/* Copyright 2016 The TensorFlow Authors 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.
==============================================================================*/

#include <stdio.h>
#include <stdlib.h>
#include <memory>
#include <set>
#include <string>
#include <utility>
#include <vector>

#include "linenoise.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/checkpoint_reader.h"
#include "tensorflow/core/framework/graph.pb.h"
#include "tensorflow/core/framework/types.h"
#include "tensorflow/core/lib/core/errors.h"
#include "tensorflow/core/lib/strings/str_util.h"
#include "tensorflow/core/platform/env.h"
#include "tensorflow/core/platform/init_main.h"
#include "tensorflow/core/protobuf/config.pb.h"
#include "tensorflow/core/util/command_line_flags.h"
#include "tensorflow/tools/tfprof/internal/tfprof_options.h"
#include "tensorflow/tools/tfprof/internal/tfprof_stats.h"
#include "tensorflow/tools/tfprof/internal/tfprof_utils.h"
#include "tensorflow/tools/tfprof/tfprof_log.pb.h"

using tensorflow::str_util::Split;

void completion(const char* buf, linenoiseCompletions* lc) {
  tensorflow::string buf_str = tensorflow::string(buf);
  if (buf_str.find(" ") == buf_str.npos) {
    for (const char* opt : tensorflow::tfprof::kCmds) {
      if (tensorflow::string(opt).find(buf_str) == 0) {
        linenoiseAddCompletion(lc, opt);
      }
    }
    return;
  }

  tensorflow::string prefix;
  int last_dash = buf_str.find_last_of(' ');
  if (last_dash != tensorflow::string::npos) {
    prefix = buf_str.substr(0, last_dash + 1);
    buf_str = buf_str.substr(last_dash + 1, tensorflow::kint32max);
  }
  for (const char* opt : tensorflow::tfprof::kOptions) {
    if (tensorflow::string(opt).find(buf_str) == 0) {
      linenoiseAddCompletion(lc, (prefix + opt).c_str());
    }
  }
}

int main(int argc, char** argv) {
  tensorflow::string FLAGS_graph_path = "";
  tensorflow::string FLAGS_run_meta_path = "";
  tensorflow::string FLAGS_op_log_path = "";
  tensorflow::string FLAGS_checkpoint_path = "";
  tensorflow::int32 FLAGS_max_depth = 4;
  tensorflow::int64 FLAGS_min_bytes = 0;
  tensorflow::int64 FLAGS_min_micros = 0;
  tensorflow::int64 FLAGS_min_params = 0;
  tensorflow::int64 FLAGS_min_float_ops = 0;
  tensorflow::string FLAGS_device_regexes = ".*";
  tensorflow::string FLAGS_order_by = "name";
  tensorflow::string FLAGS_account_type_regexes = "Variable,VariableV2";
  tensorflow::string FLAGS_start_name_regexes = ".*";
  tensorflow::string FLAGS_trim_name_regexes = "";
  tensorflow::string FLAGS_show_name_regexes = ".*";
  tensorflow::string FLAGS_hide_name_regexes;
  bool FLAGS_account_displayed_op_only = false;
  tensorflow::string FLAGS_select = "params";
  bool FLAGS_viz = false;
  tensorflow::string FLAGS_dump_to_file = "";
  for (int i = 0; i < argc; i++) {
    fprintf(stderr, "%s\n", argv[i]);
  }

  std::vector<tensorflow::Flag> flag_list = {
      tensorflow::Flag("graph_path", &FLAGS_graph_path,
                       "GraphDef proto text file name"),
      tensorflow::Flag("run_meta_path", &FLAGS_run_meta_path,
                       "RunMetadata proto binary file name"),
      tensorflow::Flag("op_log_path", &FLAGS_op_log_path,
                       "tensorflow::tfprof::OpLog proto binary file name"),
      tensorflow::Flag("checkpoint_path", &FLAGS_checkpoint_path,
                       "TensorFlow Checkpoint file name"),
      tensorflow::Flag("max_depth", &FLAGS_max_depth, "max depth"),
      tensorflow::Flag("min_bytes", &FLAGS_min_bytes, "min_bytes"),
      tensorflow::Flag("min_micros", &FLAGS_min_micros, "min micros"),
      tensorflow::Flag("min_params", &FLAGS_min_params, "min params"),
      tensorflow::Flag("min_float_ops", &FLAGS_min_float_ops, "min float ops"),
      tensorflow::Flag("device_regexes", &FLAGS_device_regexes,
                       "device regexes"),
      tensorflow::Flag("order_by", &FLAGS_order_by, "order by"),
      tensorflow::Flag("account_type_regexes", &FLAGS_start_name_regexes,
                       "start name regexes"),
      tensorflow::Flag("trim_name_regexes", &FLAGS_trim_name_regexes,
                       "trim name regexes"),
      tensorflow::Flag("show_name_regexes", &FLAGS_show_name_regexes,
                       "show name regexes"),
      tensorflow::Flag("hide_name_regexes", &FLAGS_hide_name_regexes,
                       "hide name regexes"),
      tensorflow::Flag("account_displayed_op_only",
                       &FLAGS_account_displayed_op_only,
                       "account displayed op only"),
      tensorflow::Flag("select", &FLAGS_select, "select"),
      tensorflow::Flag("dump_to_file", &FLAGS_dump_to_file, "dump to file"),
  };
  tensorflow::string usage = tensorflow::Flags::Usage(argv[0], flag_list);
  bool parse_ok = tensorflow::Flags::Parse(&argc, argv, flag_list);
  if (!parse_ok) {
    printf("%s", usage.c_str());
    return (2);
  }
  tensorflow::port::InitMain(argv[0], &argc, &argv);

