/*******************************************************************************
 *
 * MIT License
 *
 * Copyright (C) 2022-2026 Advanced Micro Devices, Inc. All rights reserved.
 *
 * 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
 * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
 * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 * SOFTWARE.
 *
 *******************************************************************************/

#pragma once

#include "CSVStackFile.hpp"
#include "ResultReporter.hpp"
#include "TimingInstrumentation.hpp"

#ifndef TENSILELITE_CLIENT_ENABLE_ROCPROFSDK
#define TENSILELITE_CLIENT_ENABLE_ROCPROFSDK 0
#endif

#include <cstddef>
#include <string>
#include <unordered_set>

#include "ProgramOptions.hpp"

namespace TensileLite
{
    namespace Client
    {
        class LogReporter : public ResultReporter
        {
        public:
            LogReporter(LogLevel                           level,
                        std::initializer_list<const char*> keys,
                        std::ostream&                      stream,
                        bool                               dumpTensors,
                        bool                               PrintWinnersOnly)
                : m_level(level)
                , m_stream(stream)
                , m_csvOutput(stream, ",")
                , m_dumpTensors(dumpTensors)
                , m_PrintWinnersOnly(PrintWinnersOnly)
            {
                for(auto const& key : keys)
                    m_csvOutput.setHeaderForKey(key, key);
            }

            LogReporter(LogLevel                           level,
                        std::initializer_list<std::string> keys,
                        std::ostream&                      stream,
                        bool                               dumpTensors,
                        bool                               PrintWinnersOnly)
                : m_level(level)
                , m_stream(stream)
                , m_csvOutput(stream, ",")
                , m_dumpTensors(dumpTensors)
                , m_PrintWinnersOnly(PrintWinnersOnly)
            {
                for(auto const& key : keys)
                    m_csvOutput.setHeaderForKey(key, key);
            }

            LogReporter(LogLevel                           level,
                        std::initializer_list<std::string> keys,
                        std::shared_ptr<std::ostream>      stream,
                        bool                               dumpTensors,
                        bool                               PrintWinnersOnly)
                : m_level(level)
                , m_stream(*stream)
                , m_ownedStream(stream)
                , m_csvOutput(stream, ",")
                , m_dumpTensors(dumpTensors)
                , m_PrintWinnersOnly(PrintWinnersOnly)
            {
                for(auto const& key : keys)
                    m_csvOutput.setHeaderForKey(key, key);
            }

            template <typename Stream>
            static std::shared_ptr<LogReporter> Default(po::variables_map const& args,
                                                        Stream&                  stream,
                                                        LogLevel level = LogLevel::Count)
            {
                bool dumpTensors      = args["dump-tensors"].as<bool>();
                bool PrintWinnersOnly = args["PrintWinnersOnly"].as<bool>();
                using namespace ResultKey;
                if(level == LogLevel::Count)
                    level = args["log-level"].as<LogLevel>();
                std::cout << "Log level: " << level << std::endl;

                PerformanceMetric metric = args["performance-metric"].as<PerformanceMetric>();
                // Default to 'DeviceEfficiency' benchmarking if CUEfficiency not specified
                const std::string perfUnit
                    = (metric == PerformanceMetric::CUEfficiency ? SpeedGFlopsPerCu : SpeedGFlops);

                return std::shared_ptr<LogReporter>(new LogReporter(level,
                                                                    {BenchmarkRunNumber,
                                                                     ProblemProgress,
#if TENSILELITE_CLIENT_ENABLE_ROCPROFSDK
                                                                     RocProfCounter,
#endif
                                                                     SolutionProgress,
                                                                     OperationIdentifier,
                                                                     ProblemSizes,
                                                                     BiasType,
                                                                     FactorDim,
                                                                     ActivationType,
                                                                     SolutionName,
                                                                     Validation,
                                                                     TimeUS,
                                                                     perfUnit,
                                                                     Empty,
                                                                     TotalGranularity,
                                                                     TilesPerCu,
                                                                     NumCus,
                                                                     Tile0Granularity,
                                                                     Tile1Granularity,
                                                                     CuGranularity,
                                                                     WaveGranularity,
                                                                     MemReadBytes,
                                                                     MemWriteBytes,
                                                                     TempEdge,
                                                                     ClockRateSys,
                                                                     ClockRateSOC,
                                                                     ClockRateMem,
                                                                     FanSpeedRPMs,
                                                                     HardwareSampleCount,
                                                                     EnqueueTime},
                                                                    stream,
                                                                    dumpTensors,
                                                                    PrintWinnersOnly));
            }

            static std::shared_ptr<LogReporter> Default(po::variables_map const& args)
            {
                return Default(args, std::cout);
            }

            virtual void reportValue_string(std::string const& key,
                                            std::string const& value) override
            {
                if(key == ResultKey::Validation)
                    acceptValidation(value);

