// Defines sigaction on msys:
#ifndef _GNU_SOURCE
#define _GNU_SOURCE
#endif

#include "run-inference.h"

#include <cassert>
#include <cinttypes>
#include <cmath>
#include <cstdio>
#include <cstring>
#include <ctime>
#include <fstream>
#include <iostream>
#include <string>
#include <vector>

#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
#include <signal.h>
#include <unistd.h>
#elif defined(_WIN32)
#define WIN32_LEAN_AND_MEAN
#ifndef NOMINMAX
#define NOMINMAX
#endif
#include <signal.h>
#include <windows.h>
#endif

#if defined(_MSC_VER)
#pragma warning(disable : 4244 4267) // possible loss of data
#endif

static console_state con_st;
static llama_context **g_ctx;

static bool is_interacting = false;

#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) ||          \
    defined(_WIN32)
void sigint_handler(int signo) {
  if (signo == SIGINT) {
    if (!is_interacting) {
      is_interacting = true;
    } else {
      console_cleanup(con_st);
      printf("\n");
      llama_print_timings(*g_ctx);
      _exit(130);
    }
  }
}
#endif

int run_inference(gpt_params params, token_callback on_token = nullptr) {

  // save choice to use color for later
  // (note for later: this is a slightly awkward choice)
  con_st.use_color = params.use_color;
  con_st.multiline_input = params.multiline_input;
  console_init(con_st);
  atexit([]() { console_cleanup(con_st); });

  if (params.perplexity) {
    printf("\n************\n");
    printf("%s: please use the 'perplexity' tool for perplexity calculations\n",
           __func__);
    printf("************\n\n");

    return 0;
  }

  if (params.embedding) {
    printf("\n************\n");
    printf("%s: please use the 'embedding' tool for embedding calculations\n",
           __func__);
    printf("************\n\n");

    return 0;
  }

  if (params.n_ctx > 2048) {
    fprintf(stderr,
            "%s: warning: model might not support context sizes greater than "
            "2048 tokens (%d specified);"
            "expect poor results\n",
            __func__, params.n_ctx);
  } else if (params.n_ctx < 8) {
    fprintf(stderr,
            "%s: warning: minimum context size is 8, using minimum size.\n",
            __func__);
    params.n_ctx = 8;
  }

  fprintf(stderr, "%s: build = %d (%s)\n", __func__, BUILD_NUMBER,
          BUILD_COMMIT);

  if (params.seed == LLAMA_DEFAULT_SEED) {
    params.seed = time(NULL);
  }

  fprintf(stderr, "%s: seed  = %u\n", __func__, params.seed);

  std::mt19937 rng(params.seed);
  if (params.random_prompt) {
    params.prompt = gpt_random_prompt(rng);
  }

  llama_init_backend(params.numa);

  llama_model *model;
  llama_context *ctx;
  g_ctx = &ctx;

  // load the model and apply lora adapter, if any
  std::tie(model, ctx) = llama_init_from_gpt_params(params);
  if (model == NULL) {
    fprintf(stderr, "%s: error: unable to load model\n", __func__);
    return 1;
  }

  // print system information
  {
    fprintf(stderr, "\n");
    fprintf(stderr, "system_info: n_threads = %d / %d | %s\n", params.n_threads,
            std::thread::hardware_concurrency(), llama_print_system_info());
  }

  // determine the maximum memory usage needed to do inference for the given
  // n_batch and n_predict parameters uncomment the "used_mem" line in llama.cpp
  // to see the results
  if (params.mem_test) {
    {
      const std::vector<llama_token> tmp(params.n_batch, llama_token_bos());
      llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
    }

    {
      const std::vector<llama_token> tmp = {
          0,
      };
      llama_eval(ctx, tmp.data(), tmp.size(), params.n_predict - 1,
                 params.n_threads);
    }

    llama_print_timings(ctx);
    llama_free(ctx);
    llama_free_model(model);

    return 0;
  }

  // export the cgraph and exit
  if (params.export_cgraph) {
    llama_eval_export(ctx, "llama.ggml");
    llama_free(ctx);
    llama_free_model(model);

    return 0;
  }

  std::string path_session = params.path_prompt_cache;
  std::vector<llama_token> session_tokens;

  if (!path_session.empty()) {
    fprintf(stderr, "%s: attempting to load saved session from '%s'\n",
            __func__, path_session.c_str());

