// Copyright (c) 2022 by Rockchip Electronics Co., Ltd. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

/*-------------------------------------------
                Includes
-------------------------------------------*/
#include "rknn_api.h"

#include <float.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <sys/time.h>

#define STB_IMAGE_IMPLEMENTATION
#include "stb/stb_image.h"
#define STB_IMAGE_RESIZE_IMPLEMENTATION
#include <stb/stb_image_resize.h>

#include "cnpy/cnpy.h"
using namespace cnpy;


/*-------------------------------------------
                  Functions
-------------------------------------------*/
static inline int64_t getCurrentTimeUs()
{
  struct timeval tv;
  gettimeofday(&tv, NULL);
  return tv.tv_sec * 1000000 + tv.tv_usec;
}

static int rknn_GetTopN(float* pfProb, float* pfMaxProb, uint32_t* pMaxClass, uint32_t outputCount, uint32_t topNum)
{
  uint32_t i, j;
  uint32_t top_count = outputCount > topNum ? topNum : outputCount;

  for (i = 0; i < topNum; ++i) {
    pfMaxProb[i] = -FLT_MAX;
    pMaxClass[i] = -1;
  }

  for (j = 0; j < top_count; j++) {
    for (i = 0; i < outputCount; i++) {
      if ((i == *(pMaxClass + 0)) || (i == *(pMaxClass + 1)) || (i == *(pMaxClass + 2)) || (i == *(pMaxClass + 3)) ||
          (i == *(pMaxClass + 4))) {
        continue;
      }

      if (pfProb[i] > *(pfMaxProb + j)) {
        *(pfMaxProb + j) = pfProb[i];
        *(pMaxClass + j) = i;
      }
    }
  }

  return 1;
}

static void dump_tensor_attr(rknn_tensor_attr* attr)
{
  std::string shape_str = attr->n_dims < 1 ? "" : std::to_string(attr->dims[0]);
  for (int i = 1; i < attr->n_dims; ++i) {
    shape_str += ", " + std::to_string(attr->dims[i]);
  }

  printf("  index=%d, name=%s, n_dims=%d, dims=[%s], n_elems=%d, size=%d, w_stride = %d, size_with_stride=%d, fmt=%s, "
         "type=%s, qnt_type=%s, "
         "zp=%d, scale=%f\n",
         attr->index, attr->name, attr->n_dims, shape_str.c_str(), attr->n_elems, attr->size, attr->w_stride,
         attr->size_with_stride, get_format_string(attr->fmt), get_type_string(attr->type),
         get_qnt_type_string(attr->qnt_type), attr->zp, attr->scale);
}

static unsigned char* load_npy(const char* input_path, rknn_tensor_attr* input_attr, int* input_type, int* input_size)
{
  int req_height  = 0;
  int req_width   = 0;
  int req_channel = 0;

  printf("Loading %s\n", input_path);

  switch (input_attr->fmt) {
  case RKNN_TENSOR_NHWC:
    req_height  = input_attr->dims[1];
    req_width   = input_attr->dims[2];
    req_channel = input_attr->dims[3];
    break;
  case RKNN_TENSOR_NCHW:
    req_height  = input_attr->dims[2];
    req_width   = input_attr->dims[3];
    req_channel = input_attr->dims[1];
    break;
  case RKNN_TENSOR_UNDEFINED:
    break;
  default:
    printf("meet unsupported layout\n");
    return NULL;
  }

  NpyArray npy_data = npy_load(input_path);

  int         type_bytes = npy_data.word_size;
  std::string typeName   = npy_data.typeName;

  printf("npy data type:%s\n", typeName.c_str());

  if (typeName == "int8") {
    *input_type = RKNN_TENSOR_INT8;
  } else if (typeName == "uint8") {
    *input_type = RKNN_TENSOR_UINT8;
  } else if (typeName == "float16") {
    *input_type = RKNN_TENSOR_FLOAT16;
  } else if (typeName == "float32") {
    *input_type = RKNN_TENSOR_FLOAT32;
  } else if (typeName == "8") {
    *input_type = RKNN_TENSOR_BOOL;
  } else if (typeName == "int64") {
    *input_type = RKNN_TENSOR_INT64;
  }

