// Copyright (c) 2019 PaddlePaddle 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 "paddle/fluid/lite/model_parser/model_parser.h"
#include <fstream>
#include "paddle/fluid/lite/scope.h"
#include "paddle/fluid/lite/tensor.h"
#include "paddle/fluid/lite/variable.h"

namespace paddle {
namespace lite {

int SizeOfType(framework::proto::VarType::Type type) {
  using Type = framework::proto::VarType::Type;
  switch (static_cast<int>(type)) {
#define DO(desc, type)            \
  case Type::VarType_Type_##desc: \
    return sizeof(type);
    DO(BOOL, bool);
    DO(FP16, float);
    DO(FP32, float);
    DO(INT8, int8_t);
    DO(INT32, int);
    DO(INT64, int64_t);
#undef DO
    default:
      LOG(FATAL) << "unknown data type";
  }
}

void TensorFromStream(std::istream &is, lite::Tensor *tensor) {
  using Type = framework::proto::VarType::Type;
  uint32_t version;
  is.read(reinterpret_cast<char *>(&version), sizeof(version));
  CHECK_EQ(version, 0U) << "Only version 0 is supported";
  // read tensor desc
  framework::proto::VarType::TensorDesc desc;
  {
    // int32_t size
    // proto buffer
    int32_t size;
    is.read(reinterpret_cast<char *>(&size), sizeof(size));
    std::unique_ptr<char[]> buf(new char[size]);
    is.read(reinterpret_cast<char *>(buf.get()), size);
    CHECK(desc.ParseFromArray(buf.get(), size)) << "Cannot parse tensor desc";
  }

  // read tensor
  std::vector<int64_t> dims;
  dims.reserve(static_cast<size_t>(desc.dims().size()));
  std::copy(desc.dims().begin(), desc.dims().end(), std::back_inserter(dims));
  tensor->Resize(dims);
  void *buf;
  size_t size = product(tensor->dims()) * SizeOfType(desc.data_type());
  // alllocate memory
  switch (static_cast<int>(desc.data_type())) {
#define DO(desc, type)                  \
  case Type::VarType_Type_##desc:       \
    buf = tensor->mutable_data<type>(); \
    break;
    DO(BOOL, bool);
    DO(FP32, float);
    DO(INT8, int8_t);
    DO(INT16, int16_t);
    DO(INT32, int32_t);
    DO(INT64, int64_t);
#undef DO
    default:
      LOG(FATAL) << "unknown type";
  }

  is.read(static_cast<char *>(buf), size);
}

void LoadLoDTensor(std::istream &is, Variable *var) {
  auto *tensor = var->GetMutable<lite::Tensor>();
  uint32_t version;
  is.read(reinterpret_cast<char *>(&version), sizeof(version));
  LOG(INFO) << "model version " << version;

  // Load LoD information
  uint64_t lod_level;
  is.read(reinterpret_cast<char *>(&lod_level), sizeof(lod_level));
  auto &lod = *tensor->mutable_lod();
  lod.resize(lod_level);
  for (uint64_t i = 0; i < lod_level; ++i) {
    uint64_t size;
    is.read(reinterpret_cast<char *>(&size), sizeof(size));
    std::vector<size_t> tmp(size / sizeof(size_t));
    is.read(reinterpret_cast<char *>(tmp.data()),
            static_cast<std::streamsize>(size));
    lod[i] = tmp;
  }

  TensorFromStream(is, tensor);
}

// TODO(Superjomn) support SelectedRows.

void ReadBinaryFile(const std::string &filename, std::string *contents) {
  std::ifstream fin(filename, std::ios::in | std::ios::binary);
  CHECK(fin.is_open()) << "Cannot open file " << filename;
  fin.seekg(0, std::ios::end);
  auto size = fin.tellg();
  contents->clear();
  contents->resize(size);
  fin.seekg(0, std::ios::beg);
  fin.read(&(contents->at(0)), contents->size());
  fin.close();
}

std::unique_ptr<framework::proto::ProgramDesc> LoadProgram(
    const std::string &path) {
  std::string desc_str;
  ReadBinaryFile(path, &desc_str);
  std::unique_ptr<framework::proto::ProgramDesc> main_program(
      new framework::proto::ProgramDesc);
  main_program->ParseFromString(desc_str);
  return main_program;
}

void LoadParams(const std::string &path) {}

void LoadModel(const std::string &model_dir, Scope *scope) {
  const std::string prog_path = model_dir + "/__model__";
  auto prog = LoadProgram(prog_path);

  auto main_block = prog->blocks(0);
  for (auto &var : main_block.vars()) {
    std::string file_path = model_dir + "/" + var.name();
    std::ifstream file(file_path);
    LoadLoDTensor(file, scope->Var(var.name()));
  }
}

}  // namespace lite
}  // namespace paddle