/*
 * Copyright (c) 2023 Arm Limited.
 *
 * SPDX-License-Identifier: MIT
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy
 * of this software and associated documentation files (the "Software"), to
 * deal in the Software without restriction, including without limitation the
 * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or
 * sell copies of the Software, and to permit persons to whom the Software is
 * furnished to do so, subject to the following conditions:
 *
 * The above copyright notice and this permission notice shall be included in all
 * copies or substantial portions of the Software.
 *
 * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
 * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
 * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 */
#ifndef ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE
#define ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE

#include "arm_compute/core/KernelDescriptors.h"
#include "arm_compute/core/Utils.h"
#include "arm_compute/core/utils/quantization/AsymmHelpers.h"

#include "tests/CL/CLAccessor.h"
#include "tests/CL/Helper.h"
#include "tests/framework/Fixture.h"
#include "tests/validation/Helpers.h"
#include "tests/validation/reference/GEMM.h"
#include "tests/validation/reference/GEMMLowp.h"
#include "tests/validation/reference/Permute.h"
#include "tests/validation/reference/ReshapeLayer.h"

#include <random>

namespace arm_compute
{
namespace test
{
namespace validation
{
using namespace arm_compute::opencl::kernels;

template <typename T, typename KernelType>
class MatMulKernelValidationFixture : public framework::Fixture
{
public:
    template <typename...>
    void setup(TensorShape shape_a, TensorShape shape_b, TensorShape output_shape, bool pretranspose_a, bool pretranspose_b, int M0, int N0, int K0, bool export_rhs_to_cl_image, DataType data_type)
    {
        // For brevity, the input shapes are assumed to be not-transposed for both Lhs and Rhs matrices.
        QuantizationInfo lhs_q_info;
        QuantizationInfo rhs_q_info;
        QuantizationInfo dst_q_info;

        if(is_data_type_quantized(data_type))
        {
            const int32_t t_max = static_cast<int32_t>(std::numeric_limits<T>::max());
            const int32_t t_min = static_cast<int32_t>(std::numeric_limits<T>::min());

            std::mt19937                           generator(library->seed());
            std::uniform_real_distribution<float>  distribution_float(-5.0f, 3.0f);
            std::uniform_int_distribution<int32_t> distribution_t(t_min, t_max);

            const float scale_lhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]
            const float scale_rhs = pow(2, distribution_float(generator)); // [2^-5, 2^3]

            const int32_t offset_lhs = distribution_t(generator);
            const int32_t offset_rhs = distribution_t(generator);

            lhs_q_info = QuantizationInfo(scale_lhs, offset_lhs);
            rhs_q_info = QuantizationInfo(scale_rhs, offset_rhs);

            const int m = shape_a.y();
            const int n = shape_b.x();
            const int k = shape_a.x();

            dst_q_info = calculate_mat_mul_dst_q_info(lhs_q_info, rhs_q_info, m, n, k, data_type);
        }

        if(pretranspose_a)
        {
            permute(shape_a, PermutationVector(1U, 0U));
        }

        if(pretranspose_b)
        {
            permute(shape_b, PermutationVector(1U, 0U));
        }

        _device_supports_export_to_cl_image = image2d_from_buffer_supported(CLKernelLibrary::get().get_device());

        if(!export_rhs_to_cl_image || _device_supports_export_to_cl_image)
        {
            _target    = compute_target(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, M0, N0, K0, export_rhs_to_cl_image, data_type, lhs_q_info, rhs_q_info, dst_q_info);
            _reference = compute_reference(shape_a, shape_b, output_shape, pretranspose_a, pretranspose_b, data_type, lhs_q_info, rhs_q_info, dst_q_info);
        }
    }

protected:
    template <typename U>
    void fill(U &&tensor, int i, float lo = -1.f, float hi = 1.f)
    {
        switch(tensor.data_type())
        {
            case DataType::F16:
            {
                arm_compute::utils::uniform_real_distribution_16bit<half> distribution{ float(lo), float(hi) };
                library->fill(tensor, distribution, i);
                break;
            }
            case DataType::F32:
            {
                std::uniform_real_distribution<float> distribution(lo, hi);
                library->fill(tensor, distribution, i);
                break;
            }
            default:
                library->fill_tensor_uniform(tensor, i);
        }
    }

    template <typename U, typename D>
    void fill_constant(U &&tensor, D value)
    {
        library->fill_tensor_value(tensor, value);
    }

