// Copyright (C) 2018-2025 Intel Corporation
// SPDX-License-Identifier: Apache-2.0
//

#pragma once

#include <iostream>
#include <string>
#include <vector>

#include "gflags/gflags.h"

/// @brief message for help argument
static const char help_message[] = "Print the usage message";

/// @brief message for images argument
static const char input_message[] =
    "Optional. Path to a folder with images and/or binaries or to specific image or binary file.\n"
    "                              In case of dynamic shapes models with several inputs provide the same number"
    " of files for each input (except cases with single file for any input):"
    "\"input1:1.jpg input2:1.bin\", \"input1:1.bin,2.bin input2:3.bin input3:4.bin,5.bin \"."
    " Also you can pass specific keys for inputs: \"random\" - for filling input with random data,"
    " \"image_info\" - for filling input with image size.\n"
    "                              You should specify either one files set to be used for all inputs (without "
    "providing "
    "input names) or separate files sets for every input of model (providing inputs names).\n"
    "Currently supported data types: bmp, bin, npy.\n"
    "If OPENCV is enabled, this functionality is extended with the following data types:\n"
    "dib, jpeg, jpg, jpe, jp2, png, pbm, pgm, ppm, sr, ras, tiff, tif.";

/// @brief message for model argument
static const char model_message[] =
    "Required. Path to an .xml/.onnx file with a trained model or to a .blob files with "
    "a trained compiled model.";

/// @brief message for performance hint
static const char hint_message[] =
    "Optional. Performance hint allows the OpenVINO device to select the right model-specific settings.\n"
    "                               'throughput' or 'tput': device performance mode will be set to THROUGHPUT.\n"
    "                               'cumulative_throughput' or 'ctput': device performance mode will be set to "
    "CUMULATIVE_THROUGHPUT.\n"
    "                               'latency': device performance mode will be set to LATENCY.\n"
    "                               'none': no device performance mode will be set.\n"
    "                              Using explicit 'nstreams' or other device-specific options, please set hint to "
    "'none'";

/// @brief message for assigning cnn calculation to device
static const char target_device_message[] =
    "Optional. Specify a target device to infer on (the list of available devices is shown below). "
    "Default value is CPU. Use \"-d HETERO:<comma-separated_devices_list>\" format to specify "
    "HETERO plugin. "
    "Use \"-d MULTI:<comma-separated_devices_list>\" format to specify MULTI plugin. "
    "The application looks for a suitable plugin for the specified device.";

/// @brief message for iterations count
static const char iterations_count_message[] =
    "Optional. Number of iterations. "
    "If not specified, the number of iterations is calculated depending on a device.";

// @brief message for enabling caching
static const char cache_dir_message[] = "Optional. Enables caching of loaded models to specified directory. "
                                        "List of devices which support caching is shown at the end of this message.";

// @brief message for single load network
static const char load_from_file_message[] = "Optional. Loads model from file directly without read_model."
                                             " All CNNNetwork options (like re-shape) will be ignored";

/// @brief message for maximum inference rate
static const char maximum_inference_rate_message[] =
    "Optional. Maximum inference rate by frame per second"
    "If not specified, default value is 0, the inference will run at maximum rate depending on a device capabilities. "
    "Tweaking this value allow better accuracy in power usage measurement by limiting the execution.";

/// @brief message for execution time
static const char execution_time_message[] = "Optional. Time in seconds to execute topology.";

static const char batch_size_message[] =
    "Optional. Batch size value. If not specified, the batch size value is determined from "
    "Intermediate Representation.";

static const char shape_message[] =
    "Optional. Set shape for model input. For example, \"input1[1,3,224,224],input2[1,4]\" or \"[1,3,224,224]\""
    " in case of one input size. This parameter affect model input shape and can be dynamic."
    " For dynamic dimensions use symbol `?` or '-1'. Ex. [?,3,?,?]."
    " For bounded dimensions specify range 'min..max'. Ex. [1..10,3,?,?].";

static const char data_shape_message[] =
    "Required for models with dynamic shapes. Set shape for input blobs."
    " In case of one input size: \"[1,3,224,224]\" or \"input1[1,3,224,224],input2[1,4]\"."
    " In case of several input sizes provide the same number for each input (except cases with single shape for any "
    "input):"
    " \"[1,3,128,128][3,3,128,128][1,3,320,320]\", \"input1[1,1,128,128][1,1,256,256],input2[80,1]\""
    " or \"input1[1,192][1,384],input2[1,192][1,384],input3[1,192][1,384],input4[1,192][1,384]\"."
    " If model shapes are all static specifying the option will cause an exception.";

static const char layout_message[] =
    "Optional. Prompts how model layouts should be treated by application. "
    "For example, \"input1[NCHW],input2[NC]\" or \"[NCHW]\" in case of one input size.";

