examples/pytorch/image-pretraining/run_mim.py/main
def main():
    # See all possible arguments in src/transformers/training_args.py
    # or by passing the --help flag to this script.
    # We now keep distinct sets of args, for a cleaner separation of concerns.

    parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
        # If we pass only one argument to the script and it's the path to a json file,
        # let's parse it to get our arguments.
        model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
    else:
        model_args, data_args, training_args = parser.parse_args_into_dataclasses()

    # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
    # information sent is the one passed as arguments along with your Python/PyTorch versions.
    send_example_telemetry("run_mim", model_args, data_args)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    if training_args.should_log:
        # The default of training_args.log_level is passive, so we set log level at info here to have that default.
        transformers.utils.logging.set_verbosity_info()

    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    transformers.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.enable_default_handler()
    transformers.utils.logging.enable_explicit_format()

    # Log on each process the small summary:
    logger.warning(
        f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
        + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
    )
    logger.info(f"Training/evaluation parameters {training_args}")

    # Detecting last checkpoint.
    last_checkpoint = None
    if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
        last_checkpoint = get_last_checkpoint(training_args.output_dir)
        if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
            raise ValueError(
                f"Output directory ({training_args.output_dir}) already exists and is not empty. "
                "Use --overwrite_output_dir to overcome."
            )
        elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
            logger.info(
                f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
                "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
            )

    # Initialize our dataset.
    ds = load_dataset(
        data_args.dataset_name,
        data_args.dataset_config_name,
        data_files=data_args.data_files,
        cache_dir=model_args.cache_dir,
        use_auth_token=True if model_args.use_auth_token else None,
    )

    # If we don't have a validation split, split off a percentage of train as validation.
    data_args.train_val_split = None if "validation" in ds.keys() else data_args.train_val_split
    if isinstance(data_args.train_val_split, float) and data_args.train_val_split > 0.0:
        split = ds["train"].train_test_split(data_args.train_val_split)
        ds["train"] = split["train"]
        ds["validation"] = split["test"]

    # Create config
    # Distributed training:
    # The .from_pretrained methods guarantee that only one local process can concurrently
    # download model & vocab.
    config_kwargs = {
        "cache_dir": model_args.cache_dir,
        "revision": model_args.model_revision,
        "use_auth_token": True if model_args.use_auth_token else None,
    }
    if model_args.config_name_or_path:
        config = AutoConfig.from_pretrained(model_args.config_name_or_path, **config_kwargs)
    elif model_args.model_name_or_path:
        config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        config = CONFIG_MAPPING[model_args.model_type]()
        logger.warning("You are instantiating a new config instance from scratch.")
        if model_args.config_overrides is not None:
            logger.info(f"Overriding config: {model_args.config_overrides}")
            config.update_from_string(model_args.config_overrides)
            logger.info(f"New config: {config}")

    # make sure the decoder_type is "simmim" (only relevant for BEiT)
    if hasattr(config, "decoder_type"):
        config.decoder_type = "simmim"

    # adapt config
    model_args.image_size = model_args.image_size if model_args.image_size is not None else config.image_size
    model_args.patch_size = model_args.patch_size if model_args.patch_size is not None else config.patch_size
    model_args.encoder_stride = (
        model_args.encoder_stride if model_args.encoder_stride is not None else config.encoder_stride
    )

    config.update(
        {
            "image_size": model_args.image_size,
            "patch_size": model_args.patch_size,
            "encoder_stride": model_args.encoder_stride,
        }
    )

    # create image processor
    if model_args.image_processor_name:
        image_processor = AutoImageProcessor.from_pretrained(model_args.image_processor_name, **config_kwargs)
    elif model_args.model_name_or_path:
        image_processor = AutoImageProcessor.from_pretrained(model_args.model_name_or_path, **config_kwargs)
    else:
        IMAGE_PROCESSOR_TYPES = {
            conf.model_type: image_processor_class for conf, image_processor_class in IMAGE_PROCESSOR_MAPPING.items()
        }
        image_processor = IMAGE_PROCESSOR_TYPES[model_args.model_type]()

