defmodule EmberEx.XCS.JIT.PartialCache do
  @moduledoc """
  Implements intelligent partial caching for LLM operations.
  
  This module provides specialized caching mechanisms that can:
  1. Cache deterministic parts of LLM pipelines while preserving stochastic parts
  2. Apply content-based hashing to identify cacheable components
  3. Use signature-based caching that preserves semantic meaning without storing content
  """
  
  require Logger
  alias EmberEx.XCS.JIT.Cache
  alias EmberEx.XCS.JIT.Profiler
  alias EmberEx.XCS.JIT.LLMDetector
  
  @doc """
  Caches the result of a partial execution within an LLM operation pipeline.
  
  ## Parameters
  
  - function_id: ID of the function being cached
  - inputs: Input data for the function
  - cache_key_fn: Function to generate a cache key from inputs (optional)
  - compute_fn: Function to compute the result if not in cache
  - metadata: Additional metadata about the function (optional)
  
  ## Returns
  
  The result of the compute_fn or the cached value
  """
  @spec cached_execution(term(), map(), function() | nil, function(), map() | nil) :: term()
  def cached_execution(function_id, inputs, cache_key_fn \\ nil, compute_fn, metadata \\ nil) do
    cache_key = generate_cache_key(function_id, inputs, cache_key_fn, metadata)
    
    # Try to get from cache
    {result, cache_status} = Profiler.profile_and_log("partial_cache_lookup", fn ->
      case Cache.get_with_state(function_id, cache_key) do
        nil -> 
          # Not in cache, compute the value
          result = compute_fn.(inputs)
          Cache.set_with_state(function_id, result, cache_key)
          {result, :miss}
          
        cached_result -> 
          {cached_result, :hit}
      end
    end)
    
    # Log cache status for analysis
    Logger.debug("Partial cache #{cache_status} for #{inspect(function_id)}")
    
    result
  end
  
  @doc """
  Analyzes a function to determine if it can be safely cached.
  
  ## Parameters
  
  - function: The function to analyze
  - inputs_sample: Sample inputs to analyze behavior (optional)
  
  ## Returns
  
  A map with analysis results:
  - :cacheable - Whether the function can be fully cached
  - :partially_cacheable - Whether parts of the function can be cached
  - :deterministic - Whether the function is deterministic
  - :llm_operation - Whether the function is an LLM operation
  - :io_bound - Whether the function performs I/O operations
  """
  @spec analyze_cacheability(function() | module(), map() | nil) :: map()
  def analyze_cacheability(function, inputs_sample \\ nil) do
    # Profile the analysis to optimize overhead
    {result, analysis_time} = Profiler.profile("analyze_cacheability", fn ->
      # Determine if this is an LLM operation
      is_llm = LLMDetector.is_llm_operation?(function)
      
      # Check if it contains I/O operations (simplified check)
      has_io = contains_io_operations?(function)
      
      # Determine determinism (simplified)
      deterministic = is_deterministic?(function, inputs_sample)
      
      %{
        cacheable: deterministic && !has_io && !is_llm,
        partially_cacheable: !deterministic && !has_io,
        deterministic: deterministic,
        llm_operation: is_llm,
        io_bound: has_io,
        analysis_time_ms: 0.0  # Placeholder, will be updated
      }
    end)
    
    # Update the result with the actual timing
    result = Map.put(result, :analysis_time_ms, analysis_time)
    
    result
  end
  
  @doc """
  Determines the optimal caching strategy for a given function or graph node.
  
  ## Parameters
  
  - target: The function or node to analyze
  - analysis: Optional pre-computed analysis
  
  ## Returns
  
  A map containing the recommended caching strategy:
  - :strategy - The caching strategy to use
  - :key_function - A function to generate cache keys
  - :invalidation_policy - When to invalidate cache entries
  """
  @spec determine_caching_strategy(term(), map() | nil) :: map()
  def determine_caching_strategy(target, analysis \\ nil) do
    analysis = analysis || analyze_cacheability(target)
    
    cond do
      analysis.cacheable ->
        # Fully cacheable, use standard caching
        %{
          strategy: :full,
          key_function: &default_key_function/2,
          invalidation_policy: :time_based
        }
        
      analysis.llm_operation && analysis.partially_cacheable ->
        # LLM operation with deterministic parts
        %{
          strategy: :prompt_only,
          key_function: &content_insensitive_key/2,
          invalidation_policy: :llm_version_based
        }
        
      analysis.partially_cacheable ->
        # Partially cacheable non-LLM operation
        %{
          strategy: :signature_based,
          key_function: &signature_based_key/2,
          invalidation_policy: :content_change_based
        }
        
      true ->
        # Not cacheable
        %{
          strategy: :none,
          key_function: nil,
          invalidation_policy: nil
        }
    end
  end
  
