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use crate::internal::*;

#[derive(Debug, Clone, new)]
pub struct AddDims {
    pub axes: Vec<usize>,
}

impl AddDims {
    fn compute_shape<D: DimLike>(&self, input: &[D]) -> TVec<D> {
        let mut shape: TVec<D> = input.iter().cloned().collect();
        for &axis in &self.axes {
            shape.insert(axis, D::one())
        }
        shape
    }

    /// Evaluates the operation given the input tensors.
    fn eval_t<T: Datum>(&self, input: Arc<Tensor>) -> TractResult<TVec<Arc<Tensor>>> {
        let shape = self.compute_shape(input.shape());
        Ok(tvec![input.into_tensor().into_array::<T>()?.into_shape(&*shape)?.into_arc_tensor()])
    }
}

impl Op for AddDims {
    fn name(&self) -> Cow<str> {
        "AddDims".into()
    }

    fn info(&self) -> TractResult<Vec<String>> {
        Ok(vec![format!("Axes: {:?}", self.axes)])
    }

    fn axes_info(&self, _model: &TypedModel, node: &TypedNode) -> TractResult<AxesInfo> {
        let mut i = 0;
        let mut axes = tvec!();
        for out in 0..node.outputs[0].fact.shape.rank() {
            if !self.axes.contains(&out) {
                axes.push(AxisInfo {
                    inputs: tvec!(Some(i)),
                    outputs: tvec!(Some(out)),
                    period: 1,
                    disposable: true,
                });
                i += 1;
            }
        }
        Ok(axes.into_iter().collect())
    }

    canonic!();
    op_as_typed_op!();
}

impl StatelessOp for AddDims {
    fn eval(&self, mut inputs: TVec<Arc<Tensor>>) -> TractResult<TVec<Arc<Tensor>>> {
        let input = args_1!(inputs);
        dispatch_datum!(Self::eval_t(input.datum_type())(self, input))
    }
}

impl InferenceRulesOp for AddDims {
    fn rules<'r, 'p: 'r, 's: 'r>(
        &'s self,
        s: &mut Solver<'r>,
        inputs: &'p [TensorProxy],
        outputs: &'p [TensorProxy],
    ) -> InferenceResult {
        check_output_arity(&outputs, 1)?;
        s.equals(&outputs[0].datum_type, &inputs[0].datum_type)?;
        s.equals(&outputs[0].rank, (&inputs[0].rank).bex() + self.axes.len() as i32)?;
        s.given(&inputs[0].shape, move |s, shape| {
            let output_shape = self.compute_shape(&shape);
            s.equals(&outputs[0].shape, output_shape)
        })
    }

    inference_op_as_op!();

    to_typed!();
}

impl TypedOp for AddDims {
    typed_op_as_op!();

    fn output_facts(&self, inputs: &[&TypedTensorInfo]) -> TractResult<TVec<TypedTensorInfo>> {
        Ok(tvec!(TypedTensorInfo::dt_shape(
            inputs[0].datum_type,
            self.compute_shape(&*inputs[0].shape.to_tvec()).as_ref(),
        )?))
    }

    fn pulsify(
        &self,
        _source: &NormalizedModel,
        node: &NormalizedNode,
        target: &mut PulsedModel,
        mapping: &HashMap<OutletId, OutletId>,
        _pulse: usize,
    ) -> TractResult<TVec<OutletId>> {
        let input = mapping[&node.inputs[0]];
        let mut fact = target.outlet_fact(input)?.clone();
        fact.shape = self.compute_shape(&fact.shape);
        fact.axis += self.axes.iter().filter(|&ax| *ax <= fact.axis).count();
        let id = target.chain_after(input, &*node.name, self.clone(), tvec!(fact))?;
        Ok(tvec!(OutletId::new(id, 0)))
    }
}