// Copyright © Advanced Micro Devices, Inc., or its affiliates.
// SPDX-License-Identifier:  MIT

#include <algorithm>
#include <gtest/gtest.h>
#include <hip/hip_runtime.h>
#include <memory>
#include <set>
#include <vector>

#include <hipdnn_frontend.hpp>
#include <hipdnn_frontend/node/BatchnormInferenceNodeVarianceExt.hpp>
#include <hipdnn_test_sdk/constants/BnInfVarExtConstants.hpp>
#include <hipdnn_test_sdk/utilities/IntegrationTestFixture.hpp>
#include <hipdnn_test_sdk/utilities/LiftingTestHelpers.hpp>
#include <hipdnn_test_sdk/utilities/TestUtilities.hpp>
#include <hipdnn_test_sdk/utilities/TestableGraph.hpp>
#include <hipdnn_test_sdk/utilities/ToVec.hpp>

using namespace hipdnn_frontend;
using namespace hipdnn_frontend::graph;
using namespace hipdnn_tests::constants;
using hipdnn_tests::IntegrationTestFixture;
using hipdnn_tests::liftGraph;
using hipdnn_tests::liftGraphWithoutFinalization;
using hipdnn_tests::TestableGraphLifting;
using hipdnn_tests::toVec;

namespace
{
// Lifts a frontend graph via build_operation_graph(handle), then
// reconstructs it with fromBackendDescriptor() for verification.
class IntegrationBatchnormInferenceVarianceExtDescriptorLifting : public IntegrationTestFixture
{
protected:
    /// Builds a standard BatchnormInferenceVarianceExt graph for round-trip testing.
    static std::shared_ptr<TestableGraphLifting> buildGraph()
    {
        auto graph = std::make_shared<TestableGraphLifting>();
        graph->set_name("BatchnormInferenceVarianceExtLiftingTestGraph")
            .set_compute_data_type(DataType::FLOAT)
            .set_intermediate_data_type(DataType::FLOAT)
            .set_io_data_type(DataType::FLOAT);

        auto x = std::make_shared<TensorAttributes>();
        x->set_uid(K_BN_INF_VAR_EXT_X_UID).set_name("x").set_data_type(DataType::FLOAT);
        x->set_dim(toVec(K_BN_INF_VAR_EXT_X_DIMS)).set_stride(toVec(K_BN_INF_VAR_EXT_X_STRIDES));

        auto mean = std::make_shared<TensorAttributes>();
        mean->set_uid(K_BN_INF_VAR_EXT_MEAN_UID).set_name("mean").set_data_type(DataType::FLOAT);
        mean->set_dim(toVec(K_BN_INF_VAR_EXT_MEAN_DIMS))
            .set_stride(toVec(K_BN_INF_VAR_EXT_MEAN_STRIDES));

        auto variance = std::make_shared<TensorAttributes>();
        variance->set_uid(K_BN_INF_VAR_EXT_VARIANCE_UID)
            .set_name("variance")
            .set_data_type(DataType::FLOAT);
        variance->set_dim(toVec(K_BN_INF_VAR_EXT_VARIANCE_DIMS))
            .set_stride(toVec(K_BN_INF_VAR_EXT_VARIANCE_STRIDES));

        auto scale = std::make_shared<TensorAttributes>();
        scale->set_uid(K_BN_INF_VAR_EXT_SCALE_UID).set_name("scale").set_data_type(DataType::FLOAT);
        scale->set_dim(toVec(K_BN_INF_VAR_EXT_SCALE_DIMS))
            .set_stride(toVec(K_BN_INF_VAR_EXT_SCALE_STRIDES));

        auto bias = std::make_shared<TensorAttributes>();
        bias->set_uid(K_BN_INF_VAR_EXT_BIAS_UID).set_name("bias").set_data_type(DataType::FLOAT);
        bias->set_dim(toVec(K_BN_INF_VAR_EXT_BIAS_DIMS))
            .set_stride(toVec(K_BN_INF_VAR_EXT_BIAS_STRIDES));

        auto epsilon = std::make_shared<TensorAttributes>(1e-5f);
        epsilon->set_uid(K_BN_INF_VAR_EXT_EPSILON_UID)
            .set_name("epsilon")
            .set_data_type(DataType::FLOAT)
            .set_dim(toVec(K_BN_INF_VAR_EXT_EPSILON_DIMS))
            .set_stride(toVec(K_BN_INF_VAR_EXT_EPSILON_STRIDES));

