def from_lists(
        cls,
        train_data: Optional[List[str]] = None,
        train_targets: Optional[List[str]] = None,
        val_data: Optional[List[str]] = None,
        val_targets: Optional[List[str]] = None,
        test_data: Optional[List[str]] = None,
        test_targets: Optional[List[str]] = None,
        predict_data: Optional[List[str]] = None,
        input_cls: Type[Input] = Seq2SeqListInput,
        transform: INPUT_TRANSFORM_TYPE = InputTransform,
        transform_kwargs: Optional[Dict] = None,
        **data_module_kwargs: Any,
    ) -> "TranslationData":
        """Load the :class:`~flash.text.seq2seq.translation.data.TranslationData` from lists of input text snippets and
        corresponding lists of target text snippets.

        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_data: The list of input text snippets to use when training.
            train_targets: The list of target text snippets to use when training.
            val_data: The list of input text snippets to use when validating.
            val_targets: The list of target text snippets to use when validating.
            test_data: The list of input text snippets to use when testing.
            test_targets: The list of target text snippets to use when testing.
            predict_data: The list of input text snippets to use when predicting.
            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.text.seq2seq.translation.data.TranslationData`.

        Examples
        ________

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.text import TranslationTask, TranslationData
            >>> datamodule = TranslationData.from_lists(
            ...     train_data=["ayay entencesay inyay igpay atinlay", "ellohay orldway"],
            ...     train_targets=["a sentence in pig latin", "hello world"],
            ...     predict_data=["ayay entencesay orfay edictionpray"],
            ...     batch_size=2,
            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            >>> model = TranslationTask()
            >>> 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...

        """

        ds_kw = {}

        return cls(
            input_cls(RunningStage.TRAINING, train_data, train_targets, **ds_kw),
            input_cls(RunningStage.VALIDATING, val_data, val_targets, **ds_kw),
            input_cls(RunningStage.TESTING, test_data, test_targets, **ds_kw),
            input_cls(RunningStage.PREDICTING, predict_data, **ds_kw),
            transform=transform,
            transform_kwargs=transform_kwargs,
            **data_module_kwargs,
        )
Load the :class:`~flash.text.seq2seq.translation.data.TranslationData` from lists of input text snippets and
        corresponding lists of target text snippets.

        To learn how to customize the transforms applied for each stage, read our
        :ref:`customizing transforms guide <customizing_transforms>`.

        Args:
            train_data: The list of input text snippets to use when training.
            train_targets: The list of target text snippets to use when training.
            val_data: The list of input text snippets to use when validating.
            val_targets: The list of target text snippets to use when validating.
            test_data: The list of input text snippets to use when testing.
            test_targets: The list of target text snippets to use when testing.
            predict_data: The list of input text snippets to use when predicting.
            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.text.seq2seq.translation.data.TranslationData`.

        Examples
        ________

        .. doctest::

            >>> from flash import Trainer
            >>> from flash.text import TranslationTask, TranslationData
            >>> datamodule = TranslationData.from_lists(
            ...     train_data=["ayay entencesay inyay igpay atinlay", "ellohay orldway"],
            ...     train_targets=["a sentence in pig latin", "hello world"],
            ...     predict_data=["ayay entencesay orfay edictionpray"],
            ...     batch_size=2,
            ... )  # doctest: +ELLIPSIS +NORMALIZE_WHITESPACE
            >>> model = TranslationTask()
            >>> 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...

        
src/flash/text/seq2seq/translation/data.py