                m_csvOutput.setValueForKey(key, value);
            }

            virtual void reportValue_uint(std::string const& key, uint64_t value) override
            {
                m_csvOutput.setValueForKey(key, value);
            }

            virtual void reportValue_int(std::string const& key, int64_t value) override
            {
                m_csvOutput.setValueForKey(key, value);
            }

            virtual void reportValue_double(std::string const& key, double value) override
            {
                m_csvOutput.setValueForKey(key, value);
            }

            virtual void reportValue_sizes(std::string const&         key,
                                           std::vector<size_t> const& value) override
            {
                std::ostringstream msg;
                msg << "(";
                streamJoin(msg, value, ",");
                msg << ")";
                reportValue_string(key, msg.str());
            }

            virtual void
                reportValue_vecOfSizes(std::string const&                      key,
                                       std::vector<std::vector<size_t>> const& value) override
            {
                std::ostringstream msg;
                msg << "(";
                for(size_t i = 0; i < value.size(); i++)
                {
                    msg << "(";
                    streamJoin(msg, value[i], ",");
                    msg << ")";
                    if(i != value.size() - 1)
                        msg << ",";
                }
                msg << ")";
                reportValue_string(key, msg.str());
            }

            void acceptValidation(std::string const& value)
            {
                if(value == "PASSED" || value == "NO_CHECK")
                    m_rowLevel = LogLevel::Normal;
                else if(value == "FAILED")
                    m_rowLevel = LogLevel::Error;
                else if(value == "WRONG_HARDWARE")
                    m_rowLevel = LogLevel::Terse;
                else if(value == "DID_NOT_SATISFY_ASSERTS")
                    m_rowLevel = LogLevel::Terse;
                else if(value == "INVALID")
                {
                    m_rowLevel = LogLevel::Error;
                    ++m_exceptionsReported;
                }
            }

            virtual bool logAtLevel(LogLevel level) override
            {
                return level <= m_level;
            }

            virtual void logMessage(LogLevel level, std::string const& message) override
            {
                if(logAtLevel(level))
                {
                    m_stream << message;
                    m_stream.flush();
                }
            }

            template <typename T>
            void logTensorTyped(LogLevel                level,
                                std::string const&      name,
                                T const*                data,
                                TensorDescriptor const& tensor,
                                T const*                ptrVal)
            {
                if(logAtLevel(level))
                {
                    if(m_dumpTensors)
                    {
                        std::string   fname = concatenate("tensor_", name, ".bin");
                        std::ofstream ofile(fname.c_str());
                        ofile.write(reinterpret_cast<const char*>(data),
                                    tensor.totalAllocatedBytes());

                        m_stream << "Dumped tensor to file " << fname << std::endl;
                    }
                    else
                    {
                        m_stream << name << ": " << tensor << std::endl;
                        WriteTensor(m_stream, data, tensor, ptrVal);
                    }
                }
            }

