text: #!/usr/bin/env python3
  import torch
  import torch.nn.functional as F
  
  from .common import (
      _run_forward,
      _format_input_baseline,
      _format_tensor_into_tuples,
      _format_additional_forward_args,
      _validate_input,
      _validate_target,
      _tensorize_baseline,
  )
  from .gradient import compute_gradients
  
  
  class Attribution:
      r"""
      All attribution algorithms extend this class. It enforces its child classes
      to extend and override core `attribute` method.
      """
  
      def __init__(self, forward_func):
          r"""
          Args:
              forward_func (callable or torch.nn.Module): This can either be an instance
                          of pytorch model or any modification of model's forward
                          function.
          """
          self.forward_func = forward_func
  
      def attribute(self, inputs):
          r"""
          This method computes and returns the attribution values for each input tensor.
          Deriving classes are responsible for implementing its logic accordingly.
  
          Specific attribution algorithms that extend this class take relevant
          additional arguments.
  
          Args:
  
              inputs (tensor or tuple of tensors):  Input for which attribution
                          is computed. It can be provided as a single tensor or
                          a tuple of multiple tensors. If multiple input tensors
                          are provided, the batch sizes must be aligned accross all
                          tensors.
  
  
          Returns:
  
              *tensor* or tuple of *tensors* of **attributions**:
              - **attributions** (*tensor* or tuple of *tensors*):
                          Attribution values for each
                          input tensor. The `attributions` have the same shape and
                          dimensionality as the inputs.
                          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.
  
          """
          raise NotImplementedError("Deriving class should implement attribute method")
  
      def has_convergence_delta(self):
          r"""
          This method informs the user whether the attribution algorithm provides
          a convergence delta (aka an approximation error) or not. Convergence
          delta may serve as a proxy of correctness of attribution algorithm's
          approximation. If deriving attribution class provides a
          `compute_convergence_delta` method, it should
          override both `compute_convergence_delta` and `has_convergence_delta` methods.
  
          Returns:
              bool:
              Returns whether the attribution algorithm
              provides a convergence delta (aka approximation error) or not.
  
          """
          return False
  
      def compute_convergence_delta(self, attributions, *args):
          r"""
          The attribution algorithms which derive `Attribution` class and provide
          convergence delta (aka approximation error) should implement this method.
          Convergence delta can be computed based on certain properties of the
          attribution alogrithms.
  
          Args:
  
                  attributions (tensor or tuple of tensors): Attribution scores that
                              are precomputed by an attribution algorithm.
                              Attributions can be provided in form of a single tensor
                              or a tuple of those. It is assumed that attribution
                              tensor's dimension 0 corresponds to the number of
                              examples, and if multiple input tensors are provided,
                              the examples must be aligned appropriately.
                  *args (optional): Additonal arguments that are used by the
                              sub-classes depending on the specific implementation
                              of `compute_convergence_delta`.
  
          Returns:
  
                  *tensor* of **deltas**:
                  - **deltas** (*tensor*):
                      Depending on specific implementaion of
                      sub-classes, convergence delta can be returned per
                      sample in form of a tensor or it can be aggregated
                      across multuple samples and returned in form of a
                      single floating point tensor.
          """
          raise NotImplementedError(
              "Deriving sub-class should implement" " compute_convergence_delta method"
          )
  
  
  class GradientAttribution(Attribution):
      r"""
      All gradient based attribution algorithms extend this class. It requires a
      forward function, which most commonly is the forward function of the model
      that we want to interpret or the model itself.
      """
  
      def __init__(self, forward_func):
          r"""
          Args:
  
              forward_func (callable or torch.nn.Module): This can either be an instance
                          of pytorch model or any modification of model's forward
                          function.
          """
          Attribution.__init__(self, forward_func)
          self.gradient_func = compute_gradients
  
      def compute_convergence_delta(
          self,
          attributions,
          start_point,
          end_point,
          target=None,
          additional_forward_args=None,
      ):
          r"""
          Here we provide a specific implementation for `compute_convergence_delta`
          which is based on a common property among gradient-based attribution algorithms.
          In the literature sometimes it is also called completeness axiom. Completeness
          axiom states that the sum of the attribution must be equal to the differences of
          NN Models's function at its end and start points. In other words:
          sum(attributions) - (F(end_point) - F(start_point)) is close to zero.
          Returned delta of this method is defined as above stated difference.
  
