#pragma once
#include <cstdio>
#include <cstdlib>

#include <memory>
#include <mutex>

#include <ATen/ATen.h>
#include <c10/util/Exception.h>
#include <torch/csrc/distributed/c10d/TraceUtils.h>
#include <torch/csrc/distributed/c10d/logger.hpp>
#include <optional>

namespace c10d {

#define DEFINE_CONSTANT(name, value) \
  static c10::IValue name = value;   \
  static std::string name##_str = value;
// Update whenever changing contents or formatting of the dump
// (minor when adding fields, major when changing existing fields)
// Also update both JSON and Pickle dumps to make use of the newly defined
// field(s).
DEFINE_CONSTANT(version_val, "2.10")
DEFINE_CONSTANT(entries_key, "entries")
DEFINE_CONSTANT(nccl_comm_key, "nccl_comm_state")
DEFINE_CONSTANT(comm_lib_version_key, "comm_lib_version")
DEFINE_CONSTANT(version_key, "version")
DEFINE_CONSTANT(pg_config_key, "pg_config")
DEFINE_CONSTANT(pg_status_key, "pg_status")
DEFINE_CONSTANT(record_id_key, "record_id")
DEFINE_CONSTANT(pg_id_key, "pg_id")
DEFINE_CONSTANT(pg_name_key, "process_group")
DEFINE_CONSTANT(collective_seq_id_key, "collective_seq_id")
DEFINE_CONSTANT(p2p_seq_id_key, "p2p_seq_id")
DEFINE_CONSTANT(is_p2p_key, "is_p2p")
DEFINE_CONSTANT(op_id_key, "op_id")
DEFINE_CONSTANT(profiling_name_key, "profiling_name")
DEFINE_CONSTANT(input_sizes_key, "input_sizes")
DEFINE_CONSTANT(input_dtypes_key, "input_dtypes")
DEFINE_CONSTANT(output_sizes_key, "output_sizes")
DEFINE_CONSTANT(output_dtypes_key, "output_dtypes")
DEFINE_CONSTANT(time_created_key, "time_created_ns")
DEFINE_CONSTANT(duration_key, "duration_ms")
DEFINE_CONSTANT(timeout_key, "timeout_ms")
DEFINE_CONSTANT(frames_key, "frames")
DEFINE_CONSTANT(state_key, "state")
DEFINE_CONSTANT(line_key, "line")
DEFINE_CONSTANT(name_key, "name")
DEFINE_CONSTANT(filename_key, "filename")
DEFINE_CONSTANT(retired_key, "retired")
DEFINE_CONSTANT(time_discovered_started_key, "time_discovered_started_ns")
DEFINE_CONSTANT(time_discovered_completed_key, "time_discovered_completed_ns")
DEFINE_CONSTANT(completed_state, "completed")
DEFINE_CONSTANT(scheduled_state, "scheduled")
DEFINE_CONSTANT(started_state, "started")
DEFINE_CONSTANT(thread_id_key, "thread_id")
DEFINE_CONSTANT(thread_name_key, "thread_name")
#undef DEFINE_CONSTANT

// Write NCCL debug info to local disk or any storage users define.
// There are some constrains we set for the debug info writer:
// 1. The writer should only be registered once.
// 2. Once registered, users cannot change it including un-register.
// 3. It is recommended to register the customized writer in the trainer setup,
//    If users don't register before calling launchAsyncDebugDump, then users
//    lose the chance to register (and the default writer will be
//    auto-registered).
class TORCH_API DebugInfoWriter {
 public:
  virtual ~DebugInfoWriter() = default;
  virtual void write(const std::string& trace);
  static DebugInfoWriter& getWriter(int rank);
  static void registerWriter(std::unique_ptr<DebugInfoWriter> writer);
  virtual std::string getWriterTarget() {
    return filename_;
  }

 protected:
  DebugInfoWriter(const std::string& namePrefix, int rank) {
    filename_ = c10::str(namePrefix, rank);
  }
  std::string filename_;

 private:
  static std::unique_ptr<DebugInfoWriter> writer_;
  static std::atomic<bool> hasWriterRegistered_;
};

template <typename EventType>
struct FlightRecorder {
  static FlightRecorder<EventType>* get() {
    // intentionally leak on exit
    // because this will hold python state that may get destructed
    static FlightRecorder<EventType>* instance =
        new FlightRecorder<EventType>();
    return instance;
  }
  FlightRecorder() {
    max_entries_ =
        getCvarInt({"TORCH_FR_BUFFER_SIZE", "TORCH_NCCL_TRACE_BUFFER_SIZE"}, 0);
    capture_cpp_stack_ = getCvarBool(
        {"TORCH_FR_CPP_STACK", "TORCH_NCCL_TRACE_CPP_STACK"}, false);
    enabled_ = max_entries_ > 0;
  }
  struct Entry {
    size_t id_; // incremented id in the trace buffer
                // used to figure out where in the circular entries
                // buffer this entry will be located to
                // update state information
    size_t pg_id_;
    std::tuple<std::string, std::string> pg_name_; // <group_name, group_desc>

