text: #!/usr/bin/env python3
  
  import torch
  
  from .._utils.common import (
      _format_attributions,
      _format_input,
      _format_input_baseline,
      _run_forward,
      _expand_additional_forward_args,
      _expand_target,
      _format_additional_forward_args,
  )
  from .._utils.attribution import PerturbationAttribution
  
  
  class FeatureAblation(PerturbationAttribution):
      def __init__(self, forward_func):
          r"""
          Args:
  
              forward_func (callable): The forward function of the model or
                          any modification of it
          """
          PerturbationAttribution.__init__(self, forward_func)
          self.use_weights = False
  
      def attribute(
          self,
          inputs,
          baselines=None,
          target=None,
          additional_forward_args=None,
          feature_mask=None,
          ablations_per_eval=1,
          **kwargs
      ):
          r""""
          A perturbation based approach to computing attribution, involving
          replacing each input feature with a given baseline / reference, and
          computing the difference in output. By default, each scalar value within
          each input tensor is taken as a feature and replaced independently. Passing
          a feature mask, allows grouping features to be ablated together. This can
          be used in cases such as images, where an entire segment or region
          can be ablated, measuring the importance of the segment (feature group).
          Each input scalar in the group will be given the same attribution value
          equal to the change in target as a result of ablating the entire feature
          group.
  
          The forward function can either return a scalar per example, or a single
          scalar for the full batch. If a single scalar is returned for the batch,
          `ablations_per_eval` must be 1, and the returned attributions will have
          first dimension 1, corresponding to feature importance across all
          examples in the batch.
  
          Args:
  
                  inputs (tensor or tuple of tensors):  Input for which ablation
                              attributions are computed. If forward_func takes a single
                              tensor as input, a single input tensor should be provided.
                              If forward_func takes multiple tensors as input, a tuple
                              of the input tensors should be provided. It is assumed
                              that for all given input tensors, dimension 0 corresponds
                              to the number of examples (aka batch size), and if
                              multiple input tensors are provided, the examples must
                              be aligned appropriately.
                  baselines (scalar, tensor, tuple of scalars or tensors, optional):
                              Baselines define reference value which replaces each
                              feature when ablated.
                              Baselines can be provided as:
                              - a single tensor, if inputs is a single tensor, with
                                  exactly the same dimensions as inputs or
                                  broadcastable to match the dimensions of inputs
                              - a single scalar, if inputs is a single tensor, which will
                                  be broadcasted for each input value in input tensor.
                              - a tuple of tensors or scalars, the baseline corresponding
                                  to each tensor in the inputs' tuple can be:
                                  - either a tensor with
                                      exactly the same dimensions as inputs or
                                      broadcastable to match the dimensions of inputs
                                  - or a scalar, corresponding to a tensor in the
                                      inputs' tuple. This scalar value is broadcasted
                                      for corresponding input tensor.
                              In the cases when `baselines` is not provided, we internally
                              use zero scalar corresponding to each input tensor.
                              Default: None
                  target (int, tuple, tensor or list, optional):  Output indices for
                              which difference is computed (for classification cases,
                              this is usually the target class).
                              If the network returns a scalar value per example,
                              no target index is necessary.
                              For general 2D outputs, targets can be either:
  
                              - a single integer or a tensor containing a single
                                  integer, which is applied to all input examples
  
                              - a list of integers or a 1D tensor, with length matching
                                  the number of examples in inputs (dim 0). Each integer
                                  is applied as the target for the corresponding example.
  
                              For outputs with > 2 dimensions, targets can be either:
  
                              - A single tuple, which contains #output_dims - 1
                                  elements. This target index is applied to all examples.
  
                              - A list of tuples with length equal to the number of
                                  examples in inputs (dim 0), and each tuple containing
                                  #output_dims - 1 elements. Each tuple is applied as the
                                  target for the corresponding example.
  