/// @brief message for execution mode
static const char api_message[] =
    "Optional. Enable Sync/Async API. When hint is throughput, default value is \"async\". "
    "When hint is latency, default value is \"sync\".";

/// @brief message for #streams for CPU inference
static const char infer_num_streams_message[] =
    "Optional. Number of streams to use for inference on the CPU or GPU devices "
    "(for HETERO and MULTI device cases use format <dev1>:<nstreams1>,<dev2>:<nstreams2> or just "
    "<nstreams>). "
    "Default value is determined automatically for a device.Please note that although the "
    "automatic selection "
    "usually provides a reasonable performance, it still may be non - optimal for some cases, "
    "especially for "
    "very small models. See sample's README for more details. "
    "Also, using nstreams>1 is inherently throughput-oriented option, "
    "while for the best-latency estimations the number of streams should be set to 1.";

/// @brief message for requests count
static const char infer_requests_count_message[] =
    "Optional. Number of infer requests. Default value is determined automatically for device.";

/// @brief message for enforcing of BF16 execution where it is possible
static const char enforce_bf16_message[] =
    "Optional. By default floating point operations execution in bfloat16 precision are enforced "
    "if supported by platform.\n"
    "                                  'true'  - enable  bfloat16 regardless of platform support\n"
    "                                  'false' - disable bfloat16 regardless of platform support";

/// @brief message for user library argument
static const char custom_extensions_library_message[] =
    "Required for custom layers (extensions). Absolute path to a shared library with the kernels "
    "implementations.";

/// @brief message for clDNN custom kernels desc
static const char custom_cldnn_message[] =
    "Required for GPU custom kernels. Absolute path to an .xml file with the kernels description.";

static constexpr char inference_only_message[] =
    "Optional. Measure only inference stage. Default option for static models. Dynamic models"
    " are measured in full mode which includes inputs setup stage,"
    " inference only mode available for them with single input data shape only."
    " To enable full mode for static models pass \"false\" value to this argument:"
    " ex. \"-inference_only=false\".";

// @brief message for inference_precision
static const char inference_precision_message[] =
    "Optional. Specifies the inference precision. Example #1: '-infer_precision bf16'. Example #2: '-infer_precision "
    "CPU:bf16,GPU:f32'";

static constexpr char inputs_precision_message[] = "Optional. Specifies precision for all input layers of the model.";

static constexpr char outputs_precision_message[] = "Optional. Specifies precision for all output layers of the model.";

static constexpr char iop_message[] =
    "Optional. Specifies precision for input and output layers by name.\n"
    "                                             Example: -iop \"input:f16, output:f16\".\n"
    "                                             Notice that quotes are required.\n"
    "                                             Overwrites precision from ip and op options for "
    "specified layers.";

static constexpr char input_image_scale_message[] =
    "Optional. Scale values to be used for the input image per channel.\n"
    "Values to be provided in the [R, G, B] format. Can be defined for desired input of the model.\n"
    "Example: -iscale data[255,255,255],info[255,255,255]\n";

static constexpr char mean_values_message[] =
    "Optional. Mean values to be used for the input image per channel. Values to be provided in the [R,G,B] format. "
    "Can be defined for desired input of the model, for example: \"--mean_values "
    "data[255,255,255],info[255,255,255]\". The exact meaning and order of channels depend on how the original model "
    "was trained. Applying the values affects performance and may cause type conversion";

static constexpr char scale_values_message[] =
    "Optional. Scale values to be used for the input image per channel. Values are provided in the [R,G,B] format. Can "
    "be defined for desired input of the model, for example: \"--scale_values data[255,255,255],info[255,255,255]\". "
    "The exact meaning and order of channels depend on how the original model was trained. If both --mean_values and "
    "--scale_values are specified, the mean is subtracted first and then scale is applied regardless of the order of "
    "options in command line. Applying the values affects performance and may cause type conversion";

