// Copyright (C) 2018-2026 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#pragma once

#include "openvino/op/op.hpp"

namespace ov {
namespace op {
namespace v0 {
// clang-format off
/// \brief Elementwise Local Response Normalization (LRN) operation.
///
/// ## Inputs
///
/// |       | Type                                    | Description                                     |
/// | ----- | --------------------------------------- | ----------------------------------------------- |
/// | `arg` | \f$N[n, c, d_1,\dots,d_n]~(n \geq 0)\f$ | A tensor of any shape and numeric element type. |
///
/// ## Output
///
/// | Type                         | Description                                                                                                                                                                                  |
/// | ---------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
/// | \f$N[n, c, d_1,\dots,d_n]\f$ | The tensor \f$T\f$, where \f$T[n, c, d_1,\dots,d_n] = \frac{N[n,i,d_1,\dots,d_n]}{ (bias + alpha * (\sum_{i=max(0,(nsize-1)/2)}^{min(C, (nsize-1)/2)+1} N[n,i,d_1,\dots,d_n]^{2}) ^ {2})}\f$ |
/// \ingroup ov_ops_cpp_api
// clang-format on
class OPENVINO_API LRN : public Op {
public:
    OPENVINO_OP("LRN", "opset1");

    /// \brief Constructs a LRN operation.
    LRN() = default;
    /// \brief Constructs a LRN operation.
    ///
    /// \param arg Node that produces the input tensor.
    LRN(const Output<Node>& arg, double alpha, double beta, double bias, size_t size);

    LRN(const Output<Node>& arg, const Output<Node>& axes, double alpha, double beta, double bias, size_t size);

    bool visit_attributes(AttributeVisitor& visitor) override;
    std::shared_ptr<Node> clone_with_new_inputs(const OutputVector& new_args) const override;
    void validate_and_infer_types() override;

    double get_alpha() const {
        return m_alpha;
    }
    void set_alpha(double alpha) {
        m_alpha = alpha;
    }
    double get_beta() const {
        return m_beta;
    }
    void set_beta(double beta) {
        m_beta = beta;
    }
    double get_bias() const {
        return m_bias;
    }
    void set_bias(double bias) {
        m_bias = bias;
    }
    size_t get_nsize() const {
        return m_size;
    }
    void set_nsize(size_t size) {
        m_size = size;
    }
    AxisSet get_reduction_axes() const;

protected:
    double m_alpha;
    double m_beta;
    double m_bias;
    size_t m_size;
};
}  // namespace v0
}  // namespace op
}  // namespace ov