        BatchnormInferenceAttributesVarianceExt attrs;
        attrs.set_name("test_op");

        auto y = graph->batchnorm_inference_variance_ext(
            x, mean, variance, scale, bias, epsilon, attrs);
        y->set_uid(K_BN_INF_VAR_EXT_Y_UID).set_output(true).set_name("y");

        return graph;
    }
};

// Builds a standard BatchnormInferenceVarianceExt graph, lowers via build_operation_graph(handle),
// lifts back with fromBackendDescriptor(), and performs comprehensive field-by-field
// validation of graph data types, tensor attributes, and operation parameters.
TEST_F(IntegrationBatchnormInferenceVarianceExtDescriptorLifting,
       BasicBatchnormInferenceVarianceExtRoundTrip)
{
    auto originalGraph = buildGraph();

    auto liftedGraph = liftGraph(*originalGraph, _handle);
    ASSERT_NE(liftedGraph, nullptr);

    // Verify graph-level data types
    EXPECT_EQ(liftedGraph->get_compute_data_type(), DataType::FLOAT);
    EXPECT_EQ(liftedGraph->get_intermediate_data_type(), DataType::FLOAT);
    EXPECT_EQ(liftedGraph->get_io_data_type(), DataType::FLOAT);

    // Verify tensors by UID
    auto tensorMap = liftedGraph->getTensorsByUid();
    ASSERT_EQ(tensorMap.size(), 7u);

    // Verify x tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_X_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_uid(), K_BN_INF_VAR_EXT_X_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_X_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_stride(), toVec(K_BN_INF_VAR_EXT_X_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_data_type(), DataType::FLOAT);

    // Verify mean tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_MEAN_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_uid(), K_BN_INF_VAR_EXT_MEAN_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_MEAN_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_MEAN_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_data_type(), DataType::FLOAT);

    // Verify variance tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_VARIANCE_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_uid(), K_BN_INF_VAR_EXT_VARIANCE_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_dim(),
              toVec(K_BN_INF_VAR_EXT_VARIANCE_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_VARIANCE_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_data_type(), DataType::FLOAT);

    // Verify scale tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_SCALE_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_uid(), K_BN_INF_VAR_EXT_SCALE_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_SCALE_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_SCALE_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_data_type(), DataType::FLOAT);

    // Verify bias tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_BIAS_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_uid(), K_BN_INF_VAR_EXT_BIAS_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_BIAS_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_BIAS_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_data_type(), DataType::FLOAT);

    // Verify y tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_Y_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_uid(), K_BN_INF_VAR_EXT_Y_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_Y_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_stride(), toVec(K_BN_INF_VAR_EXT_Y_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_data_type(), DataType::FLOAT);

    // Verify epsilon tensor
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_EPSILON_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_uid(), K_BN_INF_VAR_EXT_EPSILON_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_dim(),
              toVec(K_BN_INF_VAR_EXT_EPSILON_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_EPSILON_STRIDES));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_data_type(), DataType::FLOAT);
    EXPECT_TRUE(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_is_pass_by_value());
    ASSERT_TRUE(
        tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_compile_time_constant<float>().has_value());
    EXPECT_FLOAT_EQ(
        tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_compile_time_constant<float>().value(), 1e-5f);

    // Verify tensor names
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_name(), "x");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_name(), "mean");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_name(), "variance");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_name(), "scale");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_name(), "bias");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_name(), "y");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_name(), "epsilon");

    // Verify sub-node count and type
    auto& subNodes = liftedGraph->getSubNodes();
    ASSERT_EQ(subNodes.size(), 1u)
        << "Expected 1 operation node in lifted graph"; // NOLINT(readability-implicit-bool-conversion)

    auto* opNode = dynamic_cast<BatchnormInferenceNodeVarianceExt*>(subNodes[0].get());
    ASSERT_NE(opNode, nullptr)
        << "Expected a BatchnormInferenceNodeVarianceExt"; // NOLINT(readability-implicit-bool-conversion)

