#pragma once

#include <cstdio>
#include <cstring>
#include <cerrno>
#include <istream>
#include <ostream>
#include <fstream>

#include <c10/core/Allocator.h>
#include <c10/core/Backend.h>

#include "caffe2/core/logging.h"
#include "caffe2/serialize/istream_adapter.h"
#include "caffe2/serialize/read_adapter_interface.h"

extern "C" {
typedef struct mz_zip_archive mz_zip_archive;
}

// PyTorch containers are a special zip archive with the following layout
// archive_name.zip contains:
//    archive_name/
//        version # a file with a single decimal number written in ascii,
//                # used to establish the version of the archive format
//        model.json # overall model description, this is a json output of
//                   # ModelDef from torch.proto
//        # the following names are by convention only, model.json will
//        # refer to these files by full names
//        tensors/
//          0 # flat storage for tensor data, meta-data about shapes, etc. is
//            # in model.json
//          1
//          ...
//        # code entries will only exist for modules that have methods attached
//        code/
//          archive_name.py # serialized torch script code (python syntax, using PythonPrint)
//          archive_name_my_submodule.py # submodules have separate files
//
// The PyTorchStreamWriter also ensures additional useful properties for these files
// 1. All files are stored uncompressed.
// 2. All files in the archive are aligned to 64 byte boundaries such that
//    it is possible to mmap the entire file and get an aligned pointer to
//    tensor data.
// 3. We universally write in ZIP64 format for consistency.

// The PyTorchStreamReader also provides additional properties:
// 1. It can read zip files that are created with common
//    zip tools. This means that even though our writer doesn't compress files,
//    the reader can still read files that were compressed.
// 2. It provides a getRecordOffset function which returns the offset into the
//    raw file where file data lives. If the file was written with PyTorchStreamWriter
//    it is guarenteed to be 64 byte aligned.

// PyTorchReader/Writer handle checking the version number on the archive format
// and ensure that all files are written to a archive_name directory so they
// unzip cleanly.

// When developing this format we want to pay particular attention to the
// following use cases:
//
// -- Reading --
// 1) Reading with full random access
//   a) Reading with file api's such as fread()
//   b) mmaping the file and jumping around the mapped region
// 2) Reading with 1-pass sequential access
//      -> A reader will need to build up a data structure of parsed structures
//         as it reads
//
// -- Writing --
// 1) Writing with full random access
// 2) Writing with 1-pass sequential access
//      -> We must take care not to require updating values that have already
//         been written. We place the variable-length index at the end and do
//         not put any indicies into the header to fulfill this constraint.

// The model.json, which contains all the metadata information,
// should be written as the last file. One reason is that the size of tensor data is
// usually stable. As long as the shape and type of the tensor do not change,
// the size of the data won't change. On the other sied, the size of the
// serialized model is likely to change, so we store it as the last record, and
// we don't need to move previous records when updating the model data.

// The zip format is sufficiently flexible to handle the above use-case.
// it puts its central directory at the end of the archive and we write
// model.json as the last file when writing after we have accumulated all
// other information.

namespace caffe2 {
namespace serialize {

constexpr uint64_t kMinSupportedFileFormatVersion = 0x1L;
constexpr uint64_t kMaxSupportedFileFormatVersion = 0x1L;

// Writer-specific constants
constexpr uint64_t kFieldAlignment = 64;

class CAFFE2_API PyTorchStreamReader final {
 public:
  explicit PyTorchStreamReader(const std::string& file_name);
  explicit PyTorchStreamReader(std::istream* in);
  explicit PyTorchStreamReader(std::unique_ptr<ReadAdapterInterface> in);

  // return dataptr, size
  std::tuple<at::DataPtr, size_t> getRecord(const std::string& name);
  size_t getRecordOffset(const std::string& name);
  bool hasRecord(const std::string& name);

  ~PyTorchStreamReader();

 private:
  void init();
  size_t read(uint64_t pos, char* buf, size_t n);
  void valid(const char* what);
  size_t getRecordID(const std::string& name);

  friend size_t
  istream_read_func(void* pOpaque, uint64_t file_ofs, void* pBuf, size_t n);
  std::unique_ptr<mz_zip_archive> ar_;
  std::string archive_name_;
  std::unique_ptr<ReadAdapterInterface> in_;
};

class CAFFE2_API PyTorchStreamWriter final {
 public:
  PyTorchStreamWriter(std::string archive_name, std::ostream* out=nullptr);
  PyTorchStreamWriter(std::ostream* out)
  : PyTorchStreamWriter("archive", out) {}

  void writeRecord(const std::string& name, const void* data, size_t size, bool compress = false);
  void writeEndOfFile();

  bool finalized() const {
    return finalized_;
  }

  const std::string& archiveName() {
    return archive_name_;
  }

  ~PyTorchStreamWriter();

 private:
   void valid(const char* what);
   size_t current_pos_ = 0;
   std::unique_ptr<mz_zip_archive> ar_;
   std::string archive_name_;
   std::ostream* out_;
   std::ofstream file_stream_;
   bool finalized_ = false;
   friend size_t ostream_write_func(void *pOpaque, uint64_t file_ofs, const void *pBuf, size_t n);
};

} // namespace serialize
} // namespace caffe2