  // npy shape = NHWC
  int npy_shape[4] = {1, 1, 1, 1};

  int start = npy_data.shape.size() == 4 ? 0 : 1;
  for (size_t i = 0; i < npy_data.shape.size() && i < 4; ++i) {
    npy_shape[start + i] = npy_data.shape[i];
  }

  int height  = npy_shape[1];
  int width   = npy_shape[2];
  int channel = npy_shape[3];

  if ((input_attr->fmt != RKNN_TENSOR_UNDEFINED) &&
      (width != req_width || height != req_height || channel != req_channel)) {
    printf("npy shape match failed!, (%d, %d, %d) != (%d, %d, %d)\n", height, width, channel, req_height, req_width,
           req_channel);
    return NULL;
  }

  unsigned char* data = (unsigned char*)malloc(npy_data.num_bytes());
  if (!data) {
    return NULL;
  }

  // TODO: copy
  memcpy(data, npy_data.data<unsigned char>(), npy_data.num_bytes());

  *input_size = npy_data.num_bytes();

  return data;
}

static void save_npy(const char* output_path, float* output_data, rknn_tensor_attr* output_attr)
{
  std::vector<size_t> output_shape;

  for (uint32_t i = 0; i < output_attr->n_dims; ++i) {
    output_shape.push_back(output_attr->dims[i]);
  }

  npy_save<float>(output_path, output_data, output_shape);
}


static unsigned char* load_image(const char* image_path, rknn_tensor_attr* input_attr)
{
  int req_height  = 0;
  int req_width   = 0;
  int req_channel = 0;

  switch (input_attr->fmt) {
  case RKNN_TENSOR_NHWC:
    req_height  = input_attr->dims[1];
    req_width   = input_attr->dims[2];
    req_channel = input_attr->dims[3];
    break;
  case RKNN_TENSOR_NCHW:
    req_height  = input_attr->dims[2];
    req_width   = input_attr->dims[3];
    req_channel = input_attr->dims[1];
    break;
  default:
    printf("meet unsupported layout\n");
    return NULL;
  }

  int height  = 0;
  int width   = 0;
  int channel = 0;

  unsigned char* image_data = stbi_load(image_path, &width, &height, &channel, req_channel);
  if (image_data == NULL) {
    printf("load image failed!\n");
    return NULL;
  }

  if (width != req_width || height != req_height) {
    unsigned char* image_resized = (unsigned char*)STBI_MALLOC(req_width * req_height * req_channel);
    if (!image_resized) {
      printf("malloc image failed!\n");
      STBI_FREE(image_data);
      return NULL;
    }
    if (stbir_resize_uint8(image_data, width, height, 0, image_resized, req_width, req_height, 0, channel) != 1) {
      printf("resize image failed!\n");
      STBI_FREE(image_data);
      return NULL;
    }
    STBI_FREE(image_data);
    image_data = image_resized;
  }

  return image_data;
}

static std::vector<std::string> split(const std::string& str, const std::string& pattern)
{
  std::vector<std::string> res;
  if (str == "")
    return res;
  std::string strs = str + pattern;
  size_t      pos  = strs.find(pattern);
  while (pos != strs.npos) {
    std::string temp = strs.substr(0, pos);
    res.push_back(temp);
    strs = strs.substr(pos + 1, strs.size());
    pos  = strs.find(pattern);
  }
  return res;
}

/*-------------------------------------------
                  Main Functions
-------------------------------------------*/
int main(int argc, char* argv[])
{
  if (argc < 2) {
    printf("Usage:%s model_path [input_path] [loop_count] [core_mask]\n", argv[0]);
    return -1;
  }

  char* model_path = argv[1];
  std::vector<std::string> input_paths_split;
  int loop_count = 10;
  uint32_t core_mask = 1;
  rknn_context ctx = 0;
  uint32_t topNum = 5;
  double total_time = 0;

  if (argc > 2) {
    char* input_paths = argv[2];
    input_paths_split = split(input_paths, "#");
  }

  if (argc > 3) {
    loop_count = atoi(argv[3]);
  }

  if (argc > 4) {
    core_mask = strtoul(argv[4], NULL, 10);
  }


  // Init rknn from model path
  int ret = rknn_init(&ctx, model_path, 0, 0, NULL);

  if (ret < 0) {
    printf("rknn_init fail! ret=%d\n", ret);
    return -1;
  }

  // Get sdk and driver version
  rknn_sdk_version sdk_ver;
  ret = rknn_query(ctx, RKNN_QUERY_SDK_VERSION, &sdk_ver, sizeof(sdk_ver));
  if (ret != RKNN_SUCC) {
    printf("rknn_query fail! ret=%d\n", ret);
    rknn_destroy(ctx);
    return -1;
  }
  printf("rknn_api/rknnrt version: %s, driver version: %s\n", sdk_ver.api_version, sdk_ver.drv_version);

