//
// Copyright © 2022-2024 Arm Ltd and Contributors. All rights reserved.
// SPDX-License-Identifier: MIT
//

#include "ExecuteNetworkProgramOptions.hpp"
#include "NetworkExecutionUtils/NetworkExecutionUtils.hpp"

#include <armnn/BackendRegistry.hpp>
#include <armnn/Exceptions.hpp>
#include <armnn/utility/Assert.hpp>
#include <armnn/utility/StringUtils.hpp>
#include <armnn/Logging.hpp>

#include <fmt/format.h>

bool CheckOption(const cxxopts::ParseResult& result,
                 const char* option)
{
    // Check that the given option is valid.
    if (option == nullptr)
    {
        return false;
    }

    // Check whether 'option' is provided.
    return ((result.count(option)) ? true : false);
}

void CheckOptionDependency(const cxxopts::ParseResult& result,
                           const char* option,
                           const char* required)
{
    // Check that the given options are valid.
    if (option == nullptr || required == nullptr)
    {
        throw cxxopts::exceptions::exception("Invalid option to check dependency for");
    }

    // Check that if 'option' is provided, 'required' is also provided.
    if (CheckOption(result, option) && !result[option].has_default())
    {
        if (CheckOption(result, required) == 0 || result[required].has_default())
        {
            throw cxxopts::exceptions::exception(
                    std::string("Option '") + option + "' requires option '" + required + "'.");
        }
    }
}

void CheckOptionDependencies(const cxxopts::ParseResult& result)
{
    CheckOptionDependency(result, "tuning-level", "tuning-path");
}

void RemoveDuplicateDevices(std::vector<armnn::BackendId>& computeDevices)
{
    // Mark the duplicate devices as 'Undefined'.
    for (auto i = computeDevices.begin(); i != computeDevices.end(); ++i)
    {
        for (auto j = std::next(i); j != computeDevices.end(); ++j)
        {
            if (*j == *i)
            {
                *j = armnn::Compute::Undefined;
            }
        }
    }

    // Remove 'Undefined' devices.
    computeDevices.erase(std::remove(computeDevices.begin(), computeDevices.end(), armnn::Compute::Undefined),
                         computeDevices.end());
}

/// Takes a vector of backend strings and returns a vector of backendIDs.
/// Removes duplicate entries.
/// Can handle backend strings that contain multiple backends separated by comma e.g "CpuRef,CpuAcc"
std::vector<armnn::BackendId> GetBackendIDs(const std::vector<std::string>& backendStringsVec)
{
    std::vector<armnn::BackendId> backendIDs;
    for (const auto& backendStrings : backendStringsVec)
    {
        // Each backendStrings might contain multiple backends separated by comma e.g "CpuRef,CpuAcc"
        std::vector<std::string> backendStringVec = ParseStringList(backendStrings, ",");
        for (const auto& b : backendStringVec)
        {
            backendIDs.push_back(armnn::BackendId(b));
        }
    }

    RemoveDuplicateDevices(backendIDs);

    return backendIDs;
}

/// Provides a segfault safe way to get cxxopts option values by checking if the option was defined.
/// If the option wasn't defined it returns an empty object.
template<typename optionType>
optionType GetOptionValue(std::string&& optionName, const cxxopts::ParseResult& result)
{
    optionType out;
    if(result.count(optionName))
    {
        out = result[optionName].as<optionType>();
    }
    return out;
}

void LogAndThrowFatal(std::string errorMessage)
{
    throw armnn::InvalidArgumentException (errorMessage);
}

void CheckRequiredOptions(const cxxopts::ParseResult& result)
{

    // For each option in option-group "a) Required
    std::vector<std::string> requiredOptions{"compute",
                                             "model-path"
                                             };

    bool requiredMissing = false;
    for(auto const&  str : requiredOptions)
    {
        if(!(result.count(str) > 0))
        {
            ARMNN_LOG(error) << fmt::format("The program option '{}' is mandatory but wasn't provided.", str);
            requiredMissing = true;
        }
    }
    if(requiredMissing)
    {
        throw armnn::InvalidArgumentException ("Some required arguments are missing");
    }
}