    // Verify operation name
    EXPECT_EQ(opNode->attributes.get_name(), "test_op");
}

// After lifting, verifies tensor objects in the node attributes are the same
// shared_ptr instances as in the tensor map (pointer equality).
TEST_F(IntegrationBatchnormInferenceVarianceExtDescriptorLifting,
       BatchnormInferenceVarianceExtTensorSharingPreserved)
{
    auto originalGraph = buildGraph();

    auto liftedGraph = liftGraph(*originalGraph, _handle);
    ASSERT_NE(liftedGraph, nullptr);

    auto tensorMap = liftedGraph->getTensorsByUid();

    auto& subNodes = liftedGraph->getSubNodes();
    ASSERT_EQ(subNodes.size(), 1u);

    auto* opNode = dynamic_cast<BatchnormInferenceNodeVarianceExt*>(subNodes[0].get());
    ASSERT_NE(opNode, nullptr);

    // Verify x tensor sharing
    EXPECT_EQ(opNode->attributes.get_x()->get_uid(), K_BN_INF_VAR_EXT_X_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID].get(), opNode->attributes.get_x().get());
    // Verify mean tensor sharing
    EXPECT_EQ(opNode->attributes.get_mean()->get_uid(), K_BN_INF_VAR_EXT_MEAN_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID].get(), opNode->attributes.get_mean().get());
    // Verify variance tensor sharing
    EXPECT_EQ(opNode->attributes.get_variance()->get_uid(), K_BN_INF_VAR_EXT_VARIANCE_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID].get(),
              opNode->attributes.get_variance().get());
    // Verify scale tensor sharing
    EXPECT_EQ(opNode->attributes.get_scale()->get_uid(), K_BN_INF_VAR_EXT_SCALE_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID].get(), opNode->attributes.get_scale().get());
    // Verify bias tensor sharing
    EXPECT_EQ(opNode->attributes.get_bias()->get_uid(), K_BN_INF_VAR_EXT_BIAS_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID].get(), opNode->attributes.get_bias().get());
    // Verify y tensor sharing
    EXPECT_EQ(opNode->attributes.get_y()->get_uid(), K_BN_INF_VAR_EXT_Y_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID].get(), opNode->attributes.get_y().get());
    // Verify epsilon tensor sharing
    EXPECT_EQ(opNode->attributes.get_epsilon()->get_uid(), K_BN_INF_VAR_EXT_EPSILON_UID);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID].get(),
              opNode->attributes.get_epsilon().get());
}

// Builds a BatchnormInferenceVarianceExt graph, serializes to binary, creates a backend descriptor
// from bytes (no handle, no finalize), calls fromBackendDescriptor(), and verifies
// all fields survive the backend C API serialization path.
TEST_F(IntegrationBatchnormInferenceVarianceExtDescriptorLifting,
       BatchnormInferenceVarianceExtLiftWithoutFinalization)
{
    auto originalGraph = buildGraph();

    auto liftedGraph = liftGraphWithoutFinalization(*originalGraph);
    ASSERT_NE(liftedGraph, nullptr);

    // Verify graph-level data types
    EXPECT_EQ(liftedGraph->get_compute_data_type(), DataType::FLOAT);
    EXPECT_EQ(liftedGraph->get_intermediate_data_type(), DataType::FLOAT);
    EXPECT_EQ(liftedGraph->get_io_data_type(), DataType::FLOAT);

    // Verify the lifted graph has 1 operation node
    auto& subNodes = liftedGraph->getSubNodes();
    ASSERT_EQ(subNodes.size(), 1u);

    auto* opNode = dynamic_cast<BatchnormInferenceNodeVarianceExt*>(subNodes[0].get());
    ASSERT_NE(opNode, nullptr);

    // Verify operation name
    EXPECT_EQ(opNode->attributes.get_name(), "test_op");

    // Verify tensor dims and strides
    auto tensorMap = liftedGraph->getTensorsByUid();
    ASSERT_EQ(tensorMap.size(), 7u);