  fprintf(stderr, "%s\n", FLAGS_graph_path.c_str());

  std::vector<tensorflow::string> device_regexes =
      Split(FLAGS_device_regexes, ',', tensorflow::str_util::SkipEmpty());
  std::vector<tensorflow::string> account_type_regexes =
      Split(FLAGS_account_type_regexes, ',', tensorflow::str_util::SkipEmpty());
  std::vector<tensorflow::string> start_name_regexes =
      Split(FLAGS_start_name_regexes, ',', tensorflow::str_util::SkipEmpty());
  std::vector<tensorflow::string> trim_name_regexes =
      Split(FLAGS_trim_name_regexes, ',', tensorflow::str_util::SkipEmpty());
  std::vector<tensorflow::string> show_name_regexes =
      Split(FLAGS_show_name_regexes, ',', tensorflow::str_util::SkipEmpty());
  std::vector<tensorflow::string> hide_name_regexes =
      Split(FLAGS_hide_name_regexes, ',', tensorflow::str_util::SkipEmpty());
  std::vector<tensorflow::string> select =
      Split(FLAGS_select, ',', tensorflow::str_util::SkipEmpty());

  tensorflow::string cmd = "";
  if (argc == 1 && FLAGS_graph_path.empty()) {
    printf("1) go/tfprof: Tutorial.\n");
    printf("2) tfprof help: Detail help information.\n");
    printf(
        "3) tfprof --graph_path <GraphDef proto text file>: "
        "Profiling model structure, tensor shape and # parameters.\n");
    printf(
        "4) tfprof --graph_path <GraphDef proto text file> \\\n"
        "          --run_meta_path <RunMetadata proto binary file> \\\n"
        "          --op_log_path <tensorflow::tfprof::OpLog proto binary file> "
        "\\\n"
        "          --checkpoint_path <TensorFlow Checkpoint file>: "
        "Profiling everything!\n");
    return 0;
  } else if (argc > 1) {
    if (tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[3]) {
      tensorflow::tfprof::PrintHelp();
      return 0;
    }
    if (tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[0] ||
        tensorflow::string(argv[1]) == tensorflow::tfprof::kCmds[1]) {
      cmd = argv[1];
    }
  }

  printf("Reading Files...\n");
  std::unique_ptr<tensorflow::GraphDef> graph(new tensorflow::GraphDef());
  TF_CHECK_OK(tensorflow::tfprof::ReadGraphDef(tensorflow::Env::Default(),
                                               FLAGS_graph_path, graph.get()));

  std::unique_ptr<tensorflow::RunMetadata> run_meta(
      new tensorflow::RunMetadata());
  if (!ReadBinaryProto(tensorflow::Env::Default(), FLAGS_run_meta_path,
                       run_meta.get())
           .ok()) {
    run_meta.release();
  }

  std::unique_ptr<tensorflow::tfprof::OpLog> op_log(
      new tensorflow::tfprof::OpLog());
  if (!ReadBinaryProto(tensorflow::Env::Default(), FLAGS_op_log_path,
                       op_log.get())
           .ok()) {
    op_log.release();
  }

  std::unique_ptr<tensorflow::checkpoint::CheckpointReader> ckpt_reader;
  TF_Status* status = TF_NewStatus();
  if (!FLAGS_checkpoint_path.empty()) {
    ckpt_reader.reset(new tensorflow::checkpoint::CheckpointReader(
        FLAGS_checkpoint_path, status));
    if (TF_GetCode(status) != TF_OK) {
      fprintf(stderr, "%s\n", TF_Message(status));
      TF_DeleteStatus(status);
      return 1;
    }
    TF_DeleteStatus(status);
  }

  tensorflow::tfprof::TFStats tf_stat(std::move(graph), std::move(run_meta),
                                      std::move(op_log),
                                      std::move(ckpt_reader));
  tensorflow::tfprof::Options opts(
      FLAGS_max_depth, FLAGS_min_bytes, FLAGS_min_micros, FLAGS_min_params,
      FLAGS_min_float_ops, device_regexes, FLAGS_order_by, account_type_regexes,
      start_name_regexes, trim_name_regexes, show_name_regexes,
      hide_name_regexes, FLAGS_account_displayed_op_only, select, FLAGS_viz,
      FLAGS_dump_to_file);

  if (!cmd.empty()) {
    tf_stat.PrintGraph(cmd, opts);
    return 0;
  }

  linenoiseSetCompletionCallback(completion);
  linenoiseHistoryLoad(".tfprof_history.txt");

  for (char* line = nullptr; (line = linenoise("tfprof> ")) != nullptr;) {
    tensorflow::string line_s = tensorflow::string(line);
    free(line);

    if (line_s.empty()) {
      printf("%s", opts.ToString().c_str());
      continue;
    }
    linenoiseHistoryAdd(line_s.c_str());
    linenoiseHistorySave(".tfprof_history.txt");

    tensorflow::tfprof::Options new_opts = opts;
    tensorflow::Status s =
        tensorflow::tfprof::ParseCmdLine(line_s, &cmd, &new_opts);
    if (!s.ok()) {
      fprintf(stderr, "E: %s\n", s.ToString().c_str());
      continue;
    }
    if (cmd == tensorflow::tfprof::kCmds[2]) {
      opts = new_opts;
    } else if (cmd == tensorflow::tfprof::kCmds[3]) {
      tensorflow::tfprof::PrintHelp();
    } else {
      tf_stat.PrintGraph(cmd, new_opts);
    }
  }
  return 0;
}