    // fopen to check for existing session
    FILE *fp = std::fopen(path_session.c_str(), "rb");
    if (fp != NULL) {
      std::fclose(fp);

      session_tokens.resize(params.n_ctx);
      size_t n_token_count_out = 0;
      if (!llama_load_session_file(
              ctx, path_session.c_str(), session_tokens.data(),
              session_tokens.capacity(), &n_token_count_out)) {
        fprintf(stderr, "%s: error: failed to load session file '%s'\n",
                __func__, path_session.c_str());
        return 1;
      }
      session_tokens.resize(n_token_count_out);
      llama_set_rng_seed(ctx, params.seed);

      fprintf(stderr, "%s: loaded a session with prompt size of %d tokens\n",
              __func__, (int)session_tokens.size());
    } else {
      fprintf(stderr, "%s: session file does not exist, will create\n",
              __func__);
    }
  }

  // tokenize the prompt
  std::vector<llama_token> embd_inp;

  if (params.interactive_first || params.instruct || !params.prompt.empty() ||
      session_tokens.empty()) {
    // Add a space in front of the first character to match OG llama tokenizer
    // behavior
    params.prompt.insert(0, 1, ' ');

    embd_inp = ::llama_tokenize(ctx, params.prompt, true);
  } else {
    embd_inp = session_tokens;
  }

  const int n_ctx = llama_n_ctx(ctx);

  if ((int)embd_inp.size() > n_ctx - 4) {
    fprintf(stderr, "%s: error: prompt is too long (%d tokens, max %d)\n",
            __func__, (int)embd_inp.size(), n_ctx - 4);
    return 1;
  }

  // debug message about similarity of saved session, if applicable
  size_t n_matching_session_tokens = 0;
  if (session_tokens.size()) {
    for (llama_token id : session_tokens) {
      if (n_matching_session_tokens >= embd_inp.size() ||
          id != embd_inp[n_matching_session_tokens]) {
        break;
      }
      n_matching_session_tokens++;
    }
    if (params.prompt.empty() && n_matching_session_tokens == embd_inp.size()) {
      fprintf(stderr, "%s: using full prompt from session file\n", __func__);
    } else if (n_matching_session_tokens >= embd_inp.size()) {
      fprintf(stderr, "%s: session file has exact match for prompt!\n",
              __func__);
    } else if (n_matching_session_tokens < (embd_inp.size() / 2)) {
      fprintf(stderr,
              "%s: warning: session file has low similarity to prompt (%zu / "
              "%zu tokens); will mostly be reevaluated\n",
              __func__, n_matching_session_tokens, embd_inp.size());
    } else {
      fprintf(stderr, "%s: session file matches %zu / %zu tokens of prompt\n",
              __func__, n_matching_session_tokens, embd_inp.size());
    }
  }

  // if we will use the cache for the full prompt without reaching the end of
  // the cache, force reevaluation of the last token token to recalculate the
  // cached logits
  if (!embd_inp.empty() && n_matching_session_tokens == embd_inp.size() &&
      session_tokens.size() > embd_inp.size()) {
    session_tokens.resize(embd_inp.size() - 1);
  }

  // number of tokens to keep when resetting context
  if (params.n_keep < 0 || params.n_keep > (int)embd_inp.size() ||
      params.instruct) {
    params.n_keep = (int)embd_inp.size();
  }

  // prefix & suffix for instruct mode
  const auto inp_pfx = ::llama_tokenize(ctx, "\n\n### Instruction:\n\n", true);
  const auto inp_sfx = ::llama_tokenize(ctx, "\n\n### Response:\n\n", false);

  // in instruct mode, we inject a prefix and a suffix to each input by the user
  if (params.instruct) {
    params.interactive_first = true;
    params.antiprompt.push_back("### Instruction:\n\n");
  }

  // enable interactive mode if interactive start is specified
  if (params.interactive_first) {
    params.interactive = true;
  }