  // Get weight and internal mem size, dma used size
  rknn_mem_size mem_size;
  ret = rknn_query(ctx, RKNN_QUERY_MEM_SIZE, &mem_size, sizeof(mem_size));
  if (ret != RKNN_SUCC) {
    printf("rknn_query fail! ret=%d\n", ret);
    rknn_destroy(ctx);
    return -1;
  }
  printf("total weight size: %d, total internal size: %d\n", mem_size.total_weight_size, mem_size.total_internal_size);
  printf("total dma used size: %zu\n", (size_t)mem_size.total_dma_allocated_size);

  // Get Model Input Output Info
  rknn_input_output_num io_num;
  ret = rknn_query(ctx, RKNN_QUERY_IN_OUT_NUM, &io_num, sizeof(io_num));
  if (ret != RKNN_SUCC) {
    printf("rknn_query fail! ret=%d\n", ret);
    rknn_destroy(ctx);
    return -1;
  }
  printf("model input num: %d, output num: %d\n", io_num.n_input, io_num.n_output);

  printf("input tensors:\n");
  rknn_tensor_attr input_attrs[io_num.n_input];
  memset(input_attrs, 0, io_num.n_input * sizeof(rknn_tensor_attr));
  for (uint32_t i = 0; i < io_num.n_input; i++) {
    input_attrs[i].index = i;
    // query info
    ret = rknn_query(ctx, RKNN_QUERY_INPUT_ATTR, &(input_attrs[i]), sizeof(rknn_tensor_attr));
    if (ret < 0) {
      printf("rknn_init error! ret=%d\n", ret);
      rknn_destroy(ctx);
      return -1;
    }
    dump_tensor_attr(&input_attrs[i]);
  }

  printf("output tensors:\n");
  rknn_tensor_attr output_attrs[io_num.n_output];
  memset(output_attrs, 0, io_num.n_output * sizeof(rknn_tensor_attr));
  for (uint32_t i = 0; i < io_num.n_output; i++) {
    output_attrs[i].index = i;
    // query info
    ret = rknn_query(ctx, RKNN_QUERY_OUTPUT_ATTR, &(output_attrs[i]), sizeof(rknn_tensor_attr));
    if (ret != RKNN_SUCC) {
      printf("rknn_query fail! ret=%d\n", ret);
      rknn_destroy(ctx);
      return -1;
    }
    dump_tensor_attr(&output_attrs[i]);
  }

  // Get custom string
  rknn_custom_string custom_string;
  ret = rknn_query(ctx, RKNN_QUERY_CUSTOM_STRING, &custom_string, sizeof(custom_string));
  if (ret != RKNN_SUCC) {
    printf("rknn_query fail! ret=%d\n", ret);
    rknn_destroy(ctx);
    return -1;
  }
  printf("custom string: %s\n", custom_string.string);

  unsigned char* input_data[io_num.n_input];
  int            input_type[io_num.n_input];
  int            input_layout[io_num.n_input];
  int            input_size[io_num.n_input];
  rknn_input     inputs[io_num.n_input];
  rknn_output    outputs[io_num.n_output];

  for (int i = 0; i < io_num.n_input; i++) {
    input_data[i]   = NULL;
    input_type[i]   = RKNN_TENSOR_UINT8;
    input_layout[i] = RKNN_TENSOR_NHWC;
    input_size[i]   = input_attrs[i].n_elems * sizeof(uint8_t);
  }

  if (input_paths_split.size() > 0) {
    // Load input
    if (io_num.n_input != input_paths_split.size()) {
      printf("input missing!, need input number: %d, only get %zu inputs\n", io_num.n_input, input_paths_split.size());
      goto out;
    }
    for (int i = 0; i < io_num.n_input; i++) {
      if (strstr(input_paths_split[i].c_str(), ".npy")) {
        input_data[i] = load_npy(input_paths_split[i].c_str(), &input_attrs[i], &input_type[i], &input_size[i]);
      } else {
        // Load image
        input_data[i] = load_image(input_paths_split[i].c_str(), &input_attrs[i]);
      }

