#pragma once

#include <ATen/core/qualified_name.h>
#include <string>
#include <string_view>
#include <utility>
#include <vector>

#include <ATen/Utils.h>
#include <ATen/core/ivalue.h>
#include <ATen/core/jit_type.h>
#include <c10/util/ArrayRef.h>
#include <c10/util/FbcodeMaps.h>
#include <c10/util/intrusive_ptr.h>
#include <torch/csrc/Export.h>

namespace torch::jit {

// See Python's pickletools.py for a detailed description of each of these codes
enum class PickleOpCode : char {
  MARK = '(',
  STOP = '.',
  POP = '0',
  POP_MARK = '1',
  DUP = '2',
  FLOAT = 'F',
  INT = 'I',
  BININT = 'J',
  BININT1 = 'K',
  LONG = 'L',
  BININT2 = 'M',
  NONE = 'N',
  PERSID = 'P',
  BINPERSID = 'Q',
  REDUCE = 'R',
  STRING = 'S',
  BINSTRING = 'T',
  SHORT_BINSTRING = 'U',
  // NB: Avoid using UNICODE as it is a macro in the Windows API
  UNICODE_ = 'V',
  BINUNICODE = 'X',
  APPEND = 'a',
  BUILD = 'b',
  GLOBAL = 'c',
  DICT = 'd',
  EMPTY_DICT = '}',
  APPENDS = 'e',
  GET = 'g',
  BINGET = 'h',
  INST = 'i',
  LONG_BINGET = 'j',
  LIST = 'l',
  EMPTY_LIST = ']',
  OBJ = 'o',
  PUT = 'p',
  BINPUT = 'q',
  LONG_BINPUT = 'r',
  SETITEM = 's',
  TUPLE = 't',
  EMPTY_TUPLE = ')',
  SETITEMS = 'u',
  BINFLOAT = 'G',

  // Protocol 2
  PROTO = char('\x80'),
  NEWOBJ = '\x81',
  EXT1 = '\x82',
  EXT2 = '\x83',
  EXT4 = '\x84',
  TUPLE1 = '\x85',
  TUPLE2 = '\x86',
  TUPLE3 = '\x87',
  NEWTRUE = '\x88',
  NEWFALSE = '\x89',
  LONG1 = '\x8a',
  LONG4 = '\x8b',

  // Protocol 3 (Python 3.x)
  BINBYTES = 'B',
  SHORT_BINBYTES = 'C',

  // Protocol 4
  SHORT_BINUNICODE = char('\x8c'),
  BINUNICODE8 = '\x8d',
  BINBYTES8 = '\x8e',
  EMPTY_SET = '\x8f',
  ADDITEMS = '\x90',
  FROZENSET = '\x91',
  NEWOBJ_EX = '\x92',
  STACK_GLOBAL = '\x93',
  MEMOIZE = '\x94',
  FRAME = '\x95'
};

using ::c10::IValue;

struct WriteableTensorData {
  const char* data() const {
    return static_cast<const char*>(tensor_.storage().data());
  }
  size_t sizeInBytes() const {
    return size_;
  }
  size_t nbytes() const {
    return tensor_.storage().nbytes();
  }
  bool storageHasDeleter() const {
    return tensor_.storage().data_ptr().get_context() != nullptr;
  }

 private:
  friend TORCH_API WriteableTensorData
  getWriteableTensorData(const at::Tensor& tensor, bool to_cpu);
  at::Tensor tensor_;
  uint64_t size_;
};

class TORCH_API Pickler {
  AT_DISALLOW_COPY_AND_ASSIGN(Pickler);

 public:
  Pickler(std::function<void(const char*, size_t)> writer)
      : Pickler(std::move(writer), nullptr, nullptr, nullptr) {}

  // NOLINTNEXTLINE(cppcoreguidelines-pro-type-member-init)
  Pickler(
      std::function<void(const char*, size_t)> writer,
      std::vector<at::Tensor>* tensor_table,
      std::function<c10::QualifiedName(const c10::ClassTypePtr&)> type_renamer,
      std::vector<c10::ClassTypePtr>* memoized_class_types,
      std::function<std::string(const at::Tensor&)> get_tensor_id = nullptr,
      bool tag_aggregates = true)
      : writer_(std::move(writer)),
        tensor_table_(tensor_table),
        type_renamer_(std::move(type_renamer)),
        memoized_class_types_(memoized_class_types),
        get_tensor_id_(std::move(get_tensor_id)),
        tag_aggregates_(tag_aggregates) {}
  ~Pickler();

  // Push protocol onto the stack
  void protocol();