  // determine newline token
  auto llama_token_newline = ::llama_tokenize(ctx, "\n", false);

  if (params.verbose_prompt) {
    fprintf(stderr, "\n");
    fprintf(stderr, "%s: prompt: '%s'\n", __func__, params.prompt.c_str());
    fprintf(stderr, "%s: number of tokens in prompt = %zu\n", __func__,
            embd_inp.size());
    for (int i = 0; i < (int)embd_inp.size(); i++) {
      fprintf(stderr, "%6d -> '%s'\n", embd_inp[i],
              llama_token_to_str(ctx, embd_inp[i]));
    }
    if (params.n_keep > 0) {
      fprintf(stderr, "%s: static prompt based on n_keep: '", __func__);
      for (int i = 0; i < params.n_keep; i++) {
        fprintf(stderr, "%s", llama_token_to_str(ctx, embd_inp[i]));
      }
      fprintf(stderr, "'\n");
    }
    fprintf(stderr, "\n");
  }

  if (params.interactive) {
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__))
    struct sigaction sigint_action;
    sigint_action.sa_handler = sigint_handler;
    sigemptyset(&sigint_action.sa_mask);
    sigint_action.sa_flags = 0;
    sigaction(SIGINT, &sigint_action, NULL);
#elif defined(_WIN32)
    auto console_ctrl_handler = +[](DWORD ctrl_type) -> BOOL {
      return (ctrl_type == CTRL_C_EVENT) ? (sigint_handler(SIGINT), true)
                                         : false;
    };
    SetConsoleCtrlHandler(static_cast<PHANDLER_ROUTINE>(console_ctrl_handler),
                          true);
#endif

    fprintf(stderr, "%s: interactive mode on.\n", __func__);

    if (params.antiprompt.size()) {
      for (auto antiprompt : params.antiprompt) {
        fprintf(stderr, "Reverse prompt: '%s'\n", antiprompt.c_str());
      }
    }

    if (!params.input_prefix.empty()) {
      fprintf(stderr, "Input prefix: '%s'\n", params.input_prefix.c_str());
    }

    if (!params.input_suffix.empty()) {
      fprintf(stderr, "Input suffix: '%s'\n", params.input_suffix.c_str());
    }
  }
  fprintf(stderr,
          "sampling: repeat_last_n = %d, repeat_penalty = %f, presence_penalty "
          "= %f, frequency_penalty = %f, top_k = %d, tfs_z = %f, top_p = %f, "
          "typical_p = %f, temp = %f, mirostat = %d, mirostat_lr = %f, "
          "mirostat_ent = %f\n",
          params.repeat_last_n, params.repeat_penalty, params.presence_penalty,
          params.frequency_penalty, params.top_k, params.tfs_z, params.top_p,
          params.typical_p, params.temp, params.mirostat, params.mirostat_eta,
          params.mirostat_tau);
  fprintf(stderr,
          "generate: n_ctx = %d, n_batch = %d, n_predict = %d, n_keep = %d\n",
          n_ctx, params.n_batch, params.n_predict, params.n_keep);
  fprintf(stderr, "\n\n");

  // TODO: replace with ring-buffer
  std::vector<llama_token> last_n_tokens(n_ctx);
  std::fill(last_n_tokens.begin(), last_n_tokens.end(), 0);

  if (params.interactive) {
    const char *control_message;
    if (con_st.multiline_input) {
      control_message =
          " - To return control to LLaMa, end your input with '\\'.\n"
          " - To return control without starting a new line, end your input "
          "with '/'.\n";
    } else {
      control_message =
          " - Press Return to return control to LLaMa.\n"
          " - To return control without starting a new line, end your input "
          "with '/'.\n"
          " - If you want to submit another line, end your input with '\\'.\n";
    }
    fprintf(stderr,
            "== Running in interactive mode. ==\n"
#if defined(__unix__) || (defined(__APPLE__) && defined(__MACH__)) ||          \
    defined(_WIN32)
            " - Press Ctrl+C to interject at any time.\n"
#endif
            "%s\n",
            control_message);

    is_interacting = params.interactive_first;
  }

  bool is_antiprompt = false;
  bool input_echo = false;
  bool need_to_save_session =
      !path_session.empty() && n_matching_session_tokens < embd_inp.size();

  int n_past = 0;
  int n_remain = params.n_predict;
  int n_consumed = 0;
  int n_session_consumed = 0;

