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
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156

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

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

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

use super::NdArrayOps;

impl<E: FloatNdArrayElement, Q: QuantElement> BoolTensorOps<Self> for NdArray<E, Q> {
    fn bool_from_data<const D: usize>(
        data: TensorData,
        _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()
    }

    async fn bool_into_data<const D: usize>(
        tensor: <NdArray<E> as Backend>::BoolTensorPrimitive<D>,
    ) -> TensorData {
        let shape = tensor.shape();
        let values = tensor.array.into_iter().collect();
        TensorData::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 shape = tensor.shape();
        let values = tensor.array.into_iter().collect();
        NdArray::<E>::int_from_data(
            TensorData::new(values, shape).convert::<i64>(),
            &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(TensorData::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 output = Zip::from(&lhs.array)
            .and(&rhs.array)
            .map_collect(|&lhs_val, &rhs_val| (lhs_val == rhs_val))
            .into_shared();
        NdArrayTensor::new(output)
    }

    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>::FloatTensorPrimitive<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)
    }

    fn bool_permute<const D: usize>(
        tensor: burn_tensor::ops::BoolTensor<Self, D>,
        axes: [usize; D],
    ) -> burn_tensor::ops::BoolTensor<Self, D> {
        let array = tensor.array.permuted_axes(axes.into_dimension());
        NdArrayTensor { array }
    }

    fn bool_expand<const D1: usize, const D2: usize>(
        tensor: burn_tensor::ops::BoolTensor<Self, D1>,
        shape: Shape<D2>,
    ) -> burn_tensor::ops::BoolTensor<Self, D2> {
        NdArrayOps::expand(tensor, shape)
    }

    fn bool_flip<const D: usize>(
        tensor: burn_tensor::ops::BoolTensor<Self, D>,
        axes: &[usize],
    ) -> burn_tensor::ops::BoolTensor<Self, D> {
        NdArrayOps::flip(tensor, axes)
    }
}