  # Private helper functions
  
  defp generate_cache_key(function_id, inputs, cache_key_fn, metadata) do
    case cache_key_fn do
      nil -> default_key_function(function_id, inputs)
      _ -> cache_key_fn.(function_id, inputs, metadata)
    end
  end
  
  defp default_key_function(function_id, inputs) do
    # Simple hash-based cache key
    {function_id, :erlang.phash2(inputs)}
  end
  
  defp content_insensitive_key(function_id, inputs) do
    # Generate a key based on structure but not exact content
    # This helps cache prompt templates while varying the specific inputs
    {function_id, generate_structure_hash(inputs)}
  end
  
  defp signature_based_key(function_id, inputs) do
    # Generate a key based on input signatures but not full content
    {function_id, generate_signature_hash(inputs)}
  end
  
  defp generate_structure_hash(inputs) when is_map(inputs) do
    # Hash based on the keys and types, but not specific values
    inputs
    |> Enum.map(fn {k, v} -> {k, typeof(v)} end)
    |> :erlang.phash2()
  end
  
  defp generate_structure_hash(inputs) when is_list(inputs) do
    # For lists, hash based on length and types of elements
    inputs
    |> Enum.map(&typeof/1)
    |> :erlang.phash2()
  end
  
  defp generate_structure_hash(inputs) do
    # For other types, just hash the type
    typeof(inputs)
    |> :erlang.phash2()
  end
  
  defp generate_signature_hash(inputs) when is_map(inputs) do
    # Hash based on the keys and a limited digest of values
    inputs
    |> Enum.map(fn {k, v} -> {k, limited_digest(v)} end)
    |> :erlang.phash2()
  end
  
  defp generate_signature_hash(inputs) do
    # For non-maps, use limited digest
    limited_digest(inputs)
    |> :erlang.phash2()
  end
  
  defp limited_digest(value) when is_binary(value) do
    # For strings/binaries, hash the first few and last few chars
    # plus the length, to approximate content without full details
    size = byte_size(value)
    
    cond do
      size <= 16 ->
        # Small strings are fully hashed
        :crypto.hash(:sha256, value)
        
      true ->
        # Larger strings: hash first 8 bytes + last 8 bytes + length
        first = binary_part(value, 0, 8)
        last = binary_part(value, size - 8, 8)
        :crypto.hash(:sha256, [first, <<size::32>>, last])
    end
  end
  
  defp limited_digest(value) when is_list(value) do
    # For lists, digest the structure and first few elements
    {length(value), 
     value |> Enum.take(5) |> Enum.map(&typeof/1)}
  end
  
  defp limited_digest(value) do
    # For other types, just use the value directly
    value
  end
  
  defp typeof(value) when is_binary(value), do: :string
  defp typeof(value) when is_integer(value), do: :integer
  defp typeof(value) when is_float(value), do: :float
  defp typeof(value) when is_boolean(value), do: :boolean
  defp typeof(value) when is_map(value), do: :map
  defp typeof(value) when is_list(value), do: :list
  defp typeof(value) when is_atom(value), do: :atom
  defp typeof(value) when is_function(value), do: :function
  defp typeof(value) when is_pid(value), do: :pid
  defp typeof(value) when is_reference(value), do: :reference
  defp typeof(value) when is_tuple(value), do: :tuple
  defp typeof(_), do: :unknown
  
  defp is_deterministic?(function, inputs_sample) do
    # In a real implementation, this would use more sophisticated analysis.
    # For now, we'll use a simple approach that checks if multiple executions yield the same result
    # This is just a basic check - a full implementation would be more comprehensive
    
    # If no inputs provided, assume non-deterministic to be safe
    if inputs_sample == nil do
      false
    else
      try do
        # Try executing the function twice
        result1 = execute_function(function, inputs_sample)
        result2 = execute_function(function, inputs_sample)
        
        # Compare results (deep comparison)
        compare_results(result1, result2)
      rescue
        _ -> false
      catch
        _ -> false
      end
    end
  end
  
  defp contains_io_operations?(function) do
    # In a real implementation, this would use code analysis.
    # For now, we'll use the LLM detector as a proxy since LLM operations typically involve I/O
    # A full implementation would check for file/network/database operations
    LLMDetector.is_llm_operation?(function)
  end
  
  defp execute_function(function, inputs) when is_function(function, 1) do
    function.(inputs)
  end
  
  defp execute_function(function, inputs) when is_atom(function) and is_map(inputs) do
    if function_exported?(function, :call, 1) do
      function.call(inputs)
    else
      {:error, :not_callable}
    end
  end
  
  defp execute_function(function, _) do
    {:error, :invalid_function, function}
  end
  
  defp compare_results(result1, result2) do
    # Basic result comparison
    # In a real implementation, we would handle special cases like
    # timestamps, random values, etc.
    result1 == result2
  end
end
