1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131

// Language
use alloc::vec;
use alloc::vec::Vec;
use burn_tensor::ops::{BoolTensorOps, IntTensorOps};
use burn_tensor::{ElementConversion, Reader};
use core::ops::Range;

// Current crate
use crate::element::FloatNdArrayElement;
use crate::NdArrayDevice;
use crate::{tensor::NdArrayTensor, NdArray};

// Workspace crates
use burn_tensor::{backend::Backend, Data, Shape};

use super::NdArrayOps;

impl<E: FloatNdArrayElement> BoolTensorOps<Self> for NdArray<E> {
    fn bool_from_data<const D: usize>(
        data: Data<bool, D>,
        _device: &NdArrayDevice,
    ) -> NdArrayTensor<bool, D> {
        NdArrayTensor::from_data(data)
    }

    fn bool_shape<const D: usize>(
        tensor: &<NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> Shape<D> {
        tensor.shape()
    }

    fn bool_into_data<const D: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> Reader<Data<bool, D>> {
        let shape = tensor.shape();
        let values = tensor.array.into_iter().collect();

        Reader::Concrete(Data::new(values, shape))
    }

    fn bool_to_device<const D: usize>(
        tensor: NdArrayTensor<bool, D>,
        _device: &NdArrayDevice,
    ) -> NdArrayTensor<bool, D> {
        tensor
    }

    fn bool_reshape<const D1: usize, const D2: usize>(
        tensor: NdArrayTensor<bool, D1>,
        shape: Shape<D2>,
    ) -> NdArrayTensor<bool, D2> {
        NdArrayOps::reshape(tensor, shape)
    }

    fn bool_slice<const D1: usize, const D2: usize>(
        tensor: NdArrayTensor<bool, D1>,
        ranges: [Range<usize>; D2],
    ) -> NdArrayTensor<bool, D1> {
        NdArrayOps::slice(tensor, ranges)
    }

    fn bool_into_int<const D: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> NdArrayTensor<i64, D> {
        let data = Self::bool_into_data(tensor)
            .read_sync()
            .expect("Always sync with ndarray");
        NdArray::<E>::int_from_data(data.convert(), &NdArrayDevice::Cpu)
    }

    fn bool_device<const D: usize>(
        _tensor: &<NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> <NdArray<E> as Backend>::Device {
        NdArrayDevice::Cpu
    }

    fn bool_empty<const D: usize>(
        shape: Shape<D>,
        _device: &<NdArray<E> as Backend>::Device,
    ) -> <NdArray<E> as Backend>::BoolTensorPrimitive<D> {
        let values = vec![false; shape.num_elements()];
        NdArrayTensor::from_data(Data::new(values, shape))
    }

    fn bool_slice_assign<const D1: usize, const D2: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D1>,
        ranges: [Range<usize>; D2],
        value: <NdArray<E> as Backend>::BoolTensorPrimitive<D1>,
    ) -> <NdArray<E> as Backend>::BoolTensorPrimitive<D1> {
        NdArrayOps::slice_assign(tensor, ranges, value)
    }

    fn bool_cat<const D: usize>(
        tensors: Vec<<NdArray<E> as Backend>::BoolTensorPrimitive<D>>,
        dim: usize,
    ) -> <NdArray<E> as Backend>::BoolTensorPrimitive<D> {
        NdArrayOps::cat(tensors, dim)
    }

    fn bool_equal<const D: usize>(
        lhs: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
        rhs: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> <NdArray<E> as Backend>::BoolTensorPrimitive<D> {
        let mut array = lhs.array;
        array.zip_mut_with(&rhs.array, |a, b| *a = *a == *b);

        NdArrayTensor { array }
    }

    fn bool_not<const D: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> <NdArray<E> as Backend>::BoolTensorPrimitive<D> {
        let array = tensor.array.mapv(|a| !a).into_shared();
        NdArrayTensor { array }
    }

    fn bool_into_float<const D: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> <NdArray<E> as Backend>::TensorPrimitive<D> {
        let array = tensor.array.mapv(|a| (a as i32).elem()).into_shared();
        NdArrayTensor { array }
    }

    fn bool_swap_dims<const D: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
        dim1: usize,
        dim2: usize,
    ) -> <NdArray<E> as Backend>::BoolTensorPrimitive<D> {
        NdArrayOps::swap_dims(tensor, dim1, dim2)
    }
}