    # create model
    if model_args.model_name_or_path:
        model = AutoModelForMaskedImageModeling.from_pretrained(
            model_args.model_name_or_path,
            from_tf=bool(".ckpt" in model_args.model_name_or_path),
            config=config,
            cache_dir=model_args.cache_dir,
            revision=model_args.model_revision,
            use_auth_token=True if model_args.use_auth_token else None,
        )
    else:
        logger.info("Training new model from scratch")
        model = AutoModelForMaskedImageModeling.from_config(config)

    if training_args.do_train:
        column_names = ds["train"].column_names
    else:
        column_names = ds["validation"].column_names

    if data_args.image_column_name is not None:
        image_column_name = data_args.image_column_name
    elif "image" in column_names:
        image_column_name = "image"
    elif "img" in column_names:
        image_column_name = "img"
    else:
        image_column_name = column_names[0]

    # transformations as done in original SimMIM paper
    # source: https://github.com/microsoft/SimMIM/blob/main/data/data_simmim.py
    transforms = Compose(
        [
            Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
            RandomResizedCrop(model_args.image_size, scale=(0.67, 1.0), ratio=(3.0 / 4.0, 4.0 / 3.0)),
            RandomHorizontalFlip(),
            ToTensor(),
            Normalize(mean=image_processor.image_mean, std=image_processor.image_std),
        ]
    )

    # create mask generator
    mask_generator = MaskGenerator(
        input_size=model_args.image_size,
        mask_patch_size=data_args.mask_patch_size,
        model_patch_size=model_args.patch_size,
        mask_ratio=data_args.mask_ratio,
    )

    def preprocess_images(examples):
        """Preprocess a batch of images by applying transforms + creating a corresponding mask, indicating
        which patches to mask."""

        examples["pixel_values"] = [transforms(image) for image in examples[image_column_name]]
        examples["mask"] = [mask_generator() for i in range(len(examples[image_column_name]))]

        return examples

    if training_args.do_train:
        if "train" not in ds:
            raise ValueError("--do_train requires a train dataset")
        if data_args.max_train_samples is not None:
            ds["train"] = ds["train"].shuffle(seed=training_args.seed).select(range(data_args.max_train_samples))
        # Set the training transforms
        ds["train"].set_transform(preprocess_images)

    if training_args.do_eval:
        if "validation" not in ds:
            raise ValueError("--do_eval requires a validation dataset")
        if data_args.max_eval_samples is not None:
            ds["validation"] = (
                ds["validation"].shuffle(seed=training_args.seed).select(range(data_args.max_eval_samples))
            )
        # Set the validation transforms
        ds["validation"].set_transform(preprocess_images)

    # Initialize our trainer
    trainer = Trainer(
        model=model,
        args=training_args,
        train_dataset=ds["train"] if training_args.do_train else None,
        eval_dataset=ds["validation"] if training_args.do_eval else None,
        tokenizer=image_processor,
        data_collator=collate_fn,
    )

    # Training
    if training_args.do_train:
        checkpoint = None
        if training_args.resume_from_checkpoint is not None:
            checkpoint = training_args.resume_from_checkpoint
        elif last_checkpoint is not None:
            checkpoint = last_checkpoint
        train_result = trainer.train(resume_from_checkpoint=checkpoint)
        trainer.save_model()
        trainer.log_metrics("train", train_result.metrics)
        trainer.save_metrics("train", train_result.metrics)
        trainer.save_state()

    # Evaluation
    if training_args.do_eval:
        metrics = trainer.evaluate()
        trainer.log_metrics("eval", metrics)
        trainer.save_metrics("eval", metrics)

    # Write model card and (optionally) push to hub
    kwargs = {
        "finetuned_from": model_args.model_name_or_path,
        "tasks": "masked-image-modeling",
        "dataset": data_args.dataset_name,
        "tags": ["masked-image-modeling"],
    }
    if training_args.push_to_hub:
        trainer.push_to_hub(**kwargs)
    else:
        trainer.create_model_card(**kwargs)