defmodule EmberEx.XCS.Graph.LLMGraphBuilder do
  @moduledoc """
  Specialized graph builder for LLM operations.
  
  Builds computation graphs optimized for Language Model operations by:
  1. Identifying LLM call boundaries within computation graphs
  2. Applying differential optimization (optimizing non-LLM parts)
  3. Supporting partial caching of deterministic subgraphs
  4. Enabling batch parallelization for multiple similar requests
  """
  
  require Logger
  alias EmberEx.XCS.JIT.LLMDetector
  alias EmberEx.XCS.Graph.Node
  
  @type t :: %__MODULE__{
    batch_size: pos_integer(),
    options: map()
  }
  
  defstruct [
    batch_size: 4,
    options: %{}
  ]
  
  @doc """
  Creates a new LLM graph builder with the given options.
  
  ## Parameters
  
  - opts: Configuration options
    - `:batch_size` - Default batch size for parallel operations (default: 4)
    - Additional options specific to LLM graph building
  
  ## Returns
  
  A new LLMGraphBuilder struct
  """
  @spec new(keyword()) :: t()
  def new(opts \\ []) do
    # Extract known options
    batch_size = Keyword.get(opts, :batch_size, 4)
    
    # Convert remaining options to a map
    options = opts |> Keyword.drop([:batch_size]) |> Map.new()
    
    %__MODULE__{
      batch_size: batch_size,
      options: options
    }
  end
  
  @doc """
  Builds an optimized computation graph for LLM operations.
  
  This function analyzes a target function or operator, identifies LLM call
  boundaries, and builds a graph that:
  - Optimizes deterministic preprocessing and postprocessing components
  - Preserves stochastic LLM calls as-is
  - Supports efficient caching and execution
  
  ## Parameters
  
  - target: Function or operator to build a graph for
  - inputs: Sample inputs for tracing execution paths
  - opts: Options including:
    - `:llm_nodes` - Pre-identified LLM nodes (if available)
    - `:recursive` - Whether to analyze nested operators (default: true)
    - `:optimize_prompt` - Whether to optimize prompt construction (default: true)
    - `:optimize_postprocess` - Whether to optimize result processing (default: true)
    - `:preserve_stochasticity` - Preserve randomness in execution (default: true)
  
  ## Returns
  
  A tuple of {result, graph} containing the execution result and optimized graph
  """
  @spec build_graph(any(), map(), keyword()) :: {any(), map()}
  def build_graph(target, inputs, opts \\ []) do
    # Extract options
    llm_nodes = Keyword.get(opts, :llm_nodes, [])
    recursive = Keyword.get(opts, :recursive, true)
    optimize_prompt = Keyword.get(opts, :optimize_prompt, true)
    optimize_postprocess = Keyword.get(opts, :optimize_postprocess, true)
    preserve_stochasticity = Keyword.get(opts, :preserve_stochasticity, true)
    
    # Track execution time for metrics
    start_time = :os.system_time(:millisecond) / 1000
    
    # Phase 1: If no LLM nodes provided, trace execution to identify them
    {traced_result, trace, identified_llm_nodes} = 
      if llm_nodes == [] do
        trace_and_identify_llm_nodes(target, inputs)
      else
        # Execute normally to get a result, but use provided LLM nodes
        result = execute_original(target, inputs)
        {result, [], llm_nodes}
      end
    
    # Phase 2: Construct optimized graph with LLM boundaries
    graph = build_optimized_graph(
      target, 
      trace, 
      identified_llm_nodes,
      recursive,
      optimize_prompt,
      optimize_postprocess,
      preserve_stochasticity
    )
    
    # Record build time
    build_time = :os.system_time(:millisecond) / 1000 - start_time
    Logger.debug("LLM graph built in #{build_time}ms")
    
    # Return both the executed result and the optimized graph
    {traced_result, graph}
  end
  
  # Private helper functions
  
  # Traces execution and identifies LLM nodes in the execution path
  defp trace_and_identify_llm_nodes(target, inputs) do
    # This would use a tracing mechanism to record execution
    # For now, we'll implement a simple execution and identification
    
    # In a real implementation, we would:
    # 1. Set up execution tracing hooks
    # 2. Execute the target with provided inputs
    # 3. Record all function calls and their relationships
    # 4. Analyze the trace to identify LLM operations
    
    # Execute the target to get a result
    result = execute_original(target, inputs)
    
    # For now, we'll return an empty trace and assume
    # the entire target is one node (simplified)
    # A real implementation would extract the actual execution graph
    empty_trace = []
    
    # Identify if the target itself is an LLM node
    identified_nodes = if LLMDetector.is_llm_operator?(target) do
      [target]
    else
      []
    end
    
    {result, empty_trace, identified_nodes}
  end
  
  # Builds an optimized graph with special handling of LLM nodes
  defp build_optimized_graph(target, trace, llm_nodes, recursive, optimize_prompt, optimize_postprocess, preserve_stochasticity) do
    # This would construct the actual computation graph with
    # special handling for LLM nodes
    
    # In a real implementation, we would:
    # 1. Convert the execution trace to a proper computation graph
    # 2. Mark LLM operation nodes as special (preserved)
    # 3. Apply optimizations to non-LLM parts of the graph
    # 4. Set up appropriate caching boundaries
    
    # For now, we'll construct a simplified graph structure
    # with the target as the root node
    
    # Determine node type based on LLM detection
    is_llm = Enum.member?(llm_nodes, target)
    node_type = if is_llm, do: :llm_operation, else: :standard_operation
    
    # Basic graph structure
    %{
      root: %{
        id: generate_node_id(),
        type: node_type,
        target: target,
        is_llm: is_llm,
        preserve_stochasticity: preserve_stochasticity,
        optimize_prompt: optimize_prompt && !is_llm,
        optimize_postprocess: optimize_postprocess && !is_llm,
        children: []  # In a real implementation, this would include child nodes
      },
      nodes: [],  # Would contain all nodes in a full implementation
      edges: [],  # Would contain all edges in a full implementation
      metadata: %{
        has_llm_nodes: length(llm_nodes) > 0,
        recursive: recursive,
        optimization_level: get_optimization_level(optimize_prompt, optimize_postprocess)
      }
    }
  end
  
  # Determines the optimization level based on options
  defp get_optimization_level(optimize_prompt, optimize_postprocess) do
    case {optimize_prompt, optimize_postprocess} do
      {true, true} -> :full
      {true, false} -> :prompt_only
      {false, true} -> :postprocess_only
      {false, false} -> :minimal
    end
  end
  
  # Generates a unique ID for graph nodes
  defp generate_node_id do
    :crypto.strong_rand_bytes(8) |> Base.encode16(case: :lower)
  end
  
  # Default execution function for the original target
  defp execute_original(target, inputs) when is_atom(target) do
    # Handle module-based operators
    target.call(inputs)
  end
  
  defp execute_original(target, inputs) when is_function(target) do
    # Handle function operators
    target.(inputs)
  end
  
  defp execute_original(target, inputs) do
    # Handle other types of targets through EmberEx's operator protocol
    EmberEx.Operators.Operator.call(target, inputs)
  end
end