  // Push STOP PickleOpCode onto the stack
  void stop();

  void pushIValue(const IValue& ivalue);

  void startTuple();
  void endTuple();

  const std::vector<at::Tensor>& tensorData() {
    return tensor_data_;
  }

  void pushEmptyDict();
  void pushDict(const IValue& ivalue);
  void pushInt(int64_t value);
  void pushLong(const std::string& data);

 private:
  void pushIValueImpl(const IValue& ivalue);
  void startTypeTag();
  void endTypeTag(const IValue& value);
  void pushBool(bool value);
  void pushDouble(double value);
  void pushComplexDouble(const IValue& value);
  void pushGenericList(const IValue& ivalue);
  void pushIntList(const IValue& ivalue);
  void pushList(const IValue& ivalue);
  void pushTensor(const IValue& ivalue);
  void pushTensorReference(const IValue& ivalue);
  void pushLiteralTensor(const IValue& ivalue);
  void pushLiteralSparseTensor(const at::Tensor& tensor);
  void pushTuple(const IValue& ivalue);
  void pushString(const std::string& string);
  void pushDevice(const IValue& ivalue);
#ifdef USE_DISTRIBUTED
  void pushRRef(const IValue& ivalue);
#endif
  // unmemoized version
  void pushStringImpl(const std::string& string);
  void pushStorageOfTensor(const at::Tensor& tensor);

  void pushBinGet(uint32_t memo_id);
  void pushSpecializedList(
      const IValue& ivalue,
      const char* list_name,
      const std::function<void(const IValue&)>& item_pusher);
  void pushGlobal(std::string_view module_name, std::string_view class_name);
  // raw string data is appended directly to the byte stream
  void pushBytes(const std::string& string);
  void pushTensorData(const at::Tensor& tensor);

  // Add a BINPUT op and return the memoization id used
  size_t pushNextBinPut();

  const void* getPointer(const IValue& ivalue);

  // Caller checks that bufferPos_ > 0
  void flushNonEmpty() {
    writer_(buffer_.data(), bufferPos_);
    bufferPos_ = 0;
  }

  void flush() {
    if (bufferPos_ != 0) {
      flushNonEmpty();
    }
  }

  // These convert values to bytes and add them to the stack (NB: since T is to
  // the left of a '::', its type cannot be deduced by the compiler so one must
  // explicitly instantiate the template, i.e. push<int>(int) works, push(int)
  // does not)
  static constexpr size_t kBufferSize = 256;
  template <typename T>
  void push(std::common_type_t<T> value) {
    const char* begin = reinterpret_cast<const char*>(&value);
    if (bufferPos_ + sizeof(T) > buffer_.size()) {
      flushNonEmpty();
    }
    static_assert(sizeof(T) <= kBufferSize, "Buffer size assumption");
    memcpy(buffer_.data() + bufferPos_, begin, sizeof(T));
    bufferPos_ += sizeof(T);
  }

  // Stream to write binary data to
  // Code shouldn't call writer_ directly without first flushing.
  std::function<void(const char*, size_t)> writer_;

  // Buffer to avoid calling a writer_ on a per-byte basis.
  std::array<char, kBufferSize> buffer_;
  size_t bufferPos_{0};

  // Stack of opcodes/data
  std::vector<char> stack_;

  // External table of tensors to serialize. If this is missing, then tensors
  // are serialized directly into the pickle
  std::vector<at::Tensor>* tensor_table_;

  // TODO: only use this if necessary (add a pass to find all shared ivalues,
  // and only memoize those)
  uint32_t memo_id_ = 0;

  // Memoization of IValues that have been written (index in table is used for
  // BINPUT opcodes) to enable shared references
  c10::FastMap<const void*, uint32_t> memoized_ivalue_map_;

  // because we de-dup ivalues based on their raw pointer address in the above
  // map we need to keep all the memoized values alive during the pickle.
  // Otherwise, it is possible that a raw address gets reused for another
  // object, and we will alias it to the old object at that address.
  std::vector<IValue> memoized_ivalues_;

  std::function<c10::QualifiedName(const c10::ClassTypePtr&)> type_renamer_;

  // List of all the types that it wrote, inspect from the IValues it wrote.
  std::vector<c10::ClassTypePtr>* memoized_class_types_;

  // Function to grab next id_name for tensor storage, function is responsible
  // for returning unique ids
  std::function<std::string(const at::Tensor&)> get_tensor_id_;

  // List of tensor storages to serialize in the same binary as the pickle data
  // similar to ivalues, they are memoized using BINPUT
  std::vector<at::Tensor> tensor_data_;
  c10::FastMap<const void*, uint32_t> memoized_storage_map_;

  c10::FastMap<std::string, uint32_t> memoized_globals_map_;
  c10::FastMap<std::string, uint32_t> memoized_strings_map_;
  c10::FastMap<std::string, uint32_t> memoized_devices_map_;
  // when true, List and Dict objects will be wrapped in a
  // torch.jit._pickle.restore_type_tag call to correctly set the dynamic
  // TorchScript type for the object. When true the thing unpickling must have
  // torch installed.
  bool tag_aggregates_;
};

// returns a (tensor, record_size) for a tensor, converting it to a CPU tensor
// if it was CUDA and to_cpu is True.
TORCH_API WriteableTensorData
getWriteableTensorData(const at::Tensor& tensor, bool to_cpu = true);

// if the cls has __getstate__/__setstate__
// assert they have the right schema and return true,
// otherwise return false
bool checkHasValidSetGetState(const std::shared_ptr<c10::ClassType>& cls);

