defmodule EmberEx.XCS.JIT.BatchProcessor do
  @moduledoc """
  Handles batch processing of LLM operations to optimize parallelization.
  
  This module provides intelligent batching strategies that:
  1. Group similar LLM requests to optimize resource usage
  2. Dynamically adjust batch size based on system load
  3. Balance parallelism with overhead costs
  4. Provide automatic fallback for failed batch operations
  """
  
  require Logger
  alias EmberEx.XCS.JIT.Profiler
  
  @type batch_options :: %{
    batch_size: pos_integer(),
    max_wait_ms: non_neg_integer(),
    similarity_threshold: float(),
    dynamic_sizing: boolean()
  }
  
  @doc """
  Creates a new batch processor with the specified options.
  
  ## Parameters
  
  - opts: Batch processing options
    - :batch_size - Maximum number of operations in a batch (default: 5)
    - :max_wait_ms - Maximum time to wait for batch to fill (default: 50)
    - :similarity_threshold - Threshold for input similarity grouping (default: 0.7)
    - :dynamic_sizing - Whether to adjust batch size dynamically (default: true)
  
  ## Returns
  
  Batch options map
  """
  @spec new(keyword()) :: batch_options()
  def new(opts \\ []) do
    %{
      batch_size: Keyword.get(opts, :batch_size, 5),
      max_wait_ms: Keyword.get(opts, :max_wait_ms, 50),
      similarity_threshold: Keyword.get(opts, :similarity_threshold, 0.7),
      dynamic_sizing: Keyword.get(opts, :dynamic_sizing, true)
    }
  end
  
  @doc """
  Process a batch of operations with the same model but different inputs.
  
  ## Parameters
  
  - operations: List of operations to process in batch
  - process_fn: Function to process a single operation
  - options: Batch options
  
  ## Returns
  
  List of results corresponding to the input operations
  """
  @spec process_batch(list(map()), function(), batch_options()) :: list()
  def process_batch(operations, process_fn, options) do
    if length(operations) <= 1 do
      # If only one operation, process directly
      Enum.map(operations, process_fn)
    else
      # Measure overhead to determine if batching is worthwhile
      {batch_worth_it, _} = Profiler.profile("batch_assessment", fn ->
        assess_batch_value(operations, options)
      end)
      
      if batch_worth_it do
        process_operations_in_batch(operations, process_fn, options)
      else
        # Process sequentially if batching overhead exceeds benefit
        Enum.map(operations, process_fn)
      end
    end
  end
  
  @doc """
  Optimizes a set of LLM operations for batch processing.
  
  ## Parameters
  
  - operations: List of operations to optimize
  - options: Batch options
  
  ## Returns
  
  Optimized batch operations
  """
  @spec optimize_batch(list(map()), batch_options()) :: list(map())
  def optimize_batch(operations, options) do
    # Group by similarity to optimize batch processing
    grouped_operations = group_by_similarity(operations, options.similarity_threshold)
    
    # Determine optimal batch size based on current system conditions
    optimal_batch_size = 
      if options.dynamic_sizing do
        calculate_optimal_batch_size(grouped_operations, options.batch_size)
      else
        options.batch_size
      end
    
    # Split into batches of optimal size
    split_into_batches(grouped_operations, optimal_batch_size)
  end
  
  # Private helper functions
  
  # Assess whether batch processing would be beneficial based on operation characteristics
  defp assess_batch_value(operations, options) do
    # Heuristic: if operations have high similarity and the count is within a sweet spot,
    # batch processing is likely to be more efficient
    
    count = length(operations)
    
    cond do
      # Too few items to make batching worthwhile given the overhead
      count < 2 -> 
        false
        
      # Within ideal batch size range - likely to benefit
      count <= options.batch_size && count >= 2 -> 
        true
        
      # For larger sets, check if we can meaningfully batch them
      count > options.batch_size -> 
        # Check similarity of inputs to determine if batching makes sense
        input_similarity = calculate_average_similarity(operations)
        input_similarity >= options.similarity_threshold
        
      true -> 
        false
    end
  end
  
  # Process a set of operations in batch with proper error handling
  defp process_operations_in_batch(operations, process_fn, options) do
    Logger.debug("Processing batch of #{length(operations)} operations")
    