          This implementation assumes that both the `start_point` and `end_point` have
          the same shape and dimensionality. It also assumes that the target must have
          the same number of examples as the `start_point` and the `end_point` in case
          it is provided in form of a list or a non-singleton tensor.
  
          Args:
  
                  attributions (tensor or tuple of tensors): Precomputed attribution
                              scores. The user can compute those using any attribution
                              algorithm. It is assumed the the shape and the
                              dimensionality of attributions must match the shape and
                              the dimensionality of `start_point` and `end_point`.
                              It also assumes that the attribution tensor's
                              dimension 0 corresponds to the number of
                              examples, and if multiple input tensors are provided,
                              the examples must be aligned appropriately.
                  start_point (tensor or tuple of tensors, optional): `start_point`
                              is passed as an input to model's forward function. It
                              is the starting point of attributions' approximation.
                              It is assumed that both `start_point` and `end_point`
                              have the same shape and dimensionality.
                  end_point (tensor or tuple of tensors):  `end_point`
                              is passed as an input to model's forward function. It
                              is the end point of attributions' approximation.
                              It is assumed that both `start_point` and `end_point`
                              have the same shape and dimensionality.
                  target (int, tuple, tensor or list, optional):  Output indices for
                              which gradients are 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 (any, 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.
                              `additional_forward_args` is used both for `start_point`
                              and `end_point` when computing the forward pass.
                              Default: None
  
          Returns:
  
                  *tensor* of **deltas**:
                  - **deltas** (*tensor*):
                      This implementation returns convergence delta per
                      sample. Deriving sub-classes may do any type of aggregation
                      of those values, if necessary.
          """
          end_point, start_point = _format_input_baseline(end_point, start_point)
          additional_forward_args = _format_additional_forward_args(
              additional_forward_args
          )
          # tensorizing start_point in case it is a scalar or one example baseline
          # If the batch size is large we could potentially also tensorize only one
          # sample and expand the output to the rest of the elements in the batch
          start_point = _tensorize_baseline(end_point, start_point)
  
          attributions = _format_tensor_into_tuples(attributions)
  
          # verify that the attributions and end_point match on 1st dimension
          for attribution, end_point_tnsr in zip(attributions, end_point):
              assert end_point_tnsr.shape[0] == attribution.shape[0], (
                  "Attributions tensor and the end_point must match on the first"
                  " dimension but found attribution: {} and end_point: {}".format(
                      attribution.shape[0], end_point_tnsr.shape[0]
                  )
              )
  
          num_samples = end_point[0].shape[0]
          _validate_input(end_point, start_point)
          _validate_target(num_samples, target)
  
          def _sum_rows(input):
              return input.view(input.shape[0], -1).sum(1)
  
          with torch.no_grad():
              start_point = _sum_rows(
                  _run_forward(
                      self.forward_func, start_point, target, additional_forward_args
                  )
              )
  
              end_point = _sum_rows(
                  _run_forward(
                      self.forward_func, end_point, target, additional_forward_args
                  )
              )
              row_sums = [_sum_rows(attribution) for attribution in attributions]
              attr_sum = torch.stack([sum(row_sum) for row_sum in zip(*row_sums)])
              return attr_sum - (end_point - start_point)
  
  
  class PerturbationAttribution(Attribution):
      r"""
      All perturbation based attribution algorithms extend this class. It requires a
      forward function, which most commonly is the forward function of the model
      that we want to interpret or the model itself.
      """
  
      def __init__(self, forward_func):
          r"""
          Args:
  
              forward_func (callable or torch.nn.Module): This can either be an instance
                          of pytorch model or any modification of model's forward
                          function.
          """
          Attribution.__init__(self, forward_func)
  
  
  class InternalAttribution(Attribution):
      r"""
      Shared base class for LayerAttrubution and NeuronAttribution,
      attribution types that require a model and a particular layer.
      """
  
      def __init__(self, forward_func, layer, device_ids=None):
          r"""
          Args:
  