    CLTensor compute_target(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, const int M0, const int N0, const int K0,
                            bool export_rhs_to_cl_image, DataType data_type, const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
    {
        CLSynthetizeOperator<KernelType> matMul{};
        MatMulKernelInfo                 matmul_info;
        matmul_info.adj_lhs                = pretranspose_a;
        matmul_info.adj_rhs                = pretranspose_b;
        matmul_info.m0                     = M0;
        matmul_info.n0                     = N0;
        matmul_info.k0                     = K0;
        matmul_info.export_rhs_to_cl_image = export_rhs_to_cl_image;

        // Create tensors
        CLTensor a   = create_tensor<CLTensor>(shape_a, data_type, 1, lhs_q_info);
        CLTensor b   = create_tensor<CLTensor>(shape_b, data_type, 1, rhs_q_info);
        CLTensor dst = create_tensor<CLTensor>(output_shape, data_type, 1, dst_q_info);

        matMul.configure(a.info(), b.info(), dst.info(), matmul_info);
        ARM_COMPUTE_ASSERT(a.info()->is_resizable());
        ARM_COMPUTE_ASSERT(b.info()->is_resizable());
        ARM_COMPUTE_ASSERT(dst.info()->is_resizable());

        // Allocate tensors
        a.allocator()->allocate();
        b.allocator()->allocate();
        dst.allocator()->allocate();

        ARM_COMPUTE_ASSERT(!a.info()->is_resizable());
        ARM_COMPUTE_ASSERT(!b.info()->is_resizable());
        ARM_COMPUTE_ASSERT(!dst.info()->is_resizable());

        // Fill tensors
        fill(CLAccessor(a), 0);
        fill(CLAccessor(b), 1);

        // Compute matMul kernel
        ITensorPack tensors_pack({ { ACL_SRC_0, &a },
            { ACL_SRC_1, &b },
            { ACL_DST, &dst }
        });
        matMul.run(tensors_pack);

        return dst;
    }

    SimpleTensor<T> compute_reference(const TensorShape &shape_a, const TensorShape &shape_b, const TensorShape &output_shape, bool pretranspose_a, bool pretranspose_b, DataType data_type,
                                      const QuantizationInfo &lhs_q_info, const QuantizationInfo &rhs_q_info, const QuantizationInfo &dst_q_info)
    {
        // We collapse dimensions > 3 onto dimension 3, i.e. 5D+ tensors will look like 4D
        // This is necessary unless we choose to extend gemm reference for 5D+ tensors
        TensorShape output_shape_collapsed = output_shape.collapsed_from(Window::DimZ);
        TensorShape shape_a_collapsed      = shape_a.collapsed_from(Window::DimZ);
        TensorShape shape_b_collapsed      = shape_b.collapsed_from(Window::DimZ);

        // Create reference
        SimpleTensor<T> a{ shape_a_collapsed, data_type, 1, lhs_q_info };
        SimpleTensor<T> b{ shape_b_collapsed, data_type, 1, rhs_q_info };
        SimpleTensor<T> c{ output_shape_collapsed, data_type, 1, dst_q_info };

        // Fill reference
        fill(a, 0);
        fill(b, 1);