  // the first thing we will do is to output the prompt, so set color
  // accordingly
  console_set_color(con_st, CONSOLE_COLOR_PROMPT);

  std::vector<llama_token> embd;

  // do one empty run to warm up the model
  {
    const std::vector<llama_token> tmp = {
        llama_token_bos(),
    };
    llama_eval(ctx, tmp.data(), tmp.size(), 0, params.n_threads);
    llama_reset_timings(ctx);
  }

  while ((n_remain != 0 && !is_antiprompt) || params.interactive) {
    // predict
    if (embd.size() > 0) {
      // Note: n_ctx - 4 here is to match the logic for commandline prompt
      // handling via
      // --prompt or --file which uses the same value.
      auto max_embd_size = n_ctx - 4;
      // Ensure the input doesn't exceed the context size by truncating embd if
      // necessary.
      if ((int)embd.size() > max_embd_size) {
        auto skipped_tokens = embd.size() - max_embd_size;
        console_set_color(con_st, CONSOLE_COLOR_ERROR);
        printf("<<input too long: skipped %zu token%s>>", skipped_tokens,
               skipped_tokens != 1 ? "s" : "");
        console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
        fflush(stdout);
        embd.resize(max_embd_size);
      }

      // infinite text generation via context swapping
      // if we run out of context:
      // - take the n_keep first tokens from the original prompt (via n_past)
      // - take half of the last (n_ctx - n_keep) tokens and recompute the
      // logits in batches
      if (n_past + (int)embd.size() > n_ctx) {
        const int n_left = n_past - params.n_keep;

        // always keep the first token - BOS
        n_past = std::max(1, params.n_keep);

        // insert n_left/2 tokens at the start of embd from last_n_tokens
        embd.insert(embd.begin(),
                    last_n_tokens.begin() + n_ctx - n_left / 2 - embd.size(),
                    last_n_tokens.end() - embd.size());

        // stop saving session if we run out of context
        path_session.clear();

        // printf("\n---\n");
        // printf("resetting: '");
        // for (int i = 0; i < (int) embd.size(); i++) {
        //     printf("%s", llama_token_to_str(ctx, embd[i]));
        // }
        // printf("'\n");
        // printf("\n---\n");
      }

      // try to reuse a matching prefix from the loaded session instead of
      // re-eval (via n_past)
      if (n_session_consumed < (int)session_tokens.size()) {
        size_t i = 0;
        for (; i < embd.size(); i++) {
          if (embd[i] != session_tokens[n_session_consumed]) {
            session_tokens.resize(n_session_consumed);
            break;
          }

          n_past++;
          n_session_consumed++;

          if (n_session_consumed >= (int)session_tokens.size()) {
            ++i;
            break;
          }
        }
        if (i > 0) {
          embd.erase(embd.begin(), embd.begin() + i);
        }
      }

      // evaluate tokens in batches
      // embd is typically prepared beforehand to fit within a batch, but not
      // always
      for (int i = 0; i < (int)embd.size(); i += params.n_batch) {
        int n_eval = (int)embd.size() - i;
        if (n_eval > params.n_batch) {
          n_eval = params.n_batch;
        }
        if (llama_eval(ctx, &embd[i], n_eval, n_past, params.n_threads)) {
          fprintf(stderr, "%s : failed to eval\n", __func__);
          return 1;
        }
        n_past += n_eval;
      }

      if (embd.size() > 0 && !path_session.empty()) {
        session_tokens.insert(session_tokens.end(), embd.begin(), embd.end());
        n_session_consumed = session_tokens.size();
      }
    }

    embd.clear();

    if ((int)embd_inp.size() <= n_consumed && !is_interacting) {
      // out of user input, sample next token
      const float temp = params.temp;
      const int32_t top_k =
          params.top_k <= 0 ? llama_n_vocab(ctx) : params.top_k;
      const float top_p = params.top_p;
      const float tfs_z = params.tfs_z;
      const float typical_p = params.typical_p;
      const int32_t repeat_last_n =
          params.repeat_last_n < 0 ? n_ctx : params.repeat_last_n;
      const float repeat_penalty = params.repeat_penalty;
      const float alpha_presence = params.presence_penalty;
      const float alpha_frequency = params.frequency_penalty;
      const int mirostat = params.mirostat;
      const float mirostat_tau = params.mirostat_tau;
      const float mirostat_eta = params.mirostat_eta;
      const bool penalize_nl = params.penalize_nl;

