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

#include "low_precision/multiply_to_group_convolution.hpp"
#include <memory>

#include "openvino/pass/pattern/op/wrap_type.hpp"
#include "low_precision/network_helper.hpp"
#include "itt.hpp"
#include "openvino/core/graph_util.hpp"
#include "openvino/op/group_conv.hpp"
#include "openvino/op/negative.hpp"

namespace ov {
namespace pass {
namespace low_precision {

MultiplyToGroupConvolutionTransformation::MultiplyToGroupConvolutionTransformation(
    const Params& params,
    const PrecisionsRestriction::PrecisionsByPorts& restrictions) : CleanupTransformation(params), restrictions(restrictions), groupSize(1ul) {
    MATCHER_SCOPE(MultiplyToGroupConvolutionTransformation);
    auto matcher = pattern::wrap_type<ov::opset1::Multiply>();

    ov::graph_rewrite_callback callback = [this](pattern::Matcher& m) {
        auto op = m.get_match_root();
        if (transformation_callback(op)) {
            return false;
        }
        return transform(m);
    };

    auto m = std::make_shared<ov::pass::pattern::Matcher>(matcher, matcher_name);
    this->register_matcher(m, callback);
}

bool MultiplyToGroupConvolutionTransformation::transform(ov::pass::pattern::Matcher &m) {
    const auto multiply = m.get_match_root();
    if (!canBeTransformed(multiply)) {
        return false;
    }

    auto input = multiply->get_input_node_shared_ptr(0);
    auto constant = multiply->get_input_node_shared_ptr(1);
    auto inputIndex = 0;
    if (!ov::is_type<ov::opset1::Constant>(constant)) {
        input = multiply->get_input_node_shared_ptr(1);
        constant = multiply->get_input_node_shared_ptr(0);
        inputIndex = 1;
    }

    auto dequantization = NetworkHelper::getDequantization(multiply, defaultPrecisions, inputIndex);
    if (dequantization.data.get_node() == nullptr) {
        return false;
    }
    if (dequantization.subtractConvert != nullptr) {
        dequantization = NetworkHelper::foldDequantization(multiply, inputIndex, defaultPrecisions);
    }

    element::Type weightsPrecision = element::dynamic;
    if (updatePrecisions) {
        // try to find restrictions on weights for GroupConvolution
        if (restrictions.size() > 1ul) {
            const auto& availablePreisions = restrictions[1].second;
            if (!availablePreisions.empty()) {
                weightsPrecision = availablePreisions[0];
            }
        }

        // if restrictions are absent precisions attribute is used
        if (weightsPrecision == element::dynamic) {
            const auto precisionsAttribute = getAttribute<PrecisionsAttribute>(multiply->input(inputIndex == 0ul ? 1ul : 0ul));
            const auto precisions = precisionsAttribute == nullptr ?
                defaultPrecisions :
                precisionsAttribute.as<PrecisionsAttribute>().value();
            weightsPrecision = precisions[0];
        }
    } else {
        weightsPrecision = dequantization.data.get_element_type();
    }

    const size_t inputChannelsCount = input->get_output_partial_shape(0)[1].get_length();
    const size_t outputChannelsCount = multiply->get_output_partial_shape(0)[1].get_length();
    const size_t group = outputChannelsCount / groupSize;
    const size_t weightsSize = outputChannelsCount * inputChannelsCount / group;
    std::vector<float> weightsBuffer(weightsSize);
    const size_t kernelsCount = inputChannelsCount / group;

    if (group == 1ul) {
        for (size_t outputChannel = 0ul; outputChannel < outputChannelsCount; ++outputChannel) {
            for (size_t kernel = 0ul; kernel < kernelsCount; ++kernel) {
                const float value = (outputChannel == kernel) ? 1.f : 0.f;
                weightsBuffer[kernelsCount * outputChannel + kernel] = value;
            }
        }
    } else {
        const size_t channelsInGroup = outputChannelsCount / group;
        for (size_t outputChannel = 0ul; outputChannel < outputChannelsCount; ++outputChannel) {
            const size_t groupIndex = outputChannel / channelsInGroup;
            for (size_t kernel = 0ul; kernel < kernelsCount; ++kernel) {
                const size_t outputChannelIndexInGroup = outputChannel - groupIndex * channelsInGroup;
                const float value = (outputChannelIndexInGroup == kernel) ? 1.f : 0.f;
                weightsBuffer[kernelsCount * outputChannel + kernel] = value;
            }
        }
    }

    const PartialShape pShape = multiply->get_output_partial_shape(0);

    Shape weightsShape = Shape(pShape.rank().get_length() + 1, 1ul);
    weightsShape[0] = group;
    weightsShape[1] = outputChannelsCount / group;
    weightsShape[2] = inputChannelsCount / group;
    const auto weightsNode = std::make_shared<ov::opset1::Constant>(weightsPrecision, weightsShape, weightsBuffer);

    const size_t spatialDimsSize = pShape.rank().get_length() - 2;
    ov::Strides strides(spatialDimsSize, 1ul);
    ov::CoordinateDiff pads(spatialDimsSize, 0ul);
    ov::Strides dilations(spatialDimsSize, 1ul);

    const auto convolution = std::make_shared<ov::op::TypeRelaxed<ov::opset1::GroupConvolution>>(
        std::vector<element::Type>{ element::f32, element::f32 },
        std::vector<element::Type>{ element::f32 },
        ov::op::TemporaryReplaceOutputType(dequantization.data, element::f32).get(),
        ov::op::TemporaryReplaceOutputType(weightsNode, element::f32).get(),
        strides,
        pads,
        pads,
        dilations);
    convolution->set_friendly_name(multiply->get_friendly_name() + "/GroupConvolution");