    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_X_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_X_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_stride(), toVec(K_BN_INF_VAR_EXT_X_STRIDES));
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_MEAN_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_MEAN_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_MEAN_STRIDES));
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_VARIANCE_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_dim(),
              toVec(K_BN_INF_VAR_EXT_VARIANCE_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_VARIANCE_STRIDES));
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_SCALE_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_SCALE_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_SCALE_STRIDES));
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_BIAS_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_BIAS_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_BIAS_STRIDES));
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_Y_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_dim(), toVec(K_BN_INF_VAR_EXT_Y_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_stride(), toVec(K_BN_INF_VAR_EXT_Y_STRIDES));
    ASSERT_NE(tensorMap.count(K_BN_INF_VAR_EXT_EPSILON_UID), 0u);
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_dim(),
              toVec(K_BN_INF_VAR_EXT_EPSILON_DIMS));
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_stride(),
              toVec(K_BN_INF_VAR_EXT_EPSILON_STRIDES));

    // Verify tensor names
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_X_UID]->get_name(), "x");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_MEAN_UID]->get_name(), "mean");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_VARIANCE_UID]->get_name(), "variance");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_SCALE_UID]->get_name(), "scale");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_BIAS_UID]->get_name(), "bias");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_Y_UID]->get_name(), "y");
    EXPECT_EQ(tensorMap[K_BN_INF_VAR_EXT_EPSILON_UID]->get_name(), "epsilon");
}

// Builds a BatchnormInferenceVarianceExt graph without calling set_uid() on any tensor,
// lowers to backend, lifts, and verifies all auto-assigned UIDs are
// distinct and survive the round-trip.
TEST_F(IntegrationBatchnormInferenceVarianceExtDescriptorLifting,
       AutoAssignedUidsPreservedInLiftingRoundTrip)
{
    auto graph = std::make_shared<TestableGraphLifting>();
    graph->set_name("BatchnormInferenceVarianceExtAutoUidLiftTest")
        .set_compute_data_type(DataType::FLOAT)
        .set_intermediate_data_type(DataType::FLOAT)
        .set_io_data_type(DataType::FLOAT);

    auto x = std::make_shared<TensorAttributes>();
    x->set_name("x").set_data_type(DataType::FLOAT);
    x->set_dim(toVec(K_BN_INF_VAR_EXT_X_DIMS)).set_stride(toVec(K_BN_INF_VAR_EXT_X_STRIDES));

    auto mean = std::make_shared<TensorAttributes>();
    mean->set_name("mean").set_data_type(DataType::FLOAT);
    mean->set_dim(toVec(K_BN_INF_VAR_EXT_MEAN_DIMS))
        .set_stride(toVec(K_BN_INF_VAR_EXT_MEAN_STRIDES));

    auto variance = std::make_shared<TensorAttributes>();
    variance->set_name("variance").set_data_type(DataType::FLOAT);
    variance->set_dim(toVec(K_BN_INF_VAR_EXT_VARIANCE_DIMS))
        .set_stride(toVec(K_BN_INF_VAR_EXT_VARIANCE_STRIDES));

    auto scale = std::make_shared<TensorAttributes>();
    scale->set_name("scale").set_data_type(DataType::FLOAT);
    scale->set_dim(toVec(K_BN_INF_VAR_EXT_SCALE_DIMS))
        .set_stride(toVec(K_BN_INF_VAR_EXT_SCALE_STRIDES));

    auto bias = std::make_shared<TensorAttributes>();
    bias->set_name("bias").set_data_type(DataType::FLOAT);
    bias->set_dim(toVec(K_BN_INF_VAR_EXT_BIAS_DIMS))
        .set_stride(toVec(K_BN_INF_VAR_EXT_BIAS_STRIDES));

    auto epsilon = std::make_shared<TensorAttributes>(1e-5f);
    epsilon->set_name("epsilon")
        .set_data_type(DataType::FLOAT)
        .set_dim(toVec(K_BN_INF_VAR_EXT_EPSILON_DIMS))
        .set_stride(toVec(K_BN_INF_VAR_EXT_EPSILON_STRIDES));

    BatchnormInferenceAttributesVarianceExt attrs;
    attrs.set_name("test_auto_uid");

    auto y
        = graph->batchnorm_inference_variance_ext(x, mean, variance, scale, bias, epsilon, attrs);
    y->set_output(true).set_name("y");

    auto liftedGraph = liftGraph(*graph, _handle);
    ASSERT_NE(liftedGraph, nullptr);