      // optionally save the session on first sample (for faster prompt loading
      // next time)
      if (!path_session.empty() && need_to_save_session &&
          !params.prompt_cache_ro) {
        need_to_save_session = false;
        llama_save_session_file(ctx, path_session.c_str(),
                                session_tokens.data(), session_tokens.size());
      }

      llama_token id = 0;

      {
        auto logits = llama_get_logits(ctx);
        auto n_vocab = llama_n_vocab(ctx);

        // Apply params.logit_bias map
        for (auto it = params.logit_bias.begin(); it != params.logit_bias.end();
             it++) {
          logits[it->first] += it->second;
        }

        std::vector<llama_token_data> candidates;
        candidates.reserve(n_vocab);
        for (llama_token token_id = 0; token_id < n_vocab; token_id++) {
          candidates.emplace_back(
              llama_token_data{token_id, logits[token_id], 0.0f});
        }

        llama_token_data_array candidates_p = {candidates.data(),
                                               candidates.size(), false};

        // Apply penalties
        float nl_logit = logits[llama_token_nl()];
        auto last_n_repeat =
            std::min(std::min((int)last_n_tokens.size(), repeat_last_n), n_ctx);
        llama_sample_repetition_penalty(
            ctx, &candidates_p,
            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
            last_n_repeat, repeat_penalty);
        llama_sample_frequency_and_presence_penalties(
            ctx, &candidates_p,
            last_n_tokens.data() + last_n_tokens.size() - last_n_repeat,
            last_n_repeat, alpha_frequency, alpha_presence);
        if (!penalize_nl) {
          logits[llama_token_nl()] = nl_logit;
        }

        if (temp <= 0) {
          // Greedy sampling
          id = llama_sample_token_greedy(ctx, &candidates_p);
        } else {
          if (mirostat == 1) {
            static float mirostat_mu = 2.0f * mirostat_tau;
            const int mirostat_m = 100;
            llama_sample_temperature(ctx, &candidates_p, temp);
            id = llama_sample_token_mirostat(ctx, &candidates_p, mirostat_tau,
                                             mirostat_eta, mirostat_m,
                                             &mirostat_mu);
          } else if (mirostat == 2) {
            static float mirostat_mu = 2.0f * mirostat_tau;
            llama_sample_temperature(ctx, &candidates_p, temp);
            id = llama_sample_token_mirostat_v2(
                ctx, &candidates_p, mirostat_tau, mirostat_eta, &mirostat_mu);
          } else {
            // Temperature sampling
            llama_sample_top_k(ctx, &candidates_p, top_k, 1);
            llama_sample_tail_free(ctx, &candidates_p, tfs_z, 1);
            llama_sample_typical(ctx, &candidates_p, typical_p, 1);
            llama_sample_top_p(ctx, &candidates_p, top_p, 1);
            llama_sample_temperature(ctx, &candidates_p, temp);
            id = llama_sample_token(ctx, &candidates_p);
          }
        }
        // printf("`%d`", candidates_p.size);

        last_n_tokens.erase(last_n_tokens.begin());
        last_n_tokens.push_back(id);
      }

      // replace end of text token with newline token when in interactive mode
      if (id == llama_token_eos() && params.interactive && !params.instruct) {
        id = llama_token_newline.front();
        if (params.antiprompt.size() != 0) {
          // tokenize and inject first reverse prompt
          const auto first_antiprompt =
              ::llama_tokenize(ctx, params.antiprompt.front(), false);
          embd_inp.insert(embd_inp.end(), first_antiprompt.begin(),
                          first_antiprompt.end());
        }
      }

      // add it to the context
      embd.push_back(id);

      // echo this to console
      input_echo = true;

      // decrement remaining sampling budget
      --n_remain;
    } else {
      // some user input remains from prompt or interaction, forward it to
      // processing
      while ((int)embd_inp.size() > n_consumed) {
        embd.push_back(embd_inp[n_consumed]);
        last_n_tokens.erase(last_n_tokens.begin());
        last_n_tokens.push_back(embd_inp[n_consumed]);
        ++n_consumed;
        if ((int)embd.size() >= params.n_batch) {
          break;
        }
      }
    }