            virtual void logTensor(LogLevel                level,
                                   std::string const&      name,
                                   void const*             data,
                                   TensorDescriptor const& tensor,
                                   void const*             ptrVal) override
            {
                if(logAtLevel(level))
                {
                    if(tensor.dataType() == rocisa::DataType::Float)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<float const*>(data),
                                       tensor,
                                       reinterpret_cast<float const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::Half)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Half const*>(data),
                                       tensor,
                                       reinterpret_cast<Half const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::ComplexFloat)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<std::complex<float> const*>(data),
                                       tensor,
                                       reinterpret_cast<std::complex<float> const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::Double)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<double const*>(data),
                                       tensor,
                                       reinterpret_cast<double const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::Int32)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<int32_t const*>(data),
                                       tensor,
                                       reinterpret_cast<int32_t const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::Int8)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Int8 const*>(data),
                                       tensor,
                                       reinterpret_cast<Int8 const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::BFloat16)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<BFloat16 const*>(data),
                                       tensor,
                                       reinterpret_cast<BFloat16 const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::Float8)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Float8 const*>(data),
                                       tensor,
                                       reinterpret_cast<Float8 const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::BFloat8)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<BFloat8 const*>(data),
                                       tensor,
                                       reinterpret_cast<BFloat8 const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::Float8_fnuz)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Float8_fnuz const*>(data),
                                       tensor,
                                       reinterpret_cast<Float8_fnuz const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::BFloat8_fnuz)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<BFloat8_fnuz const*>(data),
                                       tensor,
                                       reinterpret_cast<BFloat8_fnuz const*>(ptrVal));
#ifndef _WIN32
#ifdef TENSILE_USE_FP6
                    else if(tensor.dataType() == rocisa::DataType::Float6)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Float6x32 const*>(data),
                                       tensor,
                                       reinterpret_cast<Float6x32 const*>(ptrVal));
#endif // #ifdef TENSILE_USE_FP6
#ifdef TENSILE_USE_BF6
                    else if(tensor.dataType() == rocisa::DataType::BFloat6)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<BFloat6x32 const*>(data),
                                       tensor,
                                       reinterpret_cast<BFloat6x32 const*>(ptrVal));
#endif // #ifdef TENSILE_USE_BF6
#ifdef TENSILE_USE_FP4
                    else if(tensor.dataType() == rocisa::DataType::Float4)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Float4x2 const*>(data),
                                       tensor,
                                       reinterpret_cast<Float4x2 const*>(ptrVal));
#endif // #ifdef TENSILE_USE_FP4
#endif // !_WIN32
                    else if(tensor.dataType() == rocisa::DataType::ComplexFloat)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<std::complex<float> const*>(data),
                                       tensor,
                                       reinterpret_cast<std::complex<float> const*>(ptrVal));
                    else if(tensor.dataType() == rocisa::DataType::ComplexDouble)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<std::complex<double> const*>(data),
                                       tensor,
                                       reinterpret_cast<std::complex<double> const*>(ptrVal));
#ifdef TENSILE_USE_FP6
                    else if(tensor.dataType() == rocisa::DataType::Float6)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Float6x32 const*>(data),
                                       tensor,
                                       reinterpret_cast<Float6x32 const*>(ptrVal));
#endif // #ifdef TENSILE_USE_FP6
#ifdef TENSILE_USE_BF6
                    else if(tensor.dataType() == rocisa::DataType::BFloat6)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<BFloat6x32 const*>(data),
                                       tensor,
                                       reinterpret_cast<BFloat6x32 const*>(ptrVal));
#endif // #ifdef TENSILE_USE_BF6
#ifdef TENSILE_USE_FP4
                    else if(tensor.dataType() == rocisa::DataType::Float4)
                        logTensorTyped(level,
                                       name,
                                       reinterpret_cast<Float4x2 const*>(data),
                                       tensor,
                                       reinterpret_cast<Float4x2 const*>(ptrVal));
#endif // #ifdef TENSILE_USE_FP4
                    else
                        throw std::runtime_error(
                            concatenate("Can't log tensor of type ", tensor.dataType()));
                }
            }

            /// RunListener interface functions

            virtual void setReporter(std::shared_ptr<ResultReporter> reporter) override {}

            virtual void preProblem(ContractionProblem* const problem) override
            {
                m_csvOutput.push();
                m_winner   = std::numeric_limits<double>::infinity();
                m_firstRun = true;
            }

            virtual void preSolution(ContractionSolution* const solution) override
            {
                m_csvOutput.push();
                m_rowLevel = LogLevel::Normal;
            }

            virtual void postSolution() override
            {
                ScopedTimer timer("post_solution_log");
                std::unordered_map<std::string, std::string> curRow;
                m_csvOutput.readCurrentRow(curRow);
                bool  validation    = !(curRow[ResultKey::Validation] == "FAILED"
                                    || curRow[ResultKey::Validation] == "INVALID");
                float currentTimeUS = std::stof(curRow[ResultKey::TimeUS]);
                if(m_rowLevel <= m_level
                   && (!m_PrintWinnersOnly || currentTimeUS < m_winner || !validation
                       || m_firstRun))
                {
                    if(std::isnan(currentTimeUS) && !std::stof(curRow[ResultKey::SpeedGFlops])
                       && validation)
                        std::cout << curRow[ResultKey::BenchmarkRunNumber] << ","
                                  << curRow[ResultKey::ProblemProgress] << ","
                                  << curRow[ResultKey::SolutionProgress]
                                  << ",Solution Skipped (slow or doing restoration): "
                                  << curRow[ResultKey::SolutionName] << std::endl;
                    else
                        m_csvOutput.writeCurrentRow();
                    if(validation && !std::isnan(currentTimeUS))
                    {
                        m_winner = currentTimeUS;
                    }
                    m_firstRun = false;
                }
                m_csvOutput.pop();
            }

            virtual void postProblem() override
            {
                m_csvOutput.pop();
                if(m_rowLevel <= m_level)
                    printf("++++++++++++++++++++++++\n");
            }

            virtual void finalizeReport() override {}

        private:
            LogLevel m_level;

            std::ostream&                 m_stream;
            std::shared_ptr<std::ostream> m_ownedStream;

            bool   m_firstRun         = true;
            bool   m_inSolution       = false;
            bool   m_dumpTensors      = false;
            bool   m_PrintWinnersOnly = false;
            double m_winner           = std::numeric_limits<double>::infinity();

            LogLevel m_rowLevel;

            CSVStackFile m_csvOutput;
        };
    } // namespace Client
} // namespace TensileLite