/// @brief message for #threads for CPU inference
static const char infer_num_threads_message[] = "Optional. Number of threads to use for inference on the CPU "
                                                "(including HETERO and MULTI cases).";

// @brief message for CPU threads pinning option
static const char infer_threads_pinning_message[] = "Optional. Explicit threads->cores pinning for CPU inference tasks "
                                                    "(leave empty to let the OpenVINO make a choice).";

// @brief message for switching memory allocation type option
static const char use_device_mem_message[] =
    "Optional. Switch between host and device memory allocation for input and output buffers.";

/// @brief message for latency percentile settings
static const char infer_latency_percentile_message[] =
    "Optional. Defines the percentile to be reported in latency metric. The valid range is [1, 100]. The default value "
    "is 50 (median).";

// @brief message for report_type option
static const char report_type_message[] =
    "Optional. Enable collecting statistics report. \"no_counters\" report contains "
    "configuration options specified, resulting FPS and latency. \"average_counters\" "
    "report extends \"no_counters\" report and additionally includes average PM "
    "counters values for each layer from the model. \"detailed_counters\" report "
    "extends \"average_counters\" report and additionally includes per-layer PM "
    "counters and latency for each executed infer request.";

// @brief message for report_folder option
static const char report_folder_message[] = "Optional. Path to a folder where statistics report is stored.";

// @brief message for json_stats option
static const char json_stats_message[] = "Optional. Enables JSON-based statistics output (by default reporting system "
                                         "will use CSV format). Should be used together with -report_folder option.";

// @brief message for performance counters option
static const char pc_message[] = "Optional. Report performance counters.";

// @brief message for sorted performance counters option
static const char pc_sort_message[] =
    "Optional. Report performance counters and analysis the sort hotpoint opts. "
    " \"sort\" Analysis opts time cost, print by hotpoint order "
    " \"no_sort\" Analysis opts time cost, print by normal order "
    " \"simple_sort\" Analysis opts time cost, only print EXECUTED opts by normal order";

// @brief message for performance counters for sequence option
static const char pcseq_message[] = "Optional. Report latencies for each shape in -data_shape sequence.";

// @brief message for exec_graph_path option
static const char exec_graph_path_message[] =
    "Optional. Path to a file where to store executable graph information serialized.";

// @brief message for dump config option
static const char dump_config_message[] =
    "Optional. Path to JSON file to dump OV parameters, which were set by application.";

// @brief message for load config option
static const char load_config_message[] =
    "Optional. Path to JSON file to load custom OV parameters."
    " Please note, command line parameters have higher priority then parameters from configuration file.\n"
    "                              Example 1: a simple JSON file for HW device with primary properties.\n"
    "                                       {\n"
    "                                            \"CPU\": {\"NUM_STREAMS\": \"3\", \"PERF_COUNT\": \"NO\"}\n"
    "                                       }\n"
    "                              Example 2: a simple JSON file for meta device(AUTO/MULTI) with HW device "
    "properties.\n"
    "                                       {\n"
    "                                               \"AUTO\": {\n"
    "                                                       \"PERFORMANCE_HINT\": \"THROUGHPUT\",\n"
    "                                                       \"PERF_COUNT\": \"NO\",\n"
    "                                                       \"DEVICE_PROPERTIES\": "
    "\"{CPU:{INFERENCE_PRECISION_HINT:f32,NUM_STREAMS:3},GPU:{INFERENCE_PRECISION_HINT:f32,NUM_STREAMS:5}}\"\n"
    "                                               }\n"
    "                                       }";

static const char no_warmup_message[] =
    "Optional. Skip warmup inference. Useful for benchmarking purposes in simulated environments.\n"
    "Otherwise, not recommended.";

/// @brief Define flag for showing help message <br>
DEFINE_bool(h, false, help_message);

/// @brief Declare flag for showing help message <br>
DECLARE_bool(help);

/// @brief Define parameter for set image file <br>
/// i or mif is a required parameter
DEFINE_string(i, "", input_message);

