def from_files(
        cls,
        train_files: Optional[Sequence[str]] = None,
        predict_files: Optional[Sequence[str]] = None,
        input_cls: Type[Input] = ImageClassificationFilesInput,
        transform: INPUT_TRANSFORM_TYPE = StyleTransferInputTransform,
        transform_kwargs: Optional[Dict] = None,
        **data_module_kwargs: Any
    ) -> "StyleTransferData":
        """Load the :class:`~flash.image.style_transfer.data.StyleTransferData` from lists of image files.

        The supported file extensions are: ``.jpg``, ``.jpeg``, ``.png``, ``.ppm``, ``.bmp``, ``.pgm``, ``.tif``,
        ``.tiff``, ``.webp``, and ``.npy``.
        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_files: The list of image files to use when training.
            predict_files: The list of image files to use when predicting.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
            data_module_kwargs: Additional keyword arguments to provide to the
              :class:`~flash.core.data.data_module.DataModule` constructor.

        Returns:
            The constructed :class:`~flash.image.style_transfer.data.StyleTransferData`.

        Examples
        ________

        .. testsetup::

            >>> from PIL import Image
            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
            >>> _ = [rand_image.save(f"image_{i}.png") for i in range(1, 4)]
            >>> _ = [rand_image.save(f"predict_image_{i}.png") for i in range(1, 4)]

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.image import StyleTransfer, StyleTransferData
            >>> datamodule = StyleTransferData.from_files(
            ...     train_files=["image_1.png", "image_2.png", "image_3.png"],
            ...     predict_files=["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"],
            ...     transform_kwargs=dict(image_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> model = StyleTransfer()
            >>> trainer = Trainer(fast_dev_run=True)
            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Training...
            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Predicting...

        .. testcleanup::

            >>> import os
            >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
            >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]

        """

        return cls(
            input_cls(RunningStage.TRAINING, train_files),
            predict_input=input_cls(RunningStage.PREDICTING, predict_files),
            transform=transform,
            transform_kwargs=transform_kwargs,
            **data_module_kwargs,
        )
Load the :class:`~flash.image.style_transfer.data.StyleTransferData` from lists of image files.

        The supported file extensions are: ``.jpg``, ``.jpeg``, ``.png``, ``.ppm``, ``.bmp``, ``.pgm``, ``.tif``,
        ``.tiff``, ``.webp``, and ``.npy``.
        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_files: The list of image files to use when training.
            predict_files: The list of image files to use when predicting.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            input_cls: The :class:`~flash.core.data.io.input.Input` type to use for loading the data.
            transform: The :class:`~flash.core.data.io.input_transform.InputTransform` type to use.
            transform_kwargs: Dict of keyword arguments to be provided when instantiating the transforms.
            data_module_kwargs: Additional keyword arguments to provide to the
              :class:`~flash.core.data.data_module.DataModule` constructor.

        Returns:
            The constructed :class:`~flash.image.style_transfer.data.StyleTransferData`.

        Examples
        ________

        .. testsetup::

            >>> from PIL import Image
            >>> rand_image = Image.fromarray(np.random.randint(0, 255, (64, 64, 3), dtype="uint8"))
            >>> _ = [rand_image.save(f"image_{i}.png") for i in range(1, 4)]
            >>> _ = [rand_image.save(f"predict_image_{i}.png") for i in range(1, 4)]

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.image import StyleTransfer, StyleTransferData
            >>> datamodule = StyleTransferData.from_files(
            ...     train_files=["image_1.png", "image_2.png", "image_3.png"],
            ...     predict_files=["predict_image_1.png", "predict_image_2.png", "predict_image_3.png"],
            ...     transform_kwargs=dict(image_size=(128, 128)),
            ...     batch_size=2,
            ... )
            >>> model = StyleTransfer()
            >>> trainer = Trainer(fast_dev_run=True)
            >>> trainer.fit(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Training...
            >>> trainer.predict(model, datamodule=datamodule)  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            Predicting...

        .. testcleanup::

            >>> import os
            >>> _ = [os.remove(f"image_{i}.png") for i in range(1, 4)]
            >>> _ = [os.remove(f"predict_image_{i}.png") for i in range(1, 4)]

        
src/flash/image/style_transfer/data.py