class SaliencyExplainer(FeatureAttributionExplainer):
    """Saliency explainer for computing gradient-based attributions.

    This explainer computes saliency maps using gradients of the model output
    with respect to inputs. Supports both single-target and multi-target modes
    for both single-target and multi-target scenarios.

    The saliency method computes the gradient of the output with respect to the input,
    providing a measure of how much each input feature contributes to the prediction.
    Raw gradients are returned (abs=False) to preserve sign information.

    Args:
        model: The PyTorch model whose output is to be explained.
        multi_target: Whether to use multi-target mode. When True, can compute
            attributions for multiple targets simultaneously. Defaults to False.
        internal_batch_size: Batch size for internal computations. Defaults to 64.
        grad_batch_size: Batch size for gradient computations. Defaults to 64.

    Examples:
        Single-target usage:
        >>> import torch
        >>> from torchxai.data_types import SingleTargetAcrossBatch
        >>>
        >>> model = torch.nn.Linear(10, 2)
        >>> explainer = SaliencyExplainer(model)
        >>> attributions = explainer.explain(
        ...     inputs=torch.randn(1, 10),
        ...     target=SingleTargetAcrossBatch(index=0),
        ... )
        >>> attributions.shape   # (1, 10)

        Multi-target usage:
        >>> explainer_mt = SaliencyExplainer(model, multi_target=True)
        >>> mt_attributions = explainer_mt.explain(
        ...     inputs=torch.randn(1, 10),
        ...     target=[SingleTargetAcrossBatch(index=0), SingleTargetAcrossBatch(index=1)],
        ... )
        >>> len(mt_attributions), mt_attributions[0].shape   # 2, (1, 10)
    """

    def _init_single_target_explanation_fn(self) -> Callable:
        """Initialize single-target saliency attribution function.

        Returns:
            Captum Saliency attribution function for single targets.
        """
        return partial(Saliency(self._model).attribute, abs=False)

    def _init_multi_target_explanation_fn(self) -> Callable:
        """Initialize multi-target saliency attribution function.

        Returns:
            MultiTargetSaliency attribution function for multiple targets.
        """
        return partial(
            MultiTargetSaliency(
                self._model, grad_batch_size=self._grad_batch_size
            ).attribute,
            abs=False,
        )

    def explain(
        self,
        inputs: TensorOrTupleOfTensorsGeneric,
        target: ExplanationTargetType | list[ExplanationTargetType],
        additional_forward_args: tuple[Any, ...] | None = None,
    ) -> TensorOrTupleOfTensorsGeneric | list[TensorOrTupleOfTensorsGeneric]:
        """Compute saliency attributions for the given inputs.

        Args:
            inputs: Input tensor(s) for attribution computation.
            target: An `ExplanationTargetType` (e.g. `SingleTargetAcrossBatch`) for single-target
                mode, or a list of them for multi-target mode.
            additional_forward_args: Additional arguments for model forward pass.

        Returns:
            Tensor in single-target mode. List of Tensors, one per target, in multi-target mode.

        Examples:
            >>> # Single tensor input (wrapped automatically)
            >>> attributions = explainer.explain(
            ...     inputs=torch.randn(2, 10), target=torch.tensor([0, 1])
            ... )
            >>>
            >>> # Multiple features (use OrderedDict)
            >>> attributions = explainer.explain(
            ...     inputs=OrderedDict(
            ...         {"feat1": torch.randn(2, 5), "feat2": torch.randn(2, 5)}
            ...     ),
            ...     target=torch.tensor([0, 1]),
            ... )
        """
        return self._default_explain(
            inputs=inputs,
            target=target,
            additional_forward_args=additional_forward_args,
        )