Diff Coverage

        self.tensorboard_callback.on_train_begin(self.state)
        # Checkpoint runs after log writers are armed and before progress so
        # resumed ``global_step`` is reflected in the tqdm initial position.
        self.checkpoint_callback.on_train_begin(self.state)
        self.progress_callback.on_train_begin(self.state)
        for cb in self.user_callbacks:
            cb.on_train_begin(self.state)

    def on_train_end(self):

        # actually trained.
        micro_batches = next(data_iterator)
        prepare_batch_fn = getattr(self.spec, "prepare_batch_fn", None)
        if prepare_batch_fn is not None:
            micro_batches = [
                prepare_batch_fn(batch, self.model)
                for batch in micro_batches
            ]
        self.state.global_step += 1

        super().__init__(trainer)
        train_cfg = getattr(trainer.args, 'train', None)
        ckpt_cfg = getattr(train_cfg, 'checkpoint', None)
        if ckpt_cfg is None:
            ckpt_cfg = getattr(trainer.args, 'checkpoint', None)
        self.enabled = getattr(ckpt_cfg, 'save_hf_weights', False) if ckpt_cfg else False
        self.save_steps = getattr(ckpt_cfg, 'save_steps', 0) if ckpt_cfg else 0
        self.output_dir = getattr(ckpt_cfg, 'output_dir', 'outputs') if ckpt_cfg else 'outputs'
        self._last_saved_step = -1