// Declare BackendMeta serialization and deserialization function pointer types.
using BackendMetaPtr = std::function<
    void(const at::Tensor&, std::unordered_map<std::string, bool>&)>;

// A allowlist of device type, currently available is PrivateUse1
inline std::unordered_set<c10::DeviceType>& GetBackendMetaAllowlist() {
  static std::unordered_set<c10::DeviceType> DeviceTypeAllowlist{
      c10::DeviceType::PrivateUse1};
  return DeviceTypeAllowlist;
}

// Dynamically obtain serialization function pairs
// that require the corresponding backend.
inline std::array<
    std::optional<std::pair<BackendMetaPtr, BackendMetaPtr>>,
    at::COMPILE_TIME_MAX_DEVICE_TYPES>&
GetBackendMetaSerialization() {
  // The array to save function pointer for BackendMeta serialization.
  // key is the DeviceType, value is std::pair obj.
  // value.first represent get function and value.seconde represent set function
  static std::array<
      std::optional<std::pair<BackendMetaPtr, BackendMetaPtr>>,
      at::COMPILE_TIME_MAX_DEVICE_TYPES>
      BackendMetaSerialization;
  return BackendMetaSerialization;
}

// Register function pointer of Tensor BackendMetadata for serialization.
TORCH_API inline void TensorBackendMetaRegistry(
    c10::DeviceType t,
    const BackendMetaPtr& get_fptr,
    const BackendMetaPtr& set_fptr) {
  // allowlist verification
  // Only if the devicetype is in the allowlist,
  // we allow the serialization extension to be registered for backendmeta data.
  const auto& DeviceTypeAllowlist = GetBackendMetaAllowlist();
  TORCH_CHECK(
      DeviceTypeAllowlist.find(t) != DeviceTypeAllowlist.end(),
      "It is not allowed to register the serialization method ",
      "of backendMeta data for PrivateUse1. ",
      "If you have related serialization requirements, ",
      "please expand the allowlist");
  // Register function pointer
  int device_type = static_cast<int>(t);
  auto& BackendMetaSerialization = GetBackendMetaSerialization();
  TORCH_CHECK(
      !BackendMetaSerialization[device_type].has_value(),
      "The tensor BackendMeta serialization function pointer for ",
      t,
      " has been registered.");
  BackendMetaSerialization[device_type] =
      std::optional<std::pair<BackendMetaPtr, BackendMetaPtr>>(
          std::make_pair(get_fptr, set_fptr));
}

// Return a map of Tensor Metadata which including BackendMetaData for
// serialization. For now, it only takes care of `conj` and `neg` bit.
inline std::unordered_map<std::string, bool> getTensorMetadata(
    const at::Tensor& t) {
  // We don't support serializing `ZeroTensor` as it is not public
  // facing yet.
  TORCH_CHECK(
      !t._is_zerotensor(),
      "ZeroTensor is not serializable,",
      " please file an issue if required.");
  std::unordered_map<std::string, bool> metadata{};

  // Only add meta-data if the value is not default.
  if (t.is_conj()) {
    metadata["conj"] = true;
  }
  if (t.is_neg()) {
    metadata["neg"] = true;
  }
  // Only add BackendMetaData for custom backend if the function pointer is
  // registered.
  int device_type = static_cast<int>(t.device().type());
  const auto& BackendMetaSerialization = GetBackendMetaSerialization();
  if (BackendMetaSerialization[device_type].has_value()) {
    // Pass the tensor and metadata map references as parameters to the custom
    // serialization function.
    BackendMetaPtr fptr = BackendMetaSerialization[device_type].value().first;
    fptr(t, metadata);
  }
  return metadata;
}

// set Tensor Metadata based on the map.
// Refer: getTensorMetadata
inline void setTensorMetadata(
    const at::Tensor& t,
    std::unordered_map<std::string, bool> metadata) {
  auto iter_end = metadata.end();
  auto iter_temp = metadata.find("conj");
  if (iter_temp != iter_end) {
    t._set_conj(true);
    metadata.erase(iter_temp);
  }
  iter_temp = metadata.find("neg");
  if (iter_temp != iter_end) {
    t._set_neg(true);
    metadata.erase(iter_temp);
  }
  // Only set BackendMetaData for custom backend if the function pointer is
  // registered.
  int device_type = static_cast<int>(t.device().type());
  const auto& BackendMetaSerialization = GetBackendMetaSerialization();
  if (BackendMetaSerialization[device_type].has_value()) {
    // Pass the tensor and metadata map references as parameters to the custom
    // deserialization function.
    BackendMetaPtr fptr = BackendMetaSerialization[device_type].value().second;
    fptr(t, metadata);
  }
}

// set Tensor metadata based on the map.
// NOTE: This overload is required by unpickler.cpp
inline void setTensorMetadata(
    const at::Tensor& t,
    const c10::Dict<c10::IValue, c10::IValue>& metadata_idict) {
  std::unordered_map<std::string, bool> metadata;
  for (auto& pair : metadata_idict) {
    auto key = *pair.key().toString();
    metadata[key] = pair.value().toBool();
  }
  setTensorMetadata(t, std::move(metadata));
}

} // namespace torch::jit