void CheckForDeprecatedOptions(const cxxopts::ParseResult& result)
{
    if(result.count("armnn-tflite-delegate") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'armnn-tflite-delegate' is deprecated and will be "
                              "removed soon. Please use the option 'tflite-executor' instead.";
    }
    if(result.count("concurrent") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'concurrent' is deprecated and will be "
                              "removed soon. Please use the option '\"P, thread-pool-size\"' instead.";
    }
    if(result.count("input-type") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'input-type' is deprecated and will be "
                              "removed soon. The input-types are now automatically set.";
    }
    if(result.count("input-name") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'input-name' is deprecated and will be "
                              "removed soon. The input-names are now automatically set.";
    }
    if(result.count("output-type") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'output-type' is deprecated and will be "
                              "removed soon. The output-types are now automatically set.";
    }
    if(result.count("output-name") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'output-name' is deprecated and will be "
                              "removed soon. The output-names are now automatically set.";
    }
    if(result.count("model-format") > 0)
    {
        ARMNN_LOG(warning) << "DEPRECATED: The program option 'model-format' is deprecated and will be "
                              "removed soon. The model-format is now automatically set.";
    }

}

void ProgramOptions::ValidateExecuteNetworkParams()
{
    m_ExNetParams.ValidateParams();
}

void ProgramOptions::ValidateRuntimeOptions()
{
    if (m_RuntimeOptions.m_ProfilingOptions.m_TimelineEnabled &&
        !m_RuntimeOptions.m_ProfilingOptions.m_EnableProfiling)
    {
        LogAndThrowFatal("Timeline profiling requires external profiling to be turned on");
    }
}


ProgramOptions::ProgramOptions() : m_CxxOptions{"ExecuteNetwork",
                                                "Executes a neural network model using the provided input "
                                                "tensor(s). Prints the resulting output tensor(s).\n"
                                                "\n"
                                                "The Execution Time generated by the Arm NN Core code is a\n"
                                                "more accurate measure of the time taken overall to run the model.\n"
                                                "The Inference Time printed in an Execute Network run is doing\n"
                                                "a crude measurement of overall time to check against a very inexact\n"
                                                "upper threshold and should not be used for performance analysis.\n"}
{
    try
    {
        // cxxopts doesn't provide a mechanism to ensure required options are given. There is a
        // separate function CheckRequiredOptions() for that.
        m_CxxOptions.add_options("a) Required")
                ("c,compute",
                 "Which device to run layers on by default. If a single device doesn't support all layers in the "
                 "model you can specify a second or third to fall back on. "
                 "NOTE: Multiple compute devices need to be passed as a comma separated list without whitespaces "
                 "e.g. GpuAcc,CpuAcc,CpuRef or by repeating the program option e.g. '-c CpuAcc -c CpuRef'. "
                 "Duplicates are ignored.\n"
                 "Possible choices for current machine: [ "
                 + armnn::BackendRegistryInstance().GetBackendIdsAsString() + " ]",
                 cxxopts::value<std::vector<std::string>>())

                ("m,model-path",
                 "Path to model file, e.g. .armnn, .tflite, .onnx. "
                 "DEPRECATED: .pb and .prototxt model files no longer load and are deprecated.",
                 cxxopts::value<std::string>(m_ExNetParams.m_ModelPath));

        m_CxxOptions.add_options("b) Ordering")
                ("i,input-name",
                 "Identifier of the input tensors in the network separated by a comma "
                 "and is only required for ordering input files",
                 cxxopts::value<std::string>())

                ("o,output-name",
                 "Identifier of the output tensors in the network separated by a comma "
                 "and is only required for ordering output files.",
                 cxxopts::value<std::string>());

        m_CxxOptions.add_options("c) General")
                ("b,dynamic-backends-path",
                 "Path where to load any available dynamic backend from. "
                 "If left empty (the default), dynamic backends will not be used.",
                 cxxopts::value<std::string>(m_RuntimeOptions.m_DynamicBackendsPath))

