#pragma once
#include <torch/csrc/jit/runtime/graph_executor_impl.h>

namespace torch {
namespace jit {

TORCH_API Node* createFallbackGraph(
    Block* b,
    ArrayRef<Value*> inputs,
    Graph* g);

TORCH_API void replaceBlockWithFallbackGraph(Block* b);

struct ProfilingGraphExecutorImpl : public GraphExecutorImplBase {
  ProfilingGraphExecutorImpl(
      const std::shared_ptr<Graph>& graph,
      std::string function_name);

  ExecutionPlan getPlanFor(Stack& stack, size_t remaining_bailout_depth)
      override;
  GraphExecutorState getDebugState() override;
  ~ProfilingGraphExecutorImpl() override = default;

 private:
  void runProfilingInsensitiveOptimizations(std::shared_ptr<Graph>& graph);
  void runProfilingOptimizations(std::shared_ptr<Graph>& graph);
  void replaceFallbackGraphWithFallbackFunction(Block* b);
  std::unique_ptr<ProfilingRecord> pr_;
  c10::optional<ExecutionPlan>
      profiling_plan_; // plan to run in order to profiling the code
  c10::optional<ExecutionPlan> optimized_plan_;
  // fallback functions are inserted for tensorexpr fusion groups
  // and by specialize_autogradzero. Whenever, at runtime, input
  // tensor don't match profiled properties, fallback functions are called
  // They are the deoptimized version of the logic in fusion groups
  // and/or autograd.
  // The fallback functions are owned by a GraphExecutor instance
  // They only exist in the optimized graph which is a private property
  // of the GraphExecutor and only shared with InterpreterState
  std::vector<std::unique_ptr<Function>> fallback_functions_;
  c10::optional<size_t> remaining_bailout_depth_;
};

} // namespace jit
} // namespace torch