                              Default: None
                  additional_forward_args (tuple, optional): If the forward function
                              requires additional arguments other than the inputs for
                              which attributions should not be computed, this argument
                              can be provided. It must be either a single additional
                              argument of a Tensor or arbitrary (non-tuple) type or a
                              tuple containing multiple additional arguments including
                              tensors or any arbitrary python types. These arguments
                              are provided to forward_func in order following the
                              arguments in inputs.
                              For a tensor, the first dimension of the tensor must
                              correspond to the number of examples. For all other types,
                              the given argument is used for all forward evaluations.
                              Note that attributions are not computed with respect
                              to these arguments.
                              Default: None
                  feature_mask (tensor or tuple of tensors, optional):
                              feature_mask defines a mask for the input, grouping
                              features which should be ablated together. feature_mask
                              should contain the same number of tensors as inputs.
                              Each tensor should
                              be the same size as the corresponding input or
                              broadcastable to match the input tensor. Each tensor
                              should contain integers in the range 0 to num_features
                              - 1, and indices corresponding to the same feature should
                              have the same value.
                              Note that features within each input tensor are ablated
                              independently (not across tensors).
                              If the forward function returns a single scalar per batch,
                              we enforce that the first dimension of each mask must be 1,
                              since attributions are returned batch-wise rather than per
                              example, so the attributions must correspond to the
                              same features (indices) in each input example.
                              If None, then a feature mask is constructed which assigns
                              each scalar within a tensor as a separate feature, which
                              is ablated independently.
                              Default: None
                  ablations_per_eval (int, optional): Allows ablation of multiple features
                              to be processed simultaneously in one call to forward_fn.
                              Each forward pass will contain a maximum of
                              ablations_per_eval * #examples samples.
                              For DataParallel models, each batch is split among the
                              available devices, so evaluations on each available
                              device contain at most
                              (ablations_per_eval * #examples) / num_devices
                              samples.
                              If the forward function returns a single scalar per batch,
                              ablations_per_eval must be set to 1.
                              Default: 1
                  **kwargs (Any, optional): Any additional arguments used by child
                              classes of FeatureAblation (such as Occlusion) to construct
                              ablations. These arguments are ignored when using
                              FeatureAblation directly.
                              Default: None
  
          Returns:
                  *tensor* or tuple of *tensors* of **attributions**:
                  - **attributions** (*tensor* or tuple of *tensors*):
                              The attributions with respect to each input feature.
                              If the forward function returns
                              a scalar value per example, attributions will be
                              the same size as the provided inputs, with each value
                              providing the attribution of the corresponding input index.
                              If the forward function returns a scalar per batch, then
                              attribution tensor(s) will have first dimension 1 and
                              the remaining dimensions will match the input.
                              If a single tensor is provided as inputs, a single tensor is
                              returned. If a tuple is provided for inputs, a tuple of
                              corresponding sized tensors is returned.
  
  
          Examples::
  
              >>> # SimpleClassifier takes a single input tensor of size Nx4x4,
              >>> # and returns an Nx3 tensor of class probabilities.
              >>> net = SimpleClassifier()
              >>> # Generating random input with size 2 x 4 x 4
              >>> input = torch.randn(2, 4, 4)
              >>> # Defining FeatureAblation interpreter
              >>> ablator = FeatureAblation(net)
              >>> # Computes ablation attribution, ablating each of the 16
              >>> # scalar input independently.
              >>> attr = ablator.attribute(input, target=1)
  
              >>> # Alternatively, we may want to ablate features in groups, e.g.
              >>> # grouping each 2x2 square of the inputs and ablating them together.
              >>> # This can be done by creating a feature mask as follows, which
              >>> # defines the feature groups, e.g.:
              >>> # +---+---+---+---+
              >>> # | 0 | 0 | 1 | 1 |
              >>> # +---+---+---+---+
              >>> # | 0 | 0 | 1 | 1 |
              >>> # +---+---+---+---+
              >>> # | 2 | 2 | 3 | 3 |
              >>> # +---+---+---+---+
              >>> # | 2 | 2 | 3 | 3 |
              >>> # +---+---+---+---+
              >>> # With this mask, all inputs with the same value are ablated
              >>> # simultaneously, and the attribution for each input in the same
              >>> # group (0, 1, 2, and 3) per example are the same.
              >>> # The attributions can be calculated as follows:
              >>> # feature mask has dimensions 1 x 4 x 4
              >>> feature_mask = torch.tensor([[[0,0,1,1],[0,0,1,1],
              >>>                             [2,2,3,3],[2,2,3,3]]])
              >>> attr = ablator.attribute(input, target=1, feature_mask=feature_mask)
          """
          with torch.no_grad():
              # Keeps track whether original input is a tuple or not before
              # converting it into a tuple.
              is_inputs_tuple = isinstance(inputs, tuple)
              inputs, baselines = _format_input_baseline(inputs, baselines)
              additional_forward_args = _format_additional_forward_args(
                  additional_forward_args
              )
              num_examples = inputs[0].shape[0]
              feature_mask = (
                  _format_input(feature_mask) if feature_mask is not None else None
              )
              assert (
                  isinstance(ablations_per_eval, int) and ablations_per_eval >= 1
              ), "Ablations per evaluation must be at least 1."
  