                ("P, thread-pool-size",
                 "Run the network using the Arm NN thread pool with the number of threads provided. ",
                 cxxopts::value<size_t>(m_ExNetParams.m_ThreadPoolSize)->default_value("0"))

                ("d,input-tensor-data",
                 "Path to files containing the input data as a flat array separated by whitespace. "
                 "Several paths can be passed by separating them with a comma if the network has multiple inputs "
                 "or you wish to run the model multiple times with different input data using the 'iterations' option. "
                 "If not specified, the network will be run with dummy data (useful for profiling).",
                 cxxopts::value<std::string>()->default_value(""))

                ("h,help", "Display usage information")

                ("infer-output-shape",
                 "Infers output tensor shape from input tensor shape and validate where applicable (where supported by "
                 "parser)",
                 cxxopts::value<bool>(m_ExNetParams.m_InferOutputShape)->default_value("false")->implicit_value("true"))

                ("allow-expanded-dims",
                 "If true will disregard dimensions with a size of 1 when validating tensor shapes. Tensor sizes must "
                 "still match. This is an Experimental parameter that is incompatible with infer-output-shape. "
                 "This parameter may be removed in a later update. ",
                 cxxopts::value<bool>(m_ExNetParams.m_AllowExpandedDims)->default_value("false")
                         ->implicit_value("true"))

                ("I,iterations",
                 "Number of iterations to run the network for, default is set to 1. "
                 "If you wish to run the model with different input data for every execution you can do so by "
                 "supplying more input file paths to the 'input-tensor-data' option. "
                 "Note: The number of input files provided must be divisible by the number of inputs of the model. "
                 "e.g. Your model has 2 inputs and you supply 4 input files. If you set 'iterations' to 6 the first "
                 "run will consume the first two inputs, the second the next two and the last will begin from the "
                 "start and use the first two inputs again. "
                 "Note: If the 'concurrent' option is enabled all iterations will be run asynchronously.",
                 cxxopts::value<size_t>(m_ExNetParams.m_Iterations)->default_value("1"))

                ("l,dequantize-output",
                 "If this option is enabled, all quantized outputs will be dequantized to float. "
                 "If unset, default to not get dequantized. "
                 " (Not available when executing ArmNNTfLiteDelegate or TfliteInterpreter)",
                 cxxopts::value<bool>(m_ExNetParams.m_DequantizeOutput)->default_value("false")->implicit_value("true"))

                ("p,print-intermediate-layers",
                 "If this option is enabled, the output of every graph layer will be printed.",
                 cxxopts::value<bool>(m_ExNetParams.m_PrintIntermediate)->default_value("false")
                         ->implicit_value("true"))

                ("F,print-intermediate-layers-to-file",
                 "If this option is enabled, the output of every graph layer will be printed within separate files.",
                 cxxopts::value<bool>(m_ExNetParams.m_PrintIntermediateOutputsToFile)->default_value("false")
                         ->implicit_value("true"))

                ("parse-unsupported",
                 "Add unsupported operators as stand-in layers (where supported by parser)",
                 cxxopts::value<bool>(m_ExNetParams.m_ParseUnsupported)->default_value("false")->implicit_value("true"))

                ("N,do-not-print-output",
                 "The default behaviour of ExecuteNetwork is to print the resulting outputs on the console. "
                 "This behaviour can be changed by adding this flag to your command.",
                 cxxopts::value<bool>(m_ExNetParams.m_DontPrintOutputs)->default_value("false")->implicit_value("true"))

                ("q,quantize-input",
                 "If this option is enabled, all float inputs will be quantized as appropriate for the model's inputs. "
                 " (Not available when executing ArmNNTfLiteDelegate or TfliteInterpreter)",
                 cxxopts::value<bool>(m_ExNetParams.m_QuantizeInput)->default_value("false")->implicit_value("true"))

                ("r,threshold-time",
                 "Threshold time is the maximum allowed time for inference measured in milliseconds. If the actual "
                 "inference time is greater than the threshold time, the test will fail. By default, no threshold "
                 "time is used.",
                 cxxopts::value<double>(m_ExNetParams.m_ThresholdTime)->default_value("0.0"))