/// @brief Define parameter for set model file <br>
/// It is a required parameter
DEFINE_string(m, "", model_message);

/// @brief Define execution mode
DEFINE_string(hint, "", hint_message);

/// @brief device the target device to infer on <br>
DEFINE_string(d, "CPU", target_device_message);

/// @brief Iterations count (default 0)
/// Sync mode: iterations count
/// Async mode: StartAsync counts
DEFINE_uint64(niter, 0, iterations_count_message);

/// @brief Time to execute topology in seconds
DEFINE_uint64(t, 0, execution_time_message);

/// @brief Define parameter for batch size <br>
/// Default is 0 (that means don't specify)
DEFINE_uint64(b, 0, batch_size_message);

/// @brief Define flag for input shape <br>
DEFINE_string(shape, "", shape_message);

/// @brief Define flag for input blob shape <br>
DEFINE_string(data_shape, "", data_shape_message);

/// @brief Define flag for layout shape <br>
DEFINE_string(layout, "", layout_message);

/// @brief Absolute path to extensions library with user layers <br>
/// It is a required parameter
DEFINE_string(extensions, "", custom_extensions_library_message);

/// @brief Define parameter for clDNN custom kernels path <br>
/// Default is ./lib
DEFINE_string(c, "", custom_cldnn_message);

/// @brief Define parameter for cache model dir <br>
DEFINE_string(cache_dir, "", cache_dir_message);

/// @brief Define flag for load network from model file by name without ReadNetwork <br>
DEFINE_bool(load_from_file, false, load_from_file_message);

/// @brief Define execution mode
DEFINE_string(api, "", api_message);

/// @brief Number of infer requests in parallel
DEFINE_uint64(nireq, 0, infer_requests_count_message);

/// @brief Execute infer requests at a fixed frequency
DEFINE_double(max_irate, 0, maximum_inference_rate_message);

/// @brief Number of streams to use for inference on the CPU (also affects Hetero cases)
DEFINE_string(nstreams, "", infer_num_streams_message);

/// @brief Define flag for inference only mode <br>
DEFINE_bool(inference_only, true, inference_only_message);

/// @brief Define flag for inference precision hint
DEFINE_string(infer_precision, "", inference_precision_message);

/// @brief Specify precision for all input layers of the network
DEFINE_string(ip, "", inputs_precision_message);

/// @brief Specify precision for all output layers of the network
DEFINE_string(op, "", outputs_precision_message);

/// @brief Specify precision for input and output layers by name.\n"
///        Example: -iop \"input:f16, output:f16\".\n"
///        Notice that quotes are required.\n"
///        Overwrites layout from ip and op options for specified layers.";
DEFINE_string(iop, "", iop_message);

/// @brief Define flag for using input image mean <br>
DEFINE_string(mean_values, "", mean_values_message);

/// @brief Define flag for using input image scale <br>
DEFINE_string(scale_values, "", scale_values_message);

/// @brief Number of threads to use for inference on the CPU in throughput mode (also affects Hetero
/// cases)
DEFINE_uint64(nthreads, 0, infer_num_threads_message);

// @brief Enable plugin messages
DEFINE_string(pin, "", infer_threads_pinning_message);

/// @brief Define flag for switching between host and device memory allocation for input and output buffers
DEFINE_bool(use_device_mem, false, use_device_mem_message);

/// @brief The percentile which will be reported in latency metric
DEFINE_uint64(latency_percentile, 50, infer_latency_percentile_message);

/// @brief Enables statistics report collecting
DEFINE_string(report_type, "", report_type_message);

/// @brief Path to a folder where statistics report is stored
DEFINE_string(report_folder, "", report_folder_message);

/// @brief Enables JSON-based statistics reporting
DEFINE_bool(json_stats, false, json_stats_message);

/// @brief Define flag for showing performance counters <br>
DEFINE_bool(pc, false, pc_message);

/// @brief Define flag for showing sorted performance counters <br>
DEFINE_string(pcsort, "", pc_sort_message);

/// @brief Define flag for showing performance sequence counters <br>
DEFINE_bool(pcseq, false, pcseq_message);

/// @brief Path to a file where to store executable graph information serialized
DEFINE_string(exec_graph_path, "", exec_graph_path_message);

