auto-diff 0.5.9

#![allow(clippy::redundant_closure_call)]
use tensor_rs::tensor::Tensor;
use super::{OpTrait, OpHandle, OpCall, Op};
use super::macros::new_binary_op;

use std::cell::{RefCell};
use std::rc::Rc;

use crate::var::{Var};
use crate::err::AutoDiffError;

#[cfg(feature = "use-serde")]
use serde::{Serialize, Deserialize};
#[cfg(feature = "use-serde")]
use std::any::Any;

// max_pair
new_binary_op!(MaxPair, "Max_pair",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].max_pair(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// max, in reduction
// min_pair
new_binary_op!(MinPair, "Min_pair",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].min_pair(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// min, in reduction
// arg_sort
#[cfg_attr(feature = "use-serde", derive(Serialize, Deserialize))]
pub struct ArgSort {
    #[cfg_attr(feature = "use-serde", serde(skip))]
    handle: OpHandle,
    dim: usize,
    descending: bool,
}
impl ArgSort {
    pub fn new(dim: usize, descending: bool) -> ArgSort {
        ArgSort {
            handle: OpHandle::new(),
            dim,
            descending,
        }
    }
    fn get_handle(&self) -> &OpHandle {
        &self.handle
    }
    fn get_handle_mut(&mut self) -> &mut OpHandle {
        &mut self.handle
    }
}
impl OpCall for ArgSort {
    fn call(&mut self, inputs: &[&Var])
            -> Result<Vec<Var>, AutoDiffError> {
        let new_one = ArgSort {
            handle: OpHandle::new(),
            dim: self.dim,
            descending: self.descending,
        };

        let op = Op::new(Rc::new(RefCell::new(Box::new(new_one))));

        inputs[0].called_with(op, &inputs[1..inputs.len()])
    }
}
impl OpTrait for ArgSort {

    fn get_name(&self) -> &'static str {
        "Arg_sort"
    }
    fn get_input_size(&self) -> usize {
        1
    }
    fn get_output_size(&self) -> usize {
        1
    }
    fn apply(&self, input: &[Tensor], output: &[Tensor]) {
        output[0].swap(&input[0].arg_sort(self.dim, self.descending))
    }
    fn grad(&self, input: &[Tensor], output_grad: &[Tensor], input_grad: &[Tensor]) {
        unimplemented!();
    }
    fn get_values(&self) -> Vec<Tensor> {
        Vec::new()
    }
    fn get_grads(&self) -> Vec<Tensor> {
        Vec::new()
    }
    fn set_values(&self, _v: &[Tensor]) {
    }
    #[cfg(feature = "use-serde")]
    fn as_any(&self) -> &dyn Any {
	self
    }
}
// eq_t (use eq_elem)
new_binary_op!(EqElem, "Eq_t",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].eq_t(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// equal, 0 is == 1 is !=
new_binary_op!(Equal, "Equal",
               (|a:&[Tensor], b:&[Tensor]|
                if a[0].equal(&a[1]) {
                    b[0].swap(&Tensor::zeros(&[1]))
                } else {
                    b[0].swap(&Tensor::ones(&[1]))
                }),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// ge
new_binary_op!(Ge, "Ge",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].ge(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// gt
new_binary_op!(Gt, "Gt",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].gt(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// le
new_binary_op!(Le, "Le",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].le(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// lt
new_binary_op!(Lt, "Lt",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].lt(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));
// ne
new_binary_op!(Ne, "Ne",
               (|a:&[Tensor], b:&[Tensor]|
                b[0].swap(&a[0].ne(&a[1]))
               ),
               (|input: &[Tensor], output_grad: &[Tensor],
                input_grad: &[Tensor]| {
                    unimplemented!();
               }));