                ("s,input-tensor-shape",
                 "The shape of the input tensors in the network as a flat array of integers separated by comma."
                 "Several shapes can be passed by separating them with a colon (:).",
                 cxxopts::value<std::string>())

                ("v,visualize-optimized-model",
                 "Enables built optimized model visualizer. If unset, defaults to off.",
                 cxxopts::value<bool>(m_ExNetParams.m_EnableLayerDetails)->default_value("false")
                         ->implicit_value("true"))

                ("w,write-outputs-to-file",
                 "Comma-separated list of output file paths keyed with the binding-id of the output slot. "
                 "If left empty (the default), the output tensors will not be written to a file.",
                 cxxopts::value<std::string>())

                ("x,subgraph-number",
                 "Id of the subgraph to be executed. Defaults to 0."
                 " (Not available when executing ArmNNTfLiteDelegate or TfliteInterpreter)",
                 cxxopts::value<size_t>(m_ExNetParams.m_SubgraphId)->default_value("0"))

                ("T,tflite-executor",
                 "Set the executor for the tflite model: parser, delegate, opaquedelegate, tflite.\n"
                 "parser is the ArmNNTfLiteParser, \n"
                 "delegate is the ArmNNTfLiteDelegate, \n"
                 "opaquedelegate is the ArmNNTfLiteOpaqueDelegate, \n"
                 "tflite is the TfliteInterpreter (Bypasses Arm NN and runs model in Tensorflow Lite directly)\n",
                 cxxopts::value<std::string>()->default_value("parser"))

                ("C, compare-output",
                 "Perform a per byte root mean square error calculation of the inference output with an output"
                 " file(s) that has been previously produced by running a network through ExecuteNetwork."
                 " See --write-outputs-to-file to produce an output file(s) for an execution.",
                 cxxopts::value<std::string>(m_ExNetParams.m_ComparisonFile))

                ("B, compare-output-with-backend",
                 "Perform a per byte root mean square error calculation of the output of the inference with a"
                 " different backend.",
                 cxxopts::value<std::vector<std::string>>())

                ("A, compare-with-tflite",
                 "Perform an per byte root mean square error calculation of the output of the inference with"
                 " the tflite ref model.",
                 cxxopts::value<bool>(m_ExNetParams.m_CompareWithTflite)->default_value("false")
                         ->implicit_value("true"));

        m_CxxOptions.add_options("d) Optimization")
                ("enable-fast-math",
                 "Enables fast_math options in backends that support it. Using the fast_math flag "
                 "can lead to performance improvements but may result in reduced or different precision. "
                 "Supported on CpuAcc and GpuAcc only.",
                 cxxopts::value<bool>(m_ExNetParams.m_EnableFastMath)->default_value("false")->implicit_value("true"))

                ("number-of-threads",
                 "Assign the number of threads used by the CpuAcc backend. "
                 "Input value must be between 1 and 64. "
                 "Default is set to 0 (Backend will decide number of threads to use).",
                 cxxopts::value<unsigned int>(m_ExNetParams.m_NumberOfThreads)->default_value("0"))

                ("save-cached-network",
                 "Enables saving of the cached network to a file given with the cached-network-filepath option.\n"
                 "Supported on GpuAcc only.\n"
                 "See also --cached-network-filepath",
                 cxxopts::value<bool>(m_ExNetParams.m_SaveCachedNetwork)
                         ->default_value("false")->implicit_value("true"))

                ("cached-network-filepath",
                 "If non-empty, the given file will be used to load/save the cached network. "
                 "If save-cached-network is given then the cached network will be saved to the given file. "
                 "To save the cached network a file must already exist. "
                 "If save-cached-network is not given then the cached network will be loaded from the given file. "
                 "This will remove initial compilation time of kernels and speed up the first execution.\n"
                 "Supported on GpuAcc only.",
                 cxxopts::value<std::string>(m_ExNetParams.m_CachedNetworkFilePath)->default_value(""))