              forward_func (callable or torch.nn.Module):  This can either be an instance
                          of pytorch model or any modification of model's forward
                          function.
              layer (torch.nn.Module): Layer for which output attributions are computed.
                          Output size of attribute matches that of layer output.
              device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model, which allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
          """
          Attribution.__init__(self, forward_func)
          self.layer = layer
          self.device_ids = device_ids
  
  
  class LayerAttribution(InternalAttribution):
      r"""
      Layer attribution provides attribution values for the given layer, quanitfying
      the importance of each neuron within the given layer's output. The output
      attribution of calling attribute on a LayerAttribution object always matches
      the size of the layer output.
      """
  
      def __init__(self, forward_func, layer, device_ids=None):
          r"""
          Args:
  
              forward_func (callable or torch.nn.Module):  This can either be an instance
                          of pytorch model or any modification of model's forward
                          function.
              layer (torch.nn.Module): Layer for which output attributions are computed.
                          Output size of attribute matches that of layer output.
              device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model, which allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
          """
          InternalAttribution.__init__(self, forward_func, layer, device_ids)
  
      def interpolate(layer_attribution, interpolate_dims, interpolate_mode="nearest"):
          r"""
          Interpolates given 3D, 4D or 5D layer attribution to given dimensions.
          This is often utilized to upsample the attribution of a convolutional layer
          to the size of an input, which allows visualizing in the input space.
  
          Args:
  
              layer_attribution (torch.Tensor):  Tensor of given layer attributions.
              interpolate_dims (int or tuple): Upsampled dimensions. The
                          number of elements must be the number of dimensions
                          of layer_attribution - 2, since the first dimension
                          corresponds to number of examples and the second is
                          assumed to correspond to the number of channels.
              interpolate_mode (str):  Method for interpolation, which
                          must be a valid input interpolation mode for
                          torch.nn.functional. These methods are
                          "nearest", "area", "linear" (3D-only), "bilinear"
                          (4D-only), "bicubic" (4D-only), "trilinear" (5D-only)
                          based on the number of dimensions of the given layer
                          attribution.
  
          Returns:
              *tensor* of upsampled **attributions**:
              - **attributions** (*tensor*):
                  Upsampled layer attributions with first 2 dimensions matching
                  slayer_attribution and remaining dimensions given by
                  interpolate_dims.
          """
          return F.interpolate(layer_attribution, interpolate_dims, mode=interpolate_mode)
  
  
  class NeuronAttribution(InternalAttribution):
      r"""
          Neuron attribution provides input attribution for a given neuron, quanitfying
          the importance of each input feature in the activation of a particular neuron.
          Calling attribute on a NeuronAttribution object requires also providing
          the index of the neuron in the output of the given layer for which attributions
          are required.
          The output attribution of calling attribute on a NeuronAttribution object
          always matches the size of the input.
      """
  
      def __init__(self, forward_func, layer, device_ids=None):
          r"""
          Args:
  
              forward_func (callable or torch.nn.Module):  This can either be an instance
                          of pytorch model or any modification of model's forward
                          function.
              layer (torch.nn.Module): Layer for which output attributions are computed.
                          Output size of attribute matches that of layer output.
              device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model, which allows reconstruction of
                          intermediate outputs from batched results across devices.
                          If forward_func is given as the DataParallel model itself,
                          then it is not necessary to provide this argument.
          """
          InternalAttribution.__init__(self, forward_func, layer, device_ids)
  
      def attribute(self, inputs, neuron_index, **kwargs):
          r"""
          This method computes and returns the neuron attribution values for each
          input tensor. Deriving classes are responsible for implementing
          its logic accordingly.
  
          Args:
  
                  inputs:     A single high dimensional input tensor or a tuple of them.
                  neuron_index (int or tuple): Tuple providing index of neuron in output
                          of given layer for which attribution is desired. Length of
                          this tuple must be one less than the number of
                          dimensions in the output of the given layer (since
                          dimension 0 corresponds to number of examples).
  
          Returns:
  
                  *tensor* or tuple of *tensors* of **attributions**:
                  - **attributions** (*tensor* or tuple of *tensors*):
                          Attribution values for
                          each input vector. The `attributions` have the
                          dimensionality of inputs.
          """
          raise NotImplementedError("A derived class should implement attribute method")
  