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#include "../driver/tensor_driver.hpp"
#include "../src/kernels/MIOpenReduceCalculation.hpp"
#include "get_handle.hpp"
#include "random.hpp"
#include "tensor_holder.hpp"
#include "verify.hpp"
#include <gtest/gtest.h>
#include <miopen/miopen.h>
#include <miopen/reducecalculation.hpp>

template <typename T, ReduceCalculationOp_t op>
void cpu_calculation_forward(tensor<T> input,
                             tensor<T>& ref_output,
                             int32_t dim,
                             miopenReduceCalculationNanPropagation_t nanPropagation)
{
    auto input_dims  = input.desc.GetLengths();
    auto output_dims = ref_output.desc.GetLengths();

    auto reduce_size = input_dims[dim];
    auto output_numel =
        std::accumulate(output_dims.begin(), output_dims.end(), 1LL, std::multiplies<int64_t>());

    auto inner_size = std::accumulate(
        input_dims.begin() + dim + 1, input_dims.end(), 1ULL, std::multiplies<uint64_t>());

    miopen::par_ford(output_numel)([&](size_t o) {
        size_t input_idx = (o / inner_size) * inner_size * reduce_size + o % inner_size;

        T calculation = static_cast<T>(0);

        miopen::ford(reduce_size)([&](size_t /*i*/) {
            T val = input[input_idx];
            if(nanPropagation && std::isnan(val))
            {
                val = static_cast<T>(0.0);
            }
            reduce_func<T, op>{}.calculate(calculation, val);
            input_idx += inner_size;
        });

        ref_output[o] = calculation;
    });
}

struct ReduceCalculationTestCase
{
    size_t N;
    size_t C;
    size_t D;
    size_t H;
    size_t W;
    int32_t dim;
    miopenReduceCalculationNanPropagation_t nanPropagation;
    miopenReduceCalculationOp_t reduceCalculationOp;
    friend std::ostream& operator<<(std::ostream& os, const ReduceCalculationTestCase& tc)
    {
        return os << " N:" << tc.N << " C:" << tc.C << " D:" << tc.D << " H:" << tc.H
                  << " W:" << tc.W << " dim:" << tc.dim << " NanPropagation:" << tc.nanPropagation
                  << " ReduceCalculationOp:" << tc.reduceCalculationOp;
    }

    std::vector<size_t> GetInput()
    {
        if((N != 0) && (C != 0) && (D != 0) && (H != 0) && (W != 0))
        {
            return std::vector<size_t>({N, C, D, H, W});
        }
        else if((N != 0) && (C != 0) && (H != 0) && (W != 0))
        {
            return std::vector<size_t>({N, C, H, W});
        }
        else if((N != 0) && (C != 0) && (W != 0))
        {
            return std::vector<size_t>({N, C, W});
        }
        else if((N != 0) && (W != 0))
        {
            return std::vector<size_t>({N, W});
        }
        else if((N != 0))
        {
            return std::vector<size_t>({N});
        }
        else
        {
            std::cout << "Error Input Tensor Lengths\n" << std::endl;
            return std::vector<size_t>({0});
        }
    }
};

std::vector<ReduceCalculationTestCase>
ReduceCalculationTestConfigs(miopenReduceCalculationOp_t reduceCalculationOp)
{ // n c d h w dim nanPropagation
    // clang-format off
    if(reduceCalculationOp == MIOPEN_REDUCE_CALCULATION_SUM)
    {
        return {
            { 8,    120,  0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},  //bart
            { 8,    120,  0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},
            { 8,    1023, 0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},  //gpt_neo
            { 8,    1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},
            { 8,    1023, 0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},
            { 8,    1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},
            { 16,   1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},  //gpt2
            { 16,   1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},
            { 48,   8,    0,  512, 512,   0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},  //t5
            { 48,   8,    0,  512, 512,   0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},
            { 16, 311,    0,  98,  512,   2 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM},  //rnnt
            { 16, 311,    0,  98,  512,   2 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_SUM}
        };
    }
    else if(reduceCalculationOp == MIOPEN_REDUCE_CALCULATION_PROD)
    {
        return {
            { 8,    120,  0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},  //bart
            { 8,    120,  0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},
            { 8,    1023, 0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},  //gpt_neo
            { 8,    1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},
            { 8,    1023, 0,  0,   1,     0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},
            { 8,    1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},
            { 16,   1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},  //gpt2
            { 16,   1024, 0,  0,   768,   0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},
            { 48,   8,    0,  512, 512,   0 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},  //t5
            { 48,   8,    0,  512, 512,   0 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},
            { 16, 311,    0,  98,  512,   2 , MIOPEN_REDUCE_CALCULATION_NOT_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD},  //rnnt
            { 16, 311,    0,  98,  512,   2 , MIOPEN_REDUCE_CALCULATION_PROPAGATE_NAN, MIOPEN_REDUCE_CALCULATION_PROD}
        };
    }
    return {};
    // clang-format on
}

