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// Copyright Vespa.ai. Licensed under the terms of the Apache 2.0 license. See LICENSE in the project root.
#pragma once
#include <vespa/eval/eval/tensor_function.h>
namespace vespalib::eval {
/**
* Tensor function unpacking bits into separate values.
*
* The tensor containing the packed bits must be a vector (dense
* tensor with 1 dimension) with cell type 'int8'. Bytes must be
* processed with increasing index. Bits may be unpacked in either
* 'big' or 'little' order. The result must be a vector (dense tensor
* with 1 dimension) where the dimension is 8 times larger than the
* input (since there are 8 bits packed into each int8 value).
*
* Baseline expression for 'big' bitorder (most significant bit first):
* (Note: this is the default order used by numpy unpack_bits)
* 'tensor<int8>(x[64])(bit(packed{x:(x/8)},7-(x%8)))'
*
* Baseline expression for 'little' bitorder (least significant bit first):
* (Note: make sure this is the actual order of your bits)
* 'tensor<int8>(x[64])(bit(packed{x:(x/8)},x%8))'
**/
class UnpackBitsFunction : public tensor_function::Op1
{
private:
bool _big_bitorder;
public:
UnpackBitsFunction(const ValueType &res_type_in, const TensorFunction &packed, bool big);
InterpretedFunction::Instruction compile_self(const ValueBuilderFactory &factory, Stash &stash) const override;
bool result_is_mutable() const override { return true; }
static const TensorFunction &optimize(const TensorFunction &expr, Stash &stash);
};
} // namespace
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