                ("fp16-turbo-mode",
                 "If this option is enabled, FP32 layers, "
                 "weights and biases will be converted to FP16 where the backend supports it. "
                 "If the first preferred backend does not have FP16 support, this option will be disabled. "
                 "If the value of converted FP16 is infinity, round to the closest finite FP16 value."
                 "Supported on CpuAcc and GpuAcc only.",
                 cxxopts::value<bool>(m_ExNetParams.m_EnableFp16TurboMode)
                         ->default_value("false")->implicit_value("true"))

                ("tuning-level",
                 "Sets the tuning level which enables a tuning run which will update/create a tuning file. "
                 "Available options are: 1 (Rapid), 2 (Normal), 3 (Exhaustive). "
                 "Requires tuning-path to be set, default is set to 0 (No tuning run)",
                 cxxopts::value<int>(m_ExNetParams.m_TuningLevel)->default_value("0"))

                ("tuning-path",
                 "Path to tuning file. Enables use of CL tuning",
                 cxxopts::value<std::string>(m_ExNetParams.m_TuningPath))

                ("MLGOTuningFilePath",
                 "Path to tuning file. Enables use of CL MLGO tuning.\n"
                 "Supported on GpuAcc only.",
                 cxxopts::value<std::string>(m_ExNetParams.m_MLGOTuningFilePath))

                ("R, reuse-buffers",
                 "If enabled then the IO buffers will be reused for each inference",
                 cxxopts::value<bool>(m_ExNetParams.m_ReuseBuffers)->default_value("false")->implicit_value("true"));

        m_CxxOptions.add_options("e) Profiling")
                ("a,enable-external-profiling",
                 "If enabled external profiling will be switched on",
                 cxxopts::value<bool>(m_RuntimeOptions.m_ProfilingOptions.m_EnableProfiling)
                         ->default_value("false")->implicit_value("true"))

                ("g,file-only-external-profiling",
                 "If enabled then the 'file-only' test mode of external profiling will be enabled",
                 cxxopts::value<bool>(m_RuntimeOptions.m_ProfilingOptions.m_FileOnly)
                         ->default_value("false")->implicit_value("true"))

                ("file-format",
                 "If profiling is enabled specifies the output file format",
                 cxxopts::value<std::string>(m_RuntimeOptions.m_ProfilingOptions.m_FileFormat)->default_value("binary"))

                ("j,outgoing-capture-file",
                 "If specified the outgoing external profiling packets will be captured in this binary file",
                 cxxopts::value<std::string>(m_RuntimeOptions.m_ProfilingOptions.m_OutgoingCaptureFile))

                ("k,incoming-capture-file",
                 "If specified the incoming external profiling packets will be captured in this binary file",
                 cxxopts::value<std::string>(m_RuntimeOptions.m_ProfilingOptions.m_IncomingCaptureFile))

                ("timeline-profiling",
                 "If enabled timeline profiling will be switched on, requires external profiling",
                 cxxopts::value<bool>(m_RuntimeOptions.m_ProfilingOptions.m_TimelineEnabled)
                         ->default_value("false")->implicit_value("true"))

                ("u,counter-capture-period",
                 "If profiling is enabled in 'file-only' mode this is the capture period that will be used in the test",
                 cxxopts::value<uint32_t>(m_RuntimeOptions.m_ProfilingOptions.m_CapturePeriod)->default_value("150"))

                ("e,event-based-profiling",
                 "Enables built in profiler.",
                 cxxopts::value<bool>(m_ExNetParams.m_EnableProfiling)->default_value("false")->implicit_value("true"))

                ("output-network-details",
                 "Outputs layer tensor infos and descriptors to std out along with profiling events.",
                 cxxopts::value<bool>(m_ExNetParams.m_OutputDetailsToStdOut)->default_value("false")
                         ->implicit_value("true"))

                ("output-network-details-only",
                 "Outputs layer tensor infos and descriptors to std out without profiling events.",
                 cxxopts::value<bool>(m_ExNetParams.m_OutputDetailsOnlyToStdOut)->default_value("false")
                         ->implicit_value("true"))