              # Computes initial evaluation with all features, which is compared
              # to each ablated result.
              initial_eval = _run_forward(
                  self.forward_func, inputs, target, additional_forward_args
              )
              if isinstance(initial_eval, (int, float)) or (
                  isinstance(initial_eval, torch.Tensor)
                  and (
                      len(initial_eval.shape) == 0
                      or (num_examples > 1 and initial_eval.numel() == 1)
                  )
              ):
                  single_output_mode = True
                  assert (
                      ablations_per_eval == 1
                  ), "Cannot have ablations_per_eval > 1 when function returns scalar."
                  if feature_mask is not None:
                      for single_mask in feature_mask:
                          assert single_mask.shape[0] == 1, (
                              "Cannot provide multiple masks when function returns"
                              " a scalar."
                          )
              else:
                  single_output_mode = False
                  assert (
                      isinstance(initial_eval, torch.Tensor)
                      and initial_eval[0].numel() == 1
                  ), "Target should identify a single element in the model output."
                  initial_eval = initial_eval.reshape(1, num_examples)
  
              # Initialize attribution totals and counts
              attrib_type = (
                  initial_eval.dtype
                  if isinstance(initial_eval, torch.Tensor)
                  else type(initial_eval)
              )
              total_attrib = [
                  torch.zeros_like(
                      input[0:1] if single_output_mode else input, dtype=attrib_type
                  )
                  for input in inputs
              ]
  
              # Weights are used in cases where ablations may be overlapping.
              if self.use_weights:
                  weights = [
                      torch.zeros_like(
                          input[0:1] if single_output_mode else input
                      ).float()
                      for input in inputs
                  ]
  
              # Iterate through each feature tensor for ablation
              for i in range(len(inputs)):
                  for (
                      current_inputs,
                      current_add_args,
                      current_target,
                      current_mask,
                  ) in self._ablation_generator(
                      i,
                      inputs,
                      additional_forward_args,
                      target,
                      baselines,
                      feature_mask,
                      ablations_per_eval,
                      **kwargs
                  ):
                      # modified_eval dimensions: 1D tensor with length
                      # equal to #num_examples * #features in batch
                      modified_eval = _run_forward(
                          self.forward_func,
                          current_inputs,
                          current_target,
                          current_add_args,
                      )
                      # eval_diff dimensions: (#features in batch, #num_examples, 1,.. 1)
                      # (contains 1 more dimension than inputs). This adds extra
                      # dimensions of 1 to make the tensor broadcastable with the inputs
                      # tensor.
                      if single_output_mode:
                          eval_diff = initial_eval - modified_eval
                      else:
                          eval_diff = (
                              initial_eval - modified_eval.reshape(-1, num_examples)
                          ).reshape(
                              (-1, num_examples) + (len(inputs[i].shape) - 1) * (1,)
                          )
                      if self.use_weights:
                          weights[i] += current_mask.float().sum(dim=0)
                      total_attrib[i] += (eval_diff * current_mask.to(attrib_type)).sum(
                          dim=0
                      )
  
              # Divide total attributions by counts and return formatted attributions
              if self.use_weights:
                  attrib = tuple(
                      single_attrib.float() / weight
                      for single_attrib, weight in zip(total_attrib, weights)
                  )
              else:
                  attrib = tuple(total_attrib)
              return _format_attributions(is_inputs_tuple, attrib)
  
      def _ablation_generator(
          self,
          i,
          inputs,
          additional_args,
          target,
          baselines,
          input_mask,
          ablations_per_eval,
          **kwargs
      ):
          extra_args = {}
          for key, value in kwargs.items():
              # For any tuple argument in kwargs, we choose index i of the tuple.
              if isinstance(value, tuple):
                  extra_args[key] = value[i]
              else:
                  extra_args[key] = value
  
          input_mask = input_mask[i] if input_mask is not None else None
          min_feature, num_features, input_mask = self._get_feature_range_and_mask(
              inputs[i], input_mask, **extra_args
          )
          num_examples = inputs[0].shape[0]
          ablations_per_eval = min(ablations_per_eval, num_features)
          baseline = baselines[i] if isinstance(baselines, tuple) else baselines
          if isinstance(baseline, torch.Tensor):
              baseline = baseline.reshape((1,) + baseline.shape)
  
