//
// This confidential and proprietary software may be used only as
// authorised by a licensing agreement from ARM Limited
// (C) COPYRIGHT 2020-2023 ARM Limited
// ALL RIGHTS RESERVED
// The entire notice above must be reproduced on all authorised
// copies and copies may only be made to the extent permitted
// by a licensing agreement from ARM Limited.

=== Elementwise Unary Operators

==== ABS

Elementwise absolute value operation

include::{generated}/operators/ABS.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|+infinity|+infinity|+0|+0|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    if (is_floating_point(in_out_t) && value1 == -0.0) {
        value1 = 0.0;
    }
    if (static_cast<int32_t>(value1) < 0.0) {
        value1 = apply_sub_s<in_out_t>(0, value1);
    }
    tensor_write<in_out_t>(output, shape, index, value1);
}
----

==== BITWISE_NOT

Elementwise bitwise NOT of input tensor.

include::{generated}/operators/BITWISE_NOT.adoc[]

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    in_out_t result = ~value1;
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== CEIL

Elementwise ceiling operation

include::{generated}/operators/CEIL.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|-infinity|+infinity|-0|+0|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    in_out_t result = apply_ceil<in_out_t>(value1);
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== CLZ

Elementwise count leading zeros operation

include::{generated}/operators/CLZ.adoc[]

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    in_out_t result = count_leading_zeros(value1);
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== EXP

Elementwise e to the x operation

include::{generated}/operators/EXP.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|+0|+infinity|1|1|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    in_out_t result = apply_exp<in_out_t>(value1);
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== FLOOR

Elementwise floor operation

include::{generated}/operators/FLOOR.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|-infinity|+infinity|-0|+0|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    in_out_t result = apply_floor<in_out_t>(value1);
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== LOG

Elementwise natural logarithm operation

include::{generated}/operators/LOG.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|NaN|+infinity|-infinity|-infinity|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    in_out_t result = apply_log<in_out_t>(value1);
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== LOGICAL_NOT

Elementwise logical NOT of input.

include::{generated}/operators/LOGICAL_NOT.adoc[]

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index);
    in_out_t result = !value1;
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== NEGATE

Elementwise negation operation

include::{generated}/operators/NEGATE.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|+infinity|-infinity|+0|-0|NaN
|===

[source,c++]
----
ERROR_IF(in_out_t != i8_t && input1_zp != 0) // Zero point only for int8_t
ERROR_IF(in_out_t != i8_t && output_zp != 0) // Zero point only for int8_t
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape, index);
    acc_t value = apply_sub_s<acc_t>(sign_extend<acc_t>(value1),
                                     sign_extend<acc_t>(input1_zp));
    value = apply_sub_s<acc_t>(0, value);
    value = apply_add_s<acc_t>(value, sign_extend<acc_t>(output_zp));
    in_out_t result = truncate<in_out_t>(apply_clip_s<acc_t>(value,
                                                             minimum_s<in_out_t>,
                                                             maximum_s<in_out_t>));
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== RECIPROCAL

Elementwise reciprocal operation. For integer operation, a TABLE should be used with the appropriate ranges.

include::{generated}/operators/RECIPROCAL.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|-0|+0|-infinity|+infinity|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index);
    in_out_t result = 1.0 / value1;
    tensor_write<in_out_t>(output, shape, index, result);
}
----

==== RSQRT

Elementwise reciprocal square root operation. For integer operation, a TABLE should be used with the appropriate ranges.

include::{generated}/operators/RSQRT.adoc[]

*Floating-point behavior:*
|===
|Input|-infinity|+infinity|-0|+0|NaN

|Output|NaN|+0|-infinity|+infinity|NaN
|===

[source,c++]
----
for_each(index in shape) {
    in_out_t value1 = tensor_read<in_out_t>(input1, shape1, index);
    in_out_t result;
    if (value1 < 0) {
        result = NaN;
    }
    else {
        result = 1.0 / apply_sqrt<in_out_t>(value1);
    }
    tensor_write<in_out_t>(output, shape, index, result);
}
----