    // display text
    if (input_echo) {
      if (on_token != nullptr) {
        for (auto id : embd) {
          if (!on_token(llama_token_to_str(ctx, id))) {
            n_remain = 0;
          }
        }
      }
    }
    // reset color to default if we there is no pending user input
    if (input_echo && (int)embd_inp.size() == n_consumed) {
      console_set_color(con_st, CONSOLE_COLOR_DEFAULT);
    }

    // if not currently processing queued inputs;
    if ((int)embd_inp.size() <= n_consumed) {

      // check for reverse prompt
      if (params.antiprompt.size()) {
        std::string last_output;
        for (auto id : last_n_tokens) {
          last_output += llama_token_to_str(ctx, id);
        }

        is_antiprompt = false;
        // Check if each of the reverse prompts appears at the end of the
        // output. If we're not running interactively, the reverse prompt might
        // be tokenized with some following characters so we'll compensate for
        // that by widening the search window a bit.
        for (std::string &antiprompt : params.antiprompt) {
          size_t extra_padding = params.interactive ? 0 : 2;
          size_t search_start_pos =
              last_output.length() >
                      static_cast<size_t>(antiprompt.length() + extra_padding)
                  ? last_output.length() -
                        static_cast<size_t>(antiprompt.length() + extra_padding)
                  : 0;

          if (last_output.find(antiprompt.c_str(), search_start_pos) !=
              std::string::npos) {
            if (params.interactive) {
              is_interacting = true;
              console_set_color(con_st, CONSOLE_COLOR_USER_INPUT);
            }
            is_antiprompt = true;
            fflush(stdout);
            break;
          }
        }
      }

      if (n_past > 0 && is_interacting) {
        if (params.instruct) {
          printf("\n> ");
        }

        std::string buffer;
        if (!params.input_prefix.empty()) {
          buffer += params.input_prefix;
          printf("%s", buffer.c_str());
        }

        std::string line;
        bool another_line = true;
        do {
          another_line = console_readline(con_st, line);
          buffer += line;
        } while (another_line);

        // done taking input, reset color
        console_set_color(con_st, CONSOLE_COLOR_DEFAULT);

        // Add tokens to embd only if the input buffer is non-empty
        // Entering a empty line lets the user pass control back
        if (buffer.length() > 1) {
          // append input suffix if any
          if (!params.input_suffix.empty()) {
            buffer += params.input_suffix;
            printf("%s", params.input_suffix.c_str());
          }

          // instruct mode: insert instruction prefix
          if (params.instruct && !is_antiprompt) {
            n_consumed = embd_inp.size();
            embd_inp.insert(embd_inp.end(), inp_pfx.begin(), inp_pfx.end());
          }

          auto line_inp = ::llama_tokenize(ctx, buffer, false);
          embd_inp.insert(embd_inp.end(), line_inp.begin(), line_inp.end());

          // instruct mode: insert response suffix
          if (params.instruct) {
            embd_inp.insert(embd_inp.end(), inp_sfx.begin(), inp_sfx.end());
          }

          n_remain -= line_inp.size();
        }

        input_echo = false; // do not echo this again
      }

      if (n_past > 0) {
        is_interacting = false;
      }
    }

    // end of text token
    if (!embd.empty() && embd.back() == llama_token_eos()) {
      if (params.instruct) {
        is_interacting = true;
      } else {
        fprintf(stderr, " [end of text]\n");
        break;
      }
    }

    // In interactive mode, respect the maximum number of tokens and drop back
    // to user input when reached.
    if (params.interactive && n_remain <= 0 && params.n_predict != -1) {
      n_remain = params.n_predict;
      is_interacting = true;
    }
  }

  if (!path_session.empty() && params.prompt_cache_all &&
      !params.prompt_cache_ro) {
    fprintf(stderr, "\n%s: saving final output to session file '%s'\n",
            __func__, path_session.c_str());
    llama_save_session_file(ctx, path_session.c_str(), session_tokens.data(),
                            session_tokens.size());
  }

  llama_print_timings(ctx);
  llama_free(ctx);
  llama_free_model(model);

  return 0;
}