        /* Note: Assuming the usual batch matmul dimensions A = (B x M x K), B = (B x K x N), if pretranspose_A is set to true, then A is assumed to be (B x K x M),
           therefore, A must be pre-transposed before passing it to the fixture. And, we transpose A again in the fixture to make it (B x M x K)
           in order to be able to call reference implementation that works with (B x M x K) input.
           Similarly, if pretranspose_B is set to true, then B is assumed to be (B x N x K), B must be pre-transposed before passing it to the fixture. */

        // Define transposed shapes
        TensorShape a_transposed_shape(a.shape());
        a_transposed_shape.set(0, a.shape().y());
        a_transposed_shape.set(1, a.shape().x());

        TensorShape b_transposed_shape(b.shape());
        b_transposed_shape.set(0, b.shape().y());
        b_transposed_shape.set(1, b.shape().x());

        // Define transposed tensors
        SimpleTensor<T> a_transposed{ a_transposed_shape, data_type };
        SimpleTensor<T> b_transposed{ b_transposed_shape, data_type };

        // pretranspose a if necessary
        if(pretranspose_a)
        {
            a_transposed = reference::permute<T>(a, PermutationVector(1U, 0U));
        }

        // pretranspose b if necessary
        if(pretranspose_b)
        {
            b_transposed = reference::permute<T>(b, PermutationVector(1U, 0U));
        }

        // Use transposed tensors if boolean enabled else use original tensors
        SimpleTensor<T> result = gemm_reference<T>((pretranspose_a) ? a_transposed : a, (pretranspose_b) ? b_transposed : b, c);

        // We reshape the gemm output back if the tensor is high dimensional
        if(output_shape_collapsed != output_shape)
        {
            result = reference::reshape_layer(result, output_shape);
        }

        return result;
    }

    template <typename U = T>
    typename std::enable_if < std::is_same<U, float>::value || std::is_same<U, half>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
    {
        // Setting beta to 0 will effectively disable C for the
        // computation of the reference: alpha * A * B + 0 * C
        return reference::gemm<U>(a, b, c, 1.0f, 0.f);
    }

    template <typename U = T>
    typename std::enable_if < std::is_same<U, int8_t>::value || std::is_same<U, uint8_t>::value, SimpleTensor<U >>::type gemm_reference(SimpleTensor<U> &a, SimpleTensor<U> &b, SimpleTensor<U> &c)
    {
        const UniformQuantizationInfo aq = a.quantization_info().uniform();
        const UniformQuantizationInfo bq = b.quantization_info().uniform();
        const UniformQuantizationInfo cq = c.quantization_info().uniform();

        const SimpleTensor<int32_t> result = reference::gemmlowp_matrix_multiply_core<int32_t, U, U>(a, b, c.shape(), -aq.offset, -bq.offset);

        std::vector<int32_t> gemmlowp_multipliers{ 1 };
        std::vector<int32_t> gemmlowp_shifts{ 1 };
        const int            gemmlowp_offset = cq.offset;
        const float          scale           = aq.scale * bq.scale / cq.scale;

        quantization::calculate_quantized_multiplier(scale, &gemmlowp_multipliers[0], &gemmlowp_shifts[0]);
        constexpr int32_t gemmlowp_min_bound = std::numeric_limits<int32_t>::min();
        constexpr int32_t gemmlowp_max_bound = std::numeric_limits<int32_t>::max();

        SimpleTensor<int> bias{ c.shape(), DataType::S32 };
        fill_constant(bias, static_cast<int32_t>(0));

        const SimpleTensor<U> final_result = reference::gemmlowp_quantize_down_scale_by_fixedpoint<int32_t, U>(result, bias,
                                                                                                               gemmlowp_multipliers, gemmlowp_shifts, gemmlowp_offset, gemmlowp_min_bound, gemmlowp_max_bound);
        return final_result;
    }

    CLTensor        _target{};
    SimpleTensor<T> _reference{};
    bool            _device_supports_export_to_cl_image{ true };
};

} // namespace validation
} // namespace test
} // namespace arm_compute
#endif /* ACL_TESTS_VALIDATION_FIXTURES_MATMULKERNELFIXTURE */