    std::shared_ptr<Node> lastNode = convolution;
    if (dequantization.subtract != nullptr) {
        lastNode = std::make_shared<ov::opset1::Add>(
            convolution,
            fold<ov::opset1::Negative>(foldConvert(dequantization.subtractConstant, element::f32)));
        lastNode->set_friendly_name(convolution->get_friendly_name() + "/Add");
    }

    lastNode = multiply->clone_with_new_inputs({ lastNode, constant });

    replace_node(multiply, lastNode);
    NetworkHelper::copyInfo(multiply, lastNode);

    return true;
}

bool MultiplyToGroupConvolutionTransformation::canBeTransformed(const std::shared_ptr<Node>& operation) const {
    if (!CleanupTransformation::canBeTransformed(operation)) {
        return false;
    }

    const PartialShape outPShape = operation->get_output_partial_shape(0);
    const auto rank = outPShape.rank();
    if (rank.is_dynamic()) {
        return false;
    }

    if ((rank.get_length() != 4ul) && (rank.get_length() != 5ul)) {
        return false;
    }

    if (outPShape[1].is_dynamic() || outPShape[1].get_length() % groupSize != 0) {
        return false;
    }

    auto inPShape = operation->get_input_partial_shape(0);
    if (inPShape.rank().is_dynamic() || inPShape.size() < 2 || inPShape[1].is_dynamic()) {
        return false;
    }

    Shape constShape;
    int inputIndex;
    if (const auto constant = ov::as_type_ptr<ov::opset1::Constant>(operation->get_input_node_shared_ptr(1))) {
        inputIndex = 0;
        constShape = constant->get_shape();
        if (ov::is_type<ov::opset1::Constant>(operation->get_input_node_shared_ptr(0)) ||
            (ov::is_type<ov::opset1::Subtract>(operation->get_input_node_shared_ptr(0)) &&
            ov::is_type<ov::opset1::Constant>(operation->get_input_node_shared_ptr(0)->get_input_node_shared_ptr(0)))) {
            return false;
        }
    } else if (const auto constant = ov::as_type_ptr<ov::opset1::Constant>(operation->get_input_node_shared_ptr(0))) {
        inputIndex = 1;
        constShape = constant->get_shape();
    } else {
        return false;
    }

    for (size_t i = 2; i < constShape.size(); ++i) {
        if (constShape[i] != 1) {
            return false;
        }
    }

    if (updatePrecisions && restrictions.size() > 0) {
        const auto& availablePreisions = restrictions[0].second;
        if (availablePreisions.empty()) {
            return false;
        }

        const auto dequantization = NetworkHelper::getDequantization(operation, defaultPrecisions, inputIndex);
        const element::Type parentPrecision = dequantization.data.get_element_type();
        if (std::find(availablePreisions.begin(), availablePreisions.end(), parentPrecision) == availablePreisions.end()) {
            return false;
        }
    }

    return true;
}

bool MultiplyToGroupConvolutionTransformation::isQuantized(const std::shared_ptr<const Node>& layer,
    const std::vector<ov::element::Type>& defaultPrecisions) const {
    return MultiplyToGroupConvolutionTransformation::canBeTransformedToGroupConvolution(layer);
}

bool MultiplyToGroupConvolutionTransformation::canBeTransformedToGroupConvolution(const std::shared_ptr<const Node>& layer) {
    const auto parent0 = layer->get_input_node_shared_ptr(0);
    const auto parent1 = layer->get_input_node_shared_ptr(1);

    if (!ov::is_type<ov::opset1::Constant>(parent0) && !ov::is_type<ov::opset1::Constant>(parent1)) {
        return false;
    }

    const PartialShape pShape = layer->get_output_partial_shape(0);
    const auto rank = pShape.rank();
    if (rank.is_dynamic()) {
        return false;
    }

    return (pShape.size() == 4ul) || (pShape.size() == 5ul);
}

bool MultiplyToGroupConvolutionTransformation::isDynamicOrScalar(const std::shared_ptr<const Node>& node) {
    auto getConstantIndex = [](const std::shared_ptr<const Node>& node) -> int {
        if (ov::is_type<ov::opset1::Constant>(node->get_input_node_shared_ptr(1))) {
            return 1;
        }
        if (ov::is_type<ov::opset1::Constant>(node->get_input_node_shared_ptr(0))) {
            return 0;
        }
        return -1;
    };

    const int constantIndex = getConstantIndex(node);
    if (constantIndex == -1) {
        return false;
    }

    const Input<const Node> constantInput = node->input(constantIndex);
    const auto shape = constantInput.get_partial_shape();
    if (shape.is_dynamic() || shape.rank().is_dynamic()) {
        return true;
    }

    if (std::all_of(shape.begin(), shape.end(), [](const Dimension& dimension) { return dimension == 1ul; })) {
        return true;
    }

    return false;
}

void MultiplyToGroupConvolutionTransformation::setGroupSize(const size_t groupSize) {
    this->groupSize = groupSize;
}

size_t MultiplyToGroupConvolutionTransformation::getGroupSize() const {
    return groupSize;
}

bool MultiplyToGroupConvolutionTransformation::isPrecisionPreserved(std::shared_ptr<Node> layer) const noexcept {
    return false;
}

} // namespace low_precision
} // namespace pass
} // namespace ov