                ("import-inputs-if-aligned",
                 "In & Out tensors will be imported per inference if the memory alignment allows.",
                 cxxopts::value<bool>(m_ExNetParams.m_ImportInputsIfAligned)->default_value("false")
                         ->implicit_value("true"));

        m_CxxOptions.add_options("f) Deprecated or unused")
                ("f,model-format",
                 "armnn-binary, onnx-binary, onnx-text, tflite-binary"
                 "DEPRECATED: The program option 'model-format' is deprecated and will be "
                 "removed soon. The model-format is now automatically set.",
                 cxxopts::value<std::string>())

                ("n,concurrent",
                "This option is for Arm NN internal asynchronous testing purposes. "
                "False by default. If set to true will use std::launch::async or the Arm NN thread pool, "
                "if 'thread-pool-size' is greater than 0, for asynchronous execution."
                "DEPRECATED: The program option 'concurrent' is deprecated and will be "
                "removed soon. Please use the option '\"P, thread-pool-size\"' instead.",
                cxxopts::value<bool>(m_ExNetParams.m_Concurrent)->default_value("false")->implicit_value("true"))

                ("y,input-type",
                 "The type of the input tensors in the network separated by comma. "
                 "If unset, defaults to \"float\" for all defined inputs. "
                 "Accepted values (float, int, qasymms8 or qasymmu8)."
                 "DEPRECATED: The program option 'input-type' is deprecated and will be "
                 "removed soon. The input-types are now automatically set.",
                 cxxopts::value<std::string>())

                ("z,output-type",
                 "The type of the output tensors in the network separated by comma. "
                 "If unset, defaults to \"float\" for all defined outputs. "
                 "Accepted values (float, int,  qasymms8 or qasymmu8)."
                 "DEPRECATED: The program option 'output-type' is deprecated and will be "
                 "removed soon. The output-types are now automatically set.",
                 cxxopts::value<std::string>())

                ("bf16-turbo-mode",
                 "This option is no longer being used. In order to use bf16 please set enable-fast-math "
                 "to true",
                 cxxopts::value<bool>(m_ExNetParams.m_EnableBf16TurboMode)
                        ->default_value("false")->implicit_value("true"));
    }
    catch (const std::exception& e)
    {
        ARMNN_ASSERT_MSG(false, "Caught unexpected exception");
        ARMNN_LOG(fatal) << "Fatal internal error: " << e.what();
        exit(EXIT_FAILURE);
    }
}

ProgramOptions::ProgramOptions(int ac, const char* av[]): ProgramOptions()
{
    ParseOptions(ac, av);
}

void ProgramOptions::ParseOptions(int ac, const char* av[])
{
    // Parses the command-line.
    m_CxxResult = m_CxxOptions.parse(ac, av);

    if (m_CxxResult.count("help") || ac <= 1)
    {
        std::cout << m_CxxOptions.help() << std::endl;
        exit(EXIT_SUCCESS);
    }

    CheckRequiredOptions(m_CxxResult);
    CheckOptionDependencies(m_CxxResult);
    CheckForDeprecatedOptions(m_CxxResult);

    if ((m_ExNetParams.m_OutputDetailsToStdOut ||
         m_ExNetParams.m_OutputDetailsOnlyToStdOut) &&
        !m_ExNetParams.m_EnableProfiling)
    {
        throw cxxopts::exceptions::exception("You must enable profiling if you would like to output layer details");
    }

    // Some options can't be assigned directly because they need some post-processing:
    auto computeDevices = GetOptionValue<std::vector<std::string>>("compute", m_CxxResult);
    m_ExNetParams.m_ComputeDevices = GetBackendIDs(computeDevices);
    m_ExNetParams.m_InputNames =
            ParseStringList(GetOptionValue<std::string>("input-name", m_CxxResult), ",");
    m_ExNetParams.m_InputTensorDataFilePaths =
            ParseStringList(GetOptionValue<std::string>("input-tensor-data", m_CxxResult), ",");
    m_ExNetParams.m_OutputNames =
            ParseStringList(GetOptionValue<std::string>("output-name", m_CxxResult), ",");
    m_ExNetParams.m_OutputTensorFiles =
            ParseStringList(GetOptionValue<std::string>("write-outputs-to-file", m_CxxResult), ",");
    m_ExNetParams.m_GenerateTensorData = m_ExNetParams.m_InputTensorDataFilePaths.empty();
    m_ExNetParams.m_DynamicBackendsPath = m_RuntimeOptions.m_DynamicBackendsPath;