    // Verify the tensor map has the expected number of tensors
    auto tensorMap = liftedGraph->getTensorsByUid();
    ASSERT_EQ(tensorMap.size(), 7u);

    // Verify all UIDs are distinct
    std::vector<int64_t> uids;
    uids.reserve(tensorMap.size());
    for(const auto& [uid, tensor] : tensorMap)
    {
        uids.push_back(uid);
    }
    std::sort(uids.begin(), uids.end());
    ASSERT_EQ(std::adjacent_find(uids.begin(), uids.end()), uids.end())
        << "All auto-assigned UIDs must be distinct"; // NOLINT(readability-implicit-bool-conversion)

    // Verify sub-node tensor UIDs are distinct via the node attributes
    auto& subNodes = liftedGraph->getSubNodes();
    ASSERT_EQ(subNodes.size(), 1u);

    auto* opNode = dynamic_cast<BatchnormInferenceNodeVarianceExt*>(subNodes[0].get());
    ASSERT_NE(opNode, nullptr);

    std::set<int64_t> nodeUids;
    ASSERT_NE(opNode->attributes.get_x(), nullptr);
    nodeUids.insert(opNode->attributes.get_x()->get_uid());
    ASSERT_NE(opNode->attributes.get_mean(), nullptr);
    nodeUids.insert(opNode->attributes.get_mean()->get_uid());
    ASSERT_NE(opNode->attributes.get_variance(), nullptr);
    nodeUids.insert(opNode->attributes.get_variance()->get_uid());
    ASSERT_NE(opNode->attributes.get_scale(), nullptr);
    nodeUids.insert(opNode->attributes.get_scale()->get_uid());
    ASSERT_NE(opNode->attributes.get_bias(), nullptr);
    nodeUids.insert(opNode->attributes.get_bias()->get_uid());
    ASSERT_NE(opNode->attributes.get_y(), nullptr);
    nodeUids.insert(opNode->attributes.get_y()->get_uid());
    ASSERT_NE(opNode->attributes.get_epsilon(), nullptr);
    nodeUids.insert(opNode->attributes.get_epsilon()->get_uid());
    ASSERT_EQ(nodeUids.size(), 7u)
        << "Node tensor UIDs are not all distinct"; // NOLINT(readability-implicit-bool-conversion)

    // Verify tensor dims survived the round trip
    EXPECT_EQ(opNode->attributes.get_x()->get_dim(), toVec(K_BN_INF_VAR_EXT_X_DIMS));
    EXPECT_EQ(opNode->attributes.get_x()->get_stride(), toVec(K_BN_INF_VAR_EXT_X_STRIDES));
    EXPECT_EQ(opNode->attributes.get_mean()->get_dim(), toVec(K_BN_INF_VAR_EXT_MEAN_DIMS));
    EXPECT_EQ(opNode->attributes.get_mean()->get_stride(), toVec(K_BN_INF_VAR_EXT_MEAN_STRIDES));
    EXPECT_EQ(opNode->attributes.get_variance()->get_dim(), toVec(K_BN_INF_VAR_EXT_VARIANCE_DIMS));
    EXPECT_EQ(opNode->attributes.get_variance()->get_stride(),
              toVec(K_BN_INF_VAR_EXT_VARIANCE_STRIDES));
    EXPECT_EQ(opNode->attributes.get_scale()->get_dim(), toVec(K_BN_INF_VAR_EXT_SCALE_DIMS));
    EXPECT_EQ(opNode->attributes.get_scale()->get_stride(), toVec(K_BN_INF_VAR_EXT_SCALE_STRIDES));
    EXPECT_EQ(opNode->attributes.get_bias()->get_dim(), toVec(K_BN_INF_VAR_EXT_BIAS_DIMS));
    EXPECT_EQ(opNode->attributes.get_bias()->get_stride(), toVec(K_BN_INF_VAR_EXT_BIAS_STRIDES));
    EXPECT_EQ(opNode->attributes.get_y()->get_dim(), toVec(K_BN_INF_VAR_EXT_Y_DIMS));
    EXPECT_EQ(opNode->attributes.get_y()->get_stride(), toVec(K_BN_INF_VAR_EXT_Y_STRIDES));
    EXPECT_EQ(opNode->attributes.get_epsilon()->get_dim(), toVec(K_BN_INF_VAR_EXT_EPSILON_DIMS));
    EXPECT_EQ(opNode->attributes.get_epsilon()->get_stride(),
              toVec(K_BN_INF_VAR_EXT_EPSILON_STRIDES));
}

