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
157
158
159
160

crate::ix!();

/**
  | Given a partitioned tensor $T<N, D_1,
  | ..., D_n>$, where the partitions are
  | defined as ranges on its outer-most
  | (slowest varying) dimension $N$, return
  | a tensor $T<(N + 2 * padding\_width),
  | D_1, ...,
  | 
  | D_n>$ with paddings added to the start
  | and end of each range.
  | 
  | Optionally, different paddings can
  | be provided for beginning and end.
  | 
  | Paddings provided must be a tensor $T<D_1,
  | ...,
  | 
  | D_n>$. If no padding is provided, add
  | zero padding. If no lengths vector is
  | provided, add padding only once, at
  | the start and end of data.
  | 
  | Github Links:
  | 
  | - https://github.com/pytorch/pytorch/blob/master/caffe2/operators/sequence_ops.cc
  |
  */
#[USE_OPERATOR_CONTEXT_FUNCTIONS]
pub struct AddPaddingOp<Context> {

    storage:                    OperatorStorage,
    context:                    Context,

    start_padding_width:        i32,
    end_padding_width:          i32,

    /**
      | Scratch space required by the CUDA version
      | 
      | {Context::GetDeviceType()};
      |
      */
    lengths_prefix_sum_buffer:  Tensor,

    /// {Context::GetDeviceType()};
    lengths_prefix_sum:         Tensor,
}

num_inputs!{AddPadding, (1,4)}

num_outputs!{AddPadding, (1,2)}

inputs!{AddPadding, 
    0 => ("data_in",       "*(type: Tensor)* Input data ($T<N, D_1, ..., D_n>$)."),
    1 => ("lengths",       "*(type: Tensor`<int>`)* Number of elements in each range. sum(lengths) = N."),
    2 => ("start_padding", "*(type: Tensor`<int>`)* [OPTIONAL] Padding data for range start ($T<D_1, ..., D_n>$)."),
    3 => ("end_padding",   "*(type: Tensor`<int>`)* [OPTIONAL] Padding for range end. If not provided, `start_padding` is used ($T<D_1, ..., D_n>$).")
}

outputs!{AddPadding, 
    0 => ("data_out",    "*(type: Tensor)* Padded data tensor ($T<N + 2*padding_width, D_1, ..., D_n>$)."),
    1 => ("lengths_out", "*(type: Tensor`<int>`)* [OPTIONAL] Lengths for each padded range.")
}

args!{AddPadding, 
    0 => ("padding_width",     "*(type: int)* Number of copies of padding to add around each range."),
    1 => ("end_padding_width", "*(type: int)* [OPTIONAL] Specifies a different end-padding width. If this is not set, will use same as `padding_width`.")
}

impl<Context> AddPaddingOp<Context> {

    pub fn new<Args>(args: Args) -> Self {
    
        todo!();
        /*
            : Operator<Context>(std::forward<Args>(args)...),
            startPaddingWidth_( this->template GetSingleArgument<int>("padding_width", 1)),
            endPaddingWidth_( this->template GetSingleArgument<int>("end_padding_width", -1)) 

        CAFFE_ENFORCE_GE(startPaddingWidth_, 0);
        if (endPaddingWidth_ < 0) {
          endPaddingWidth_ = startPaddingWidth_;
        }
        */
    }
    
    #[inline] pub fn run_on_device(&mut self) -> bool {
        
        todo!();
        /*
            if (startPaddingWidth_ == 0 && endPaddingWidth_ == 0) {
          Output(0)->CopyFrom(Input(0), true /*async*/);
          if (OutputSize() == 2) {
            Output(1)->CopyFrom(Input(1), true /*async*/);
          }
          return true;
        }
        return DispatchHelper<TensorTypes<float, double, int, int64_t, bool>>::call(
            this, Input(0));
        */
    }
    
    #[inline] pub fn do_run_with_type<T>(&mut self) -> bool {
    
        todo!();
        /*
            const auto& in = Input(0);
        CAFFE_ENFORCE_GE(in.dim(), 1);
        const int32_t outer_size = in.sizes()[0];
        const auto block_size = in.size_from_dim(1);

        // if no lengths is provided, assume it is a single full-span entry
        const int32_t* lengths_ptr = nullptr;
        int32_t lengths_size = 1;
        if (InputSize() > 1) {
          const auto& lengths = Input(1);
          lengths_ptr = lengths.template data<int32_t>();
          lengths_size = lengths.numel();
        }

        // fetch paddings
        // input_size == 2 : pad with zeros
        // input_size == 3 : start and end paddings are the same
        // input_size == 4 : different start and end paddings
        const T* padding_start_ptr = nullptr;
        const T* padding_end_ptr = nullptr;
        if (InputSize() >= 3) {
          auto& padding_start = Input(2);
          CAFFE_ENFORCE_EQ(block_size, padding_start.numel());
          padding_start_ptr = padding_start.template data<T>();
        }
        if (InputSize() == 4) {
          auto& padding_end = Input(3);
          CAFFE_ENFORCE_EQ(block_size, padding_end.numel());
          padding_end_ptr = padding_end.template data<T>();
        } else {
          padding_end_ptr = padding_start_ptr;
        }

        auto out_dims = in.sizes().vec();
        out_dims[0] += (startPaddingWidth_ + endPaddingWidth_) * lengths_size;
        auto* out = Output(0, std::move(out_dims), at::dtype<T>());

        const auto* in_ptr = in.template data<T>();
        auto* out_ptr = out->template mutable_data<T>();

        return MakePadding<T>(
            in_ptr,
            out_ptr,
            lengths_ptr,
            lengths_size,
            outer_size,
            padding_start_ptr,
            padding_end_ptr,
            block_size);
        */
    }
}