    m_RuntimeOptions.m_EnableGpuProfiling = m_ExNetParams.m_EnableProfiling;

    std::string tfliteExecutor = GetOptionValue<std::string>("tflite-executor", m_CxxResult);

    if (tfliteExecutor.size() == 0 || tfliteExecutor == "parser")
    {
        m_ExNetParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteParser;
    }
    else if (tfliteExecutor == "opaquedelegate")
    {
        m_ExNetParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteOpaqueDelegate;
    }
    else if (tfliteExecutor == "delegate")
    {
        m_ExNetParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate;
    }
    else if (tfliteExecutor == "tflite")
    {
        m_ExNetParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter;
    }
    else
    {
        ARMNN_LOG(info) << fmt::format("Invalid tflite-executor option '{}'.", tfliteExecutor);
        throw armnn::InvalidArgumentException ("Invalid tflite-executor option");
    }

    // For backwards compatibility when deprecated options are used
    if (m_ExNetParams.m_EnableDelegate)
    {
        m_ExNetParams.m_TfLiteExecutor = ExecuteNetworkParams::TfLiteExecutor::ArmNNTfLiteDelegate;
    }

    // Set concurrent to true if the user expects to run inferences asynchronously
    if (m_ExNetParams.m_Concurrent)
    {
        m_ExNetParams.m_ThreadPoolSize = 1;
    }

    // There's an odd combination of parameters to be handled here. It appears that setting m_ThreadPoolSize greater
    // than 0 implies using the asynchronous mode. However, TfLite executor does not support an asynchronous mode of
    // execution
    if (m_ExNetParams.m_ThreadPoolSize > 0)
    {
        m_ExNetParams.m_Concurrent = true;
    }
    if (m_ExNetParams.m_Concurrent &&
        m_ExNetParams.m_TfLiteExecutor == ExecuteNetworkParams::TfLiteExecutor::TfliteInterpreter)
    {
        ARMNN_LOG(info) << "The TfLite runtime does not support an asynchronous mode of execution. Parameters "
                           "\"n,concurrent\" or \"P, thread-pool-size\" will be ignored.";
    }

    // Parse input tensor shape from the string we got from the command-line.
    std::vector<std::string> inputTensorShapesVector =
            ParseStringList(GetOptionValue<std::string>("input-tensor-shape", m_CxxResult), ":");

    if (!inputTensorShapesVector.empty())
    {
        m_ExNetParams.m_InputTensorShapes.reserve(inputTensorShapesVector.size());

        for(const std::string& shape : inputTensorShapesVector)
        {
            std::stringstream ss(shape);
            std::vector<unsigned int> dims = ParseArray(ss);

            m_ExNetParams.m_InputTensorShapes.push_back(
                    armnn::TensorShape{static_cast<unsigned int>(dims.size()), dims.data()});
        }
    }

    // We have to validate ExecuteNetworkParams first so that the tuning path and level is validated
    ValidateExecuteNetworkParams();

    // Parse CL tuning parameters to runtime options
    if (!m_ExNetParams.m_TuningPath.empty())
    {
        m_RuntimeOptions.m_BackendOptions.emplace_back(
            armnn::BackendOptions
            {
                "GpuAcc",
                {
                    {"TuningLevel", m_ExNetParams.m_TuningLevel},
                    {"TuningFile", m_ExNetParams.m_TuningPath.c_str()},
                    {"KernelProfilingEnabled", m_ExNetParams.m_EnableProfiling},
                    {"MLGOTuningFilePath", m_ExNetParams.m_MLGOTuningFilePath}
                }
            }
        );
    }

    ValidateRuntimeOptions();

    auto comparisonComputDevices = GetOptionValue<std::vector<std::string>>("compare-output-with-backend", m_CxxResult);

    if (!comparisonComputDevices.empty())
    {
        m_ExNetParams.m_ComparisonComputeDevices = GetBackendIDs(comparisonComputDevices);
    }
}