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use crate::infer::*;
use crate::internal::*;
use tract_ndarray::*;

#[derive(Debug, Clone, new, Default)]
pub struct Split {
    axis: usize,
    outputs: usize,
    split: Option<Vec<usize>>,
}

impl Split {
    fn split_dims<D: DimLike>(&self, input: D) -> TractResult<TVec<D>> {
        if let Some(ref split) = self.split.as_ref() {
            Ok(split.iter().map(|&d| D::from(d)).collect())
        } else {
            Ok(tvec!(input/self.outputs;self. outputs))
        }
    }

    fn eval_t<T: Datum>(&self, input: Arc<Tensor>) -> TractResult<TVec<Arc<Tensor>>> {
        let mut current = 0;
        let input = input.to_array_view::<T>()?;
        Ok(self
            .split_dims(input.shape()[self.axis])?
            .iter()
            .map(|&d| {
                let slice = if d > 0 {
                    input.slice_axis(Axis(self.axis), (current..current + d).into()).to_owned()
                } else {
                    let mut shape: TVec<usize> = input.shape().into();
                    shape[self.axis] = 0;
                    ArrayD::<T>::default(&*shape)
                };
                current += d;
                slice.into_arc_tensor()
            })
            .collect())
    }
}

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

    not_a_typed_op!();
    not_a_pulsed_op!();
}

impl StatelessOp for Split {
    /// Evaluates the operation given the input tensors.
    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 Split {
    fn rules<'r, 'p: 'r, 's: 'r>(
        &'s self,
        s: &mut Solver<'r>,
        inputs: &'p [TensorProxy],
        outputs: &'p [TensorProxy],
    ) -> InferenceResult {
        check_input_arity(&inputs, 1)?;
        check_output_arity(&outputs, self.outputs)?;
        (0..self.outputs).try_for_each(|i| {
            s.equals(&inputs[0].datum_type, &outputs[i].datum_type)?;
            s.equals(&inputs[0].rank, &outputs[i].rank)
        })?;
        s.given(&inputs[0].shape, move |s, shape| {
            let dims = self.split_dims(shape[self.axis].clone())?;
            for i in 0..self.outputs {
                let mut shape = shape.clone();
                shape[self.axis] = dims[i].clone();
                s.equals(&outputs[i].shape, shape)?;
            }
            Ok(())
        })?;
        Ok(())
    }

    fn nboutputs(&self) -> TractResult<usize> {
        Ok(self.outputs)
    }

    as_op!();

    fn to_typed(
        &self,
        _source: &InferenceModel,
        node: &InferenceNode,
        target: &mut TypedModel,
        mapping: &HashMap<OutletId, OutletId>,
    ) -> TractResult<TVec<OutletId>> {
        let input = target.outlet_fact(mapping[&node.inputs[0]])?.clone();
        let wire = mapping[&node.inputs[0]];
        let mut outputs = tvec!();
        let mut current = 0.to_dim();
        for len in self.split_dims(input.shape.dim(self.axis))? {
            let end = current.clone() + len;
            outputs.push(
                target.wire_node(
                    format!("{}-{}..{}", node.name, current, end),
                    crate::ops::array::Slice::new(self.axis, current, end.clone()),
                    &[wire],
                )?[0],
            );
            current = end;
        }
        Ok(outputs)
    }
}