    // collective_seq_id and p2p_seq_id refer to actual kernel launches (e.g. 1
    // per coalesced group).
    // collective_seq_id only increments for true collective operations (over
    // all ranks in the group). p2p_seq_id only increments over non-collective
    // operations in the group. op_id refers to logical operations (e.g. one per
    // op inside coalesced group)
    size_t collective_seq_id_;
    size_t p2p_seq_id_;
    size_t op_id_;
    std::string profiling_name_;

    std::shared_ptr<torch::CapturedTraceback> traceback_;
    // we borrow pointers to start_ and end_ so we can query the state
    // on reporting. However, once the event is completed, the call
    // to `complete` will clear these.
    EventType *start_, *end_;

    // timestamp when the entry was created, likely close to the time the work
    // was 'enqueued'- not necessarily started
    c10::time_t time_created_;

    // configured timeout for this entry
    c10::time_t timeout_ms_;

    // Is this a P2P event?
    bool isP2P_;

    std::optional<float> duration_;

    // timestamp when our CPU threads discovered that the kernel started.
    // will always be _after_ it actually started, and can be very late
    // if the watchdog thread got stuck on CUDA APIs.
    std::optional<c10::time_t> time_discovered_started_;

    // timestamp when our CPU threads discovered that the kernel completed.
    // will always be _after_ it actually completed, and can be the same time
    // as the discovery of the start if the watchdog thread is stuck on CUDA
    // APIs
    std::optional<c10::time_t> time_discovered_completed_;

    // size information for input/output tensors
    c10::SmallVector<int64_t, 4> input_dims_;
    std::vector<c10::ScalarType> input_dtypes_;
    c10::SmallVector<int64_t, 4> output_dims_;
    std::vector<c10::ScalarType> output_dtypes_;
    c10::SmallVector<int64_t, 8> sizes_; // flattened from inputs, outputs
    std::thread::id thread_id_;
    std::string thread_name_;
    bool retired_ = false; // is this work entry no longer in the workMetaList_?
                           // a retired but not completed event has timed out

    // Returns the traceback of current entry, in string form.
    // Note: `getTraceback` invokes `torch::symbolize`, which may need to
    // acquire the GIL. If you don't want to block the current thread or take
    // the risk of a GIL deadlock, you can use an asynchronous calling mechanism
    // like std::async.
    TORCH_API std::string getTraceback();
  };

  bool enabled_ = false;
  bool capture_cpp_stack_ = false;
  std::mutex mutex_;
  std::vector<Entry> entries_;
  size_t max_entries_ = 0;
  size_t next_ = 0;
  size_t id_ = 0;
  std::map<size_t, std::shared_ptr<ProcessGroupStatus>> all_pg_status_ = {};
  std::map<std::tuple<std::string, std::string>, std::vector<uint64_t>>
      pg_name_to_ranks_ = {};
  std::string comm_lib_version_;

  std::optional<size_t> record(
      size_t pg_id,
      const std::tuple<std::string, std::string>& pg_name,
      size_t collective_seq_id,
      size_t p2p_seq_id,
      size_t op_id,
      std::string profiling_name,
      const std::vector<at::Tensor>& inputs,
      const std::vector<at::Tensor>& outputs,
      EventType* start,
      EventType* end,
      std::chrono::milliseconds timeout_ms,
      std::shared_ptr<ProcessGroupStatus> pg_status,
      bool isP2P);

  TORCH_API void record_pg_ranks(
      const std::tuple<std::string, std::string>& pg_name,
      std::vector<uint64_t> ranks);

  void record_accelerator_version(const std::string comm_lib_version);

  void update_state(Entry& r);

  std::vector<Entry> dump_entries();

  // Returns the entry with the given id, if it exists. Otherwise, returns
  // std::nullopt.
  TORCH_API std::optional<Entry> getEntry(std::optional<size_t> id);

  /*
  Mark an Event as completed and free its events.
  This is called by the watchdog thread, and is asynchronous from the
  perspective of the main thread.
  compute_duration defaults to true since retire_id is only called in the
  watchdog thread, which is currently a place we call cuda APIs which may hang,
  but care should be taken to avoid computing duration in any function that must
  never hang. (timing must also be enabled for compute_duration - see
  TORCH_NCCL_ENABLE_TIMING).
  */
  TORCH_API void retire_id(
      std::optional<size_t> id,
      bool compute_duration = true);

  const c10::List<c10::IValue> getCollectiveTrace(
      bool includeStacktraces,
      bool onlyActive);

  // dump pg_entries
  const c10::Dict<c10::IValue, c10::IValue> getPgConfig();

  const std::map<std::string, std::map<std::string, std::string>>
  getPgConfigJson();

  // dump pg_status
  const c10::Dict<c10::IValue, c10::IValue> getPgStatus();

  const std::map<std::string, std::map<std::string, std::string>>
  getPgStatusJson();

  std::string dump_json(
      const std::optional<std::unordered_map<
          std::string,
          std::unordered_map<std::string, std::string>>>& extraDumpMap,
      bool includeCollectives,
      bool onlyActive);

  std::string dump(
      const std::optional<std::unordered_map<
          std::string,
          std::unordered_map<std::string, std::string>>>& extraDumpMap,
      bool includeCollectives,
      bool includeStackTraces,
      bool onlyActive);
};

// Dumps the fr traces and additional information about the Process
// Group.
TORCH_API std::string dump_fr_trace(
    bool includeCollectives,
    bool includeStackTraces,
    bool onlyActive);

// Dumps the fr traces and additional information about the Process
// Group in JSON formatted string.
// We don't include stack traces in JSON format as it is far too much data.
TORCH_API std::string dump_fr_trace_json(
    bool includeCollectives,
    bool onlyActive);
} // namespace c10d
