defmodule EmberEx.XCS.JIT.Strategies.TraceStrategy do
  @moduledoc """
  A JIT optimization strategy based on execution tracing.
  
  This strategy analyzes operation execution patterns and optimizes based on 
  observed execution traces. It's particularly effective for operations with 
  predictable execution flows.
  """
  
  alias EmberEx.Operators.Operator
  # Uncomment when needed
  # alias EmberEx.XCS.JIT.Analysis
  
  @typedoc """
  Analysis result containing execution trace information and optimization opportunities
  """
  @type analysis_result :: %{
    score: integer(),
    rationale: String.t(),
    execution_trace: list(map()),
    hot_paths: list(list(atom())),
    optimization_targets: list(map())
  }
  
  @doc """
  Returns the name of this strategy.
  """
  @spec name() :: String.t()
  def name, do: "trace"
  
  @doc """
  Analyzes an operator by tracing its execution with the given inputs.
  
  This function:
  1. Executes the operator with the provided inputs
  2. Collects the execution trace (calls, data flow, execution times)
  3. Identifies hot paths and optimization opportunities
  4. Returns an analysis result with a score indicating optimization potential
  
  ## Parameters
    * `operator` - The operator to analyze
    * `inputs` - Sample inputs for trace execution
    
  ## Returns
    * An analysis result containing trace information and optimization score
  """
  @spec analyze(Operator.t(), map()) :: analysis_result()
  def analyze(operator, inputs) do
    # Install tracing hooks to collect execution data
    {trace_data, _execution_time} = trace_execution(operator, inputs)
    
    # Analyze the trace data
    hot_paths = identify_hot_paths(trace_data)
    optimization_targets = identify_optimization_targets(trace_data, hot_paths)
    
    # Calculate optimization score based on potential gains
    {score, rationale} = calculate_score(trace_data, hot_paths, optimization_targets)
    
    %{
      score: score,
      rationale: rationale,
      execution_trace: trace_data,
      hot_paths: hot_paths,
      optimization_targets: optimization_targets
    }
  end
  
  @doc """
  Compiles an optimized version of the operator based on trace analysis.
  
  This function:
  1. Uses the analysis results to create an execution plan
  2. Specializes the operator for the identified execution patterns
  3. Applies trace-based optimizations like function inlining and constant folding
  4. Returns an optimized operator that follows the same interface
  
  ## Parameters
    * `operator` - The original operator to optimize
    * `inputs` - Sample inputs used for optimization planning
    * `analysis` - Analysis results from the `analyze/2` function
  
  ## Returns
    * An optimized operator with improved performance characteristics
  """
  @spec compile(Operator.t(), map(), any()) :: Operator.t()
  def compile(operator, _inputs, analysis) do
    # First validate that we have a proper analysis result
    case analysis do
      # Analysis is a valid map with a score
      %{score: score} when is_integer(score) ->
        if score < 30 do
          # Not worth optimizing
          operator
        else
          # Apply optimizations based on analysis
          optimized_operator = apply_optimizations(operator, analysis)
          
          # Deal with potential nil return
          if optimized_operator == nil do
            require Logger
            Logger.warning("Trace strategy could not produce a valid optimized operator, returning original")
            operator
          else
            optimized_operator
          end
        end
        
      # Invalid or missing analysis structure
      _ ->
        require Logger
        Logger.warning("TraceStrategy received invalid analysis: #{inspect(analysis)}")
        operator
    end
  rescue
    error ->
      require Logger
      Logger.warning("TraceStrategy error during compilation: #{inspect(error)}")
      operator
  end
  
  # Private implementation functions
  
  @spec trace_execution(Operator.t(), map()) :: {list(map()), float()}
  defp trace_execution(operator, inputs) do
    # Start timer
    start_time = :os.system_time(:microsecond)
    
    # Create a trace context to collect data
    trace_ctx = %{
      calls: [],
      data_flow: [],
      current_path: []
    }
    
    # Execute with tracing
    trace_result = with_tracing(operator, inputs, trace_ctx)
    
    # End timer
    end_time = :os.system_time(:microsecond)
    execution_time = (end_time - start_time) / 1000.0
    
    {trace_result.calls, execution_time}
  end
  
  @spec with_tracing(Operator.t(), map(), map()) :: map()
  defp with_tracing(operator, inputs, trace_ctx) do
    # In a real implementation, we would use metaprogramming or instrumentation
    # to track calls through the operator's execution path
    
    # For now, we just execute the operator and return a minimal trace
    result = Operator.call(operator, inputs)
    
    # Add a synthetic trace entry
    updated_calls = [
      %{
        target: operator,
        inputs: inputs,
        result: result,
        execution_time: 1.0, # Mock time
        path: trace_ctx.current_path
      } 
      | trace_ctx.calls
    ]
    
    %{trace_ctx | calls: updated_calls}
  end
  
  @spec identify_hot_paths(list(map())) :: list(list(atom()))
  defp identify_hot_paths(_trace_data) do
    # In a real implementation, we would analyze the trace data
    # to find frequently executed paths or paths with high execution time
    
    # For now, return a mock hot path
    [[:prepare_inputs, :compute, :process_outputs]]
  end
  
  @spec identify_optimization_targets(list(map()), list(list(atom()))) :: list(map())
  defp identify_optimization_targets(_trace_data, _hot_paths) do
    # Find optimization opportunities based on trace patterns
    # This could include function inlining, constant folding, etc.
    
    # Mock optimization targets
    [
      %{
        type: :inline_function,
        target: :prepare_inputs,
        reason: "Frequently called with similar arguments"
      },
      %{
        type: :constant_folding,
        target: :compute,
        reason: "Some computation paths use constant inputs"
      }
    ]
  end
  
  @spec calculate_score(list(map()), list(list(atom())), list(map())) :: {integer(), String.t()}
  defp calculate_score(_trace_data, hot_paths, optimization_targets) do
    # Calculate a score based on the potential optimization impact
    
    # Basic algorithm: score based on number of hot paths and optimization targets
    base_score = length(hot_paths) * 10 + length(optimization_targets) * 15
    
    # Cap the score at 100
    score = min(base_score, 100)
    
    rationale = "Found #{length(hot_paths)} hot execution paths and " <>
                "#{length(optimization_targets)} optimization opportunities"
    
    {score, rationale}
  end
  
  @spec apply_optimizations(Operator.t(), analysis_result()) :: Operator.t()
  defp apply_optimizations(operator, _analysis) do
    # Apply each optimization target to the operator
    
    # This is where the actual optimization would happen
    # For now, just return the original operator
    operator
  end
  
  # This function was removed as it was unused, but kept as a comment 
  # for potential future implementation
  #
  # @spec wrap_operator(Operator.t(), Operator.t(), analysis_result()) :: Operator.t()
  # defp wrap_operator(optimized_op, original_op, _analysis) do
  #   # Create a wrapper that maintains the original interface
  #   # but delegates to the optimized implementation
  #   
  #   # In a full implementation, this would create a proper wrapper
  #   # For simplicity, we'll just create a function that wraps the optimized op
  #   # and ensures it implements the Operator protocol
  #   case optimized_op do
  #     # If it's already an operator struct, return it
  #     %{__struct__: _} = op -> op 
  #     
  #     # If it's a function, wrap it in a MapOperator
  #     fun when is_function(fun, 1) ->
  #       MapOperator.new(fun)
  #       
  #     # As a fallback, return the original operator
  #     _ -> original_op
  #   end
  # end
end
