

        pytorch_model_path = os.path.join(checkpoint_folder, "pytorch_model.bin")
        torch.save({}, pytorch_model_path)
        return control


class LoadBestPeftModelCallback(TrainerCallback):
    def on_train_end(
        self,
        args: TrainingArguments,
        state: TrainerState,
        control: TrainerControl,
        **kwargs,
    ):
        print(f"Loading best peft model from {state.best_model_checkpoint} (score: {state.best_metric}).")
        best_model_path = os.path.join(state.best_model_checkpoint, "adapter_model.bin")
        adapters_weights = torch.load(best_model_path)
        model = kwargs["model"]
        set_peft_model_state_dict(model, adapters_weights)
        return control