template <typename T = float>
struct ReduceCalculationTest : public ::testing::TestWithParam<ReduceCalculationTestCase>
{
protected:
    void SetUp() override
    {
        auto&& handle            = get_handle();
        reducecalculation_config = GetParam();
        auto gen_value = [](auto...) { return prng::gen_descreet_uniform_sign<T>(1e-2, 100); };

        dim                 = reducecalculation_config.dim;
        nanPropagation      = reducecalculation_config.nanPropagation;
        reduceCalculationOp = reducecalculation_config.reduceCalculationOp;

        auto in_dims = reducecalculation_config.GetInput();

        input = tensor<T>{in_dims}.generate(gen_value);

        std::vector<size_t> out_dims;

        for(int i = 0; i < in_dims.size(); i++)
        {
            if(i != dim)
            {
                out_dims.push_back(in_dims[i]);
            }
        }

        output = tensor<T>{out_dims};
        std::fill(output.begin(), output.end(), std::numeric_limits<T>::quiet_NaN());

        ref_output = tensor<T>{out_dims};
        std::fill(ref_output.begin(), ref_output.end(), std::numeric_limits<T>::quiet_NaN());

        std::vector<size_t> workspace_dims;
        ws_sizeInBytes = miopen::GetReduceCalculationWorkspaceSize(
            handle, input.desc, output.desc, dim, reduceCalculationOp);
        if(ws_sizeInBytes == static_cast<size_t>(-1))
            GTEST_SKIP();

        workspace_dims.push_back(ws_sizeInBytes / sizeof(T));
        if(ws_sizeInBytes != 0)
        {
            workspace = tensor<T>{workspace_dims};
            std::fill(workspace.begin(), workspace.end(), std::numeric_limits<T>::quiet_NaN());
            workspace_dev = handle.Write(workspace.data);
        }

        input_dev  = handle.Write(input.data);
        output_dev = handle.Write(output.data);
    }
    void RunTest()
    {
        auto&& handle = get_handle();

        if(reduceCalculationOp == MIOPEN_REDUCE_CALCULATION_SUM)
        {
            cpu_calculation_forward<T, ReduceCalculationOp_t::Sum>(
                input, ref_output, dim, nanPropagation);
        }
        else if(reduceCalculationOp == MIOPEN_REDUCE_CALCULATION_PROD)
        {
            cpu_calculation_forward<T, ReduceCalculationOp_t::Prod>(
                input, ref_output, dim, nanPropagation);
        }

        miopenStatus_t status;

        status = miopen::ReduceCalculationForward(handle,
                                                  workspace_dev.get(),
                                                  ws_sizeInBytes,
                                                  input.desc,
                                                  input_dev.get(),
                                                  output.desc,
                                                  output_dev.get(),
                                                  nanPropagation,
                                                  dim,
                                                  reduceCalculationOp);

        EXPECT_EQ(status, miopenStatusSuccess);

        output.data = handle.Read<T>(output_dev, output.data.size());
    }

    void Verify()
    {
        // Computation error of fp16 is ~2^13 (=8192) bigger than
        // the one of fp32 because mantissa is shorter by 13 bits.
        auto threshold = std::is_same<T, float>::value ? 1.5e-5 : 8.2e-2;

        // bf16 mantissa has 7 bits, by 3 bits shorter than fp16.
        if(std::is_same<T, bfloat16>::value)
            threshold *= 80.0;
        auto error = miopen::rms_range(ref_output, output);

        EXPECT_TRUE(miopen::range_distance(ref_output) == miopen::range_distance(output));
        EXPECT_TRUE(error < threshold * 10) << "Error output beyond tolerance Error:" << error
                                            << ",  Thresholdx10: " << threshold * 10;
    }
    ReduceCalculationTestCase reducecalculation_config;

    tensor<T> input;
    tensor<T> output;
    tensor<T> workspace;

    tensor<T> ref_output;

    miopen::Allocator::ManageDataPtr input_dev;
    miopen::Allocator::ManageDataPtr output_dev;
    miopen::Allocator::ManageDataPtr workspace_dev;

    size_t ws_sizeInBytes;

    int32_t dim;
    miopenReduceCalculationNanPropagation_t nanPropagation;
    miopenReduceCalculationOp_t reduceCalculationOp;
};
