#!/usr/bin/env python3
from typing import Any, Callable, List, Tuple, Union

from torch import Tensor
from torch.nn import Module

from ..._utils.attribution import GradientAttribution, NeuronAttribution
from ..._utils.gradient import construct_neuron_grad_fn
from ..._utils.typing import TensorOrTupleOfTensorsGeneric
from ..integrated_gradients import IntegratedGradients


class NeuronIntegratedGradients(NeuronAttribution, GradientAttribution):
    r"""
    Approximates the integral of gradients for a particular neuron
    along the path from a baseline input to the given input.
    If no baseline is provided, the default baseline is the zero tensor.
    More details regarding the integrated gradient method can be found in the
    original paper here:
    https://arxiv.org/abs/1703.01365

    Note that this method is equivalent to applying integrated gradients
    where the output is the output of the identified neuron.
    """

    def __init__(
        self,
        forward_func: Callable,
        layer: Module,
        device_ids: Union[None, List[int]] = None,
    ):
        r"""
        Args:

            forward_func (callable):  The forward function of the model or any
                          modification of it
            layer (torch.nn.Module): Layer for which attributions are computed.
                          Output size of attribute matches this layer's input or
                          output dimensions, depending on whether we attribute to
                          the inputs or outputs of the layer, corresponding to
                          attribution of each neuron in the input or output of
                          this layer.
                          Currently, it is assumed that the inputs or the outputs
                          of the layer, depending on which one is used for
                          attribution, can only be a single tensor.
            device_ids (list(int)): Device ID list, necessary only if forward_func
                          applies a DataParallel model. This 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.
        """
        NeuronAttribution.__init__(self, forward_func, layer, device_ids)
        GradientAttribution.__init__(self, forward_func)

    def attribute(
        self,
        inputs: TensorOrTupleOfTensorsGeneric,
        neuron_index: Union[int, Tuple[int, ...]],
        baselines: Union[None, Tensor, Tuple[Tensor, ...]] = None,
        additional_forward_args: Any = None,
        n_steps: int = 50,
        method: str = "gausslegendre",
        internal_batch_size: Union[None, int] = None,
        attribute_to_neuron_input: bool = False,
    ) -> TensorOrTupleOfTensorsGeneric:
        r"""
        Args:

            inputs (tensor or tuple of tensors):  Input for which neuron integrated
                        gradients 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, and if multiple input tensors
                        are provided, the examples must be aligned appropriately.
            neuron_index (int or tuple): 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).
                            An integer may be provided instead of a tuple of
                            length 1.
            baselines (scalar, tensor, tuple of scalars or tensors, optional):
                        Baselines define the starting point from which integral
                        is computed.
                        Baselines can be provided as:

                        - a single tensor, if inputs is a single tensor, with
                          exactly the same dimensions as inputs or the first
                          dimension is one and the remaining dimensions match
                          with 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 matching dimensions to
                            corresponding tensor in the inputs' tuple
                            or the first dimension is one and the remaining
                            dimensions match with the corresponding
                            input tensor.

                          - 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
            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. It will be
                        repeated for each of `n_steps` along the integrated
                        path. 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
            n_steps (int, optional): The number of steps used by the approximation
                        method. Default: 50.
            method (string, optional): Method for approximating the integral,
                        one of `riemann_right`, `riemann_left`, `riemann_middle`,
                        `riemann_trapezoid` or `gausslegendre`.
                        Default: `gausslegendre` if no method is provided.
            internal_batch_size (int, optional): Divides total #steps * #examples
                        data points into chunks of size internal_batch_size,
                        which are computed (forward / backward passes)
                        sequentially.
                        For DataParallel models, each batch is split among the
                        available devices, so evaluations on each available
                        device contain internal_batch_size / num_devices examples.
                        If internal_batch_size is None, then all evaluations are
                        processed in one batch.
                        Default: None
            attribute_to_neuron_input (bool, optional): Indicates whether to
                        compute the attributions with respect to the neuron input
                        or output. If `attribute_to_neuron_input` is set to True
                        then the attributions will be computed with respect to
                        neuron's inputs, otherwise it will be computed with respect
                        to neuron's outputs.
                        Note that currently it is assumed that either the input
                        or the output of internal neuron, depending on whether we
                        attribute to the input or output, is a single tensor.
                        Support for multiple tensors will be added later.
                        Default: False

        Returns:
            *tensor* or tuple of *tensors* of **attributions**:
            - **attributions** (*tensor* or tuple of *tensors*):
                        Integrated gradients for particular neuron with
                        respect to each input feature.
                        Attributions will always be the same size as the provided
                        inputs, with each value providing the attribution of the
                        corresponding input index.
                        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::

            >>> # ImageClassifier takes a single input tensor of images Nx3x32x32,
            >>> # and returns an Nx10 tensor of class probabilities.
            >>> # It contains an attribute conv1, which is an instance of nn.conv2d,
            >>> # and the output of this layer has dimensions Nx12x32x32.
            >>> net = ImageClassifier()
            >>> neuron_ig = NeuronIntegratedGradients(net, net.conv1)
            >>> input = torch.randn(2, 3, 32, 32, requires_grad=True)
            >>> # To compute neuron attribution, we need to provide the neuron
            >>> # index for which attribution is desired. Since the layer output
            >>> # is Nx12x32x32, we need a tuple in the form (0..11,0..31,0..31)
            >>> # which indexes a particular neuron in the layer output.
            >>> # For this example, we choose the index (4,1,2).
            >>> # Computes neuron integrated gradients for neuron with
            >>> # index (4,1,2).
            >>> attribution = neuron_ig.attribute(input, (4,1,2))
        """
        ig = IntegratedGradients(self.forward_func)
        ig.gradient_func = construct_neuron_grad_fn(
            self.layer, neuron_index, self.device_ids, attribute_to_neuron_input
        )
        # Return only attributions and not delta
        return ig.attribute(
            inputs,
            baselines,
            additional_forward_args=additional_forward_args,
            n_steps=n_steps,
            method=method,
            internal_batch_size=internal_batch_size,
        )