          # Repeat features and additional args for batch size.
          all_features_repeated = [
              torch.cat([inputs[j]] * ablations_per_eval, dim=0)
              for j in range(len(inputs))
          ]
          additional_args_repeated = (
              _expand_additional_forward_args(additional_args, ablations_per_eval)
              if additional_args is not None
              else None
          )
          target_repeated = _expand_target(target, ablations_per_eval)
  
          num_features_processed = min_feature
          while num_features_processed < num_features:
              current_num_ablated_features = min(
                  ablations_per_eval, num_features - num_features_processed
              )
  
              # Store appropriate inputs and additional args based on batch size.
              if current_num_ablated_features != ablations_per_eval:
                  current_features = [
                      feature_repeated[0 : current_num_ablated_features * num_examples]
                      for feature_repeated in all_features_repeated
                  ]
                  current_additional_args = (
                      _expand_additional_forward_args(
                          additional_args, current_num_ablated_features
                      )
                      if additional_args is not None
                      else None
                  )
                  current_target = _expand_target(target, current_num_ablated_features)
              else:
                  current_features = all_features_repeated
                  current_additional_args = additional_args_repeated
                  current_target = target_repeated
  
              # Store existing tensor before modifying
              original_tensor = current_features[i]
              # Construct ablated batch for features in range num_features_processed
              # to num_features_processed + current_num_ablated_features and return
              # mask with same size as ablated batch. ablated_features has dimension
              # (current_num_ablated_features, num_examples, + inputs[i].shape[1:])
              ablated_features, current_mask = self._construct_ablated_input(
                  current_features[i].reshape(
                      (current_num_ablated_features, num_examples)
                      + current_features[i].shape[1:]
                  ),
                  input_mask,
                  baseline,
                  num_features_processed,
                  num_features_processed + current_num_ablated_features,
                  **extra_args
              )
  
              # current_features[i] has dimension
              # (current_num_ablated_features * num_examples, inputs[i].shape[1:]),
              # which can be provided to the model as input.
              current_features[i] = ablated_features.reshape(
                  (-1,) + ablated_features.shape[2:]
              )
              yield tuple(
                  current_features
              ), current_additional_args, current_target, current_mask
              # Replace existing tensor at index i.
              current_features[i] = original_tensor
              num_features_processed += current_num_ablated_features
  
      def _construct_ablated_input(
          self, expanded_input, input_mask, baseline, start_feature, end_feature, **kwargs
      ):
          r"""
          Ablates given expanded_input tensor with given feature mask, feature range,
          and baselines. expanded_input shape is (`num_features`, `num_examples`, ...)
          with remaining dimensions corresponding to remaining original tensor
          dimensions and `num_features` = `end_feature` - `start_feature`.
          input_mask has same number of dimensions as original input tensor (one less
          than `expanded_input`), and can have first dimension either 1, applying same
          feature mask to all examples, or `num_examples`. baseline is expected to
          be broadcastable to match `expanded_input`.
  
          This method returns the ablated input tensor, which has the same
          dimensionality as `expanded_input` as well as the corresponding mask with
          either the same dimensionality as `expanded_input` or second dimension
          being 1. This mask contains 1s in locations which have been ablated (and
          thus counted towards ablations for that feature) and 0s otherwise.
          """
          current_mask = torch.stack(
              [input_mask == j for j in range(start_feature, end_feature)], dim=0
          ).long()
          ablated_tensor = (
              expanded_input * (1 - current_mask).to(expanded_input.dtype)
          ) + (baseline * current_mask.to(expanded_input.dtype))
          return ablated_tensor, current_mask
  
      def _get_feature_range_and_mask(self, input, input_mask, **kwargs):
          if input_mask is None:
              # Obtain feature mask for selected input tensor, matches size of
              # 1 input example, (1 x inputs[i].shape[1:])
              input_mask = torch.reshape(
                  torch.arange(torch.numel(input[0]), device=input.device),
                  input[0:1].shape,
              ).long()
          return (
              torch.min(input_mask).item(),
              torch.max(input_mask).item() + 1,
              input_mask,
          )
  