// Copyright (C) 2018-2021 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#pragma once

#include <cstddef>
#include <memory>
#include <string>
#include <vector>

#include "ngraph/node.hpp"
#include "ngraph/op/util/activation_functions.hpp"
#include "ngraph/op/util/fused_op.hpp"
#include "ngraph/op/util/rnn_cell_base.hpp"

namespace ngraph
{
    namespace op
    {
        namespace v3
        {
            ///
            /// \brief      Class for GRU cell node.
            ///
            /// \note       It follows notation and equations defined as in ONNX standard:
            ///             https://github.com/onnx/onnx/blob/master/docs/Operators.md#GRU
            ///
            ///             Note this class represents only single *cell* and not whole GRU *layer*.
            ///
            class NGRAPH_API GRUCell : public util::RNNCellBase
            {
            public:
                static constexpr NodeTypeInfo type_info{"GRUCell", 3};
                const NodeTypeInfo& get_type_info() const override { return type_info; }
                GRUCell();
                ///
                /// \brief      Constructs GRUCell node.
                ///
                /// \param[in]  X                     The input tensor with shape: [batch_size,
                ///                                   input_size].
                /// \param[in]  initial_hidden_state  The hidden state tensor at current time step
                ///                                   with shape: [batch_size, hidden_size].
                /// \param[in]  W                     The weight tensor with shape:
                ///                                   [gates_count * hidden_size, input_size].
                /// \param[in]  R                     The recurrence weight tensor with shape:
                ///                                   [gates_count * hidden_size, hidden_size].
                /// \param[in]  hidden_size           The number of hidden units for recurrent cell.
                ///
                GRUCell(const Output<Node>& X,
                        const Output<Node>& initial_hidden_state,
                        const Output<Node>& W,
                        const Output<Node>& R,
                        std::size_t hidden_size);

                ///
                /// \brief      Constructs GRUCell node.
                ///
                /// \param[in]  X                     The input tensor with shape: [batch_size,
                ///                                   input_size].
                /// \param[in]  initial_hidden_state  The hidden state tensor at current time step
                ///                                   with shape: [batch_size, hidden_size].
                /// \param[in]  W                     The weight tensor with shape:
                ///                                   [gates_count * hidden_size, input_size].
                /// \param[in]  R                     The recurrence weight tensor with shape:
                ///                                   [gates_count * hidden_size, hidden_size].
                /// \param[in]  hidden_size           The number of hidden units for recurrent cell.
                /// \param[in]  activations           The vector of activation functions used inside
                ///                                   recurrent cell.
                /// \param[in]  activations_alpha     The vector of alpha parameters for activation
                ///                                   functions in order respective to activation
                ///                                   list.
                /// \param[in]  activations_beta      The vector of beta parameters for activation
                ///                                   functions in order respective to activation
                ///                                   list.
                /// \param[in]  clip                  The value defining clipping range [-clip,
                ///                                   clip] on input of activation functions.
                ///
                GRUCell(const Output<Node>& X,
                        const Output<Node>& initial_hidden_state,
                        const Output<Node>& W,
                        const Output<Node>& R,
                        std::size_t hidden_size,
                        const std::vector<std::string>& activations,
                        const std::vector<float>& activations_alpha,
                        const std::vector<float>& activations_beta,
                        float clip,
                        bool linear_before_reset);

                ///
                /// \brief      Constructs GRUCell node.
                ///
                /// \param[in]  X                     The input tensor with shape: [batch_size,
                ///                                   input_size].
                /// \param[in]  initial_hidden_state  The hidden state tensor at current time step
                ///                                   with shape: [batch_size, hidden_size].
                /// \param[in]  W                     The weight tensor with shape: [gates_count *
                ///                                   hidden_size, input_size].
                /// \param[in]  R                     The recurrence weight tensor with shape:
                ///                                   [gates_count * hidden_size, hidden_size].
                /// \param[in]  hidden_size           The number of hidden units for recurrent cell.
                /// \param[in]  B                     The sum of biases (weight and recurrence) for
                ///                                   update, reset and hidden gates.
                ///                                   If linear_before_reset := true then biases for
                ///                                   hidden gates are
                ///                                   placed separately (weight and recurrence).
                ///                                   Shape: [gates_count * hidden_size] if
                ///                                   linear_before_reset := false
                ///                                   Shape: [(gates_count + 1) * hidden_size] if
                ///                                   linear_before_reset := true
                /// \param[in]  activations           The vector of activation functions used inside
                ///                                   recurrent cell.
                /// \param[in]  activations_alpha     The vector of alpha parameters for activation
                ///                                   functions in order respective to activation
                ///                                   list.
                /// \param[in]  activations_beta      The vector of beta parameters for activation
                ///                                   functions in order respective to activation
                ///                                   list.
                /// \param[in]  clip                  The value defining clipping range [-clip,
                ///                                   clip] on input of activation functions.
                /// \param[in]  linear_before_reset   Whether or not to apply the linear
                ///                                   transformation before multiplying by the
                ///                                   output of the reset gate.
                ///
                GRUCell(const Output<Node>& X,
                        const Output<Node>& initial_hidden_state,
                        const Output<Node>& W,
                        const Output<Node>& R,
                        const Output<Node>& B,
                        std::size_t hidden_size,
                        const std::vector<std::string>& activations =
                            std::vector<std::string>{"sigmoid", "tanh"},
                        const std::vector<float>& activations_alpha = {},
                        const std::vector<float>& activations_beta = {},
                        float clip = 0.f,
                        bool linear_before_reset = false);

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

                bool get_linear_before_reset() const { return m_linear_before_reset; }

            private:
                /// brief Add and initialize bias input to all zeros.
                void add_default_bias_input();

                ///
                /// \brief The Activation function f.
                ///
                util::ActivationFunction m_activation_f;
                ///
                /// \brief The Activation function g.
                ///
                util::ActivationFunction m_activation_g;

                static constexpr std::size_t s_gates_count{3};
                ///
                /// \brief Control whether or not apply the linear transformation.
                ///
                /// \note The linear transformation may be applied when computing the output of
                ///       hidden gate. It's done before multiplying by the output of the reset gate.
                ///
                bool m_linear_before_reset;
            };
        } // namespace v3
    }     // namespace op
} // namespace ngraph