    # In a production implementation, we would use proper batching APIs
    # For now, we'll use Task to parallelize within BEAM
    
    # Apply process_fn to each operation in parallel, with a reasonable timeout
    timeout_ms = max(options.max_wait_ms * 5, 5000)
    
    try do
      # Using Task.async_stream for controlled parallelism
      operations
      |> Task.async_stream(process_fn, 
         max_concurrency: options.batch_size,
         timeout: timeout_ms)
      |> Enum.map(fn {:ok, result} -> result end)
    rescue
      e ->
        # On batch processing error, fall back to sequential processing
        Logger.warning("Batch processing failed, falling back to sequential: #{inspect(e)}")
        Enum.map(operations, process_fn)
    catch
      :exit, _ ->
        # On timeout, fall back to sequential processing
        Logger.warning("Batch processing timed out, falling back to sequential")
        Enum.map(operations, process_fn)
    end
  end
  
  # Group operations by similarity to optimize batching
  defp group_by_similarity(operations, _threshold) do
    # In a real implementation, this would use semantic similarity metrics
    # For this example, we'll use a simplified approach based on operation shape
    Enum.group_by(operations, fn op ->
      # Simplified grouping by shape/structure of inputs
      # A real implementation would use embedding similarity
      input_shape = compute_input_shape(op)
      input_shape
    end)
    |> Map.values()
    |> Enum.sort_by(&length/1, :desc)
  end
  
  # Calculate the average similarity between operations
  defp calculate_average_similarity(operations) do
    # In a real implementation, this would calculate actual embeddings and similarities
    # For now, we'll use a simplified approach based on input shapes
    
    if length(operations) <= 1 do
      1.0
    else
      # Count operations with similar shapes
      shapes = Enum.map(operations, &compute_input_shape/1)
      unique_shapes = Enum.uniq(shapes)
      
      # More unique shapes = less similarity
      similarity = 1.0 - (length(unique_shapes) - 1) / length(operations)
      max(0.0, similarity)
    end
  end
  
  # Compute a simple representation of input shape for similarity grouping
  defp compute_input_shape(operation) do
    # Extract input fields and their types
    # This is a simplified implementation that would be more sophisticated in practice
    
    inputs = Map.get(operation, :inputs, %{})
    
    inputs
    |> Enum.map(fn {k, _v} -> k end)
    |> Enum.sort()
  end
  
  # Calculate optimal batch size based on current conditions
  defp calculate_optimal_batch_size(grouped_operations, max_batch_size) do
    # In a real implementation, this would consider:
    # - Current system load
    # - Historical performance metrics 
    # - Memory usage patterns
    # - Network latency profiles
    
    # For now, we'll use a simpler heuristic based on group sizes
    avg_group_size = 
      if length(grouped_operations) > 0 do
        grouped_operations
        |> Enum.map(&length/1)
        |> Enum.sum()
        |> Kernel./(length(grouped_operations))
      else
        1
      end
      
    # Find a batch size that balances parallelism with overhead
    base_size = max(2, min(round(avg_group_size), max_batch_size))
    
    # Adjust for current system load (simplified)
    load_factor = get_system_load_factor()
    adjusted_size = max(2, round(base_size * load_factor))
    
    # Cap at maximum batch size
    min(adjusted_size, max_batch_size)
  end
  
  # Get a factor representing current system load (0.5 to 1.0)
  # Lower values indicate higher load (smaller batches)
  defp get_system_load_factor do
    # In a real implementation, this would monitor actual system metrics
    # For this example, we'll return a reasonable default
    0.8
  end
  
  # Split a list of grouped operations into batches of specified size
  defp split_into_batches(grouped_operations, batch_size) do
    # Flatten groups and split into batches
    grouped_operations
    |> Enum.flat_map(fn group ->
      Enum.chunk_every(group, batch_size)
    end)
  end
end