/// @brief Define flag for loading configuration file <br>
DEFINE_string(load_config, "", load_config_message);

/// @brief Define flag for dumping configuration file <br>
DEFINE_string(dump_config, "", dump_config_message);

/// @brief Skips warmup inference and measures only the first inference
DEFINE_bool(no_warmup, false, no_warmup_message);

/**
 * @brief This function show a help message
 */
static void show_usage() {
    std::cout << "usage: benchmark_app [OPTION]" << std::endl;
    std::cout << std::endl;
    std::cout << "Options:" << std::endl;
    std::cout << "    -h, --help                    " << help_message << std::endl;
    std::cout << "    -m  <path>                    " << model_message << std::endl;
    std::cout << "    -i  <path>                    " << input_message << std::endl;
    std::cout << "    -d  <device>                  " << target_device_message << std::endl;
    std::cout << "    -hint  <performance hint> (latency or throughput or cumulative_throughput or none)   "
              << hint_message << std::endl;
    std::cout << "    -niter  <integer>             " << iterations_count_message << std::endl;
    std::cout << "    -max_irate \"<float>\"        " << maximum_inference_rate_message << std::endl;
    std::cout << "    -t                            " << execution_time_message << std::endl;
    std::cout << std::endl;
    std::cout << "Input shapes" << std::endl;
    std::cout << "    -b  <integer>                 " << batch_size_message << std::endl;
    std::cout << "    -shape                        " << shape_message << std::endl;
    std::cout << "    -data_shape                   " << data_shape_message << std::endl;
    std::cout << "    -layout                       " << layout_message << std::endl;
    std::cout << std::endl;
    std::cout << "Advanced options" << std::endl;
    std::cout << "    -extensions  <absolute_path>  " << custom_extensions_library_message << std::endl;
    std::cout << "    -c  <absolute_path>           " << custom_cldnn_message << std::endl;
    std::cout << "    -cache_dir  <path>            " << cache_dir_message << std::endl;
    std::cout << "    -load_from_file               " << load_from_file_message << std::endl;
    std::cout << "    -api <sync/async>             " << api_message << std::endl;
    std::cout << "    -nireq  <integer>             " << infer_requests_count_message << std::endl;
    std::cout << "    -nstreams  <integer>          " << infer_num_streams_message << std::endl;
    std::cout << "    -inference_only         " << inference_only_message << std::endl;
    std::cout << "    -infer_precision        " << inference_precision_message << std::endl;
    std::cout << "    -no_warmup                    " << no_warmup_message << std::endl;
    std::cout << std::endl;
    std::cout << "Preprocessing options:" << std::endl;
    std::cout << "    -ip   <value>           " << inputs_precision_message << std::endl;
    std::cout << "    -op   <value>           " << outputs_precision_message << std::endl;
    std::cout << "    -iop  <value>           " << iop_message << std::endl;
    std::cout << "    -mean_values   [R,G,B]  " << mean_values_message << std::endl;
    std::cout << "    -scale_values  [R,G,B]  " << scale_values_message << std::endl;
    std::cout << std::endl;
    std::cout << "Device-specific performance options:" << std::endl;
    std::cout << "    -nthreads  <integer>          " << infer_num_threads_message << std::endl;
    std::cout << "    -pin  <string>  \"YES\" / \"NO\" " << infer_threads_pinning_message << std::endl;
    std::cout << "    -use_device_mem           " << use_device_mem_message << std::endl;
    std::cout << std::endl;
    std::cout << "Statistics dumping options:" << std::endl;
    std::cout << "    -latency_percentile     " << infer_latency_percentile_message << std::endl;
    std::cout << "    -report_type  <type>    " << report_type_message << std::endl;
    std::cout << "    -report_folder          " << report_folder_message << std::endl;
    std::cout << "    -json_stats             " << json_stats_message << std::endl;
    std::cout << "    -pc                     " << pc_message << std::endl;
    std::cout << "    -pcsort                 " << pc_sort_message << std::endl;
    std::cout << "    -pcseq                  " << pcseq_message << std::endl;
    std::cout << "    -exec_graph_path        " << exec_graph_path_message << std::endl;
    std::cout << "    -dump_config            " << dump_config_message << std::endl;
    std::cout << "    -load_config            " << load_config_message << std::endl;
}