// Exercises the JSON serialize/deserialize path with a handle (full finalization)
// for a batchnorm inference variance ext graph.
TEST_F(IntegrationBatchnormInferenceVarianceExtDescriptorLifting, JsonRoundTripWithHandle)
{
    auto originalGraph = buildGraph();

    auto result = originalGraph->validate();
    ASSERT_EQ(result.code, ErrorCode::OK) << result.err_msg;

    // Serialize to JSON (auto-lowers internally)
    std::string jsonData;
    result = originalGraph->serialize(jsonData);
    ASSERT_EQ(result.code, ErrorCode::OK) << result.err_msg;
    ASSERT_FALSE(jsonData.empty());

    // Deserialize from JSON with handle
    auto liftedGraph = std::make_shared<TestableGraphLifting>();
    result = liftedGraph->deserialize(_handle, jsonData);
    ASSERT_EQ(result.code, ErrorCode::OK) << result.err_msg;

    // Verify graph-level attributes
    EXPECT_EQ(liftedGraph->get_name(), "BatchnormInferenceVarianceExtLiftingTestGraph");
    EXPECT_EQ(liftedGraph->get_compute_data_type(), DataType::FLOAT);
    EXPECT_EQ(liftedGraph->get_intermediate_data_type(), DataType::FLOAT);
    EXPECT_EQ(liftedGraph->get_io_data_type(), DataType::FLOAT);

    // Verify tensor count (x, mean, variance, scale, bias, epsilon, y = 7)
    auto tensorMap = liftedGraph->getTensorsByUid();
    ASSERT_EQ(tensorMap.size(), 7u)
        << "Expected 7 tensors in lifted batchnorm inference variance ext graph";

    // Verify tensors
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_X_UID,
                                      "x",
                                      toVec(K_BN_INF_VAR_EXT_X_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_X_STRIDES),
                                      DataType::FLOAT);
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_MEAN_UID,
                                      "mean",
                                      toVec(K_BN_INF_VAR_EXT_MEAN_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_MEAN_STRIDES),
                                      DataType::FLOAT);
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_VARIANCE_UID,
                                      "variance",
                                      toVec(K_BN_INF_VAR_EXT_VARIANCE_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_VARIANCE_STRIDES),
                                      DataType::FLOAT);
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_SCALE_UID,
                                      "scale",
                                      toVec(K_BN_INF_VAR_EXT_SCALE_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_SCALE_STRIDES),
                                      DataType::FLOAT);
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_BIAS_UID,
                                      "bias",
                                      toVec(K_BN_INF_VAR_EXT_BIAS_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_BIAS_STRIDES),
                                      DataType::FLOAT);
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_EPSILON_UID,
                                      "epsilon",
                                      toVec(K_BN_INF_VAR_EXT_EPSILON_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_EPSILON_STRIDES),
                                      DataType::FLOAT);
    hipdnn_tests::verifyTensorInGraph(tensorMap,
                                      K_BN_INF_VAR_EXT_Y_UID,
                                      "y",
                                      toVec(K_BN_INF_VAR_EXT_Y_DIMS),
                                      toVec(K_BN_INF_VAR_EXT_Y_STRIDES),
                                      DataType::FLOAT);

    // Verify sub-node count and type
    auto& subNodes = liftedGraph->getSubNodes();
    ASSERT_EQ(subNodes.size(), 1u) << "Expected 1 operation node in lifted graph";

    auto* opNode = dynamic_cast<BatchnormInferenceNodeVarianceExt*>(subNodes[0].get());
    ASSERT_NE(opNode, nullptr) << "Expected a BatchnormInferenceNodeVarianceExt";

    EXPECT_EQ(opNode->attributes.get_name(), "test_op");
}

} // namespace