      if (!input_data[i]) {
        goto out;
      }
    }
  } else {
    for (int i = 0; i < io_num.n_input; i++) {
      input_data[i] = (unsigned char*)malloc(input_size[i]);
      memset(input_data[i], 0x00, input_size[i]);
    }
  }


  memset(inputs, 0, io_num.n_input * sizeof(rknn_input));
  for (int i = 0; i < io_num.n_input; i++) {
    inputs[i].index        = i;
    inputs[i].pass_through = 0;
    inputs[i].type         = (rknn_tensor_type)input_type[i];
    inputs[i].fmt          = (rknn_tensor_format)input_layout[i];
    inputs[i].buf          = input_data[i];
    inputs[i].size         = input_size[i];
  }

  // Set input
  ret = rknn_inputs_set(ctx, io_num.n_input, inputs);
  if (ret < 0) {
    printf("rknn_input_set fail! ret=%d\n", ret);
    goto out;
  }

  rknn_set_core_mask(ctx, (rknn_core_mask)core_mask);

  // Warmup
  printf("Warmup ...\n");
  for (int i = 0; i < 5; ++i) {
    int64_t start_us  = getCurrentTimeUs();
    ret               = rknn_run(ctx, NULL);
    int64_t elapse_us = getCurrentTimeUs() - start_us;
    if (ret < 0) {
      printf("rknn run error %d\n", ret);
      goto out;
    }
    printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us);
  }


  // Run
  printf("Begin perf ...\n");
  for (int i = 0; i < loop_count; ++i) {
    int64_t start_us  = getCurrentTimeUs();
    ret               = rknn_run(ctx, NULL);
    int64_t elapse_us = getCurrentTimeUs() - start_us;
    if (ret < 0) {
      printf("rknn run error %d\n", ret);
      return -1;
    }
    total_time += elapse_us / 1000.f;
    printf("%4d: Elapse Time = %.2fms, FPS = %.2f\n", i, elapse_us / 1000.f, 1000.f * 1000.f / elapse_us);
  }
  printf("\nAvg Time %.2fms, Avg FPS = %.3f\n\n", total_time/loop_count, loop_count * 1000.f / total_time);

  // Get output
  memset(outputs, 0, io_num.n_output * sizeof(rknn_output));
  for (uint32_t i = 0; i < io_num.n_output; ++i) {
    outputs[i].want_float  = 1;
    outputs[i].index       = i;
    outputs[i].is_prealloc = 0;
  }

  ret = rknn_outputs_get(ctx, io_num.n_output, outputs, NULL);
  if (ret < 0) {
    printf("rknn_outputs_get fail! ret=%d\n", ret);
    goto out;
  }

  // save output
  for (uint32_t i = 0; i < io_num.n_output; i++) {
    char output_path[PATH_MAX];
    memset(output_path, 0x00, sizeof(output_path));
    sprintf(output_path, "rt_output%d.npy", i);
    printf("Save output to %s\n", output_path);
    save_npy(output_path, (float*)outputs[i].buf, &output_attrs[i]);
  }

  // Get top 5
  for (uint32_t i = 0; i < io_num.n_output; i++) {
    uint32_t MaxClass[topNum];
    float    fMaxProb[topNum];
    float*   buffer    = (float*)outputs[i].buf;
    uint32_t sz        = outputs[i].size / sizeof(float);
    int      top_count = sz > topNum ? topNum : sz;

    rknn_GetTopN(buffer, fMaxProb, MaxClass, sz, topNum);

    printf("---- Top%d ----\n", top_count);
    for (int j = 0; j < top_count; j++) {
      printf("%8.6f - %d\n", fMaxProb[j], MaxClass[j]);
    }
  }

  // release outputs
  ret = rknn_outputs_release(ctx, io_num.n_output, outputs);

out:
  // destroy
  rknn_destroy(ctx);

  for (int i = 0; i < io_num.n_input; i++) {
    if (input_data[i] != NULL) {
      free(input_data[i]);
    }
  }

  return 0;
}